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arXiv 2025年

作者： Madampe, Kashumi Grundy, John Arachchilage, Nalin HumaniSE Lab Department of Software Systems and Cybersecurity Monash University Melbourne Australia School of Computing Technologies RMIT University Melbourne Australia

With the popularising of generative AI, the existence of AI-based programming assistants for developers is no surprise. Developers increasingly use them for their work, including generating code to fulfil the data protection requirements (privacy) of the apps they build. We wanted to know if the reality is the same as expectations of AI-based programming assistants when trying to fulfil software privacy requirements, and the challenges developers face when using AI-based programming assistants and how these can be improved. To this end, we conducted a survey with 51 developers worldwide. We found that AI-based programming assistants need to be improved in order for developers to better trust them with generating code that ensures privacy. In this paper, we provide some practical recommendations for developers to consider following when using AI-based programming assistants for privacy-related code development, and some key further research directions. © 2025, CC BY-NC-ND.

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Pipelining Graph Construction and Agent-based Computation over Distributed Memory

Pipelining Graph Construction and Agent-based Computation ov...

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IEEE International Conference on Big Data

作者： Yan Hong Munehiro Fukuda Computing and Software Systems University of Washington Bothell Bothell

ISBN: (纸本)9781665480468

Graph streaming has received substantial attention for the past 10+ years to cope with large-scale graph computation. Two major approaches, one using conventional data-streaming tools and the other accessing graph databases, facilitate continuous analysis of endlessly flowing graphs and query-based incremental construction of huge graphs, respectively. However, some scientific graphs including biological networks need to stay in memory for repetitive but various analyses. Although a cluster system, thus distributed memory can entirely handle a big graph in memory, a challenge is substantial overhead incurred by loading graphs into memory. A solution is hiding such graph-loading and construction overheads with graph computation in a pipelined fashion. We adapted this pipelining approach for agent-based graph computing where thousands of agents traverse a graph for finding its attributes and shape. We used the multi-agent spatial simulation (MASS) library to implement the concept. A huge graph is incrementally constructed in batches, each spawning and walking agents over the corresponding subgraph, and thus all eventually completing a given computation. We coded and ran two MASS benchmark programs: triangle counting and connected components, with which we evaluated our pipelined graph processing. The best performance was obtained once the batch size shrunk enough to fit cache memory, regardless of the number of cluster nodes. For a single node execution of connected components over a 140MB graph, our graph-pipelining implementation performed 7.7 times faster than non-pipelining execution. Its parallel execution with 24 cluster nodes achieved 8.3 times speed-up as compared to the pipelined single-node execution.

关键词： Shape Scalability Loading XML Big Data Size measurement Libraries

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Assessing Empathy in Mental Health Caregivers using Conversational AI

Assessing Empathy in Mental Health Caregivers using Conversa...

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Raghav Naswa Sugam Jaiswal Remya Mavila Weichao Yuwen Bill Erdly Dong Si Computing and Software Systems Division University of Washington Bothell USA School of Nursing and Healthcare Leadership University of Washington Tacoma USA

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

Mental health issues are a significant concern in the United States, with a considerable proportion of adults experiencing some form of mental illness. Unfortunately, a significant portion of these adults do not receive any mental health treatment. Effective communication characterized by empathy is essential for enhancing the well-being of individuals with mental health issues, and the burden of care for these folks falls largely upon family caregivers. However, many caregivers lack the necessary skills to engage with these people in a compassionate and understanding manner. Caregiver training can empower family members and friends to provide in-home treatment to people with mental health issues. To address this challenge, a conversational AI chatbot was developed to train caregivers in empathetic communication. The chatbot engages in interactive conversations with caregivers and offers guidance on compassionate and empathetic communication. Our study demonstrates that conversational AI can serve as a valuable tool for training caregivers, leading to improved patient outcomes through enhanced communication skills.

关键词： Training Machine learning algorithms Data analysis Text categorization Mental health Oral communication Medical services

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Applying Smarta to the analysis of tourist networks

arXiv

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arXiv 2025年

作者： Lloret-Climent, Miguel Nescolarde-Selva, Josué Antonio Alonso-Stenberg, Kristian Montoyo-Guijarro, Andrés Gutiérrez-Vázquez, Yoan Department of Applied Mathematics University of Alicante Alicante03690 Spain Department of Software and Computing Systems University of Alicante Alicante03690 Spain

The framework of the present study was the destination life cycle model, a classical model that describes the development of tourist destinations. We examined mass tourism in Benidorm based on tourist accommodation supply and demand statistics over the January 2016 - October 2018 period, provided by Spain’s National Institute for Statistics. The objective was to analyze the life cycle and competitiveness of Benidorm’s tourism system, interpret whether the tourism product was sustainable, and at what stage in the cycle Benidorm is currently in. To do this, we used Smarta software, which, based on network analysis, enables to interpret the system’s virtuous cycles and analyze causality by observing relationship patterns in the system’s attractors, thus complementing typical processing based on causal maps and the study of social networks. The results obtained by this application (which has been developed by our research team), show 6 sets of attractors that mark the trends of the tourist system. Finally, the analysis of the significant variables of these attractors have helped us to justify that the tourist system of Benidorm is in the rejuvenation phase. © 2025, CC BY-NC-ND.

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VisioSLR: A Vision Data-Driven Framework for Sign Language Video Recognition and Performance Evaluation on Fine-Tuned YOLO Models

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Procedia Computer Science 2025年 257卷 85-92页

作者： Leila Ismail Nada Shahin Henok Tesfaye Alain Hennebelle Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Independent Researcher

With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively.

关键词： Artificial intelligence Computer vision Deep learning Fine-tuning Machine translation Neural machine translation Sign language Sign language recognition Transfer learning YOLO

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Rare Collision Risk Estimation of Autonomous Vehicles with Multi-Agent Situation Awareness

Rare Collision Risk Estimation of Autonomous Vehicles with M...

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International Conference on Intelligent Transportation

作者： Mahdieh Zaker Henk A.P. Blom Sadegh Soudjani Abolfazl Lavaei School of Computing Newcastle University United Kingdom Delft University of Technology The Netherlands Max Planck Institute for Software Systems Germany

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

This paper offers a formal framework for the rare collision risk estimation of autonomous vehicles (AVs) with multi-agent situation awareness, affected by different sources of noise in a complex dynamic environment. The estimation framework consists of two complementary parts: modeling formalism and a rare event estimation method using sequential Monte Carlo (MC) simulation instead of importance sampling. By defining incremental levels of severity that must be passed before a collision, a sequence of MC simulations can be applied from one level to the next. This particular sequential MC method consists of the simulation of an Interacting Particle System (IPS) in combination with Fixed Assignment Splitting (FAS) of particles that reach the next level. We model AVs equipped with the situation awareness as general stochastic hybrid systems (GSHS), including the IPS-FAS relevant severity levels, and assess the probability of collision in a lane-change scenario where two self-driving vehicles simultaneously intend to switch lanes into a shared one while utilizing the time-to-collision measure for decision-making as required. The IPS-FAS method is subsequently used to estimate collision risk for this GSHS model of the lane-changing scenario. The results show that in contrast to straightforward MC simulation, IPS-FAS is able to quantify the very low collision risk for the scenario of interest.

关键词： Monte Carlo methods Noise Decision making Estimation Stochastic processes Switches IP networks Vehicle dynamics Autonomous vehicles Intelligent transportation systems

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DICFaaS: Parallel and Scalable Data Integrity Computation for Serverless systems

DICFaaS: Parallel and Scalable Data Integrity Computation fo...

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Tianchen Xiong Bo Peng School of Software Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Serverless computing is extensively employed in distributed storage computing scenarios owing to its dynamic responsiveness and on-demand allocation capabilities. Within distributed storage computing, data integrity computation holds significant importance. Nevertheless, current serverless systems fail to consider the CPU-intensive nature of data integrity computation, leading to prolonged CPU occupancy, increased latency, and substantial performance deterioration, particularly in parallel scenarios. Therefore, there is an imperative need for a solution that can efficiently handle this kind of computation in serverless systems. In this paper, we introduce DICFaaS, a high-performance serverless system specialized for parallel and scalable data integrity computation. DICFaaS can recognize the offloading potential of servers and automatically offload CPU-intensive data integrity computation. The offloading function of DICFaaS can be easily integrated into other existing serverless systems. We evaluate the effectiveness of DICFaaS for data integrity computation compared to state-of-the-art serverless systems. For example, DICFaaS delivers up to $17 \times$ latency improvement over AWS Lambda and up to $42 \times$ latency improvement over OpenFaaS when the data size is 200M. It also saves up to $\mathbf{6 0 \%}$ CPU overhead at runtime compared to OpenFaaS.

关键词： Runtime Data integrity Heuristic algorithms Scalability Serverless computing Distributed databases Quality of service Parallel processing Dynamic scheduling Hardware

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Multi-Function LUT for FPGAs

Multi-Function LUT for FPGAs

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2023 Seminar on Microelectronics, Dielectrics and Plasmas, Theory and Practical Applications, MDP 2023

作者： Sovetov, Stanislav I. Tyurin, Sergey F. Perm National Research Polytechnic University Department of Automatic and Telemechanic Perm Russia Perm National Research Polytechnic University Department of Automatic and Telemechanic Russia Perm State University Department of Software Computing Systems Perm Russia

ISBN: (纸本)9798350383577

FPGA's basic element is the Look Up Table (LUT). Known LUTs implement only one logic function for a given configuration in canonical normal form (CDNF). In this case, there is always a part of inactive pass transistors (PT). Previously developed LUT implements two functions simultaneously. A further modification of this method is proposed with the implementation of more than two functions of the same variables for one configuration. This provides the effect of reducing the number of transistors in comparison with the implementation in several LUTs. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Rare Collision Risk Estimation of Autonomous Vehicles with Multi-Agent Situation Awareness

arXiv

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arXiv 2024年

作者： Zaker, Mahdieh Blom, Henk A.P. Soudjani, Sadegh Lavaei, Abolfazl School of Computing Newcastle University United Kingdom Delft University of Technology Netherlands Max Planck Institute For Software Systems Germany

This paper offers a formal framework for the rare collision risk estimation of autonomous vehicles (AVs) with multi-agent situation awareness, affected by different sources of noise in a complex dynamic environment. In our proposed setting, the situation awareness is considered for one of the ego vehicles by aggregating a range of diverse information gathered from other vehicles into a vector. We model AVs equipped with the situation awareness as general stochastic hybrid systems (GSHS) and assess the probability of collision in a lane-change scenario where two self-driving vehicles simultaneously intend to switch lanes into a shared one, while utilizing the time-to-collision measure for decision-making as required. Due to the substantial data requirements of simulation-based methods for the rare collision risk estimation, we leverage a multi-level importance splitting technique, known as interacting particle system-based estimation with fixed assignment splitting (IPS-FAS). This approach allows us to estimate the probability of a rare event by employing a group of interacting particles. Specifically, each particle embodies a system trajectory and engages with others through resampling and branching, focusing computational resources on trajectories with the highest probability of encountering the rare event. The effectiveness of our proposed approach is demonstrated through an extensive simulation of a lane-change scenario. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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MP-GAN: Cyber-Attack Detection and Localization for Cyber-Physical systems with Multi-Process Generative Adversarial Networks*

MP-GAN: Cyber-Attack Detection and Localization for Cyber-Ph...

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2023 International Conference on Artificial Intelligence of Things and systems, AIoTSys 2023

作者： Zhou, Yikui Wang, Jie Tang, Junnan Gou, Chao Jiang, Zigui Li, Dan Ng, See-Kiong Sun-Yat Sen University School of Software Engineering Zhuhai China Sun-Yat Sen University School of Intelligent Systems Engineering Shenzhen China National University of Singapore Institute of Data Science School of Computing Singapore

ISBN: (纸本)9798350312270

Cyber-Physical System (CPS) integrates sensing, computation, cybernetics, and networking to control a hybrid physical system consisting of different functional subsystems, making the production process more intelligent and controllable. However, cyber-attacks during its operation will lead to abnormal system behaviors or even system breakdowns. In recent years, data-driven anomaly detection methods have been adopted to judge whether the CPS system is under cyber-attacks based on rich sensor measurements to avoid further economic losses or safety issues. However, the multi-process essence of CPS has not been adequately addressed in existing works to locate the processes or points being attacked for in-time actions. In this work, we proposed a Multi-Process Generative Adversarial Network (MP-GAN) framework to detect anomalous CPS statuses and locate cyber-attacks. Specifically, the Long-Short-Term-Memory Recurrent Neural Networks (LSTM-RNN) was adopted as the base model to capture the temporal correlation of time series distributions, and the data was transferred between latent space and data space in a bi-directional manner, which regulated the generator to generate more realistic samples and thus better grasping the underline principles of the system. Moreover, parallel generators were employed to capture system performances at different physical processes, thus localizing the attacked CPS processes. Experiments on three CPS datasets, two collected from the Secure Water Treatment (SWaT) system and one collected from the water Distribution (WADI) system, showed that the proposed MP-GAN framework effectively reported anomalies caused by various cyber-attacks inserted in these complex multiprocess CPSs, which outperforms the state-of-the-art methods and can also locate most of the detected irregularities at the corresponding stages where the attacks were inserted. © 2023 IEEE.

关键词： Cyber Physical System

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