This document describes techniques for creating compact and fast n-gram language models. It presents several implementations of n-gram language models that are both small in size and fast to query. The most compact implementation can store the 4 billion n-grams from the Google Web1T corpus in just 23 bits per n-gram, using techniques like implicitly encoding words and applying variable-length encodings to context deltas. It also discusses methods for improving query speed, such as a novel language model caching technique that speeds up both their implementations and SRILM by up to 300%.
In this paper, we apply grammar-based pre-processing prior to using the Prediction by Partial Matching
(PPM) compression algorithm. This achieves significantly better compression for different natural
language texts compared to other well-known compression methods. Our method first generates a grammar
based on the most common two-character sequences (bigraphs) or three-character sequences (trigraphs) in
the text being compressed and then substitutes these sequences using the respective non-terminal symbols
defined by the grammar in a pre-processing phase prior to the compression. This leads to significantly
improved results in compression for various natural languages (a 5% improvement for American English,
10% for British English, 29% for Welsh, 10% for Arabic, 3% for Persian and 35% for Chinese). We
describe further improvements using a two pass scheme where the grammar-based pre-processing is
applied again in a second pass through the text. We then apply the algorithms to the files in the Calgary
Corpus and also achieve significantly improved results in compression, between 11% and 20%, when
compared with other compression algorithms, including a grammar-based approach, the Sequitur
algorithm.
This document discusses unsupervised techniques for deciphering encrypted documents. It presents an approach using noisy-channel modeling and expectation-maximization (EM) to find the most likely plaintext given a ciphertext. As an example, it applies this method to deciphering a basic English letter substitution cipher. The algorithm initializes substitution probabilities uniformly, then iterates to adjust the probabilities based on plaintext and ciphertext letter co-occurrence counts to converge on the most probable plaintext.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
ANALYZING ARCHITECTURES FOR NEURAL MACHINE TRANSLATION USING LOW COMPUTATIONA...ijnlc
With the recent developments in the field of Natural Language Processing, there has been a rise in the use
of different architectures for Neural Machine Translation. Transformer architectures are used to achieve
state-of-the-art accuracy, but they are very computationally expensive to train. Everyone cannot have such
setups consisting of high-end GPUs and other resources. We train our models on low computational
resources and investigate the results. As expected, transformers outperformed other architectures, but
there were some surprising results. Transformers consisting of more encoders and decoders took more
time to train but had fewer BLEU scores. LSTM performed well in the experiment and took comparatively
less time to train than transformers, making it suitable to use in situations having time constraints.
A Compression & Encryption Algorithms on DNA Sequences Using R 2 P & Selectiv...IJMERJOURNAL
ABSTRACT: The size of DNA (Deoxyribonucleic Acid) sequences is varying in the range of millions to billions of nucleotides and two or three times bigger annually. Therefore efficient lossless compression technique, data structures to efficiently store, access, secure communicate and search these large datasets are necessary. This compression algorithm for genetic sequences, based on searching the exact repeat, reverse and palindrome (R2P) substring substitution and create a Library file. The R2P substring is replaced by corresponding ASCII character where for repeat, selecting ASCII characters ranging from 33 to 33+72, for reverse from 33+73 to 33+73+72 and for palindrome from 179 to 179+72. The selective encryption technique, the data are encrypted either in the library file or in compressed file or in both, also by using ASCII code and online library file acting as a signature. Selective encryption, where a part of message is encrypted keeping the remaining part unencrypted, can be a viable proposition for running encryption system in resource constraint devices. The algorithm can approach a moderate compression rate, provide strong data security, the running time is very few second and the complexity is O(n2 ). Also the compressed data again compressed by renounced compressor for reducing the compression rate & ratio. This techniques can approach a compression rate of 2.004871bits/base.
This document discusses sequence learning from acoustic models to end-to-end automatic speech recognition (ASR) systems. It covers feedforward neural networks, recurrent neural networks including LSTM, connectionist temporal classification, and building an end-to-end ASR system. Experimental results on a low-resource language are also presented. Key papers on the topics are referenced.
Deep Packet Inspection with Regular Expression MatchingEditor IJCATR
Deep packet inspection directs, persists, filters and logs IP-based applications and Web services traffic based on content
encapsulated in a packet's header or payload, regardless of the protocol or application type. In content scanning, the packet payload is
compared against a set of patterns specified as regular expressions. With deep packet inspection in place through a single intelligent
network device, companies can boost performance without buying expensive servers or additional security products. They are typically
matched through deterministic finite automata (DFAs), but large rule sets need a memory amount that turns out to be too large for
practical implementation. Many recent works have proposed improvements to address this issue, but they increase the number of
transitions (and then of memory accesses) per character. This paper presents a new representation for DFAs, orthogonal to most of the
previous solutions, called delta finite automata (FA), which considerably reduces states and transitions while preserving a transition
per character only, thus allowing fast matching. A further optimization exploits Nth order relationships within the DFA by adopting
the concept of temporary transitions.
ADAPTIVE AUTOMATA FOR GRAMMAR BASED TEXT COMPRESSIONcsandit
The Internet and the ubiquitous presence of computing devices anywhere is generating a
continuously growing amount of information. However, the information entropy is not uniform.
It allows the use of data compression algorithms to reduce the demand for more powerful
processors and larger data storage equipment. This paper presents an adaptive rule-driven
device - the adaptive automata - as the device to identify repetitive patterns to be compressed in
a grammar based lossless data compression scheme.
In this paper, we apply grammar-based pre-processing prior to using the Prediction by Partial Matching
(PPM) compression algorithm. This achieves significantly better compression for different natural
language texts compared to other well-known compression methods. Our method first generates a grammar
based on the most common two-character sequences (bigraphs) or three-character sequences (trigraphs) in
the text being compressed and then substitutes these sequences using the respective non-terminal symbols
defined by the grammar in a pre-processing phase prior to the compression. This leads to significantly
improved results in compression for various natural languages (a 5% improvement for American English,
10% for British English, 29% for Welsh, 10% for Arabic, 3% for Persian and 35% for Chinese). We
describe further improvements using a two pass scheme where the grammar-based pre-processing is
applied again in a second pass through the text. We then apply the algorithms to the files in the Calgary
Corpus and also achieve significantly improved results in compression, between 11% and 20%, when
compared with other compression algorithms, including a grammar-based approach, the Sequitur
algorithm.
This document discusses unsupervised techniques for deciphering encrypted documents. It presents an approach using noisy-channel modeling and expectation-maximization (EM) to find the most likely plaintext given a ciphertext. As an example, it applies this method to deciphering a basic English letter substitution cipher. The algorithm initializes substitution probabilities uniformly, then iterates to adjust the probabilities based on plaintext and ciphertext letter co-occurrence counts to converge on the most probable plaintext.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
ANALYZING ARCHITECTURES FOR NEURAL MACHINE TRANSLATION USING LOW COMPUTATIONA...ijnlc
With the recent developments in the field of Natural Language Processing, there has been a rise in the use
of different architectures for Neural Machine Translation. Transformer architectures are used to achieve
state-of-the-art accuracy, but they are very computationally expensive to train. Everyone cannot have such
setups consisting of high-end GPUs and other resources. We train our models on low computational
resources and investigate the results. As expected, transformers outperformed other architectures, but
there were some surprising results. Transformers consisting of more encoders and decoders took more
time to train but had fewer BLEU scores. LSTM performed well in the experiment and took comparatively
less time to train than transformers, making it suitable to use in situations having time constraints.
A Compression & Encryption Algorithms on DNA Sequences Using R 2 P & Selectiv...IJMERJOURNAL
ABSTRACT: The size of DNA (Deoxyribonucleic Acid) sequences is varying in the range of millions to billions of nucleotides and two or three times bigger annually. Therefore efficient lossless compression technique, data structures to efficiently store, access, secure communicate and search these large datasets are necessary. This compression algorithm for genetic sequences, based on searching the exact repeat, reverse and palindrome (R2P) substring substitution and create a Library file. The R2P substring is replaced by corresponding ASCII character where for repeat, selecting ASCII characters ranging from 33 to 33+72, for reverse from 33+73 to 33+73+72 and for palindrome from 179 to 179+72. The selective encryption technique, the data are encrypted either in the library file or in compressed file or in both, also by using ASCII code and online library file acting as a signature. Selective encryption, where a part of message is encrypted keeping the remaining part unencrypted, can be a viable proposition for running encryption system in resource constraint devices. The algorithm can approach a moderate compression rate, provide strong data security, the running time is very few second and the complexity is O(n2 ). Also the compressed data again compressed by renounced compressor for reducing the compression rate & ratio. This techniques can approach a compression rate of 2.004871bits/base.
This document discusses sequence learning from acoustic models to end-to-end automatic speech recognition (ASR) systems. It covers feedforward neural networks, recurrent neural networks including LSTM, connectionist temporal classification, and building an end-to-end ASR system. Experimental results on a low-resource language are also presented. Key papers on the topics are referenced.
Deep Packet Inspection with Regular Expression MatchingEditor IJCATR
Deep packet inspection directs, persists, filters and logs IP-based applications and Web services traffic based on content
encapsulated in a packet's header or payload, regardless of the protocol or application type. In content scanning, the packet payload is
compared against a set of patterns specified as regular expressions. With deep packet inspection in place through a single intelligent
network device, companies can boost performance without buying expensive servers or additional security products. They are typically
matched through deterministic finite automata (DFAs), but large rule sets need a memory amount that turns out to be too large for
practical implementation. Many recent works have proposed improvements to address this issue, but they increase the number of
transitions (and then of memory accesses) per character. This paper presents a new representation for DFAs, orthogonal to most of the
previous solutions, called delta finite automata (FA), which considerably reduces states and transitions while preserving a transition
per character only, thus allowing fast matching. A further optimization exploits Nth order relationships within the DFA by adopting
the concept of temporary transitions.
ADAPTIVE AUTOMATA FOR GRAMMAR BASED TEXT COMPRESSIONcsandit
The Internet and the ubiquitous presence of computing devices anywhere is generating a
continuously growing amount of information. However, the information entropy is not uniform.
It allows the use of data compression algorithms to reduce the demand for more powerful
processors and larger data storage equipment. This paper presents an adaptive rule-driven
device - the adaptive automata - as the device to identify repetitive patterns to be compressed in
a grammar based lossless data compression scheme.
Learning R via Python…or the other way aroundSid Xing
The document discusses similarities and differences between R and Python programming languages. Both support functional and object-oriented programming paradigms. However, Python is designed more for general application development while R focuses on data analysis functions. Their syntax also differs, with Python emphasizing readability through whitespace and R favoring brevity through nesting. Examples are provided to demonstrate equivalent functionality in each language.
Обзор последних разработок в области text-to-speech.
+Аудио-файлы с синтетической речью
https://soundcloud.com/user-872135531/sets/samples-of-synthesized-speech-for-modern-text-to-speech-systems-review
Sequence to Sequence Learning with Neural NetworksNguyen Quang
This document discusses sequence to sequence learning with neural networks. It summarizes a seminal paper that introduced a simple approach using LSTM neural networks to map sequences to sequences. The approach uses two LSTMs - an encoder LSTM to map the input sequence to a fixed-dimensional vector, and a decoder LSTM to map the vector back to the target sequence. The paper achieved state-of-the-art results on English to French machine translation, showing the potential of simple neural models for sequence learning tasks.
Efficiency lossless data techniques for arabic text compressionijcsit
This document summarizes a study that evaluated the efficiency of the LZW and BWT data compression techniques on Arabic text files of different sizes and categories (vowelized, partially vowelized, unvowelized). It found that the enhanced LZW technique, which took advantage of Arabic letters having a single case, achieved the highest compression ratios. The enhanced LZW performed better than standard LZW and BWT for all categories of Arabic text. While LZW generally compressed Arabic text better than English text, BWT only performed better on vowelized Arabic versus English. The study concluded the enhanced LZW was the most effective technique for compressing Arabic texts.
Hardware Implementations of RS Decoding Algorithm for Multi-Gb/s Communicatio...RSIS International
In this paper, we have designed the VLSI hardware for a novel RS decoding algorithm suitable for Multi-Gb/s Communication Systems. Through this paper we show that the performance benefit of the algorithm is truly witnessed when implemented in hardware thus avoiding the extra processing time of Fetch-Decode-Execute cycle of traditional microprocessor based computing systems. The new algorithm with less time complexity combined with its application specific hardware implementation makes it suitable for high speed real-time systems with hard timing constraints. The design is implemented as a digital hardware using VHDL
Non autoregressive neural text-to-speech reviewJune-Woo Kim
Non autoregressive neural text-to-speech, Peng, Kainan, et al. "Non-autoregressive neural text-to-speech." International Conference on Machine Learning. PMLR, 2020. review by June-Woo Kim
Stay Tuned for the Video Presentation on this new Lossless Data Compressor founded by P. B. Alipour since 2009. Thesis report available at: http://www.bth.se/fou/cuppsats.nsf/bbb56322b274389dc1256608004f052b/d6e604432ce79795c125775c0078148a!OpenDocument
Advances in Parallel Computing for Computational Mechanics. MPI for Python an...guest23e06a
The document describes advances in parallel computing for computational mechanics using MPI for Python and the Domain Decomposition Method presented by Mario Storti et al. It outlines the presentation which includes an introduction to PETSc-FEM, a parallel finite element program, and recent developments including MPI for Python and an interface strip preconditioner for the Domain Decomposition Method.
Performance analysis and implementation for nonbinary quasi cyclic ldpc decod...ijwmn
Non-binary low-density parity check (NB-LDPC) codes are an extension of binary LDPC codes with
significantly better performance. Although various kinds of low-complexity iterative decoding algorithms
have been proposed, there is a big challenge for VLSI implementation of NBLDPC decoders due to its high
complexity and long latency. In this brief, highly efficient check node processing scheme, which the
processing delay greatly reduced, including Min-Max decoding algorithm and check node unit are
proposed. Compare with previous works, less than 52% could be reduced for the latency of check node
unit. In addition, the efficiency of the presented techniques is design to demonstrate for the (620, 310) NBQC-
LDPC decoder.
This document summarizes a research paper on improving regular expression matching using a look-ahead finite automata approach. It proposes LaFA, which optimizes regular expression detection by allowing out-of-order detection, systematically reordering the detection sequence, and sharing states among automata. LaFA uses three main modules - a timestamp lookup module to detect non-repeating patterns, a character lookup module to check individual characters, and a repetition detection module to handle repeating patterns. The system architecture involves capturing packets, extracting payloads, and using these modules in parallel to match regular expressions and improve detection speed for intrusion detection systems.
Align, Disambiguate and Walk : A Unified Approach forMeasuring Semantic Simil...Koji Matsuda
The document presents a unified approach for measuring semantic similarity between texts at multiple levels (sense, word, text) using semantic signatures. It generates semantic signatures through multi-seeded random walks over the WordNet graph. It then aligns and disambiguates words and senses to extract sense "seeds" for the signatures. Finally, it calculates signature similarity using measures like cosine similarity, weighted overlap, and top-k Jaccard. The approach provides a unified framework for semantic similarity that can be applied to various NLP tasks.
This document describes a middleware called SWING that integrates the data mining system AL-QUIN and the ontological engineering tool Protege-2000. This integration enables AL-QUIN to perform semantic web mining tasks by making it compliant with Semantic Web standards and interoperable with Protege-2000. The middleware suggests a methodology for building semantic web mining systems by upgrading existing data mining systems to work with ontological engineering tools.
Aes cryptography algorithm based on intelligent blum blum-shub prn gs publica...zaidinvisible
This document summarizes a study that proposes enhancing the Advanced Encryption Standard (AES) algorithm by using an intelligent Blum-Blum-Shub (BBS) pseudo-random number generator to generate the initial encryption key. The AES algorithm is described along with its standard steps of sub-bytes, shift rows, mix columns, and add round key. Issues with the security of AES's public key are discussed. The study then introduces BBS and Iterated Local Search (ILS) metaheuristics and describes how combining them can generate strong cryptographic keys. An example is provided to demonstrate encrypting a message with the enhanced AES approach using an intelligent BBS-generated key. The study concludes the method increases encryption efficiency and
Arabic Phoneme Recognition using Hierarchical Neural Fuzzy Petri Net and LPC ...CSCJournals
The basic idea behind the proposed hierarchical phoneme recognition is that phonemes can be classified into specific phoneme types which can be organized within a hierarchical tree structure. The recognition principle is based on “divide and conquer” in which a large problem is divided into many smaller, easier to solve problems whose solutions can be combined to yield a solution to the complex problem. Fuzzy Petri net (FPN) is a powerful modeling tool for fuzzy production rules based knowledge systems. For building hierarchical classifier using Neural Fuzzy Petri net (NFPN), Each node of the hierarchical tree is represented by a NFPN. Every NFPN in the hierarchical tree is trained by repeatedly presenting a set of input patterns along with the class to which each particular pattern belongs. The feature vector used as input to the NFPN is the LPC parameters.
This document discusses a proposed look-ahead finite automata (LaFA) system for improving regular expression (RE) detection speed in network intrusion detection and prevention systems (NIDPS). The LaFA approach aims to address scalability issues with existing RE detection methods by optimizing the detection sequence, sharing states among automata for different REs, and using specialized buffered lookup modules for detection. These modules include a timestamps lookup module, character lookup module, and repetition detection module that can perform "look ahead" operations to more efficiently detect variable string patterns in REs. The proposed LaFA architecture and detection modules are described and compared to existing deterministic and nondeterministic finite automata approaches.
Lossless LZW Data Compression Algorithm on CUDAIOSR Journals
This document discusses implementing the LZW lossless data compression algorithm on CUDA. It begins with background on data compression techniques, including lossless vs lossy compression. It then provides details on the LZW algorithm, how it works, and its advantages over other dictionary-based algorithms like LZ77 and LZ78. The document discusses how CUDA and GPUs can be used for parallel computing. It reviews related work porting compression algorithms to CUDA. Finally, it proposes implementing LZW on CUDA to take advantage of GPU parallelism for faster compression times compared to CPU implementations. Performance comparisons from previous works porting other algorithms to CUDA suggest GPU implementations can achieve significant speedups.
UTTERANCE-LEVEL SEQUENTIAL MODELING FOR DEEP GAUSSIAN PROCESS BASED SPEECH S...Tomoki Koriyama
The document proposes incorporating a simple recurrent unit (SRU) into deep Gaussian processes (DGP) for speech synthesis to enable utterance-level sequential modeling. The SRU-DGP model outperformed feedforward DGP and LSTM RNN baselines in subjective evaluations, and achieved faster speech generation than an LSTM RNN. Experimental results on a Japanese speech corpus showed the SRU-DGP model yielded smaller spectral distortion than other neural network and Bayesian neural network baselines. Future work will investigate incorporating other differentiable components like attention into the DGP framework.
BERT is a pre-trained language representation model that uses the Transformer architecture. It is pre-trained using two unsupervised tasks: masked language modeling and next sentence prediction. BERT can then be fine-tuned on downstream NLP tasks like question answering and text classification. When fine-tuned on SQuAD, BERT achieved state-of-the-art results by using the output hidden states to predict the start and end positions of answers within paragraphs. Later work like RoBERTa and ALBERT improved on BERT by modifying pre-training procedures and model architectures.
Analysis of LDPC Codes under Wi-Max IEEE 802.16eIJERD Editor
The LDPC codes have been shown to be by-far the best coding scheme capable for transmitting message over noisy channel. The main aim of this paper is to study the behaviour of LDPC codes on under IEEE 802.16e guidelines. The rate- ½ LDPC codes have been implemented on AWGN channel and the result shows that they can be used on such channels with low BER performance. The BER can be further minimized by increasing the block length.
The document proposes a new indexing technique called Cache-Sensitive Search Trees (CSS-trees) for main memory databases. CSS-trees augment binary search trees by storing a directory structure on top of a sorted array to improve cache performance without significant extra space. The document compares CSS-trees to other indexing techniques such as hash indexes, binary search trees, B+-trees, and T-trees in terms of lookup time and space usage. Experimental results show that CSS-trees outperform other techniques, with lookup times over 2x faster than binary search, due to better cache behavior from improved data locality.
Cache conscious index mechanism for main-memory databases Red Over
This document proposes the Cache-Sensitive T-tree (CST-tree), a cache-conscious index structure for main-memory databases. The CST-tree aims to improve cache behavior by decreasing node size to increase cache hits compared to traditional T-trees. It does not store the entire middle key array in each node to save space. Experimental results show the CST-tree provides better performance than T-trees in main memory databases, with performance between CSB+-trees and full CSB+-trees. It also uses less space than full CSB+-trees.
Learning R via Python…or the other way aroundSid Xing
The document discusses similarities and differences between R and Python programming languages. Both support functional and object-oriented programming paradigms. However, Python is designed more for general application development while R focuses on data analysis functions. Their syntax also differs, with Python emphasizing readability through whitespace and R favoring brevity through nesting. Examples are provided to demonstrate equivalent functionality in each language.
Обзор последних разработок в области text-to-speech.
+Аудио-файлы с синтетической речью
https://soundcloud.com/user-872135531/sets/samples-of-synthesized-speech-for-modern-text-to-speech-systems-review
Sequence to Sequence Learning with Neural NetworksNguyen Quang
This document discusses sequence to sequence learning with neural networks. It summarizes a seminal paper that introduced a simple approach using LSTM neural networks to map sequences to sequences. The approach uses two LSTMs - an encoder LSTM to map the input sequence to a fixed-dimensional vector, and a decoder LSTM to map the vector back to the target sequence. The paper achieved state-of-the-art results on English to French machine translation, showing the potential of simple neural models for sequence learning tasks.
Efficiency lossless data techniques for arabic text compressionijcsit
This document summarizes a study that evaluated the efficiency of the LZW and BWT data compression techniques on Arabic text files of different sizes and categories (vowelized, partially vowelized, unvowelized). It found that the enhanced LZW technique, which took advantage of Arabic letters having a single case, achieved the highest compression ratios. The enhanced LZW performed better than standard LZW and BWT for all categories of Arabic text. While LZW generally compressed Arabic text better than English text, BWT only performed better on vowelized Arabic versus English. The study concluded the enhanced LZW was the most effective technique for compressing Arabic texts.
Hardware Implementations of RS Decoding Algorithm for Multi-Gb/s Communicatio...RSIS International
In this paper, we have designed the VLSI hardware for a novel RS decoding algorithm suitable for Multi-Gb/s Communication Systems. Through this paper we show that the performance benefit of the algorithm is truly witnessed when implemented in hardware thus avoiding the extra processing time of Fetch-Decode-Execute cycle of traditional microprocessor based computing systems. The new algorithm with less time complexity combined with its application specific hardware implementation makes it suitable for high speed real-time systems with hard timing constraints. The design is implemented as a digital hardware using VHDL
Non autoregressive neural text-to-speech reviewJune-Woo Kim
Non autoregressive neural text-to-speech, Peng, Kainan, et al. "Non-autoregressive neural text-to-speech." International Conference on Machine Learning. PMLR, 2020. review by June-Woo Kim
Stay Tuned for the Video Presentation on this new Lossless Data Compressor founded by P. B. Alipour since 2009. Thesis report available at: http://www.bth.se/fou/cuppsats.nsf/bbb56322b274389dc1256608004f052b/d6e604432ce79795c125775c0078148a!OpenDocument
Advances in Parallel Computing for Computational Mechanics. MPI for Python an...guest23e06a
The document describes advances in parallel computing for computational mechanics using MPI for Python and the Domain Decomposition Method presented by Mario Storti et al. It outlines the presentation which includes an introduction to PETSc-FEM, a parallel finite element program, and recent developments including MPI for Python and an interface strip preconditioner for the Domain Decomposition Method.
Performance analysis and implementation for nonbinary quasi cyclic ldpc decod...ijwmn
Non-binary low-density parity check (NB-LDPC) codes are an extension of binary LDPC codes with
significantly better performance. Although various kinds of low-complexity iterative decoding algorithms
have been proposed, there is a big challenge for VLSI implementation of NBLDPC decoders due to its high
complexity and long latency. In this brief, highly efficient check node processing scheme, which the
processing delay greatly reduced, including Min-Max decoding algorithm and check node unit are
proposed. Compare with previous works, less than 52% could be reduced for the latency of check node
unit. In addition, the efficiency of the presented techniques is design to demonstrate for the (620, 310) NBQC-
LDPC decoder.
This document summarizes a research paper on improving regular expression matching using a look-ahead finite automata approach. It proposes LaFA, which optimizes regular expression detection by allowing out-of-order detection, systematically reordering the detection sequence, and sharing states among automata. LaFA uses three main modules - a timestamp lookup module to detect non-repeating patterns, a character lookup module to check individual characters, and a repetition detection module to handle repeating patterns. The system architecture involves capturing packets, extracting payloads, and using these modules in parallel to match regular expressions and improve detection speed for intrusion detection systems.
Align, Disambiguate and Walk : A Unified Approach forMeasuring Semantic Simil...Koji Matsuda
The document presents a unified approach for measuring semantic similarity between texts at multiple levels (sense, word, text) using semantic signatures. It generates semantic signatures through multi-seeded random walks over the WordNet graph. It then aligns and disambiguates words and senses to extract sense "seeds" for the signatures. Finally, it calculates signature similarity using measures like cosine similarity, weighted overlap, and top-k Jaccard. The approach provides a unified framework for semantic similarity that can be applied to various NLP tasks.
This document describes a middleware called SWING that integrates the data mining system AL-QUIN and the ontological engineering tool Protege-2000. This integration enables AL-QUIN to perform semantic web mining tasks by making it compliant with Semantic Web standards and interoperable with Protege-2000. The middleware suggests a methodology for building semantic web mining systems by upgrading existing data mining systems to work with ontological engineering tools.
Aes cryptography algorithm based on intelligent blum blum-shub prn gs publica...zaidinvisible
This document summarizes a study that proposes enhancing the Advanced Encryption Standard (AES) algorithm by using an intelligent Blum-Blum-Shub (BBS) pseudo-random number generator to generate the initial encryption key. The AES algorithm is described along with its standard steps of sub-bytes, shift rows, mix columns, and add round key. Issues with the security of AES's public key are discussed. The study then introduces BBS and Iterated Local Search (ILS) metaheuristics and describes how combining them can generate strong cryptographic keys. An example is provided to demonstrate encrypting a message with the enhanced AES approach using an intelligent BBS-generated key. The study concludes the method increases encryption efficiency and
Arabic Phoneme Recognition using Hierarchical Neural Fuzzy Petri Net and LPC ...CSCJournals
The basic idea behind the proposed hierarchical phoneme recognition is that phonemes can be classified into specific phoneme types which can be organized within a hierarchical tree structure. The recognition principle is based on “divide and conquer” in which a large problem is divided into many smaller, easier to solve problems whose solutions can be combined to yield a solution to the complex problem. Fuzzy Petri net (FPN) is a powerful modeling tool for fuzzy production rules based knowledge systems. For building hierarchical classifier using Neural Fuzzy Petri net (NFPN), Each node of the hierarchical tree is represented by a NFPN. Every NFPN in the hierarchical tree is trained by repeatedly presenting a set of input patterns along with the class to which each particular pattern belongs. The feature vector used as input to the NFPN is the LPC parameters.
This document discusses a proposed look-ahead finite automata (LaFA) system for improving regular expression (RE) detection speed in network intrusion detection and prevention systems (NIDPS). The LaFA approach aims to address scalability issues with existing RE detection methods by optimizing the detection sequence, sharing states among automata for different REs, and using specialized buffered lookup modules for detection. These modules include a timestamps lookup module, character lookup module, and repetition detection module that can perform "look ahead" operations to more efficiently detect variable string patterns in REs. The proposed LaFA architecture and detection modules are described and compared to existing deterministic and nondeterministic finite automata approaches.
Lossless LZW Data Compression Algorithm on CUDAIOSR Journals
This document discusses implementing the LZW lossless data compression algorithm on CUDA. It begins with background on data compression techniques, including lossless vs lossy compression. It then provides details on the LZW algorithm, how it works, and its advantages over other dictionary-based algorithms like LZ77 and LZ78. The document discusses how CUDA and GPUs can be used for parallel computing. It reviews related work porting compression algorithms to CUDA. Finally, it proposes implementing LZW on CUDA to take advantage of GPU parallelism for faster compression times compared to CPU implementations. Performance comparisons from previous works porting other algorithms to CUDA suggest GPU implementations can achieve significant speedups.
UTTERANCE-LEVEL SEQUENTIAL MODELING FOR DEEP GAUSSIAN PROCESS BASED SPEECH S...Tomoki Koriyama
The document proposes incorporating a simple recurrent unit (SRU) into deep Gaussian processes (DGP) for speech synthesis to enable utterance-level sequential modeling. The SRU-DGP model outperformed feedforward DGP and LSTM RNN baselines in subjective evaluations, and achieved faster speech generation than an LSTM RNN. Experimental results on a Japanese speech corpus showed the SRU-DGP model yielded smaller spectral distortion than other neural network and Bayesian neural network baselines. Future work will investigate incorporating other differentiable components like attention into the DGP framework.
BERT is a pre-trained language representation model that uses the Transformer architecture. It is pre-trained using two unsupervised tasks: masked language modeling and next sentence prediction. BERT can then be fine-tuned on downstream NLP tasks like question answering and text classification. When fine-tuned on SQuAD, BERT achieved state-of-the-art results by using the output hidden states to predict the start and end positions of answers within paragraphs. Later work like RoBERTa and ALBERT improved on BERT by modifying pre-training procedures and model architectures.
Analysis of LDPC Codes under Wi-Max IEEE 802.16eIJERD Editor
The LDPC codes have been shown to be by-far the best coding scheme capable for transmitting message over noisy channel. The main aim of this paper is to study the behaviour of LDPC codes on under IEEE 802.16e guidelines. The rate- ½ LDPC codes have been implemented on AWGN channel and the result shows that they can be used on such channels with low BER performance. The BER can be further minimized by increasing the block length.
The document proposes a new indexing technique called Cache-Sensitive Search Trees (CSS-trees) for main memory databases. CSS-trees augment binary search trees by storing a directory structure on top of a sorted array to improve cache performance without significant extra space. The document compares CSS-trees to other indexing techniques such as hash indexes, binary search trees, B+-trees, and T-trees in terms of lookup time and space usage. Experimental results show that CSS-trees outperform other techniques, with lookup times over 2x faster than binary search, due to better cache behavior from improved data locality.
Cache conscious index mechanism for main-memory databases Red Over
This document proposes the Cache-Sensitive T-tree (CST-tree), a cache-conscious index structure for main-memory databases. The CST-tree aims to improve cache behavior by decreasing node size to increase cache hits compared to traditional T-trees. It does not store the entire middle key array in each node to save space. Experimental results show the CST-tree provides better performance than T-trees in main memory databases, with performance between CSB+-trees and full CSB+-trees. It also uses less space than full CSB+-trees.
The document summarizes the Log-Structured Merge-Tree (LSM-Tree) data structure. It is designed to provide low-cost indexing for files with high insert rates. It works by deferring and batching index changes, cascading them from an in-memory to disk-based components in an efficient manner similar to merge sort. This reduces disk arm movements compared to traditional indexes like B-trees. The LSM-tree is most useful when insert operations are more common than retrieval operations.
Bitmap Indexes for Relational XML Twig Query ProcessingKyong-Ha Lee
This document discusses using bitmap indexes to support holistic twig join (HTJ) algorithms for processing XML queries on data stored in a relational database. It presents two types of bitmap indexes - a basic index on single columns and a hybrid "bitTwig" index combining path and ancestor indexes. Experiments show the basic index performs best for shallow XML while a tag-level index with skipping works best for deep XML. Future work includes evaluating more HTJ algorithms and index structures.
Database Research on Modern Computing ArchitectureKyong-Ha Lee
This document provides an overview of a talk on database research related to modern computing architecture given on September 10, 2010. The talk discusses the immense changes in computer hardware, including a variety of computing resources and increasing intra-node parallelism. It also covers how database technology can facilitate modern hardware features like parallelism. Specific topics covered include memory hierarchy changes, the memory wall problem, and latency issues compared to increasing bandwidth.
Study: The Future of VR, AR and Self-Driving CarsLinkedIn
We asked LinkedIn members worldwide about their levels of interest in the latest wave of technology: whether they’re using wearables, and whether they intend to buy self-driving cars and VR headsets as they become available. We asked them too about their attitudes to technology and to the growing role of Artificial Intelligence (AI) in the devices that they use. The answers were fascinating – and in many cases, surprising.
This SlideShare explores the full results of this study, including detailed market-by-market breakdowns of intention levels for each technology – and how attitudes change with age, location and seniority level. If you’re marketing a tech brand – or planning to use VR and wearables to reach a professional audience – then these are insights you won’t want to miss.
In this paper, we apply grammar-based pre-processing prior to using the Prediction by Partial Matching
(PPM) compression algorithm. This achieves significantly better compression for different natural
language texts compared to other well-known compression methods. Our method first generates a grammar
based on the most common two-character sequences (bigraphs) or three-character sequences (trigraphs) in
the text being compressed and then substitutes these sequences using the respective non-terminal symbols
defined by the grammar in a pre-processing phase prior to the compression. This leads to significantly
improved results in compression for various natural languages (a 5% improvement for American English,
10% for British English, 29% for Welsh, 10% for Arabic, 3% for Persian and 35% for Chinese). We
describe further improvements using a two pass scheme where the grammar-based pre-processing is
applied again in a second pass through the text. We then apply the algorithms to the files in the Calgary
Corpus and also achieve significantly improved results in compression, between 11% and 20%, when
compared with other compression algorithms, including a grammar-based approach, the Sequitur
algorithm.
In this paper, we apply grammar-based pre-processing prior to using the Prediction by Partial Matching (PPM) compression algorithm. This achieves significantly better compression for different natural language texts compared to other well-known compression methods. Our method first generates a grammar based on the most common two-character sequences (bigraphs) or three-character sequences (trigraphs) in the text being compressed and then substitutes these sequences using the respective non-terminal symbols defined by the grammar in a pre-processing phase prior to the compression. This leads to significantly improved results in compression for various natural languages (a 5% improvement for American English, 10% for British English, 29% for Welsh, 10% for Arabic, 3% for Persian and 35% for Chinese). We describe further improvements using a two pass scheme where the grammar-based pre-processing is applied again in a second pass through the text. We then apply the algorithms to the files in the Calgary Corpus and also achieve significantly improved results in compression, between 11% and 20%, when compared with other compression algorithms, including a grammar-based approach, the Sequitur algorithm
ANALYZING ARCHITECTURES FOR NEURAL MACHINE TRANSLATION USING LOW COMPUTATIO...kevig
With the recent developments in the field of Natural Language Processing, there has been a rise in the use
of different architectures for Neural Machine Translation. Transformer architectures are used to achieve
state-of-the-art accuracy, but they are very computationally expensive to train. Everyone cannot have such
setups consisting of high-end GPUs and other resources. We train our models on low computational
resources and investigate the results. As expected, transformers outperformed other architectures, but
there were some surprising results. Transformers consisting of more encoders and decoders took more
time to train but had fewer BLEU scores. LSTM performed well in the experiment and took comparatively
less time to train than transformers, making it suitable to use in situations having time constraints
ANALYZING ARCHITECTURES FOR NEURAL MACHINE TRANSLATION USING LOW COMPUTATIONA...kevig
With the recent developments in the field of Natural Language Processing, there has been a rise in the use
of different architectures for Neural Machine Translation. Transformer architectures are used to achieve
state-of-the-art accuracy, but they are very computationally expensive to train. Everyone cannot have such
setups consisting of high-end GPUs and other resources. We train our models on low computational
resources and investigate the results. As expected, transformers outperformed other architectures, but
there were some surprising results. Transformers consisting of more encoders and decoders took more
time to train but had fewer BLEU scores. LSTM performed well in the experiment and took comparatively
less time to train than transformers, making it suitable to use in situations having time constraints.
This project report presents an analysis of compression algorithms like Huffman encoding, run length encoding, LZW, and a variant of LZW. Testing was conducted on these algorithms to analyze compression rates, compression and decompression times, and memory usage. Huffman encoding achieved around 40% compression while run length encoding resulted in negligible compression. LZW performed better than its variant in compression but the variant used significantly less memory. The variant balanced compression performance and memory usage better than standard LZW for large files.
Arabic named entity recognition using deep learning approachIJECEIAES
Most of the Arabic Named Entity Recognition (NER) systems depend massively on external resources and handmade feature engineering to achieve state-of-the-art results. To overcome such limitations, we proposed, in this paper, to use deep learning approach to tackle the Arabic NER task. We introduced a neural network architecture based on bidirectional Long Short-Term Memory (LSTM) and Conditional Random Fields (CRF) and experimented with various commonly used hyperparameters to assess their effect on the overall performance of our system. Our model gets two sources of information about words as input: pre-trained word embeddings and character-based representations and eliminated the need for any task-specific knowledge or feature engineering. We obtained state-of-the-art result on the standard ANERcorp corpus with an F1 score of 90.6%.
Ngs de novo assembly progresses and challengesScott Edmunds
This document discusses the progresses and challenges of de novo genome assembly using next-generation sequencing data, including improvements made to error correction, contig construction, scaffolding, gap closure, and computational performance that have increased assembly quality and scalability; however, challenges still remain around resolving repeats and assembling heterozygous diploid genomes accurately.
The document proposes BergSump, a new framework for analyzing I/O automata. BergSump aims to confirm that superblocks and flip-flop gates are generally incompatible. It discusses related work on XML, wireless networks, and cryptography. The implementation section outlines version 5.9 of BergSump and plans to release the code under an open source license. The evaluation analyzes BergSump's performance and shows its median complexity is better than prior solutions. The conclusion argues that BergSump can successfully observe many sensor networks at once.
Code GPU with CUDA - Optimizing memory and control flowMarina Kolpakova
This document provides tips for optimizing memory and control flow when programming with CUDA GPUs. It discusses different types of GPU memory like registers, shared memory, and global memory. It describes efficient memory access patterns and techniques to improve coalesced memory loads. The document also covers optimizing control flow by reducing warp divergence and synchronizations. It recommends tuning block configurations to improve occupancy and performance.
The document discusses various data compression techniques. It defines data compression as the process of reducing the size of data through the use of compression algorithms. There are two main types of compression: lossless, where the original and decompressed data are identical, and lossy, where some data may be lost during compression. Common lossless techniques mentioned include run-length encoding, Huffman coding, and Lempel-Ziv encoding, while lossy methods discussed are JPEG for images and MPEG for video. The document also provides examples to illustrate how several of these compression algorithms work.
The document discusses various data compression techniques. It defines data compression as the process of reducing the size of data through the use of compression algorithms. There are two main types of compression: lossless, where the original and decompressed data are identical, and lossy, where some data may be lost during compression. Common lossless techniques mentioned include run-length encoding, Huffman coding, and Lempel-Ziv encoding, while lossy methods discussed are JPEG for images and MPEG for video. The document also provides examples to illustrate how several of these compression algorithms work.
IEEE 2014 NS2 NETWORKING PROJECTS Fast regular expression matching using sma...IEEEBEBTECHSTUDENTPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
EMPLOYING PIVOT LANGUAGE TECHNIQUE THROUGH STATISTICAL AND NEURAL MACHINE TRA...ijnlc
The quality of Neural Machine Translation (NMT) systems like Statistical Machine Translation (SMT) systems, heavily depends on the size of training data set, while for some pairs of languages, high-quality parallel data are poor resources. In order to respond to this low-resourced training data bottleneck reality, we employ the pivoting approach in both neural MT and statistical MT frameworks. During our experiments on the Persian-Spanish, taken as an under-resourced translation task, we discovered that, the aforementioned method, in both frameworks, significantly improves the translation quality in comparison to the standard direct translation approach.
Doppl is a new programming language being developed that aims to provide natural syntax for parallel programming. The language is focused on shared memory applications and message passing between tasks. This first development diary outlines the goals of the language, which include allowing programmers to model algorithms as state machines and represent data as attribute arrays to improve cache performance. Future iterations will explore additional features like loop structures and type systems.
This document proposes a novel framework called smooth sparse coding for learning sparse representations of data. It incorporates feature similarity or temporal information present in data sets via non-parametric kernel smoothing. The approach constructs codes that represent neighborhoods of samples rather than individual samples, leading to lower reconstruction error. It also proposes using marginal regression rather than lasso for obtaining sparse codes, providing a dramatic speedup of up to two orders of magnitude without sacrificing accuracy. The document contributes a framework for incorporating domain information into sparse coding, sample complexity results for dictionary learning using smooth sparse coding, an efficient marginal regression training procedure, and successful application to classification tasks with improved accuracy and speed.
Deep Learning for Machine Translation: a paradigm shift - Alberto Massidda - ...Codemotion
In beginning there was the "rule based" machine translation, like Babelfish, that didn't work at all. Then came the Statistical Machine translation, powering the like of Google Translate, and all was good. Nowadays, it's all about Deep Learning and the Neural Machine Translation is the state of the art, with unmatched translation fluency. Let's dive into the internals of a Neural Machine Translation system, explaining the principles and the advantages over the past.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document discusses test data compression techniques for system-on-chip designs. It presents the XMatchPro algorithm that combines dictionary-based and bit mask-based compression to significantly reduce testing time and memory requirements. The algorithm was applied to benchmarks and achieved a 92% compression efficiency while improving decompression efficiency by up to 90% compared to other techniques without additional overhead. International lossless compression techniques are also overviewed, including dictionary-based Lempel-Ziv methods that have been implemented in hardware using systolic arrays or content addressable memories.
Local Applications of Large Language Models based on RAG.pptxlwz614595250
We present an approach. We can deploy large models locally in an efficiently realizable way. Targeting some domain-specific applications will help a lot.
A MULTI-LAYER HYBRID TEXT STEGANOGRAPHY FOR SECRET COMMUNICATION USING WORD T...IJNSA Journal
This paper introduces a multi-layer hybrid text steganography approach by utilizing word tagging and recoloring. Existing approaches are planned to be either progressive in getting imperceptibility, or high hiding limit, or robustness. The proposed approach does not use the ordinary sequential inserting process and overcome issues of the current approaches by taking a careful of getting imperceptibility, high hiding limit, and robustness through its hybrid work by using a linguistic technique and a format-based technique. The linguistic technique is used to divide the cover text into embedding layers where each layer consists of a sequence of words that has a single part of speech detected by POS tagger, while the format-based technique is used to recolor the letters of a cover text with a near RGB color coding to embed 12 bits from the secret message in each letter which leads to high hidden capacity and blinds the embedding, moreover, the robustness is accomplished through a multi-layer embedding process, and the generated stego key significantly assists the security of the embedding messages and its size. The experimental results comparison shows that the purpose approach is better than currently developed approaches in providing an ideal balance between imperceptibility, high hiding limit, and robustness criteria.
A MULTI-LAYER HYBRID TEXT STEGANOGRAPHY FOR SECRET COMMUNICATION USING WORD T...IJNSA Journal
This paper introduces a multi-layer hybrid text steganography approach by utilizing word tagging and recoloring. Existing approaches are planned to be either progressive in getting imperceptibility, or high hiding limit, or robustness. The proposed approach does not use the ordinary sequential inserting process and overcome issues of the current approaches by taking a careful of getting imperceptibility, high hiding limit, and robustness through its hybrid work by using a linguistic technique and a format-based technique. The linguistic technique is used to divide the cover text into embedding layers where each layer consists of a sequence of words that has a single part of speech detected by POS tagger, while the format-based technique is used to recolor the letters of a cover text with a near RGB color coding to embed 12 bits from the secret message in each letter which leads to high hidden capacity and blinds the embedding, moreover, the robustness is accomplished through a multi-layer embedding process, and the generated stego key significantly assists the security of the embedding messages and its size. The experimental results comparison shows that the purpose approach is better than currently developed approaches in providing an ideal balance between imperceptibility, high hiding limit, and robustness criteria.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
FREE A4 Cyber Security Awareness Posters-Social Engineering part 3Data Hops
Free A4 downloadable and printable Cyber Security, Social Engineering Safety and security Training Posters . Promote security awareness in the home or workplace. Lock them Out From training providers datahops.com
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
Pauls klein 2011-lm_paper(3)
1. Faster and Smaller N -Gram Language Models
Adam Pauls Dan Klein
Computer Science Division
University of California, Berkeley
{adpauls,klein}@cs.berkeley.edu
Abstract and Raj, 2001; Hsu and Glass, 2008), our meth-
ods are conceptually based on tabular trie encodings
N -gram language models are a major resource wherein each n-gram key is stored as the concatena-
bottleneck in machine translation. In this pa- tion of one word (here, the last) and an offset encod-
per, we present several language model imple- ing the remaining words (here, the context). After
mentations that are both highly compact and presenting a bit-conscious basic system that typifies
fast to query. Our fastest implementation is
such approaches, we improve on it in several ways.
as fast as the widely used SRILM while re-
quiring only 25% of the storage. Our most First, we show how the last word of each entry can
compact representation can store all 4 billion be implicitly encoded, almost entirely eliminating
n-grams and associated counts for the Google its storage requirements. Second, we show that the
n-gram corpus in 23 bits per n-gram, the most deltas between adjacent entries can be efficiently en-
compact lossless representation to date, and coded with simple variable-length encodings. Third,
even more compact than recent lossy compres- we investigate block-based schemes that minimize
sion techniques. We also discuss techniques
the amount of compressed-stream scanning during
for improving query speed during decoding,
including a simple but novel language model
lookup.
caching technique that improves the query To speed up our language models, we present two
speed of our language models (and SRILM) approaches. The first is a front-end cache. Caching
by up to 300%. itself is certainly not new to language modeling, but
because well-tuned LMs are essentially lookup ta-
bles to begin with, naive cache designs only speed
1 Introduction up slower systems. We present a direct-addressing
For modern statistical machine translation systems, cache with a fast key identity check that speeds up
language models must be both fast and compact. our systems (or existing fast systems like the widely-
The largest language models (LMs) can contain as used, speed-focused SRILM) by up to 300%.
many as several hundred billion n-grams (Brants Our second speed-up comes from a more funda-
et al., 2007), so storage is a challenge. At the mental change to the language modeling interface.
same time, decoding a single sentence can trig- Where classic LMs take word tuples and produce
ger hundreds of thousands of queries to the lan- counts or probabilities, we propose an LM that takes
guage model, so speed is also critical. As al- a word-and-context encoding (so the context need
ways, trade-offs exist between time, space, and ac- not be re-looked up) and returns both the probabil-
curacy, with many recent papers considering small- ity and also the context encoding for the suffix of the
but-approximate noisy LMs (Chazelle et al., 2004; original query. This setup substantially accelerates
Guthrie and Hepple, 2010) or small-but-slow com- the scrolling queries issued by decoders, and also
pressed LMs (Germann et al., 2009). exploits language model state equivalence (Li and
In this paper, we present several lossless meth- Khudanpur, 2008).
ods for compactly but efficiently storing large LMs Overall, we are able to store the 4 billion n-grams
in memory. As in much previous work (Whittaker of the Google Web1T (Brants and Franz, 2006) cor-
2. pus, with associated counts, in 10 GB of memory, we encode counts, probabilities and/or back-off
which is smaller than state-of-the-art lossy language weights in our model. In general, the number of bits
model implementations (Guthrie and Hepple, 2010), per value required to encode all value ranks for a
and significantly smaller than the best published given language model will vary – we will refer to
lossless implementation (Germann et al., 2009). We this variable as v .
are also able to simultaneously outperform SRILM
in both total size and speed. Our LM toolkit, which 2.2 Trie-Based Language Models
is implemented in Java and compatible with the stan- The data structure of choice for the majority of
dard ARPA file formats, is available on the web.1 modern language model implementations is a trie
(Fredkin, 1960). Tries or variants thereof are
2 Preliminaries implemented in many LM tool kits, including
Our goal in this paper is to provide data structures SRILM (Stolcke, 2002), IRSTLM (Federico and
that map n-gram keys to values, i.e. probabilities Cettolo, 2007), CMU SLM (Whittaker and Raj,
or counts. Maps are fundamental data structures 2001), and MIT LM (Hsu and Glass, 2008). Tries
and generic implementations of mapping data struc- represent collections of n-grams using a tree. Each
tures are readily available. However, because of the node in the tree encodes a word, and paths in the
sheer number of keys and values needed for n-gram tree correspond to n-grams in the collection. Tries
language modeling, generic implementations do not ensure that each n-gram prefix is represented only
work efficiently “out of the box.” In this section, once, and are very efficient when n-grams share
we will review existing techniques for encoding the common prefixes. Values can also be stored in a trie
keys and values of an n-gram language model, tak- by placing them in the appropriate nodes.
ing care to account for every bit of memory required Conceptually, trie nodes can be implemented as
by each implementation. records that contain two entries: one for the word
To provide absolute numbers for the storage re- in the node, and one for either a pointer to the par-
quirements of different implementations, we will ent of the node or a list of pointers to children. At
use the Google Web1T corpus as a benchmark. This a low level, however, naive implementations of tries
corpus, which is on the large end of corpora typically can waste significant amounts of space. For exam-
employed in language modeling, is a collection of ple, the implementation used in SRILM represents a
nearly 4 billion n-grams extracted from over a tril- trie node as a C struct containing a 32-bit integer
lion tokens of English text, and has a vocabulary of representing the word, a 64-bit memory2 pointer to
about 13.5 million words. the list of children, and a 32-bit floating point num-
ber representing the value stored at a node. The total
2.1 Encoding Values storage for a node alone is 16 bytes, with additional
overhead required to store the list of children. In
In the Web1T corpus, the most frequent n-gram
total, the most compact implementation in SRILM
occurs about 95 billion times. Storing this count
uses 33 bytes per n-gram of storage, which would
explicitly would require 37 bits, but, as noted by
require around 116 GB of memory to store Web1T.
Guthrie and Hepple (2010), the corpus contains only
While it is simple to implement a trie node in this
about 770 000 unique counts, so we can enumerate
(already wasteful) way in programming languages
all counts using only 20 bits, and separately store an
that offer low-level access to memory allocation like
array called the value rank array which converts the
C/C++, the situation is even worse in higher level
rank encoding of a count back to its raw count. The
programming languages. In Java, for example, C-
additional array is small, requiring only about 3MB,
style structs are not available, and records are
but we save 17 bits per n-gram, reducing value stor-
most naturally implemented as objects that carry an
age from around 16GB to about 9GB for Web1T.
additional 64 bits of overhead.
We can rank encode probabilities and back-offs in
2
the same way, allowing us to be agnostic to whether While 32-bit architectures are still in use today, their lim-
ited address space is insufficient for modern language models
1
http://code.google.com/p/berkeleylm/ and we will assume all machines use a 64-bit architecture.
3. n−1
Despite its relatively large storage requirements, encode c(w1 ). We will refer to this encoding as a
the implementation employed by SRILM is still context encoding.
widely in use today, largely because of its speed – to Note that typically, n-grams are encoded in tries
our knowledge, SRILM is the fastest freely available in the reverse direction (first-rest instead of last-
language model implementation. We will show that rest), which enables a more efficient computation of
we can achieve access speeds comparable to SRILM back-offs. In our implementations, we found that the
but using only 25% of the storage. speed improvement from switching to a first-rest en-
coding and implementing more efficient queries was
2.3 Implicit Tries modest. However, as we will see in Section 4.2, the
A more compact implementation of a trie is de- last-rest encoding allows us to exploit the scrolling
scribed in Whittaker and Raj (2001). In their imple- nature of queries issued by decoders, which results
mentation, nodes in a trie are represented implicitly in speedups that far outweigh those achieved by re-
as entries in an array. Each entry encodes a word versing the trie.
with enough bits to index all words in the language
model (24 bits for Web1T), a quantized value, and 3 Language Model Implementations
a 32-bit3 offset that encodes the contiguous block In the previous section, we reviewed well-known
of the array containing the children of the node. techniques in language model implementation. In
Note that 32 bits is sufficient to index all n-grams in this section, we combine these techniques to build
Web1T; for larger corpora, we can always increase simple data structures in ways that are to our knowl-
the size of the offset. edge novel, producing language models with state-
Effectively, this representation replaces system- of-the-art memory requirements and speed. We will
level memory pointers with offsets that act as logical also show that our data structures can be very effec-
pointers that can reference other entries in the array, tively compressed by implicitly encoding the word
rather than arbitrary bytes in RAM. This represen- wn , and further compressed by applying a variable-
tation saves space because offsets require fewer bits length encoding on context deltas.
than memory pointers, but more importantly, it per-
mits straightforward implementation in any higher- 3.1 Sorted Array
level language that provides access to arrays of inte-
A standard way to implement a map is to store an
gers.4
array of key/value pairs, sorted according to the key.
2.4 Encoding n-grams Lookup is carried out by performing binary search
on a key. For an n-gram language model, we can ap-
Hsu and Glass (2008) describe a variant of the im- ply this implementation with a slight modification:
plicit tries of Whittaker and Raj (2001) in which we need n sorted arrays, one for each n-gram order.
each node in the trie stores the prefix (i.e. parent). n−1
We construct keys (wn , c(w1 )) using the context
This representation has the property that we can re- encoding described in the previous section, where
n
fer to each n-gram w1 by its last word wn and the the context offsets c refer to entries in the sorted ar-
n−1 n−1
offset c(w1 ) of its prefix w1 , often called the ray of (n − 1)-grams. This data structure is shown
context. At a low-level, we can efficiently encode graphically in Figure 1.
n−1
this pair (wn , c(w1 )) as a single 64-bit integer, Because our keys are sorted according to their
where the first 24 bits refer to wn and the last 40 bits context-encoded representation, we cannot straight-
3
The implementation described in the paper represents each forwardly answer queries about an n-gram w with-
32-bit integer compactly using only 16 bits, but this represen- out first determining its context encoding. We can
tation is quite inefficient, because determining the full 32-bit do this efficiently by building up the encoding in-
offset requires a binary search in a look up table. crementally: we start with the context offset of the
4
Typically, programming languages only provide support
for arrays of bytes, not bits, but it is of course possible to simu-
unigram w1 , which is simply its integer representa-
late arrays with arbitrary numbers of bits using byte arrays and tion, and use that to form the context encoding of the
bit manipulation. 2
bigram w1 = (w2 , c(w1 )). We can find the offset of
4. 3-grams 2-grams 1-grams
. .
. .
w c val w c val w val
1933 15176583 6879 00004498 .
1933 15176585 6879 00004502 .
1933 15176593 6879 00004530 “slept”
.
“slept” 1933 15176613 “cat” 6879 00004568
.
1933 15179801 6879 00004588
“left”
1933 15180051 6879 00004598 “the”
1933 15180053 6879 00004668 “had”
1935 15176585 6880 00004669 .
.
“ran” 1935 15176589 “dog” 6880 00004568
“dog”
1935 15176591 6880 00004577
. . .
. . .
24 40 v
bits bits bits
64 bits
Figure 1: Our S ORTED implementation of a trie. The dotted paths correspond to “the cat slept”, “the cat ran”, and “the
dog ran”. Each node in the trie is an entry in an array with 3 parts: w represents the word at the node; val represents
the (rank encoded) value; and c is an offset in the array of n − 1 grams that represents the parent (prefix) of a node.
Words are represented as offsets in the unigram array.
the bigram using binary search, and form the context n
insert an n-gram w1 into the hash table, we must
encoding of the trigram, and so on. Note, however, form its context encoding incrementally from the
that if our queries arrive in context-encoded form, offset of w1 . However, unlike the sorted array im-
queries are faster since they involve only one binary plementation, at query time, we only need to be
search in the appropriate array. We will return to this able to check equality between the query key wn = 1
later in Section 4.2 (wn , c(w1 )) and a key w1n = (wn , c(w1n−1 )) in
n−1
This implementation, S ORTED, uses 64 bits for the table. Equality can easily be checked by first
the integer-encoded keys and v bits for the values. checking if wn = wn , then recursively checking
Lookup is linear in the length of the key and log- equality between wn−1 and w1n−1 , though again,
1
arithmic in the number of n-grams. For Web1T equality is even faster if the query is already context-
(v = 20), the total storage is 10.5 bytes/n-gram or encoded.
about 37GB. This H ASH data structure also uses 64 bits for
integer-encoded keys and v bits for values. How-
3.2 Hash Table ever, to avoid excessive hash collisions, we also al-
Hash tables are another standard way to implement locate additional empty space according to a user-
associative arrays. To enable the use of our context defined parameter that trades off speed and time –
encoding, we require an implementation in which we used about 40% extra space in our experiments.
we can refer to entries in the hash table via array For Web1T, the total storage for this implementation
offsets. For this reason, we use an open address hash is 15 bytes/n-gram or about 53 GB total.
map that uses linear probing for collision resolution. Look up in a hash map is linear in the length of
As in the sorted array implementation, in order to an n-gram and constant with respect to the number
5. of n-grams. Unlike the sorted array implementa- (a) Context-Encoding (b) Context Deltas (c) Bits Required
w c val !w !c val |!w| |!c| |val|
tion, the hash table implementation also permits ef- 1933 15176585 3 1933 15176585 3 24 40 3
ficient insertion and deletion, making it suitable for 1933 15176587 2 +0 +2 1 2 3 3
stream-based language models (Levenberg and Os- 1933 15176593 1 +0 +5 1 2 3 3
1933 15176613 8 +0 +40 8 2 9 6
borne, 2009). 1933 15179801 1 +0 +188 1 2 12 3
1935 15176585 298 +2 15176585 298 4 36 15
3.3 Implicitly Encoding wn 1935 15176589 1 +0 +4 1 2 6 3
Value rank
The context encoding we have used thus far still for header
(d) Compressed Array key
wastes space. This is perhaps most evident in the
1933 15176585 563097887 956 3 0 +0 +2 2 +0 +5 1 +0 +40 8 . . .
sorted array representation (see Figure 1): all n-
Logical Number True if
grams ending with a particular word wi are stored Header
key offset of of bits all !w in
this block in this block are
contiguously. We can exploit this redundancy by block 0
storing only the context offsets in the main array,
using as many bits as needed to encode all context Figure 2: Compression using variable-length encoding.
offsets (32 bits for Web1T). In auxiliary arrays, one (a) A snippet of an (uncompressed) context-encoded ar-
for each n-gram order, we store the beginning and ray. (b) The context and word deltas. (c) The number
end of the range of the trie array in which all (wi , c) of bits required to encode the context and word deltas as
well as the value ranks. Word deltas use variable-length
keys are stored for each wi . These auxiliary arrays
block coding with k = 1, while context deltas and value
are negligibly small – we only need to store 2n off- ranks use k = 2. (d) A snippet of the compressed encod-
sets for each word. ing array. The header is outlined in bold.
The same trick can be applied in the hash table
implementation. We allocate contiguous blocks of of the key array used in our sorted array implemen-
the main array for n-grams which all share the same tation. While we have already exploited the fact that
last word wi , and distribute keys within those ranges the 24 word bits repeat in the previous section, we
using the hashing function. note here that consecutive context offsets tend to be
This representation reduces memory usage for quite close together. We found that for 5-grams, the
keys from 64 bits to 32 bits, reducing overall storage median difference between consecutive offsets was
for Web1T to 6.5 bytes/n-gram for the sorted imple- about 50, and 90% of offset deltas were smaller than
mentation and 9.1 bytes for the hashed implementa- 10000. By using a variable-length encoding to rep-
tion, or about 23GB and 32GB in total. It also in- resent these deltas, we should require far fewer than
creases query speed in the sorted array case, since to 32 bits to encode context offsets.
find (wi , c), we only need to search the range of the We used a very simple variable-length coding to
array over which wi applies. Because this implicit encode offset deltas, word deltas, and value ranks.
encoding reduces memory usage without a perfor- Our encoding, which is referred to as “variable-
mance cost, we will assume its use for the rest of length block coding” in Boldi and Vigna (2005),
this paper. works as follows: we pick a (configurable) radix
r = 2k . To encode a number m, we determine the
3.4 A Compressed Implementation number of digits d required to express m in base r.
3.4.1 Variable-Length Coding We write d in unary, i.e. d − 1 zeroes followed by
The distribution of value ranks in language mod- a one. We then write the d digits of m in base r,
eling is Zipfian, with far more n-grams having low each of which requires k bits. For example, using
counts than high counts. If we ensure that the value k = 2, we would encode the decimal number 7 as
rank array sorts raw values by descending order of 010111. We can choose k separately for deltas and
frequency, then we expect that small ranks will oc- value indices, and also tune these parameters to a
cur much more frequently than large ones, which we given language model.
can exploit with a variable-length encoding. We found this encoding outperformed other
To compress n-grams, we can exploit the context standard prefix codes, including Golomb
encoding of our keys. In Figure 2, we show a portion codes (Golomb, 1966; Church et al., 2007)
6. and Elias γ and δ codes. We also experimented 6GB less than the storage required by Germann et
with the ζ codes of Boldi and Vigna (2005), which al. (2009), which is the best published lossless com-
modify variable-length block codes so that they pression to date.
are optimal for certain power law distributions.
We found that ζ codes performed no better than 4 Speeding up Decoding
variable-length block codes and were slightly more
complex. Finally, we found that Huffman codes In the previous section, we provided compact and
outperformed our encoding slightly, but came at a efficient implementations of associative arrays that
much higher computational cost. allow us to query a value for an arbitrary n-gram.
However, decoders do not issue language model re-
3.4.2 Block Compression quests at random. In this section, we show that lan-
We could in principle compress the entire array of guage model requests issued by a standard decoder
key/value pairs with the encoding described above, exhibit two patterns we can exploit: they are highly
but this would render binary search in the array im- repetitive, and also exhibit a scrolling effect.
possible: we cannot jump to the mid-point of the ar-
ray since in order to determine what key lies at a par- 4.1 Exploiting Repetitive Queries
ticular point in the compressed bit stream, we would In a simple experiment, we recorded all of the
need to know the entire history of offset deltas. language model queries issued by the Joshua de-
Instead, we employ block compression, a tech- coder (Li et al., 2009) on a 100 sentence test set.
nique also used by Harb et al. (2009) for smaller Of the 31 million queries, only about 1 million were
language models. In particular, we compress the unique. Therefore, we expect that keeping the re-
key/value array in blocks of 128 bytes. At the be- sults of language model queries in a cache should be
ginning of the block, we write out a header consist- effective at reducing overall language model latency.
ing of: an explicit 64-bit key that begins the block; To this end, we added a very simple cache to
a 32-bit integer representing the offset of the header our language model. Our cache uses an array of
key in the uncompressed array;5 the number of bits key/value pairs with size fixed to 2b − 1 for some
of compressed data in the block; and the variable- integer b (we used 24). We use a b-bit hash func-
length encoding of the value rank of the header key. tion to compute the address in an array where we
The remainder of the block is filled with as many will always place a given n-gram and its fully com-
compressed key/value pairs as possible. Once the puted language model score. Querying the cache is
block is full, we start a new block. See Figure 2 for straightforward: we check the address of a key given
a depiction. by its b-bit hash. If the key located in the cache ar-
When we encode an offset delta, we store the delta ray matches the query key, then we return the value
of the word portion of the key separately from the stored in the cache. Otherwise, we fetch the lan-
delta of the context offset. When an entire block guage model probability from the language model
shares the same word portion of the key, we set a and place the new key and value in the cache, evict-
single bit in the header that indicates that we do not ing the old key in the process. This scheme is often
encode any word deltas. called a direct-mapped cache because each key has
To find a key in this compressed array, we first exactly one possible address.
perform binary search over the header blocks (which Caching n-grams in this way reduces overall la-
are predictably located every 128 bytes), followed tency for two reasons: first, lookup in the cache is
by a linear search within a compressed block. extremely fast, requiring only a single evaluation of
Using k = 6 for encoding offset deltas and k = 5 the hash function, one memory lookup to find the
for encoding value ranks, this C OMPRESSED im- cache key, and one equality check on the key. In
plementation stores Web1T in less than 3 bytes per contrast, even our fastest (H ASH) implementation
n-gram, or about 10.2GB in total. This is about may have to perform multiple memory lookups and
5
We need this because n-grams refer to their contexts using equality checks in order to resolve collisions. Sec-
array offsets. ond, when calculating the probability for an n-gram
7. the cat + fell down W MT 2010 W EB 1T
Representation
the cat fell LM 0.76
Order #n-grams Order #n-grams
Explicit 1gm 4,366,395 1gm 13,588,391
cat fell down LM 0.12 2gm 61,865,588 2gm 314,843,401
“the cat”
3gm 123,158,761 3gm 977,069,902
18569876 fell LM 0.76 4gm 217,869,981 4gm 1,313,818,354
Encoding
5gm 269,614,330 5gm 1,176,470,663
Context
3576410
“cat fell” Total 676,875,055 Total 3,795,790,711
35764106 down LM 0.12
Table 1: Sizes of the two language models used in our
experiments.
Figure 3: Queries issued when scoring trigrams that are
created when a state with LM context “the cat” combines
with “fell down”. In the standard explicit representation n-grams exhibit a scrolling effect, shown in Fig-
of an n-gram as list of words, queries are issued atom- ure 3: the n − 1 suffix words of one n-gram form
ically to the language model. When using a context- the n − 1 prefix words of the next.
encoding, a query from the n-gram “the cat fell” returns
the context offset of “cat fell”, which speeds up the query As discussed in Section 3, our LM implementa-
of “cat fell down”. tions can answer queries about context-encoded n-
not in the language model, language models with grams faster than explicitly encoded n-grams. With
back-off schemes must in general perform multiple this in mind, we augment the values stored in our
n−1
queries to fetch the necessary back-off information. language model so that for a key (wn , c(w1 )),
n
we store the offset of the suffix c(w2 ) as well as
Our cache retains the full result of these calculations
and thus saves additional computation. the normal counts/probabilities. Then, rather than
Federico and Cettolo (2007) also employ a cache represent the LM context in the decoder as an ex-
in their language model implementation, though plicit list of words, we can simply store context off-
based on traditional hash table cache with linear sets. When we query the language model, we get
probing. Unlike our cache, which is of fixed size, back both a language model score and context offset
their cache must be cleared after decoding a sen- ˆ n−1
c(w1 ), where w1 ˆ n−1 is the the longest suffix of
n−1
tence. We would not expect a large performance in- w1 contained in the language model. We can then
crease from such a cache for our faster models since quickly form the context encoding of the next query
our H ASH implementation is already a hash table by simply concatenating the new word with the off-
with linear probing. We found in our experiments ˆ n−1
set c(w1 ) returned from the previous query.
that a cache using linear probing provided marginal
In addition to speeding up language model
performance increases of about 40%, largely be-
queries, this approach also automatically supports an
cause of cached back-off computation, while our
equivalence of LM states (Li and Khudanpur, 2008):
simpler cache increases performance by about 300%
in standard back-off schemes, whenever we compute
even over our H ASH LM implementation. More tim-
the probability for an n-gram (wn , c(wn−1 )) when
1
ing results are presented in Section 5.
wn−1 is not in the language model, the result will be
1
ˆ n−1
the same as the result of the query (wn , c(w1 ). It
4.2 Exploiting Scrolling Queries
is therefore only necessary to store as much of the
Decoders with integrated language models (Och and context as the language model contains instead of
Ney, 2004; Chiang, 2005) score partial translation all n − 1 words in the context. If a decoder main-
hypotheses in an incremental way. Each partial hy- tains LM states using the context offsets returned
pothesis maintains a language model context con- by our language model, then the decoder will au-
sisting of at most n − 1 target-side words. When tomatically exploit this equivalence and the size of
we combine two language model contexts, we create the search space will be reduced. This same effect is
several new n-grams of length of n, each of which exploited explicitly by some decoders (Li and Khu-
generate a query to the language model. These new danpur, 2008).
8. W MT 2010 W EB 1T
LM Type bytes/ bytes/ bytes/ Total LM Type bytes/ bytes/ bytes/ Total
key value n-gram Size key value n-gram Size
SRILM-H – – 42.2 26.6G Gzip – – 7.0 24.7G
SRILM-S – – 33.5 21.1G T-MPHR† – – 3.0 10.5G
H ASH 5.6 6.0 11.6 7.5G C OMPRESSED 1.3 1.6 2.9 10.2G
S ORTED 4.0 4.5 8.5 5.5G
TPT – – 7.5∗∗ 4.7G∗∗ Table 3: Memory usages of several language model im-
C OMPRESSED 2.1 3.8 5.9 3.7G plementations on the W EB 1T. A † indicates lossy com-
pression.
Table 2: Memory usages of several language model im-
plementations on the W MT 2010 language model. A We compare against three baselines. The first two,
∗∗
indicates that the storage in bytes per n-gram is re- SRILM-H and SRILM-S, refer to the hash table-
ported for a different language model of comparable size, and sorted array-based trie implementations pro-
and the total size is thus a rough projection. vided by SRILM. The third baseline is the Tightly-
Packed Trie (TPT) implementation of Germann et
5 Experiments al. (2009). Because this implementation is not freely
available, we use their published memory usage in
5.1 Data bytes per n-gram on a language model of similar
To test our LM implementations, we performed size and project total usage.
experiments with two different language models. The memory usage of all of our models is con-
Our first language model, W MT 2010, was a 5- siderably smaller than SRILM – our H ASH imple-
gram Kneser-Ney language model which stores mentation is about 25% the size of SRILM-H, and
probability/back-off pairs as values. We trained this our S ORTED implementation is about 25% the size
language model on the English side of all French- of SRILM-S. Our C OMPRESSED implementation
English corpora provided6 for use in the WMT 2010 is also smaller than the state-of-the-art compressed
workshop, about 2 billion tokens in total. This data TPT implementation.
was tokenized using the tokenizer.perl script In Table 3, we show the results of our C OM -
provided with the data. We trained the language PRESSED implementation on W EB 1T and against
model using SRILM. We also extracted a count- two baselines. The first is compression of the ASCII
based language model, W EB 1T, from the Web1T text count files using gzip, and the second is the
corpus (Brants and Franz, 2006). Since this data is Tiered Minimal Perfect Hash (T-MPHR) of Guthrie
provided as a collection of 1- to 5-grams and asso- and Hepple (2010). The latter is a lossy compres-
ciated counts, we used this data without further pre- sion technique based on Bloomier filters (Chazelle
processing. The make up of these language models et al., 2004) and additional variable-length encod-
is shown in Table 1. ing that achieves the best published compression of
W EB 1T to date. Our C OMPRESSED implementa-
5.2 Compression Experiments tion is even smaller than T-MPHR, despite using a
We tested our three implementations (H ASH, lossless compression technique. Note that since T-
S ORTED, and C OMPRESSED) on the W MT 2010 MPHR uses a lossy encoding, it is possible to re-
language model. For this language model, there are duce the storage requirements arbitrarily at the cost
about 80 million unique probability/back-off pairs, of additional errors in the model. We quote here the
so v ≈ 36. Note that here v includes both the storage required when keys7 are encoded using 12-
cost per key of storing the value rank as well as the bit hash codes, which gives a false positive rate of
(amortized) cost of storing two 32 bit floating point about 2−12 =0.02%.
numbers (probability and back-off) for each unique 7
value. The results are shown in Table 2. Guthrie and Hepple (2010) also report additional savings
by quantizing values, though we could perform the same quan-
6
www.statmt.org/wmt10/translation-task.html tization in our storage scheme.
9. LM Type No Cache Cache Size nificantly less space. S ORTED is slower but of
C OMPRESSED 9264±73ns 565±7ns 3.7G course more memory efficient, and C OMPRESSED
S ORTED 1405±50ns 243±4ns 5.5G is the slowest but also the most compact repre-
H ASH 495±10ns 179±6ns 7.5G
SRILM-H 428±5ns 159±4ns 26.6G
sentation. In H ASH +S CROLL, we issued queries
H ASH +S CROLL 323±5ns 139±6ns 10.5G to the language model using the context encoding,
which speeds up queries substantially. Finally, we
Table 4: Raw query speeds of various language model note that our direct-mapped cache is very effective.
implementations. Times were averaged over 3 runs on The query speed of all models is boosted substan-
the same machine. For H ASH +S CROLL, all queries were tially. In particular, our C OMPRESSED implementa-
issued to the decoder in context-encoded form, which tion with caching is nearly as fast as SRILM-H with-
speeds up queries that exhibit scrolling behaviour. Note out caching, and even the already fast H ASH imple-
that memory usage is higher than for H ASH because we mentation is 300% faster in raw query speed with
store suffix offsets along with the values for an n-gram.
caching enabled.
LM Type No Cache Cache Size We also measured the effect of LM performance
C OMPRESSED 9880±82s 1547±7s 3.7G on overall decoder performance. We modified
SRILM-H 1120±26s 938±11s 26.6G Joshua to optionally use our LM implementations
H ASH 1146±8s 943±16s 7.5G during decoding, and measured the time required
to decode all 2051 sentences of the 2008 News
Table 5: Full decoding times for various language model test set. The results are shown in Table 5. With-
implementations. Our H ASH LM is as fast as SRILM out caching, SRILM-H and H ASH were comparable
while using 25% of the memory. Our caching also re- in speed, while C OMPRESSED introduces a perfor-
duces total decoding time by about 20% for our fastest
mance penalty. With caching enabled, overall de-
models and speeds up C OMPRESSED by a factor of 6.
Times were averaged over 3 runs on the same machine. coder speed is improved for both H ASH and SRILM-
H, while the C OMPRESSED implementation is only
5.3 Timing Experiments about 50% slower that the others.
We first measured pure query speed by logging all
LM queries issued by a decoder and measuring
6 Conclusion
the time required to query those n-grams in isola- We have presented several language model imple-
tion. We used the the Joshua decoder8 with the mentations which are state-of-the-art in both size
W MT 2010 model to generate queries for the first and speed. Our experiments have demonstrated im-
100 sentences of the French 2008 News test set. This provements in query speed over SRILM and com-
produced about 30 million queries. We measured the pression rates against state-of-the-art lossy compres-
time9 required to perform each query in order with sion. We have also described a simple caching tech-
and without our direct-mapped caching, not includ- nique which leads to performance increases in over-
ing any time spent on file I/O. all decoding time.
The results are shown in Table 4. As expected,
H ASH is the fastest of our implementations, and Acknowledgements
comparable10 in speed to SRILM-H, but using sig-
This work was supported by a Google Fellowship for the
8
We used a grammar trained on all French-English data first author and by BBN under DARPA contract HR0011-
provided for WMT 2010 using the make scripts provided 06-C-0022. We would like to thank David Chiang, Zhifei
at http://sourceforge.net/projects/joshua/files Li, and the anonymous reviewers for their helpful com-
/joshua/1.3/wmt2010-experiment.tgz/download
9 ments.
All experiments were performed on an Amazon EC2 High-
Memory Quadruple Extra Large instance, with an Intel Xeon nately, it is not completely fair to compare our LMs against ei-
X5550 CPU running at 2.67GHz and 8 MB of cache. ther of these numbers: although the JNI overhead slows down
10
Because we implemented our LMs in Java, we issued SRILM, implementing our LMs in Java instead of C++ slows
queries to SRILM via Java Native Interface (JNI) calls, which down our LMs. In the tables, we quote times which include
introduces a performance overhead. When called natively, we the JNI overhead, since this reflects the true cost to a decoder
found that SRILM was about 200 ns/query faster. Unfortu- written in Java (e.g. Joshua).
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