Using Negative Detectors for Identifying Adversarial Data Manipulation in Machine Learning
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With the increased popularity of Machine Learning (ML) in real-world applications, adversarial attacks are emerging to subvert the ML-based decision support systems. It appears that the existing adversarial defenses are ineffective against adaptive attacks since these are highly depend on knowledge of prior attacks and the ML model architecture. To alleviate the challenges, We propose a negative filtering strategy that does not require any adversarial knowledge and can work independent of ML models. This filtering strategy relies on salient features of clean (training) data and employs a complementary approach to cover possible attack surface in an application. Our empirical experiments with different data sets demonstrate that the negative filters could effectively detect wide-range of adversarial inputs and update itself to protect against adaptive attacks.
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Robustness in deep learning
Dr Murari Mandal from NUS presented as part of 3 days OpenPOWER Industry summit about Robustness in Deep learning where he talked about AI Breakthroughs , Performance improments in AI models , Adversarial attacks , Attacks on semantic segmentation , Attacs on object detector , Defending Against adversarial attacks and many other areas.
Ubiquitious Computing system : Integrating RFID with Face Recognition systems
This document describes a student attendance system that integrates RFID and face recognition technologies. RFID tags are used to record when students enter the classroom, while a camera and face recognition software verify students' identities. The system aims to automate attendance tracking and prevent illegal use of RFID cards. It discusses the components, workings, and standards of RFID systems as well as an overview of face recognition using principal component analysis. MATLAB is used to implement a prototype that detects faces in images and recognizes students with over 95% accuracy based on a real-time face database. The integrated system aims to provide efficient, secure, and error-free attendance tracking.
Automated parameter optimization should be included in future
defect predict...
Automated parameter optimization should be included in future
defect predict...Chakkrit (Kla) Tantithamthavorn
Automated parameter optimization techniques like Caret can substantially improve the performance of defect prediction models over using default parameter settings. When applied to 18 datasets using 26 classification techniques, Caret optimized models improved average AUC performance by up to 40 percentage points for some techniques. Caret optimized models also tended to be more stable than default models, with the stability ratio being lower than 1 for 35% of techniques studied. Overall, automated parameter optimization can significantly enhance both the performance and stability of defect prediction models.4. Classification.pdf
Machine Learning Classification models:
Logistic Regression
K-Nearest Neighbors (K-NN)
Support Vector Machine
Kernel SVM
Naïve Bayes
Decision Tree Classification
Random Forest Classification
Final PPT.pdf
This document summarizes an autonomous driving model called YOLOP that can perform object and traffic sign detection, drivable area segmentation, and lane detection. It was tested on the BDD100K dataset, achieving mAP of 76.5% for detection, 91.5% for segmentation, and 70.5% for lane detection. The model was then trained on the German Traffic Sign Recognition Benchmark (GTRSB) and German Traffic Sign Detection Benchmark (GTDSB) datasets, achieving up to 96.8% accuracy. An alternate training method focused on bounding boxes first before classifying signs, improving mAP. A prototype bot was constructed using the model for moderate speed autonomous driving.
Presentation on BornoNet Research Paper and Python Basics
The slides are of a presentation on BornoNet Research Paper and Python basics done by our team recently in our Mobile and Telecommunication course of undergraduate studies.
Intro to Multitarget Tracking for CURVE
Tracking involves estimating a target's state based on uncertain sensor measurements and calculating the accuracy of the estimate. Effective tracking requires modeling target motion, sensor measurements, and measurement origin uncertainty. Tracking performance is evaluated using mean-square estimation error. Data association and filtering techniques have been developed to track maneuvering targets in clutter using multiple sensors, with information fusion improving accuracy.
Andrew Bossy. Data Imputation Using Reverse ML
This document discusses various methods for data imputation and restoration using reverse machine learning. It describes types of missing data and common imputation techniques like mean, median, mode, regression, KNN, and MICE. Advanced generative models for imputation including VAE, GAIN, MisGAN, and VIGAN are also covered. The document proposes training autoencoders with dropout and regularization as a reverse machine learning technique for data restoration, finding it achieves better accuracy than classical methods.
Deep_Learning__INAF_baroncelli.pdf
The document discusses improving signal/background discrimination for the AGILE telescope using machine learning models and feature selection techniques. It explores automatically selecting features using methods like Pearson correlation, mutual information, lasso, and extra trees. These selected features are then used to train gradient boosting and random forest models. The best performing model used all 239 features with gradient boosting, achieving an F1 score of 0.9094 and ROC AUC of 0.9111. Lasso-selected features (120 features) also performed well with gradient boosting with an F1 score of 0.9074. This demonstrates that automatic feature selection can identify informative features and build effective models for signal/background discrimination.
Outlier detection method introduction
Introduce some basic Outlier Detection Method
(ABOD, HBOD, kNN, LOF, OC-SVM, PCA, Isolation Forest, AE)
Recent advances on low-rank and sparse decomposition for moving object detection
(RFIA 2016) Recent advances on low-rank and sparse decomposition for moving object detection: matrix and tensor-based approaches. RFIA 2016, workshop/atelier: Enjeux dans la détection d’objets mobiles par soustraction de fond.
COSMOS-ASME-IDETC-2014
One of the primary drawbacks plaguing wider acceptance of surrogate models is their low fidelity in general. This issue can be in a large part attributed to the lack of automated model selection techniques, particularly ones that do not make limiting assumptions regarding the choice of model types and kernel types. A novel model selection technique was recently developed to perform optimal model search concurrently at three levels: (i) optimal model type (e.g., RBF), (ii) optimal kernel type (e.g., multiquadric), and (iii) optimal values of hyper-parameters (e.g., shape parameter) that are conventionally kept constant. The error measures to be minimized in this optimal model selection process are determined by the Predictive Estimation of Model Fidelity (PEMF) method, which has been shown to be significantly more accurate than typical cross-validation-based error metrics. In this paper, we make the following important advancements to the PEMF-based model selection framework, now called the Concurrent Surrogate Model Selection or COS- MOS framework: (i) The optimization formulation is modified through binary coding to allow surrogates with differing num- bers of candidate kernels and kernels with differing numbers of hyper-parameters (which was previously not allowed). (ii) A robustness criterion, based on the variance of errors, is added to the existing criteria for model selection. (iii) A larger candidate pool of 16 surrogate-kernel combinations is considered for selection − possibly making COSMOS one of the most comprehensive surrogate model selection framework (in theory and implementation) currently available. The effectiveness of the COSMOS framework is demonstrated by successfully applying it to four benchmark problems (with 2-30 variables) and an airfoil design problem. The optimal model selection results illustrate how diverse models provide important tradeoffs for different problems.
SASA Presentation 2013
This document summarizes and compares different regression techniques for predictive modeling, including linear regression, regression trees, and support vector regression (SVR). It discusses the theory, algorithms, and applications of each technique. A simulation study is conducted in R to assess the predictive performance of each regression method on various datasets and evaluate their advantages and disadvantages. The document concludes that while each regression technique has strengths for certain problem types, SVR and random forests often have highly generalized predictive performance.
Adaptive Hyper-Parameter Tuning for Black-box LiDAR Odometry [IROS2021]
Adaptive Hyper-Parameter Tuning for Black-box LiDAR Odometry
Kenji Koide, Masashi Yokozuka, Shuji Oishi, and Atsuhiko Banno
Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2021), pp. 7708-7714, Prague, Czech Republic, Sep., 2021
https://staff.aist.go.jp/k.koide/
VSSML17 Review. Summary Day 1 Sessions
Valencian Summer School in Machine Learning 2017 - Day 1
Lectures Review: Summary Day 1 Sessions. By Mercè Martín (BigML).
https://bigml.com/events/valencian-summer-school-in-machine-learning-2017
Andy Bosyi: Data Imputation using Reverse ML
Andy Bosyi: Data Imputation using Reverse ML
Data Science Online Camp 2023 Winter
Website: https://dscamp.org
Youtube: https://www.youtube.com/channel/UCeHtPZ_ZLZ-nHFMUCXY81RQ
FB: https://www.facebook.com/people/Data-Science-Camp/100064240830422/
Adaptive relaying
This document discusses applications of artificial intelligence techniques like neural networks, expert systems, and fuzzy logic to power system protection. It provides examples of using these techniques for transmission line fault classification, distance relaying, machine winding protection, transformer differential relaying, and transformer fault diagnosis. The applications aim to make protection schemes more adaptive, reliable, and capable of handling changing system conditions. AI techniques can introduce new concepts like integrated, adaptive, and predictive protection to design smarter digital relays.
Similar to Using Negative Detectors for Identifying Adversarial Data Manipulation in Machine Learning (20)
Deep Recurrent Neural Network for Multi-target Filtering
Deep Recurrent Neural Network for Multi-target Filtering
Responsible AI in Industry: Practical Challenges and Lessons Learned
Responsible AI in Industry: Practical Challenges and Lessons Learned
Realtime pothole detection system using improved CNN Models
Realtime pothole detection system using improved CNN Models
Ubiquitious Computing system : Integrating RFID with Face Recognition systems
Ubiquitious Computing system : Integrating RFID with Face Recognition systems
Automated parameter optimization should be included in future
defect predict...
Automated parameter optimization should be included in future
defect predict...
Presentation on BornoNet Research Paper and Python Basics
Presentation on BornoNet Research Paper and Python Basics
Recent advances on low-rank and sparse decomposition for moving object detection
Recent advances on low-rank and sparse decomposition for moving object detection
Adaptive Hyper-Parameter Tuning for Black-box LiDAR Odometry [IROS2021]
Adaptive Hyper-Parameter Tuning for Black-box LiDAR Odometry [IROS2021]
More from Kishor Datta Gupta
GAN introduction.pptx
Generative adversarial networks (GANs) are a class of machine learning frameworks where two neural networks contest with each other in a game. One network generates new data instances, while the other evaluates them for authenticity. The generator creates synthetic instances to fool the discriminator, while the discriminator learns to identify the generator's fakes from true instances.
Interpretable Learning Model for Lower Dimensional Feature Space: A Case stud...
Detecting brown spot in rice leaf is an urgent complication in the agricultural field as Brown Spot disease lessen the rice yield remarkably. Several segmentation techniques have been applied to identify and extract the infected portion of the rice-leaf and machine learning algorithms such as decision trees, support vector machines are applied to detect this infection. In particular, a combination of Convolution Neural Networks with these algorithms has also tried to resolve this problem. Although this attempt has achieved success in providing accuracy (96.8%), these kinds of approaches raise issues regarding the size and interpretability of feature space and interpretability of the decision model. Indeed, Deep learning networks automatically create a feature space that usually contains a massive number of features (numerous of them are not necessarily appropriate). This vast number of features extends the non-interpretability of the machine learning model. Furthermore, training the model with these many features is computationally expensive. To resolve these issues, we propose a method to extract a few interpretable features from rice-leaf images and construct a low-dimensional feature space; however, interpretation shows that they deserve significant credit for the decent accuracy of our classification model.
A safer approach to build recommendation systems on unidentifiable data
Conference: 14th International Conference on Agents and Artificial Intelligence (ICAART 2022)
In recent years, data security has been one of the biggest concerns, and individuals have grown increasingly worried about the security of their personal information. Personalization typically necessitates the collection of individual data for analysis, exposing customers to privacy concerns. Companies create an illusion of safety to make people feel safe using a mainstream word, "encryption". Though encryption protects personal data from an external breach, the companies can still exploit personal data collected from users as they own the encryption keys. We present a naive yet secure approach for recommending movies to consumers without collecting any personally identifiable information. Our proposed approach can assist a movie recommendation system understand user preferences using the user's movie watch-time and watch history only. We conducted a comprehensive and comparative study on the performance of three deep reinforcement learning architectures, namely DQN, DDQN, and D3QN, on the same task. We observed that D3QN outperformed the other two architectures and achieved a precision of 0.880, recall of 0.805, and F1 score of 0.830. The results show that we can build a competitive movie recommendation system using unidentifiable data.
Adversarial Attacks and Defense
This document summarizes a presentation on machine learning models, adversarial attacks, and defense strategies. It discusses adversarial attacks on machine learning systems, including GAN-based attacks. It then covers various defense strategies against adversarial attacks, such as filter-based adaptive defenses and outlier-based defenses. The presentation also addresses issues around bias in AI systems and the need for explainable and accountable AI.
Who is responsible for adversarial defense
The document discusses adversarial attacks (AAs) on AI/ML systems. It outlines different types of attacks like poisoning attacks, evasion attacks, and Trojan attacks. It also describes various evasion-based attack methods like one pixel attacks and gradient attacks. Additionally, it notes that AAs can be transferable between models and are less effective in physical environments. The document discusses current defense strategies like retraining models, input reconstruction, and model modifications. However, it notes limitations like reduced accuracy and vulnerability to adaptive attacks. It also does not sufficiently test defense practicality or computational costs. In conclusion, the document argues that defending against adaptive attacks and Trojan attacks is particularly challenging and requires end-to-end protections.
Zero shot learning
Zero-shot learning allows a model to recognize classes that it was not trained on by utilizing auxiliary information about both seen and unseen classes during training. The model is trained to predict this auxiliary information, like word embeddings or manually designed features, for the seen classes. During testing, the model predicts the auxiliary information for an unseen class and assigns it to the class whose auxiliary information is closest, even if that class was not part of the training data. This allows the model to generalize to new classes without requiring labeled examples of those classes.
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...
—Recommendation is crucial in both academia andindustry, and various techniques are proposed such as content-based collaborative filtering, matrix factorization, logistic re-gression, factorization machines, neural networks and multi-armed bandits. However, most of the previous studies sufferfrom two limitations: (1) considering the recommendation asa static procedure and ignoring the dynamic interactive naturebetween users and the recommender systems; (2) focusing on theimmediate feedback of recommended items and neglecting thelong-term rewards. To address the two limitations, in this paperwe propose a novel recommendation framework based on deepreinforcement learning, called DRR. The DRR framework treatsrecommendation as a sequential decision making procedure andadopts an “Actor-Critic” reinforcement learning scheme to modelthe interactions between the users and recommender systems,which can consider both the dynamic adaptation and long-term rewards. Further more, a state representation module isincorporated into DRR, which can explicitly capture the interac-tions between items and users. Three instantiation structures aredeveloped. Extensive experiments on four real-world datasets areconducted under both the offline and online evaluation settings.The experimental results demonstrate the proposed DRR methodindeed outperforms the state-of-the-art competitors
Machine learning in computer security
Machine learning can be applied in various areas of computer security like network security, endpoint protection, application security, user behavior analysis, and process behavior analysis. Some common machine learning techniques that are useful for security include regression for prediction and detection of anomalies, classification to identify threats and attacks, and clustering for forensic analysis and to detect outliers. Example applications of machine learning in security include using regression to detect anomalies in network traffic, classification to identify malware, and clustering to separate malware from legitimate files.
Policy Based reinforcement Learning for time series Anomaly detection
This document discusses a policy-based reinforcement learning approach called PTAD for time series anomaly detection. PTAD formulates anomaly detection as a Markov Decision Process and uses an asynchronous actor-critic algorithm to learn a stochastic policy. The agent takes as input current and previous time series data and actions, and outputs a decision of normal or anomalous. It is rewarded based on a confusion matrix calculation. Experimental results show PTAD achieves best performance both within and across datasets by adjusting to different behaviors. The stochastic policy allows exploring precision-recall tradeoffs. While interesting, it is not compared to neural network based techniques like autoencoders.
Cyber intrusion
This document discusses intrusion detection systems (IDS). It covers the key components of an IDS, including methods of intrusion detection like audit trail processing, on-the-fly processing, profiles of normal behavior, signatures of abnormal behavior, and parameter pattern matching. The document also discusses building network-based IDS using tools like Snort and host-based IDS. It provides examples of labs to analyze network and wireless intrusion detection using machine learning techniques.
understanding the pandemic through mining covid news using natural language p...
This document summarizes a research presentation on analyzing Covid-19 news reports from newspapers in developed and developing countries using natural language processing. It introduces the research aim to understand how newspapers portray the pandemic using NLP techniques on reports from the US and Bangladesh. The researchers collected over 1000 news articles to create the NNK Dataset, which they preprocessed and analyzed to extract keywords, sentiments, and case numbers. Word clouds of frequent terms and numeric extractions showed how coverage evolved over time. The dataset was made publicly available to encourage further analysis of portraying pandemics through newspapers.
Different representation space for MNIST digit
The document discusses using different representation spaces for digits when classifying MNIST data. It shows that classifying digits when each digit has its own best representation space that other digits are compared to leads to much higher accuracy, ranging from 97-99%, compared to using the same representation space for all digits which only achieves 64% accuracy.
"Can NLP techniques be utilized as a reliable tool for medical science?" -Bui...
Artificial intelligence persists on being a right-hand tool for many branches of biology. From preliminary advices and treatments, such as understanding if symptoms related to fever or cold, to critical detection of cancerous cell or classification of X-rays, traditional machine learning and deep learning techniques achieved remarkable feats. However, total dependency on machine-based prediction is yet a far fetched concept. In this paper, we provide a framework utilizing several Natural Language Processing (NLP) algorithms to construct a comparative analysis. We create an ensemble of top-performing algorithms to accomplish classification task on medical reports. We compare both the traditional machine learning and deep learning techniques and evaluate their probabilities of being reliable on analyzing medical diagnosis. We concluded that an ensemble approach can provide reliable outcomes with accuracy over 92% and that the current state of the art is unequipped to provide the result with the standard needed for health sectors but an ensemble of these techniques can be a pathway for future research direction.
Conference: IEEE 11th Annual Information Technology, Electronics and Mobile Communication Conference (IEEE IEMCON 2020)At: Vancouver
Clustering report
This document discusses clustering clean and adversarial images from the MNIST dataset using K-means, LDA, and T-SNE clustering methods. It contains 10,000 clean images and 10,000 adversarial images generated using the FGSM attack method from 10 classes in MNIST. The document applies principal component analysis to extract features from the images before clustering them to visualize how the different methods group the clean and adversarial samples.
Basic digital image concept
This document discusses basic digital image concepts including image data representations, color channels, bit depth, CMYK vs RGB color models, image blur filters using kernels, and kernel operations used in convolutional neural networks (CNN).
An empirical study on algorithmic bias (aiml compsac2020)
In all goal-oriented selection activities, an existence of certain level of bias is unavoidable and may be desired for efficient artificial intelligence based decision support systems. However, a fair independent comparison of all eligible entities is essential to alleviate explicit bias in competitive marketplace. For example, searching online for a good or service, it is expected that the underlying algorithm will provide fair results by searching all available entities in the category mentioned. However, a biased search can make a narrow or collaborative query, ignoring competitive outcomes, resulting customers in costing more or getting lower quality products or services for the money they spend. This paper describes algorithmic bias in different contexts with examples and scenarios, best practices to detect bias, and two case studies to identify algorithmic bias.
Hybrid pow-pos-based-system against majority attack-in-cryptocurrency system ...
This document proposes a hybrid PoW-PoS consensus protocol to prevent 51% attacks in cryptocurrency systems. The key points are:
1) A 51% attack occurs when a single entity gains over 50% of the network's computational power, allowing them to manipulate the blockchain.
2) Existing solutions have limitations like special validators, inconsistent block times, or reliance on another network.
3) The proposed solution uses PoW and PoS sequentially in each node, adjusting difficulty to maintain consistent block times. It penalizes malicious nodes and distributes profits proportionally.
4) Analysis shows the hybrid protocol maintains decentralization and the blockchain properties better than alternatives like Casper, Decred or Komodo
Shamir secret sharing: Alternative of hashing for authentication
Hashing is vulnerable to quantum attack. So keep password text hidden using hash in quantum computer era is not viable. Shamir's secret sharing can be used for authentication purpose as a alternative of hashing
A Genetic Algorithm Approach to Optimize Dispatching for A Micro-grid Energy ...
Distributed network reconfiguration techniques are used widely to optimize power distribution systems. As renewable energy generation are very stochastic in nature, network reconfiguration with this stochastic nature does not provide the optimal solution. To address this problem a three-objective genetic algorithm approach has been taken in this project to find the optimal solution of energy scheduling throughout a day, simultaneously using the concept of network reconfiguration. In this research paper, we have applied a genetic algorithm approach, in order to optimize dispatching power with reconfiguring the network and scheduling the power sources. Our proposed methods shows that, it is possible to get 1MW less line lose compared to general condition.
Multi level ransomware analysis MALCON 2019 conference
Ransomware attacks in recent years have proved expensive due to significant damages and obstructions these caused in various sectors such as health, insurance, business, and education. Several malware detection methods have been proposed to uncover different malware families, but the problem remained unsolved due to the continuously evolving malware. In this work, we proposed a multi-level big data mining framework combining Reverse engineering, Natural Language Processing(NLP) and Machine Learning(ML) approaches. The framework analyzes the ransomware at different levels (i.e., Dynamic link library, function call and assembly instruction level) via different supervised ML algorithms. Apache Spark was employed for faster processing of large generated feature set. Portable Executable (PE) parser and Objectdump tool of Linux system were used to get the raw data from the ransomware and normal binaries that were processed further using our custom-built NLP processing. The n-gram probabilities, term-frequency and inverse document frequency (TF-IDF) were used to generate the final feature sets. Experiments were performed with different ‘N’ values of n-gram language model that shows that the ransomware detection accuracy is inversely proportional to the value of N. Among the five chosen supervised classifiers, Logistic regression outperformed others with a detection rate of 98.59% for generated TF-IDFs trigrams at combined multi-level, which is an improved accuracy compared to individual levels.
More from Kishor Datta Gupta (20)
Interpretable Learning Model for Lower Dimensional Feature Space: A Case stud...
Interpretable Learning Model for Lower Dimensional Feature Space: A Case stud...
A safer approach to build recommendation systems on unidentifiable data
A safer approach to build recommendation systems on unidentifiable data
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...
Policy Based reinforcement Learning for time series Anomaly detection
Policy Based reinforcement Learning for time series Anomaly detection
understanding the pandemic through mining covid news using natural language p...
understanding the pandemic through mining covid news using natural language p...
"Can NLP techniques be utilized as a reliable tool for medical science?" -Bui...
"Can NLP techniques be utilized as a reliable tool for medical science?" -Bui...
An empirical study on algorithmic bias (aiml compsac2020)
An empirical study on algorithmic bias (aiml compsac2020)
Hybrid pow-pos-based-system against majority attack-in-cryptocurrency system ...
Hybrid pow-pos-based-system against majority attack-in-cryptocurrency system ...
Shamir secret sharing: Alternative of hashing for authentication
Shamir secret sharing: Alternative of hashing for authentication
A Genetic Algorithm Approach to Optimize Dispatching for A Micro-grid Energy ...
A Genetic Algorithm Approach to Optimize Dispatching for A Micro-grid Energy ...
Multi level ransomware analysis MALCON 2019 conference
Multi level ransomware analysis MALCON 2019 conference
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Understanding Inductive Bias in Machine Learning
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEM
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
International Conference on NLP, Artificial Intelligence, Machine Learning an...
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
官方认证美国密歇根州立大学毕业证学位证书原版一模一样
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A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Recycled Concrete Aggregate in Construction Part III
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Casting-Defect-inSlab continuous casting.pdf
Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting
Recycled Concrete Aggregate in Construction Part II
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
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Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
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Using Negative Detectors for Identifying Adversarial Data Manipulation in Machine Learning
- 1. Using Negative Detectors for Identifying Adversarial Data Manipulation Presented by : Kishor Datta Gupta
- 2. Adversarial Attack (AA) on AI/ML “Manipulation of training data, Machine Learning (ML) model architecture, or manipulate testing data in a way that will result in wrong output from ML”
- 3. Limitation of AA Defense Strategies • Generate Adversarial Example and Retrain the model • Limitations: Reduce the accuracy of learning model Retrain: • Using PCA, low-pass filtering, JPEG compression, soft thresholding techniques as pre-processing technique. • Limitation: Vulnerable to adaptive attack. Input Reconstruction or Transformation: • Modifying the ML architecture to detect adversarial attack • Limitations: Require Modification of learning models. Model Modification:
- 4. We need an adaptive defense strategy which don’t modify the learning model and don’t require the adversarial knowledge. Problem Definition Detect adversarial input using only the knowledge of non-adversarial data, Converting it as an Outlier detection problem
- 5. Relevant Outlier Detection models Type Abbr Algorithm Linear Model MCD Minimum Covariance Determinant (use the mahalanobis distances as the outlier scores) OCSVM One-Class Support Vector Machines LMDD Deviation-based Outlier Detection (LMDD) Proximity-Based LOF Local Outlier Factor COF Connectivity-Based Outlier Factor CBLOF Clustering-Based Local Outlier Factor LOCI LOCI: Fast outlier detection using the local correlation integral HBOS Histogram-based Outlier Score SOD Subspace Outlier Detection ROD Rotation-based Outlier Detection Probabilistic ABOD Angle-Based Outlier Detection COPOD COPOD: Copula-Based Outlier Detection FastABOD Fast Angle-Based Outlier Detection using approximation MAD Median Absolute Deviation (MAD) SOS Stochastic Outlier Selection Outlier Ensembles IForest Isolation Forest FB Feature Bagging LSCP LSCP: Locally Selective Combination of Parallel Outlier Ensembles XGBOD Extreme Boosting Based Outlier Detection (Supervised) LODA Lightweight On-line Detector of Anomalies Neural Networks AutoEncoder Fully connected AutoEncoder (use reconstruction error as the outlier score) VAE Variational AutoEncoder (use reconstruction error as the outlier score) Beta-VAE Variational AutoEncoder (all customized loss term by varying gamma and capacity) SO_GAAL Single-Objective Generative Adversarial Active Learning MO_GAAL Multiple-Objective Generative Adversarial Active Learning
- 11. Comparison
- 12. Summary We devised an adaptive negative filtering methodology to detect adversarial attacks that does not modify the ML model or information about the ML model. Our strategy can be implemented in any ML-based system without expensive retraining. Adaptive attacks are ineffective in our negative filtering approach. Further works will be needed to conduce comprehensive experiments for testing various attacks using datasets from different domains.
- 13. Q/A