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2023 Iberian Languages Evaluation Forum, IberLEF 2023

作者： Martínez-Murillo, Iván Sepúlveda-Torres, Robiert Saquete, Estela LLoret, Elena Palomar, Manuel GPLSI research group Dept. of Software and Computing Systems University of Alicante Ctra. San Vicente s/n San Vicente del Raspeig Alicante03690 Spain

AuTexTification is a shared task within the IberLEF workshop which aims to determine whether a text has been generated by an Artificial Intelligence (AI) or a human. The objective of this paper is to report the participation and results of the GPLSI team from the University of Alicante (Spain) in subtask 1: Human or Generated of the AuTexTification challenge for English and Spanish languages. We propose and experiment with different approaches based on Transfer Learning;Ensemble Learning;fine-tuning existing language models, such as RoBERTa or RemBERT;or relying on linguistic features. Our best models for both languages were trained through Transfer Learning techniques, obtaining the 6th and 8th position in the English and Spanish versions of this subtask, respectively. Results obtained in the Spanish-version were close to the top-performing team. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Learning systems

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Nostalgic Analysis of Location Based Tweets 13th

Nostalgic Analysis of Location Based Tweets

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13th International Conference on Social Informatics, SocInfo 2022

作者： Gao, Larissa Hong, Lingzi Mashhadi, Afra Computing and Software Systems University of Washington Bothell United States Information Science University of North Texas Denton United States

ISBN: (纸本)9783031190964

Social media posts have been increasingly used as mediums for gaining insight into a wide spectrum of people’s experiences including those in relation to physical spaces. We examine features of social media posts that characterized users’ memories of different visited Points-of-Interest (POI). Our results revealed that these recall of memories often mention other users, making them a collective experience. Furthermore, our analysis shows that nostalgic tweets are more than just tweets that mention the past but include an intertwine the past experiences with a positive outlook on the future and present. We also note that while these tweets do not mention specific locations in their content, their originating geo-location reflects places with a greater sense of belonging present. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Location

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Agent-Based Parallelization of a Multi-Dimensional Semantic Database Model

Agent-Based Parallelization of a Multi-Dimensional Semantic ...

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IEEE International Conference on Information Reuse and Integration (IRI)

作者： Alex Li Munehiro Fukuda Division of Computing and Software Systems University of Washington Bothell Bothell WA

Responses to database queries that may be even identical should vary if they are given under a different user context. For instance, queries for wild animals in the context of the ocean versus mountains should be different. Announced in 1993 [1], Mathematical Model of Meaning (MMM) provides users with capabilities to extract data items tightly coupled under different semantic spaces. Such a space is created dynamically with user-defined impression words to compute semantic equivalence and similarity between data items. MMM computes semantic correlations between the key and other data items to achieve dynamic data querying. However, a semantic space creation and a data correlative calculation are computationally demanding. We consider MMM as a practical database application of multi-agent technologies, construct a space over a cluster system, and have multi-agents explore for a given target and its surrounding data items. We use the Multi-Agent Spatial Simulation (MASS) library to implement an agent-based semantic database system and to measure its parallel execution. Compared to a sequential MMM implementation, MASS-based parallelization yielded a 22-time speedup when creating a space, mainly achieved with matrix multiplication. MASS also reduced the time required for distance sorting of multi-dimensional vectors by 23.7%.

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Impact of Artificial Intelligence on Decision-Making in Information systems

Impact of Artificial Intelligence on Decision-Making in Info...

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Advanced Research in computing (ICARC), International Conference on

作者： RWMR Dharani Rajasinghe Dept. of Software Engineering & Information Systems Faculty of Computing NSBM Green University Homagama Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

The study examines how artificial intelligence (AI) affects information systems (IS) decision-making, emphasizing how AI improves precision, speed, and flexibility in a range of industries. AI presents potential and problems, including ethical, legal, and societal ramifications, as it revolutionizes organizational decision-making. In order to demonstrate AI's function in intricate IS systems, this paper examines a variety of AI strategies used in decision-making, assesses their efficacy using accepted metrics, and examines real-world case studies. Key findings address issues including data bias, lack of transparency, and job displacement while highlighting the advantages of AI in processing massive information and producing useful insights. The recommendations highlight the necessity of worker reskilling, sector-specific adaptability, explainable AI (XAI) models, and regulatory frameworks. In order to facilitate sustainable AI integration in IS for improved organizational and social results, this research emphasizes the significance of striking a balance between AI-driven innovation and ethical issues.

关键词： Industries Surveys Ethics Adaptation models Technological innovation Explainable AI Law Decision making Data models Information systems

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Enhancing Blockchain Adoption through Tailored software Engineering: An Industrial-grounded Study in Education Credentialing

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Distributed Ledger Technologies: Research and Practice 2023年第4期2卷 1-24页

作者： Zoey Ziyi Li Han Wang Dragan Gasevic Jiangshan Yu Joseph K. Liu Department of Software Systems & Cybersecurity Monash University Australia Department of Human Centred Computing Monash University Australia

Recent years have witnessed a marked increase in both academic proposals and industrial adoptions of blockchain technology. However, a majority of the projects remain at the stage of prototype proposals and their real-world deployment has not met the anticipated level. This gap can be attributed to three major barriers - technical difficulties, human factors, and social context. Most of the existing research leans towards addressing the technical challenges, leaving the human and social aspects inadequately explored. Moreover, a lack of practical insights in the existing blockchain software engineering frameworks further exacerbates the adoption problem. To address these gaps, we introduce a Blockchain-oriented software Engineering Approach for Higher Adoption Possibility (BOSE-HAP). This approach emphasizes collaboration, reflective thinking, and iterative development, aiming to bolster implementation consistency and stimulate industry adoption. We have applied this approach in the design, development, and launch of a blockchain credentialing product, ***, in the context of a university-level summer school. The product achieves industry-accepted System Usability Score and has seen successful real-world deployment. In addition, this study embed usability considerations throughout the process, involved a total of 112 stakeholders across different development stages, with 25 of them participating in our in-depth interviews and usability testing. Drawing from our firsthand experience and industrial-grounded findings, we deliver eight reflections and propose five best practice suggestions relevant to blockchain adoption. We believe these insights will provide invaluable guidance for both academic researchers and industry practitioners involved in the field of blockchain technology.

关键词： Blockchain software engineering education credentialing design-based thinking usability testing STGT

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Influence of donor age on liver transplantation outcomes: A multivariate analysis and comparative study

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World Journal of Gastrointestinal Surgery 2024年第2期16卷 331-344页

作者： Miran Bezjak Ivan Stresec Branislav Kocman Stipislav Jadrijević Tajana Filipec Kanizaj Miro Antonijević Bojana Dalbelo Bašić Danko Mikulić Department of Surgery University Hospital MerkurZagreb 10000Croatia Department of Electronics MicroelectronicsComputer and Intelligent SystemsFaculty of Electrical Engineering and ComputingZagreb 10000Croatia Department of Gastroenterology University Hospital MerkurZagreb 10000Croatia Development of Software Nanobit Ltd.Zagreb 10000Croatia

BACKGROUND The growing disparity between the rising demand for liver transplantation(LT)and the limited availability of donor organs has prompted a greater reliance on older liver ***,utilizing livers from elderly donors has been associated with outcomes inferior to those achieved with grafts from younger *** accounting for additional risk factors,we hypothesize that the utili-zation of older liver grafts has a relatively minor impact on both patient survival and graft *** To evaluate the impact of donor age on LT outcomes using multivariate analysis and comparing young and elderly donor *** In the period from April 2013 to December 2018,656 adult liver transplants were performed at the University Hospital *** multivariate Cox propor-tional hazards models were developed to independently assess the significance of donor *** age was treated as a continuous *** approach involved univariate and multivariate analysis,including variable selection and assessment of interactions and ***,to exemplify the similarity of using young and old donor liver grafts,the group of 87 recipients of elderly donor liver grafts(≥75 years)was compared to a group of 124 recipients of young liver grafts(≤45 years)from the *** rates of the two groups were estimated using the Kaplan-Meier method and the log-rank test was used to test the differences between *** Using multivariate Cox analysis,we found no statistical significance in the role of donor age within the constructed *** when retained during the entire model development,the donor age's impact on survival remained insignificant and transformations and interactions yielded no substantial effects on *** insigni-ficance and low coefficient values suggest that donor age does not impact patient survival in our ***,there was no statistical evidence that the five developed models did not adhe

关键词： Liver transplantation Elderly donors Survival analysis Postoperative complications Cox proportional hazard models

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Multimodal Fusion Strategies for Emotion Recognition

Multimodal Fusion Strategies for Emotion Recognition

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International Joint Conference on Neural Networks (IJCNN)

作者： David Ortiz-Perez Manuel Benavent-Lledo David Mulero-Pérez David Tomás Jose Garcia-Rodriguez Department of Computer Technology University of Alicante Spain Department of Software and Computing Systems University of Alicante Spain

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Emotions play a crucial role in our daily lives, influencing how we face challenges throughout the day and shaping our behavior, even when we are not consciously aware of them. Detecting emotional states in others relies on comprehending the collective impact of a variety of actions that emotions can produce, such as facial expressions, posture, tone of voice, or speech. To address this challenge, we propose a multimodal transformer-based model designed to recognize the emotional moods of individuals using video data, including audio and text transcriptions. Consequently, our model extracts the most relevant information from each modality to make a final prediction. Throughout this work, different fusion architectures have been integrated with transformer-based models to determine the optimal combination. This study examines the performance of individual modalities and their combinations using the CMU-MOSEI dataset. This dataset encompasses preprocessed video, audio, and text data. Our best model achieves a weighted accuracy of 85.59% on this dataset, surpassing previous works for this task.

关键词： Emotion recognition Text recognition Mood Neural networks Predictive models Transformers Robustness

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Promise and Peril of Collaborative Code Generation Models: Balancing Effectiveness and Memorization

arXiv

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arXiv 2024年

作者： Chen, Zhi Jiang, Lingxiao Centre for Research on Intelligent Software Engineering School of Computing and Information Systems Singapore Management University Singapore

In the rapidly evolving field of machine learning, training models with datasets from various locations and organizations presents significant challenges due to privacy and legal concerns. The exploration of effective collaborative training settings, which are capable of leveraging valuable knowledge from distributed and isolated datasets, is increasingly *** study investigates key factors that impact the effectiveness of collaborative training methods in code next-token prediction, as well as the correctness and utility of the generated code, showing the promise of such methods. Additionally, we evaluate the memorization of different participant training data across various collaborative training settings, including centralized, federated, and incremental training, showing their potential risks in leaking data. Our findings indicate that the size and diversity of code datasets are pivotal factors influencing the success of collaborative trained code models. We demonstrate that federated learning achieves competitive performance compared to centralized training while offering better data protection, as evidenced by lower memorization ratios in the generated code. However, federated learning can still produce verbatim code snippets from hidden training data, potentially violating data privacy or copyright. Our study further explores the patterns of effectiveness and memorization in incremental learning, emphasizing the importance of the sequence in which individual participant datasets are introduced. Also, we identify the memorization phenomenon of cross-organizational clones as a prevalent challenge in both centralized and federated learning scenarios. Our findings highlight the persistent risk of data leakage during inference, even when training data remains unseen. We conclude with strategic recommendations for practitioners and researchers to optimize the use of multisource datasets, thereby propelling the cross-organizational collaboration forward. Cop

关键词： Collaborative learning

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Evaluating software Development Agents: Patch Patterns, Code Quality, and Issue Complexity in Real-World GitHub Scenarios

arXiv

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arXiv 2024年

作者： Chen, Zhi Jiang, Lingxiao Centre for Research on Intelligent Software Engineering School of Computing and Information Systems Singapore Management University Singapore

In recent years, AI-based software engineering has progressed from pre-trained models to advanced agentic workflows, with software Development Agents representing the next major leap. These agents, capable of reasoning, planning, and interacting with external environments, offer promising solutions to complex software engineering tasks. However, while much research has evaluated code generated by large language models (LLMs), comprehensive studies on agent-generated patches, particularly in real-world settings, are lacking. This study addresses that gap by evaluating 4,892 patches from 10 top-ranked agents on 500 real-world GitHub issues from SWE-Bench Verified, focusing on their impact on code quality. Our analysis shows no single agent dominated, with 170 issues unresolved, indicating room for improvement. Even for patches that passed unit tests and resolved issues, agents made different file and function modifications compared to the gold patches from repository developers, revealing limitations in the benchmark’s test case coverage. Most agents maintained code reliability and security, avoiding new bugs or vulnerabilities;while some agents increased code complexity, many reduced code duplication and minimized code smells. Finally, agents performed better on simpler codebases, suggesting that breaking complex tasks into smaller sub-tasks could improve effectiveness. This study provides the first comprehensive evaluation of agent-generated patches on real-world GitHub issues, offering insights to advance AI-driven software development. © 2024, CC BY.

关键词： software design

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TPTO: A Transformer-PPO based Task Offloading Solution for Edge computing Environments 29

TPTO: A Transformer-PPO based Task Offloading Solution for E...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Gholipour, Niloofar De Assuncao, Marcos Dias Agarwal, Pranav Gascon-Samson, Julien Buyya, Rajkumar Univ. of Quebec Dept. of Software Engineering and IT École de Technologie Supérieure Montreal Canada The Univ. of Melbourne CLOUDS Lab School of Computing and Information Systems Australia

ISBN: (纸本)9798350330717

Emerging applications in healthcare, autonomous vehicles, and wearable assistance require interactive and low-latency data analysis services. Unfortunately, cloud-centric architectures cannot fulfill the low-latency demands of these applications, as user devices are often distant from cloud data centers. Edge computing aims to reduce the latency by enabling processing tasks to be offloaded to resources located at the network's edge. However, determining which tasks must be offloaded to edge servers to reduce the latency of application requests is not trivial, especially if the tasks present dependencies. This paper proposes a Deep Reinforcement Learning (DRL) approach called TPTO, which leverages Transformer Networks and Proximal Policy Optimization (PPO) to offload dependent tasks of IoT applications in edge computing. We consider users with various preferences, where devices can offload computation to an edge server via wireless channels. Performance evaluation results demonstrate that under fat application graphs, TPTO is more effective than state-of-the-art methods, such as Greedy, HEFT, and MRLCO, by reducing latency by 30.24%, 29.61%, and 12.41%, respectively. In addition, TPTO presents a training time approximately 2.5 times faster than an existing DRL approach. © 2023 IEEE.

关键词： Edge computing reinforcement learning task offloading Transformers

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