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Intent Recognition in Smart Homes with ProbLog

Intent Recognition in Smart Homes with ProbLog

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IEEE Annual conference on Pervasive Computing and Communications workshops (PerCom)

作者： Gary Smith Ron Petrick Vaishak Belle Edinburgh Centre for Robotics Edinburgh Scotland

Smart homes and assisted living facilities are better able to improve the lives of residents if they can perform intent recognition. If the system understands the goals of residents it can select appropriate assistive actions in advance, for example. The increasing availability of cheap sensors and smart devices provides fine grained data about residents and the ambient environment. Making full use of this data for intent recognition requires both relational and probabilistic reasoning. In this paper we present a framework for performing combined logical and probabilistic reasoning about intentions using data from simple switch sensors. We employ ProbLog, a probabilistic extension to Prolog, to infer the most probable intention given observations of the actions of the agent and sensor readings of important aspects of the environment.

关键词： Performance evaluation Pervasive computing conferences Smart homes Switches Probabilistic logic Cognition

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Symbolic AI for XAI: Evaluating LFIT Inductive programming for Fair and Explainable Automatic Recruitment

Symbolic AI for XAI: Evaluating LFIT Inductive Programming f...

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IEEE Winter Applications and Computer Vision workshops (WACVW)

作者： Alfonso Ortega Julian Fierrez Aythami Morales Zilong Wang Tony Ribeiro School of Engineering Universidad Autonoma de Madrid Laboratoire des Sciences du Numérique de Nantes National Institute of Informatics Japan

ISBN: (纸本)9781665419673;9781665429993

Machine learning methods are growing in relevance for biometrics and personal information processing in domains such as forensics, e-health, recruitment, and e-learning. In these domains, white-box (human-readable) explanations of systems built on machine learning methods can become crucial. Inductive logic programming (ILP) is a subfield of symbolic AI aimed to automatically learn declarative theories about the process of data. Learning from Interpretation Transition (LFIT) is an ILP technique that can learn a propositional logic theory equivalent to a given blackbox system (under certain conditions). The present work takes a first step to a general methodology to incorporate accurate declarative explanations to classic machine learning by checking the viability of LFIT in a specific AI application scenario: fair recruitment based on an automatic tool generated with machine learning methods for ranking Curricula Vitae that incorporates soft biometric information (gender and ethnicity). We show the expressiveness of LFIT for this specific problem and propose a scheme that can be applicable to other domains.

关键词： Training Machine learning algorithms Biometrics (access control) conferences Resumes Neural networks Tools

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Worrisome Patterns in Developers: A Survey in Cryptography

Worrisome Patterns in Developers: A Survey in Cryptography

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IEEE/ACM international conference on Automated Software Engineering - workshops (ASE workshops)

作者： Mohammadreza Hazhirpasand Mohammad Ghafari Oscar Nierstrasz University of Bern Bern Switzerland University of Auckland Auckland New Zealand

ISBN: (纸本)9781665411851

We surveyed 97 developers who had used cryptography in open-source projects, in the hope of identifying developer security and cryptography practices. We asked them about individual and company-level practices, and divided respondents into three groups (i.e., high, medium, and low) based on their level of knowledge. We found differences between the high-profile developers and the other two groups. For instance, high-profile developers have more years of experience in programming, have attended more security and cryptography courses, have more background in security, are highly concerned about security, and tend to use security tools more than the other two groups. Never-theless, we observed worrisome patterns among all participants such as the high usage of unreliable sources like Stack Overflow, and the low rate of security tool usage.

关键词： Training conferences Companies programming Cryptography Open source software Software engineering

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Model-based Poisson co-clustering for Attributed Networks

Model-based Poisson co-clustering for Attributed Networks

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IEEE international conference on Data Mining workshops (ICDM workshops)

作者： Paul Riverain Simon Fossier Mohamed Nadif Thales Research & Technology Université de Paris Paris France Thales Research & Technology Palaiseau France Université de Paris Paris France

ISBN: (纸本)9781665424288

Co-clustering aims at simultaneously partitioning both dimensions of a data matrix. It has demonstrated better performances than one-sided clustering for high-dimensional data. In this paper, we focus on the task of attributed network clustering and propose to consider the edges of these networks as a form of pairwise semi-supervision between data points. We present a probabilistic model for co-clustering based on the Poisson Latent Block Model (LBM) and incorporate probabilistic Must Link relationships in the model using Hidden Markov Random Fields (HMRF). We present two inference algorithms based on Variational and Classification EM. Experiments on the task of node clustering in attributed networks confirm the interest of our approach and demonstrate the effectiveness of our algorithms.

关键词： Deep learning Hidden Markov models Clustering algorithms Stochastic processes Probabilistic logic Inference algorithms Data models

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Care-full Design Sprints, Online? Addressing Gaps in Cultural Access and Inclusion during Covid-19 with Vulnerable Communities in London and Tokyo 21

Care-full Design Sprints, Online? Addressing Gaps in Cultura...

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10th international conference on Communities and Technologies: Wicked Problems in the Age of Tech, C and T 2021

作者： Braybrooke, Kit Janes, Stephanie Sato, Chihiro King's College London United Kingdom Keio University Japan

ISBN: (纸本)9781450390569

What does it mean to invite vulnerable communities to the table in times of crisis not just as subjects, but as co-designers, in ways that facilitate nourishing and care-full relations? In this paper, we present the case of an online design sprint involving two groups of diverse participants in London and Tokyo as the Covid-19 pandemic unfolded. This modified design sprint model, which we describe as a 'care-full design stroll', integrated co-design approaches with ethics of care to offer remote cultural experiences aimed at addressing inequalities of access and inclusion faced by the arts and cultural sectors in Japan and the UK. We analyse data from ethnographic observations, interviews and surveys in both nations to illustrate the challenges and opportunities of facilitating design sprints online. Our findings show how care-full co-design, underpinned by concepts of thinking-with and working-alongside, can be facilitated in online-only and/or limited terrains, in ways that nourish cultural organisations and their publics in times of great uncertainty. We conclude with a set of six design principles which provide practical recommendations for the effective facilitation of future care-full co-design sprints for groups working in a variety of settings. © 2021 ACM.

关键词： Computer programming

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STONE: Signal Temporal logic Neural Network for Time Series Classification

STONE: Signal Temporal Logic Neural Network for Time Series ...

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IEEE international conference on Data Mining workshops (ICDM workshops)

作者： Ruixuan Yan Agung Julius Maria Chang Achille Fokoue Tengfei Ma Rosario Uceda-Sosa Rensselaer Polytechnic Institute Troy NY USA IBM T.J. Watson Research Center IBM Research Yorktown Heights NY USA

ISBN: (纸本)9781665424288

In this paper, we propose a neuro-symbolic frame-work called signal temporal logic neural network (STONE) that combines the characteristics of neural networks and temporal logics. Weighted Signal Temporal logic (wSTL) formulas are recursively composed of subformulas connected using logical and temporal operators. The quantitative semantics of wSTL is defined such that the quantitative satisfaction of subformulas with higher weights have a more significant influence on the quantitative satisfaction of a wSTL formula. In the STONE, each neuron represents a component of a wSTL formula, and the output of STONE corresponds to the quantitative satisfaction of a wSTL formula. We use STONE to represent wSTL formulas and classify time-series data. WSTL formulas are more interpretable and human-readable than classical time series classification models. The STONE is end-to-end differentiable, which allows learning of wSTL formulas to be done using back-propagation. Experiments on benchmark time-series datasets show that STONE is comparable to the state-of-the-art time series classification models and the wSTL learning algorithm is faster than the traditional STL learning algorithm.

关键词： conferences Time series analysis Semantics Neurons Benchmark testing Classification algorithms Data mining

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international workshops on AI Approaches to the Complexity of Legal Systems, AICOL 2018 and AICOL 2020, held jointly with the international Workshop on Explainable and Responsible AI and Law, XAILA 2020

International Workshops on AI Approaches to the Complexity o...

引用

international workshops on AI Approaches to the Complexity of Legal Systems, AICOL 2018 and AICOL 2020, held jointly with the international Workshop on Explainable and Responsible AI and Law, XAILA 2020

ISBN: (纸本)9783030898106

The proceedings contain 23 papers. The special focus in this conference is on AI Approaches to the Complexity of Legal Systems. The topics include: Towards Grad-CAM Based Explainability in a Legal Text Processing Pipeline. Extended Version;Making Things Explainable vs Explaining: Requirements and Challenges Under the GDPR;explaining Arguments at the Dutch National Police;like Circles in the Water: Responsibility as a System-Level Function;the Rule of Law and Compliance: Legal Quadrant and Conceptual Clustering;the Web of Data’s Role in Legal Ecosystems to Address Violent Extremism Fuelled by Hate Speech in Social Media;SPIRIT: Semantic and Systemic Interoperability for Identity Resolution in Intelligence Analysis;timeLex: A Suite of Tools for Processing Temporal Information in Legal Texts;inferring the Meaning of Non-personal, Anonymized, and Anonymous Data;identification of Legislative Errors Through Knowledge Representation and Interpretive Argumentation;Challenges in the Implementation of Privacy Enhancing Semantic Technologies (PESTs) Supporting GDPR;publication of Court Records: Circumventing the Privacy-Transparency Trade-Off;challenges in the Digital Representation of Privacy Terms;the Use of Decentralized and Semantic Web Technologies for Personal Data Protection and Interoperability;lexdatafication: Italian Legal Knowledge Modelling in Akoma Ntoso;A Critical Reflection on ODRL;automating Normative Control for Healthcare Research;principles and Semantics: Modelling Violations for Normative Reasoning;towards a Formal Framework for Partial Compliance of Business Processes;a Case Study Integrating Knowledge Graphs and Intuitionistic logic;logical Comparison of Cases.

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JavaBERT: Training a Transformer-Based Model for the Java programming Language

JavaBERT: Training a Transformer-Based Model for the Java Pr...

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IEEE/ACM international conference on Automated Software Engineering - workshops (ASE workshops)

作者： Nelson Tavares De Sousa Wilhelm Hasselbring Software Engineering Group Kiel University Kiel Germany

ISBN: (纸本)9781665411851

Code quality is and will be a crucial factor while developing new software code, requiring appropriate tools to ensure functional and reliable code. Machine learning techniques are still rarely used for software engineering tools, missing out the potential benefits of its application. Natural language processing has shown the potential to process text data regarding a variety of tasks. We argue, that such models can also show similar benefits for software code processing. In this paper, we investigate how models used for natural language processing can be trained upon software code. We introduce a data retrieval pipeline for software code and train a model upon Java software code. The resulting model, JavaBERT, shows a high accuracy on the masked language modeling task showing its potential for software engineering tools.

关键词： Java Computer languages Vocabulary Codes Pipelines Software Data models

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An Empirical Study of Parallelizing Test Execution Using CUDA Unified Memory and OpenMP GPU Offloading

An Empirical Study of Parallelizing Test Execution Using CUD...

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IEEE international conference on Software Testing Verification and Validation Workshop, ICSTW

作者： Taghreed Bagies Ali Jannesari King Abdulaziz University Jeddah Saudi Arabia Iowa State University Ames Iowa USA

ISBN: (纸本)9781665444576

The execution of software testing is costly and time-consuming. To accelerate the test execution, researchers have applied several methods to run the testing in parallel. One method of parallelizing the test execution is by using a GPU to distribute test inputs among several threads running in parallel. In this paper, we investigate three programming models CUDA Unified Memory, CUDA Non-Unified Memory, and OpenMP GPU offloading to parallelize the test execution and discuss the challenges using these programming models. We use eleven benchmarks and parallelize their test suites by using these models. Our study shows some limitations (e.g. cache size, branch divergence, and load imbalance) when using GPUs to execute the testing in parallel.

关键词： Software testing conferences Instruction sets Graphics processing units Life estimation programming Benchmark testing

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A Static Analysis Framework for Detecting Bugs in Introductory Programs

A Static Analysis Framework for Detecting Bugs in Introducto...

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IEEE international conference on Software Reliability Engineering workshops (ISSRE Wksp)

作者： Wenchu Xu Yanran Ma State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Big Data Lab Foreign Language School Nanjing China

ISBN: (纸本)9781665426046

Introductory programming courses are crucial to students who begin to learn programming languages. However, current introductory program judgment systems only give a pass or fail by the percentage of passed test cases. This limitation hinders the learning progress because students may unaware of potential bugs hidden in their programs. Detecting and reporting these bugs can help them avoid introducing the same kinds of bugs in other programs. However, state-of-the-art bug detection methods focus on precision and scalability, but are usually costly to develop detection methods for various kinds of bugs. To simplify the development of bug detection while keeping a reasonable precision, this paper presents a static detection framework to detect software bugs. Then, we apply the method to introductory programs and successfully detect all bugs with a false positive rate of 28.57 %.

关键词： Computer languages Scalability conferences Computer bugs Prototypes Static analysis programming

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