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2021 CHI Conference on human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021

作者： Signer, Madlaina Signer, Alexandra Sorkine-Hornung, Olga Department of Computer Science Eth Zurich Switzerland Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh United States

ISBN: (纸本)9781450380966

We propose a novel design of engineered, structured materials that leverages fast fabrication technologies, pushing them towards mass-fabrication. Specifically, our metamaterial is designed to be laser cut, to approximate the volumetric shape and allow for locally varying compliance. Traditional mechanical metamaterials consist of intricate cells arranged on a 3-dimensional grid, limiting them to 3D printing-which is slow. Our metamaterial is designed for laser cutting, which is drastically faster. Our structures are best described as ruffled strips of thin sheet material, such as paper, plastics, metals, etc. Users can interactively define the ruffles' anisotropic stiffness directions and local density. Our computational design tool assists users by automatically optimizing the ruffle to fill the shape's volume, and exporting the flat ruffle design ready for cutting. We demonstrate how such ruffled metamaterials can be utilized for, e.g., custom toys with locally varying compliance, custom packaging material, or lightweight formwork for architectural shells. © 2021 ACM.

关键词： Fabrication

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Intelligent Detection System for Multi-Step Cyber-Attack Based on Machine Learning 15

Intelligent Detection System for Multi-Step Cyber-Attack Bas...

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15th International Conference on Developments in eSystems Engineering, DeSE 2023

作者： Alheeti, Khattab M. Ali Alzahrani, Abdulkareem Jasim, Omar Hammad Al-Dosary, Duaa Ahmed, Hamsa M. Al-Ani, Muzhir Shaban University of Anbar Computer Networking Systems Department Ramadi Iraq Al Baha University Faculty of Computer Science and Information Technology Al Baha Saudi Arabia Human Resources Department Ramadi Iraq College of Computer Sciences and Information Technology University of Anbar Anbar Iraq University of Human Development College of Science and Technology Department of Information Technology Sulaymaniyah Iraq

ISBN: (纸本)9798350335149

Cyber-attacks involve stifling processes and activities, conciliating data, or restricting data access by carefully modifying computer systems and networks with malware. There has been a significant increase in these types of attacks over time. Due to the rise in complexity and structure, advanced defensive methods are needed. In the face of growing security threats, traditional methods of identifying cyber-attacks are ineffective. In this paper, the intelligent of intrusion a detection system is suggested. Moreover, the suggested system attempts to evaluate the capability of the k-nearest neighbour's algorithm (KNN) in terms of distinguishing between authentic and tampered data. A reliable dataset named the Multi-Step Cyber-Attack Dataset (MSCAD) is utilized to determine the behavior Among the new sorts of attacks. Moreover, 60% of the dataset was utilized for training the model, and a remaining 40% was used for testing. Evaluation metrics like accuracy, precision, recall, and F1 score are used. Experiments suggest that the proposed system-based KNN could enhance detection performance. Moreover, the suggested approach increases detection accuracy while minimizing false alarms. © 2023 IEEE.

关键词： Network security

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3DG: A Framework for Using Generative AI for Handling Sparse Learner Performance Data From Intelligent Tutoring Systems

3DG: A Framework for Using Generative AI for Handling Sparse...

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2024 Joint of International Conference on Learning Analytics and Knowledge Workshops, LAK-WS 2024

作者： Zhang, Liang Lin, Jionghao Borchers, Conrad Cao, Meng Hu, Xiangen Institute for Intelligent Systems University of Memphis MemphisTN38152 United States Department of Electrical and Computer Engineering University of Memphis MemphisTN38152 United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA15213 United States Department of Psychology University of Memphis MemphisTN38152 United States

Learning performance data (e.g., quiz scores and attempts) is significant for understanding learner engagement and knowledge mastery level. However, the learning performance data collected from Intelligent Tutoring Systems (ITSs) often suffers from sparsity, impacting the accuracy of learner modeling and knowledge assessments. To address this, we introduce the 3DG framework (3-Dimensional tensor for Densification and Generation), a novel approach combining tensor factorization with advanced generative models, including Generative Adversarial Network (GAN) and Generative Pre-trained Transformer (GPT), for enhanced data imputation and augmentation. The framework operates by first representing the data as a three-dimensional tensor, capturing dimensions of learners, questions, and attempts. It then densifies the data through tensor factorization and augments it using Generative AI models, tailored to individual learning patterns identified via clustering. Applied to data from an AutoTutor lesson by the Center for the Study of Adult Literacy (CSAL), the 3DG framework effectively generated scalable, personalized simulations of learning performance. Comparative analysis revealed GAN’s superior reliability over GPT-4 in this context, underscoring its potential in addressing data sparsity challenges in ITSs and contributing to the advancement of personalized educational technology. © 2024 CEUR-WS. All rights reserved.

关键词： Generative adversarial networks

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Framing Theory on Music Streaming Platforms: How Vocabulary Influences Music Playlist Decision-Making and Expectations

Framing Theory on Music Streaming Platforms: How Vocabulary ...

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2024 ACM International Conference on Intelligent User Interfaces Workshops, IUI-WS 2024

作者： van Bree, Lotte Graus, Mark Ferwerda, Bruce Department of Computer Science and Informatics School of Engineering Jönköping University Jönköping Sweden Human-Centric SARL Paris France

To improve decision-making and prevent information overload, music streaming services try to offer users personalized content. Vocabulary can be an important component in achieving personalization, in addition to recommender systems that ensure users are given access to their musical preferences. One way to apply personalization through vocabulary is to use personal pronouns. However, it is still unclear how the use of vocabulary personalization affects users’ decisions and expectations when it comes to music streaming services. This study aims to explore how vocabulary framing in playlist titles influences decision-making and the content expectations of the music playlist. Despite challenges in making informed vocabulary choices, our results show a substantial impact of personalized vocabulary in playlist titles on user choice and overall expectations. Users distinctly favor playlists with personalized vocabulary, emphasizing their crucial role in enhancing the music streaming experience. © 2024 Copyright for this paper by its authors.

关键词： Decision making

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Interactive Data Fusion of Neural Radiance Fields for Industrial Facility Inspection in Virtual Reality

Interactive Data Fusion of Neural Radiance Fields for Indust...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Ke Li Susanne Schmidt Tim Rolff Reinhard Bacher Wim Leemans Frank Steinicke Deutsches Elektronen-Synchrotron DESY Germany Department of Informatics Human-Computer Interaction Universität Hamburg

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

Real-world industrial facilities often present complex equipment that requires extensive inspection and maintenance for their operations. In this work, we present a virtual reality (VR) framework that supports virtual facility inspection and maintenance tasks by using neural radiance fields (NeRF) models to replicate, store, and visualize the appearance of complex industrial facilities. To overcome the performance bottleneck of VR NeRF rendering, we present two novel interactive data fusion techniques that can merge a NeRF model with its' corresponding CAD model through a mixed reality tunneling effect and contextual 3D NeRF drawing interaction. Technical benchmarking results and preliminary expert feedback are presented for the initial evaluation of our framework.

关键词： Solid modeling Three-dimensional displays Data integration Virtual reality Inspection Rendering (computer graphics) Industrial facilities

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Zero-shot Persuasive Chatbots with LLM-Generated Strategies and Information Retrieval

Zero-shot Persuasive Chatbots with LLM-Generated Strategies ...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Furumai, Kazuaki Legaspi, Roberto Vizcarra, Julio Yamazaki, Yudai Nishimura, Yasutaka Semnani, Sina J. Ikeda, Kazushi Shi, Weiyan Lam, Monica S. Human-Centered AI Laboratories KDDI Research Inc. Saitama Japan Computer Science Department Stanford University StanfordCA United States

ISBN: (纸本)9798891761681

Persuasion plays a pivotal role in a wide range of applications from health intervention to the promotion of social good. Persuasive chatbots employed responsibly for social good can be an enabler of positive individual and social change. Existing methods rely on fine-tuning persuasive chatbots with task-specific training data which is costly, if not infeasible, to collect. Furthermore, they employ only a handful of pre-defined persuasion strategies. We propose PersuaBot, a zero-shot chatbot based on Large Language Models (LLMs) that is factual and more persuasive by leveraging many more nuanced strategies. PersuaBot uses an LLM to first generate natural responses, from which the strategies used are extracted. To combat hallucination of LLMs, Persuabot replace any unsubstantiated claims in the response with retrieved facts supporting the extracted strategies. We applied our chatbot, PersuaBot, to three significantly different domains needing persuasion skills: donation solicitation, recommendations, and health intervention. Our experiments on simulated and human conversations show that our zero-shot approach is more persuasive than prior work, while achieving factual accuracy surpassing state-of-the-art knowledge-oriented chatbots. © 2024 Association for Computational Linguistics.

关键词： Zero-shot learning

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Conversational Product Recommendation using LLM 4

Conversational Product Recommendation using LLM

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Chang, Ting-Jui Lin, Lydia Hsiao-Mei Tsai, Richard Tzong-Han National Central University Department of Computer Science and Information Engineering Taoyuan Taiwan National Taipei University of Technology Department of Interaction Design Taipei Taiwan Research Center for Humanities and Social Sciences Academia Sinica Taipei Taiwan

ISBN: (纸本)9798350360721

It is expected to deploy chatbots as sales assistants on e-commerce platforms soon. In recent years, the capabilities demonstrated by large language models (LLMs) indicate their suitability for this role. However, a dialogue process is not fully established. Most existing works concentrate on searching for ideal products, such as Retrieval-Augmented Generation (RAG) and product search. Many proposed methods rely on product reviews. However, in the case of some e-commerce platform products, valuable reviews are often lacking, rendering these approaches impractical. Thus, we studied how to assist users and select a set of candidate products. Accordingly, we simulated sales conversations based on the product features provided by the seller, using LLM and reliable content while addressing the cold-start problem. Specifically, we used product features to generate customer persona for user simulation. LLM was used to play the role of sales assistant in giving recommendations. The experimental results showed that practical conversation was created between customers and chatbots in Traditional Chinese. The code and results are available at: https://***/terryobe-ncu/CPR_LLM © 2024 IEEE.

关键词： Sales

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Enhancing Cancer Gene Prediction through Aligned Fusion of Multiple PPI Networks Using Graph Transformer Models

Enhancing Cancer Gene Prediction through Aligned Fusion of M...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Han, Zebei Yu, Gufeng Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China

ISBN: (纸本)9798350337488

Despite the considerable progress that has been made in cancer research, identifying cancer genes remains a significant challenge due to the intricate nature of the disease. Given the importance of incorporating gene interaction relationships in identifying potential cancer genes, Graph Neural Networks (GNNs) have garnered increasing attention for their ability to model gene associations effectively. Particularly, recent studies have demonstrated the superiority of GNNs in deciphering the knowledge embedded in Protein-Protein interaction (PPI) networks for cancer gene prediction. However, these studies primarily focused on a single PPI network, overlooking the valuable insights encapsulated within other PPI networks. Additionally, previous endeavors often centered around pan-cancer datasets, neglecting the importance of predicting specific cancer genes. To address these limitations, we present a novel method called MPIT, which employs Graph Transformer Networks (GTNs) to identify specific cancer driver genes. MPIT effectively integrates data from diverse PPI and multi-omics data via the alignment and fusion of gene representations learned from different PPI networks. We collect three distinct cancer cell line datasets to assess the model performance. Our experimental findings demonstrate the superiority of MPIT over the existing methods, achieving the state-of-the-art performance across all three datasets. © 2023 IEEE.

关键词： Cancer genes feature alignment feature fusion Graph Transformer Network specific cancer genes

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A DV-Hop dynamic weight positioning model with genetic algorithm

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International Journal of Wireless and Mobile Computing 2021年第2期20卷 139-145页

作者： Wang, Penghong Fan, Tian Cai, Xingjuan Li, Wuchao School of Computer Science and Technology Taiyuan University of Science and Technology Taiyuan Shanxi003024 China Research Centre of Intelligent Interface and Human Computer Interaction Department of Computer Science and Technology Harbin Institute of Technology Harbin Heilongjiang150006 China Jiaxing Vocational Technical College Jiaxing Zhejiang China

Wireless Sensor Networks (WSNs), as an important part of the Internet of Things (IoT), are widely deployed to the location estimation in WSNs. However, at present, the study of DV-Hop algorithm is still limited to the improvement of the sensor nodes in the ordinary region. In this paper, aiming at the complex areas under different application backgrounds, based on the analysis of the localisation principle of the traditional DV-Hop model based on optimisation algorithm, a dynamic weight positioning model based on genetic algorithm (DWMGA-DV-Hop) is proposed. The algorithm performs simulation experiments on three different types of test by adopting dynamic weight calculation model. And experimental results demonstrate that, under different test sets, compared with the standard DV-Hop algorithm, TW-PSODV-Hop and OCS-DV-Hop algorithm, this algorithm has the highest positioning accuracy and stability. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Genetic algorithms

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Auto-PIP: Real-time Identification of Critical Performance Inflection Points in Software Stress Testing 35

Auto-PIP: Real-time Identification of Critical Performance I...

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35th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2024

作者： Zhang, Shenglin Xiong, Xiao Li, Mengyao Sun, Yongqian Zhao, Yongxin Chen, Xia Deng, Bowen Pei, Dan Nankai University China Haihe Laboratory of Information Technology Application Innovation China Tianjin Key Laboratory of Software Experience and Human Computer Interaction China Huawei Cloud Tsinghua University Beijing National Research Center for Information Science and Technology China

ISBN: (纸本)9798350367041

Conducting stress testing is essential to ensure the stability and performance of software systems post-launch. Among its various aspects, the real-time identification of the "Smooth Load Area"(SLA) is crucial for establishing performance benchmarks, identifying bottlenecks, guiding optimization strategies, and strategically allocating resources. However, existing methods are incapable of real-time inflection point detection or require manual intervention. This paper proposes Auto-PIP, an automated identification framework for performance inflection points based on key performance indicators (KPIs) during performance stress tests. Auto-PIP integrates trend testing algorithms with unsupervised anomaly detection algorithms to identify optimal operating points and estimate maximum capacity. Auto-PIP has been deployed in Huawei Cloud, and the evaluation results show that Auto-PIP demonstrated 100% accuracy in optimal inflection point detection, a 41.7% leap over baselines, and 83.9% accuracy in maximum point identification, a 10.7% gain. Additionally, we have released the dataset to the public to promote ongoing research. © 2024 IEEE.

关键词： Software testing

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