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ACM SIGMOD international conference on Management of Data (SIGMOD)

作者： Shanghooshabad, Ali Mohammadi Kurmanji, Meghdad Ma, Qingzhi Shekelyan, Michael Almasi, Mehrdad Triantafillou, Peter Univ Warwick Coventry W Midlands England

ISBN: (纸本)9781450383431

Modern databases face formidable challenges when called to join (several) massive tables. Joins (especially when entailing many-tomany joins) are very time- and resource-consuming, join results can be too big to keep in memory, and performing analytics/learning tasks over them costs dearly in terms of time, resources, and money (in the cloud). Moreover, although random sampling is a promising idea to mitigate the above problems, the current state of the art leaves lots of room for improvements. With this paper we contribute a principled solution, coined PGMJoins. PGMJoins adapts probabilistic graphical models to deriving provably random samples of the join result for (n-way) key-joins, many-to-many joins, and cyclic and acyclic joins. PGMJoins contributes optimizations both for deriving the structure of the graph and for PGM inference. It also contributes a novel Sum-Product Message Passing Algorithm (SP-MPA) to make a uniform sample of the joint distribution (join result) efficiently and a novel way to deal with cyclic joins. Despite the use of PGMs, the learned joint distribution is not approximated and the uniform samples are drawn from the true distribution. Our experimentation using queries and datasets from TPC-H, JOB, TPC-DS, and Twitter shows PGMJoins to outperform the state of the art (by 2X-28X).

关键词： Join Queries Random sampling PGMs for data management

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CITRisk 2022 - Proceedings of the 3rd international Workshop on Computational and Information Technologies for Risk-Informed Systems, co-located with 22nd international Scientific and Technical conference on Information Technologies in Education and Management, ITEM 2022

CITRisk 2022 - Proceedings of the 3rd International Workshop...

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3rd international Workshop on Computational and Information Technologies for Risk-Informed Systems, CITRisk 2022

The proceedings contain 14 papers. The topics discussed include: infrastructure cybersecurity under complex man-made threats conditions;game and system models of conflict resolution methods and infrastructure cybersecurity under active attacks;reducing average risk by providing invariance of two-dimensional binary images;the probabilistic graphical model for multi-hazard risk evaluation of critical infrastructure impairment;the random forest algorithm as an element of statistical learning for disease prediction;mobile cyber-physical system for monitoring the health of patients with cardiovascular diseases;the problem of software implementation iterative algorithms of inductive modeling;non-parametric methods of finding changepoints in multidimensional time series;risk of vertical separation loss at en-route phase of airplane flight;the risks assessment of delivery failures for application-to-person SMS market;and ML-based approach for credit risk assessment using parallel calculations.

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Generative Machine Learning for Resource-Aware 5G and IoT Systems

Generative Machine Learning for Resource-Aware 5G and IoT Sy...

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IEEE international conference on Communications (ICC)

作者： Piatkowski, Nico Mueller-Roemer, Johannes S. Hasse, Peter Bachorek, Adam Werner, Tim Birnstill, Pascal Morgenstern, Andreas Stobbe, Lutz Fraunhofer IAIS St Augustin Germany Fraunhofer IGD Darmstadt Germany Tech Univ Darmstadt Darmstadt Germany Fraunhofer FOKUS Berlin Germany Fraunhofer IESE Kaiserslautern Germany Fraunhofer IOSB AST Ilmenau Germany Fraunhofer IOSB Karlsruhe Germany Fraunhofer IZM Wessling Germany

ISBN: (纸本)9781728194417

Extrapolations predict that the sheer number of Internet-of-Things (IoT) devices will exceed 40 billion in the next five years. Hand-crafting specialized energy models and monitoring sub-systems for each type of device is error prone, costly, and sometimes infeasible. In order to detect abnormal or faulty behavior as well as inefficient resource usage autonomously, it is of tremendous importance to endow upcoming IoT and 5G devices with sufficient intelligence to deduce an energy model from their own resource usage data. Such models can in-turn be applied to predict upcoming resource consumption and to detect system behavior that deviates from normal states. To this end, we investigate a special class of undirected probabilistic graphical model, the so-called integer Markov random fields (IntMRF). On the one hand, this model learns a full generative probability distribution over all possible states of the system-allowing us to predict system states and to measure the probability of observed states. On the other hand, IntMRFs are themselves designed to consume as less resources as possible-e.g., faithful modelling of systems with an exponentially large number of states, by using only 8-bit unsigned integer arithmetic and less than 16KB memory. We explain how IntMRFs can be applied to model the resource consumption and the system behavior of an IoT device and a 5G core network component, both under various workloads. Our results suggest, that the machine learning model can represent important characteristics of our two test systems and deliver reasonable predictions of the power consumption.

关键词： generative model probabilistic graphical model internet of things 5G core energy model

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Fall Detection in Hospital Rooms with probabilistic graphical models

Fall Detection in Hospital Rooms with Probabilistic Graphica...

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Workshops on Image Processing Theory, Tools and Applications, IPTA

作者： Jacobo González de Frutos Mia Sandra Nicole Siemon Kamal Nasrollahi Dept. of Electronic Systems Aalborg University Aalborg Denmark Dept. of Architecture Design and Media Technology Aalborg University and Milestone Systems Aalborg Denmark

ISBN: (数字)9798331541842

ISBN: (纸本)9798331541859

Unintentional injury deaths, primarily caused by simple falls among elderly individuals, have consistently ranked as the second most common cause of death over the years, as documented by the World Health Organization. This not only poses a significant health concern but also imposes a substantial financial burden on hospitals, as insurance companies typically do not cover such events. This project aims to address this challenge by developing a simple Artificial Intelligence system based on Video Anomaly Detection to detect fall situations that require less computational power and protect patients' privacy. The system comprises a Keypoint Detector for estimating the patient's pose and a discrete Bayesian Network to detect whether a person has fallen or not. The approach has been trained and tested under the public dataset CAUCAFall achieving an average accuracy of 90.62%, Area Under the ROC Curve of 88.71%, Region-Based Detection Criterion of 86.23% and Track-Based Detection Criterion of 98.39%. The obtained results demonstrate highly competitive performance, approaching the levels achieved by the most recent State-of-the-art works tested on CAUCAFall. Our unsupervised methodology to fall detection is privacy-preserving, computationally lightweight, and learns patterns from the training data in seconds on a conventional 30GB memory Google Cloud Virtual Machine with 8 AMD EPYC 7B13s Intel Broadwell CPUs.

关键词： Privacy graphical models Hospitals Image processing Training data Insurance probabilistic logic Fall detection Older adults Injuries

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Dynamic inference in probabilistic graphical models 12

Dynamic inference in probabilistic graphical models

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12th Innovations in Theoretical Computer Science conference, ITCS 2021

作者： Feng, Weiming He, Kun Sun, Xiaoming Yin, Yitong State Key Laboratory for Novel Software Technology Nanjing University China Shenzhen University China Shenzhen Institute of Computing Sciences China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783959771771

probabilistic graphical models, such as Markov random fields (MRFs), are useful for describing high-dimensional distributions in terms of local dependence structures. The probabilistic inference is a fundamental problem related to graphical models, and sampling is a main approach for the problem. In this paper, we study probabilistic inference problems when the graphical model itself is changing dynamically with time. Such dynamic inference problems arise naturally in today’s application, e.g. multivariate time-series data analysis and practical learning procedures. We give a dynamic algorithm for sampling-based probabilistic inferences in MRFs, where each dynamic update can change the underlying graph and all parameters of the MRF simultaneously, as long as the total amount of changes is bounded. More precisely, suppose that the MRF has n variables and polylogarithmic-bounded maximum degree, and N(n) independent samples are sufficient for the inference for a polynomial function N(·). Our algorithm dynamically maintains an answer to the inference problem using Oe(nN(n)) space cost, and Oe(N(n) + n) incremental time cost upon each update to the MRF, as long as the Dobrushin-Shlosman condition is satisfied by the MRFs. This well-known condition has long been used for guaranteeing the efficiency of Markov chain Monte Carlo (MCMC) sampling in the traditional static setting. Compared to the static case, which requires Ω(nN(n)) time cost for redrawing all N(n) samples whenever the MRF changes, our dynamic algorithm gives a Ω(min e {n, N(n)})-factor speedup. Our approach relies on a novel dynamic sampling technique, which transforms local Markov chains (a.k.a. single-site dynamics) to dynamic sampling algorithms, and an "algorithmic Lipschitz" condition that we establish for sampling from graphical models, namely, when the MRF changes by a small difference, samples can be modified to reflect the new distribution, with cost proportional to the difference on MRF. © Weiming F

关键词： Sampling

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On generating random Gaussian graphical models

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international JOURNAL OF APPROXIMATE REASONING 2020年 125卷 240-250页

作者： Cordoba, Irene Varando, Gherardo Bielza, Concha Larranaga, Pedro Univ Politecn Madrid Madrid Spain Univ Copenhagen Copenhagen Denmark

Structure learning methods for covariance and concentration graphs are often validated on synthetic models, usually obtained by randomly generating: (i) an undirected graph, and (ii) a compatible symmetric positive definite (SPD) matrix. In order to ensure positive definiteness in (ii), a dominant diagonal is usually imposed. In this work we investigate different methods to generate random symmetric positive definite matrices with undirected graphical constraints. We show that if the graph is chordal it is possible to sample uniformly from the set of correlation matrices compatible with the graph, while for general undirected graphs we rely on a partial orthogonalization method. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Concentration graph Covariance graph Positive definite matrix simulation Undirected graphical model Algorithm validation

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Trust Quantification for Autonomous Medical Advisory Systems 18

Trust Quantification for Autonomous Medical Advisory Systems

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18th international conference on Privacy, Security, and Trust (PST)

作者： Thomas, Mini Samavi, Reza Doyle, Thomas E. McMaster Univ Comp & Software Hamilton ON Canada Ryerson Univ Elect & Comp Engn Toronto ON Canada Vector Inst Toronto ON Canada McMaster Univ Elect & Comp Engn Hamilton ON Canada

ISBN: (纸本)9781665401845

Autonomous Medical Advisory Systems (AMAS) integrate sensors and implement learning technologies to provide intelligent and real-time recommendations. In this paper, we propose a formal framework for quantifying trust using the Bayesian network for the sensor layer of AMAS systems. First, we identify the various factors influencing trust in this context. We make the factors granular enough such that the probability of the trust for the factor to be in a specific state can be measured. Then, using a probabilistic graphical model, we impose a compact structure to the identified factors such that the posterior probability of the trustworthiness of the entire system or its constituents can be computed. Parameterized cases of Bayesian network are simulated in MATLAB to demonstrate the applicability and scalability of the model for trust inference.

关键词： Privacy Scalability probabilistic logic Sensor systems Real-time systems Mathematical models Bayes methods

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Unified Identification of Anomalies on the Edge: A Hybrid Sequential PGM Approach

Unified Identification of Anomalies on the Edge: A Hybrid Se...

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IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Javad Forough Monowar Bhuyan Erik Elmroth Dept. of Computing Science Umeå University Umeå Sweden

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Edge cloud resources, just as many other computing resources, are prone to both performance and security anomalies due to their decentralized nature and real-time requirements for processing of data. Their behaviour initially observed as anomalous may, however, in many cases be rather generic and hard to detect. To be able to address such anomalies, it is instrumental to determine whether the anomaly is a "Security" threat or only a "Performance" concern. Therefore, in this paper, we develop an anomaly detection model capable of distinguishing between security and performance anomalies. The model is based on sequential modeling and probabilistic graphical Model (PGM), which leverage historical information and dependencies between previous predictions to classify future anomalies accurately. The evaluation of our proposed model shows its superior performance on our testbed and benchmark datasets. Accordingly, the model achieves an average 5%, and 3% higher F1 score compared to state-of-the-art methods in binary and multi-label anomaly detection cases, respectively. Moreover, our testing time analysis demonstrates the ability of the proposed model in early detection of such anomalies on the edge cloud.

关键词： Analytical models graphical models Image edge detection Computational modeling Predictive models Benchmark testing Real-time systems

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Reverse Engineering the Hamming Code with Automatic Graph Learning 3

Reverse Engineering the Hamming Code with Automatic Graph Le...

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3rd international conference on Artificial Intelligence in Information and Communication (IEEE ICAIIC)

作者： Jacobsen, Noah B. Mission Solut Grp Inc Data Sci N Charleston SC 29406 USA

ISBN: (纸本)9781728176383

probabilistic graphical models arc used extensively across the information theory, artificial intelligence and machine learning disciplines. In this paper, we work towards realizing a generalized graph-based framework for automated learning in intelligent systems. The proposed automatic graph learning framework employs factor graphs. i.e. Tanner graphs from coding theory. to represent an arbitrary stochastic system of variables and factorized realizations of their joint probability density function. We develop algorithms that are capable of learning statistical relationships between system variables, which involves constructing an appropriate factor graph representation and generating estimates of its component probability density unctions, from training data. In this paper, automatic graph learning is used to reverse engineer the Hamming code, based on training data comprised of input-output codeword pairs. We show that automatic graph learning is capable of replicating known decoder performance with an order of magnitude less training data than a multi-layer dense neural network.

关键词： learning theory probabilistic graph models factor graphs Bayesian and neural networks probabilistic inference sum-product algorithm

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Enhancing Text Anonymisation: A Study on CRF, LSTM, and ELMo for Advanced Entity Recognition

Enhancing Text Anonymisation: A Study on CRF, LSTM, and ELMo...

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Panhellenic conference on Electronics and Telecommunications (PACET)

作者： Ilias Siniosoglou Dimitris Asimopoulos Vasileios Argyriou Thomas Lagkas Anastasios Lytos Ioannis D. Moscholios Sotirios K. Goudos Panagiotis Sarigiannidis Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece R&D Department MetaMind Innovations P.C. Kozani Greece Department of Networks and Digital Media Kingston University Kingston upon Thames United Kingdom Department of Computer Science International Hellenic University Greece Sidroco Holdings Ltd. Nicosia Cyprus Department of Informatics and Telecommunications Department University of Peloponnese Tripoli Greece Physics Department Aristotle University of Thessaloniki Thessaloniki Greece

It is essential to create effective strategies for safeguarding private data while preserving the value of textual data in the face of rising privacy concerns in the digital era. In this research, we give a thorough investigation of Conditional Random Fields (CRF), Long Short-Term Memory (LSTM), and Embeddings from Language models (ELMo)-based text anonymisation methods [1]. The strengths of CRF, LSTM, and ELMo are combined in the proposed approach to overcome the difficulty of text anonymisation. LSTM is a recurrent neural network architecture that can model long-term dependencies, CRF is a probabilistic graphical model that captures the dependencies among the sequence of words, and ELMo offers contextual word representations utilizing deep bidirectional language models. These models are leveraged in order to identify and subsequently obfuscate sensitive information in the frame of textual anonymisation. Experimental results show that when compared to conventional methods, all three methods—CRF, LSTM, and ELMo—achieve considerable improvements in text anonymisation.

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