Ambient Intelligence in Adaptive Online Experiments
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The document outlines the thesis defense of Violeta Damjanovic for her PhD in ambient intelligence and adaptive online experiments. The thesis addresses integrating probabilistic knowledge from pervasive semantic web environments into ontological models to enable adaptive and intelligent experimental environments. The proposed solution involves mechanisms for transforming probabilistic asynchronous process knowledge into ontologies and collecting ambient process knowledge to develop an adaptive semantic ambient system called AmIART. The thesis is expected to contribute to integrating the pervasive semantic web into online experimenting systems and adaptive systems considering uncertain knowledge.
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This document discusses using a combination of long short-term memory (LSTM) and convolutional neural networks (CNN) for visual question answering (VQA). It proposes extracting image features from CNNs and encoding question semantics with LSTMs. A multilayer perceptron would then combine the image and question representations to predict answers. The methodology aims to reduce statistical biases in VQA datasets by focusing attention on relevant image regions. It was implemented in Keras with TensorFlow using pre-trained CNNs for images and word embeddings for questions. The proposed approach analyzes local image features and question semantics to improve VQA classification accuracy over methods relying solely on language.
Performance Evaluation of Neural Classifiers Through Confusion Matrices To Di...
In this paper we have aimed to diagnose skin conditions using Artificial Intelligence (AI) based classifier algorithms and do the performance analyses of those presented algorithms through confusion matrices. These algorithms are being used in a large array of different areas including medicine, and display very distinct characteristics in the sense that they are grouped under different categories such as supervised, unsupervised, statistical, or optimization. The objective of this study is to diagnose skin conditions using seven different well-known and popular as well as emerging Artificial Intelligence based algorithms and to help general practitioners and/or dermatologists develop a careful and supportive approach that leads to a probable diagnosis of skin conditions or diseases. These algorithms we chose as neural classifiers include Back- Propagation (BP), Random Forest (RF), Support Vector Machines (SVMs), Linear Vector Quantization (LVQ), Self-Organizing Maps (SOMs), Naïve Bayes, and finally Bayesian Networks. All of these algorithms have been tested and their results of diagnosing skin conditions/diseases by using data set from Dermatology Database have been compared.
Survey of Various Approaches of Emotion Detection Via Multimodal Approach
This document summarizes various approaches for multimodal emotion detection using features extracted from text, audio, and video data. It discusses how combining multiple modalities can provide more comprehensive insight into a user's emotions compared to a single modality. The document reviews related literature on audio-video, text-video, and multimodal emotion classification systems. It also describes approaches for feature extraction from text, such as identifying semantic words and concepts, and from video, including face detection and facial feature extraction. The proposed system aims to predict emotions by combining features from Twitter text and real-time video captured while a user completes a depression questionnaire.
Neural networks and deep learning
Neural networks and deep learning are machine learning techniques inspired by the human brain. Neural networks consist of interconnected nodes that process input data and pass signals to other nodes. The main types discussed are artificial neural networks (ANNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). ANNs can learn nonlinear relationships between inputs and outputs. CNNs are effective for image processing by learning relevant spatial features. RNNs capture sequential dependencies in data like text. Deep learning uses neural networks with many layers to learn complex patterns in large datasets.
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The document discusses soft computing and its techniques, including artificial neural networks (ANN). It provides an overview of ANN, including how biological neurons inspired the basic ANN model. A neuron has inputs, outputs, weights, and an activation function. Networks can be single or multilayer. Learning involves updating weights to minimize error, with backpropagation commonly used for multilayer networks. Applications include pattern recognition, function approximation, and parameter estimation. A simple example is provided to estimate the slope and intercept of a line using ANN.
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We will use 7 emotions namely - We have used 7 emotions namely - 'Angry', 'Disgust'濫, 'Fear', 'Happy', 'Neutral', 'Sad'☹️, 'Surprise' to train and test our algorithm using Convolution Neural Networks.
abstrakty přijatých příspěvků.doc
This document contains summaries of several papers related to artificial intelligence and neural networks:
1. The first paper discusses using recurrent neural networks to plan robot motions in variable environments.
2. The second paper describes using neural network models to classify brain signals and mind states from EEG data.
3. The third paper proposes using coarticulation composite models in acoustic-phonetic decoding to improve recognition rates beyond phonemes, diphones, and triphones.
Nature Inspired Reasoning Applied in Semantic Web
1) Neural networks are computational structures inspired by biological neural networks and have been successfully used to solve complex tasks like image recognition and natural language processing.
2) Neural networks consist of interconnected nodes that perform simple mathematical functions to produce outputs. The connections between nodes and their weights can be modified through training to solve problems.
3) Nature inspired algorithms like neural networks are well-suited for semantic web problems because they can process large amounts of information quickly to find good enough solutions.
Query Aware Determinization of Uncertain Objects
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Abstract: To cope with society’s demand for transparency and corruption prevention, the Brazilian Federal General Comptroller (CGU) has carried out a number of actions, including: awareness campaigns aimed at the private sector; campaigns to educate the public; research initiatives; and regular inspections and audits of municipalities and states. Although CGU has collected information from hundreds of different sources - Revenue Agency, Federal Police, and others - the process of fusing all this data has not been efficient enough to meet the needs of CGU’s decision makers. Therefore, it is natural to change the focus from data fusion to knowledge fusion. As a consequence, traditional syntactic methods must be augmented with techniques that represent and reason with the semantics of databases. However, commonly used approaches fail to deal with uncertainty, a dominant characteristic in corruption prevention. This paper presents the use of Probabilistic OWL (PR-OWL) to design and test a model that performs information fusion to detect possible frauds in procurements involving Federal money. To design this model, a recently developed tool for creating PR-OWL ontologies was used with support from PR-OWL specialists and careful guidance from a fraud detection specialist from CGU.
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UnBBayes Overview
UnBBayes is a probabilistic network framework written in Java. It has both a GUI and an API with inference, sampling, learning and evaluation. It supports BN, ID, MSBN, OOBN, HBN, MEBN/PR-OWL, structure, parameter and incremental learning.
The overview is presented through a slides potpourri from different presentations the Artificial Intelligence Group (GIA) from University of Brasilia (UnB) has given since 1999. It covers BN, ID, MSBN, UnBBayes Server, and MEBN.
This presentation was given by Rommel Carvalho when he started his PhD at George Mason University on the Friday seminar called Krypton (http://krypton.c4i.gmu.edu/).
Probabilistic Ontology: Representation and Modeling Methodology
Oral Defense of Doctoral Dissertation
Volgenau School of Engineering, George Mason University
Rommel Novaes Carvalho
Bachelor of Science, University of Brasília, Brazil, 2003
Master of Science, University of Brasília, Brazil, 2008
Probabilistic Ontology: Representation and Modeling Methodology
Tuesday, June 28, 2011, 2:00pm -- 4:00pm
Nguyen Engineering Building, Room 4705
Committee
Kathryn Laskey, Chair
Paulo Costa
Kuo-Chu Chang
David Schum
Larry Kerschberg
Fabio Cozman
Abstract
The past few years have witnessed an increasingly mature body of research on the Semantic Web (SW), with new standards being developed and more complex problems being addressed. As complexity increases in SW applications, so does the need for principled means to cope with uncertainty in SW applications. Several approaches addressing uncertainty representation and reasoning in the SW have emerged. Among these is Probabilistic Web Ontology Language (PR-OWL), which provides Web Ontology Language (OWL) constructs for representing Multi-Entity Bayesian Network (MEBN) theories. However, there are several important ways in which the initial version PR-OWL 1.0 fails to achieve full compatibility with OWL. Furthermore, although there is an emerging literature on ontology engineering, little guidance is available on the construction of probabilistic ontologies.
This research proposes a new syntax and semantics, defined as PR-OWL 2.0, which improves compatibility between PR-OWL and OWL in two important respects. First, PR-OWL 2.0 follows the approach suggested by Poole et al. to formalizing the association between random variables from probabilistic theories with the individuals, classes and properties from ontological languages such as OWL. Second, PR-OWL 2.0 allows values of random variables to range over OWL datatypes.
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Advanced Computational Intelligence: An International Journal (ACII) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of computational intelligence. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced computational intelligence concepts and establishing new collaborations in these areas.
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The document discusses ontology design patterns (ODPs), which are reusable solutions to common modeling problems when building ontologies. It provides an overview of the history and use of ODPs, including in the domain of bio-ontologies. Various types of ODPs are described, such as extensional patterns and content patterns. The advantages of using ODPs in ontology engineering are improved modularity, reusability, communication and more. Guidelines for documenting and applying ODPs are also presented.
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The document discusses ontologizing business processes by using an ontology-based approach to model business processes at the strategic, functional and operational levels. It proposes using model-driven architecture and ontology engineering principles to develop ontologies for Web Services Description Language (WSDL) and Business Process Execution Language (BPEL) elements. The results define the relationships between WSDL and BPEL elements and relevant ontology classes. Transition rules for linking activities in a business process are also discussed.
Developing Ontologies for Collaborative Engineering in Mechatronics
The document discusses developing ontologies for collaborative engineering in mechatronics. It presents several ontology development methodologies and describes how the ImportNET project is using these to develop an ontology landscape for mechatronic domains. This includes developing ontologies to model mechatronic engineering processes and artifacts using foundational ontologies like DOLCE and aligning the ontologies.
DOLCE and Pi-Calculus Rendezvous Semantics for Business Processes
1st International Workshop on Knowledge Reuse and Reengineering over the Semantic Web (KRRSW 2008) hosted by the 5th ESWC 2008, Costa Adeje, Tenerife, Canary Islands
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Ontology Design Patterns for the Semantic Business Processes
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zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
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
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Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
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Monitoring and Managing Anomaly Detection on OpenShift.pdf
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
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9. What is Camel K?
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11. What is a Jupyter Notebook?
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HCL Notes and Domino License Cost Reduction in the World of DLAU
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
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With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
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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.
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In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
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At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
Main news related to the CCS TSI 2023 (2023/1695)
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
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This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
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We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
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Dandelion Hashtable: beyond billion requests per second on a commodity server
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Ambient Intelligence in Adaptive Online Experiments
- 1. Ambient ntelligence in Adaptive Online Experiments Violeta Damjanovic PhD Thesis Defense : November 18, 2008 PhD Supervisor : Prof. Dr. Vladan Devedzic FON, University of Belgrade, Serbia
- 17. The operational semantics of the probabilistic asynchronous algebra (Herescu, 2002)
- 18. Probabilistic Asynchronous Process Algebra - Metamodel
- 19. Part 5: Proposed solution … to integrate probabilistic asynchronous process‘ knowledge into ontologies Transformation mechanism pa2OWL … to collect and ontologize knowledge on ambient processes AmIART-specific components P-OWL … to enable adaptive, user-oriented and intelligent experimental environment Adaptive & Semantic Ambient System AmIART
- 20. AmIART: conceptual model Part 5: Proposed solution
- 21. P-OWL: AmIART-specific components Adaptation Manager pa2OWL Web Agent System Part 5: Proposed solution
- 22. Adaptation Manager Part 5: Proposed solution
- 23. Web Agent System & pa2OWL Part 5: Proposed solution
- 24. pa2OWL: Implementation details
- 25. Part 5: Proposed solution Transformation Rule
- 26. Part 5: Proposed solution