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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Wang, Hongyue Deng, Jialin Kong, Dehui Fang, Ziqi Luo, Hong Pasumarthy, Nandini Patibanda, Rakesh Abewickrema, Sasindu Fang, Xiao Zoe Elvitigala, Don Samitha Mueller, Florian Exertion Games Lab. Department of Human-Centred Computing Monash University Melbourne VIC Australia Diverse-ability Interaction Lab. School of Computer Science Bristol United Kingdom Human-Centered Systems Lab (H-Lab) Karlsruhe Institute of Technology Karlsruhe Germany Imperial College London London United Kingdom Exertion Games Lab. Department of Human-Centred Computing Monash University Melbourne Australia College of Computer Science Zhejiang University Zhejiang Hangzhou China

ISBN: (纸本)9798400713958

Sound plays a crucial role in shaping diners’ perceptions and enhancing the dining experience. Recognizing this, designers have started to integrate auditory elements into interactive interfaces that connect diners with food. However, how chefs incorporate sound into their culinary practices remains underexplored. To bridge this gap, we introduced GastroConcerto, an auditory dining system that allows chefs to link specific sounds with ingredients, enabling the creation of unique sonic dishes. Our ultimate goal is to enrich chefs’ creative repertoire through novel auditory interactions. © 2025 Copyright held by the owner/author(s).

关键词： auditory interface chefs’ culinary practices Human-food interaction

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Gain and Impedance Bandwidth Enhancement of a Loop Antenna for X-Band Applications

Gain and Impedance Bandwidth Enhancement of a Loop Antenna f...

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Antennas and Propagation & USNC/URSI National Radio science Meeting

作者： Sagiru Gaya Mohamed Saeed AlShehhi Khaled Al-Wahedi Mohammed A. Abou-Khousa Department of Electrical Engineering and Computer Science Khalifa University of Science and Technology Abu Dhabi UAE smart sensing lab (SSL) Khalifa University of Science (KU)

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

This paper presents the new design for a rectangular loop antenna (RLA) with enhanced impedance bandwidth and realized gain. The newly proposed RLA includes a truncated rectangular loop and two parasitic elements positioned on either side of the loop, strategically employed to enhance bandwidth. Additionally, to concentrate radiated power, a metallic sheet is positioned beneath the antenna at a distance of 3.35 mm. Through simulations, the study illustrates that the suggested design achieves a notable 60% improvement in bandwidth and a gain enhancement of approximately 6.6 dBi. The antenna operates within the frequency range of 10.2 GHz to 11.7 GHz, achieving a maximum gain of 11 dBi at a resonance frequency of 11 GHz. Given its performance in the X-band frequency range, the proposed RLA emerges as a promising candidate for applications such as synthetic aperture radar (SAR) imaging.

关键词： Antenna measurements Resonant frequency Loop antennas Bandwidth Resonance Radar polarimetry Frequency measurement

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Application of Generative Graph Models in Biological Network Regeneration: A Selective Review and Qualitative Analysis

Application of Generative Graph Models in Biological Network...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Binon Teji Swarup Roy Pietro Hiram Guzzi Dinabandhu Bhandari Department of Computer Applications Network Reconstruction & Analysis (NETRA) Lab Sikkim University Gangtok India School of Computer Science and Biomedical Engineering University of Catanzaro Italy Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Biological networks are essential for understanding the complex cellular mechanisms of living organisms. Simulating biological networks allows researchers to model and understand complex cellular processes without the need for extensive and costly experiments. The ability to generate synthetic graphs that closely resemble these real-world, complex biological processes is a vital research area. Graph generation in the context of biological networks is particularly significant because it can lead to insights into cellular functions, disease mechanisms, and therapeutic targets. Recent advances in deep learning, particularly in graph generative models, have opened new avenues for applications in the biological domain. These advancements have the potential to revolutionize our understanding of biological systems. However, despite the development of numerous effective graph generation models, there has been limited work assessing the qualitative aspects of the generated graphs within the context of biological networks. Addressing this gap is crucial for ensuring that synthetic graphs are not only structurally accurate but also biologically *** various graph generation models are available, their application to biological network recreation has been limited. In this paper, we focus on graph generation, specifically edge and node-independent models for biological networks. We assess four candidate models across four gene expression networks. Our systematic assessment examines the models’ qualitative aspects, including graph structural properties, generation diversity, and computational efficiency. Our findings highlight the strengths and limitations of current models, offering insights to guide the development of more robust graph generation techniques that accurately replicate biological network characteristics.

关键词： Analytical models Accuracy Systematics Reviews Biological system modeling Computational modeling Neural networks Predictive models Biology Organisms

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RJoules: An Energy Measurement Tool for R 23

RJoules: An Energy Measurement Tool for R

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Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering

作者： Rajrupa Chattaraj Sridhar Chimalakonda Research in Intelligent Systems and Human Analytics Lab Department of Computer Science and Engineering Indian Institute of Technology Tirupati India

ISBN: (纸本)9798350329964

With the exponential growth of data, the demand for effective data analysis tools has increased significantly. R language, known for its statistical modeling and data analysis capabilities, has become one of the most popular programming languages among data scientists and researchers. As the importance of energy-aware software systems continues to rise, several studies investigate the impact of source code and different stages of machine learning model training on energy consumption. However, existing studies in this domain primarily focus on programming languages like Python and Java, resulting in a lack of energy measuring tools for other programming languages such as R. To address this gap, we propose "RJoules", a tool designed to measure the energy consumption of R code snippets. We evaluate the correctness and performance of RJoules by applying it to four machine learning algorithms on three different systems. Our aim is to support developers and practitioners in building energy-aware systems in R. The demonstration of the tool is available at https://***/yMKFuvAM-DE and related artifacts at https://***/RJoules/.

关键词： energy measurement

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Shared Bodily Fusion: Leveraging Inter-Body Electrical Muscle Stimulation for Social Play

Shared Bodily Fusion: Leveraging Inter-Body Electrical Muscl...

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2024 ACM Designing Interactive Systems Conference, DIS 2024

作者： Patibanda, Rakesh Overdevest, Nathalie Nisal, Shreyas Saini, Aryan Elvitigala, Don Samitha Knibbe, Jarrod van den Hoven, Elise Mueller, Florian Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia School of Electrical Engineering and Computer Science Faculty of Engineering The University of Queensland St LuciaQLD Australia Materialising Memories & Visualisation Institute University of Technology Sydney Sydney Australia Eindhoven University of Technology Eindhoven Netherlands

ISBN: (纸本)9798400705830

Traditional games like "Tag" rely on shared control via inter-body interactions (IBIs) – touching, pushing, and pulling – that foster emotional and social connection. Digital games largely limit IBIs, with players using their bodies as input to control virtual avatars instead. Our "Shared Bodily Fusion" approach addresses this by fusing players’ bodies through a mediating computer, creating a shared input and output system. We demonstrate this approach with "Hidden Touch", a game where a novel social electrical muscle stimulation system transforms touch (input) into muscle actuations (output), facilitating IBIs. Through a study (n=27), we identifed three player experience themes. Informed by these fndings and our design process, we mapped their trajectories across our three experiential spaces – threshold, tolerance, and precision – which collectively form our design framework. This framework facilitates the creation of future digital games where IBIs are intrinsic, ultimately promoting the many benefts of social play. © 2024 Copyright held by the owner/author(s).

关键词： Muscle

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Code Review Automation Via Multi-task Federated LLM - An Empirical Study

arXiv

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arXiv 2024年

作者： Kumar, Jahnavi Chimalakonda, Sridhar Research in Intelligent Software & Human Analytics Lab Department of Computer Science and Engineering Indian Institute of Technology Tirupati India

Code review is a crucial process before deploying code to production, as it validates the code, provides suggestions for improvements, and identifies errors such as missed edge cases. In projects with regular production releases, the effort required for peer code-reviews remains high. Consequently, there has been significant interest from software engineering (SE) researchers in automating the code review process. Previous research on code review automation has typically approached the task as three independent sub-tasks: review necessity prediction, review comment generation, and code refinement. Our study attempts to (i) leverage the relationships between the sub-tasks of code review automation, by developing a multi-task model that addresses all tasks in an integrated manner, and (ii) increase model robustness on unseen data via collaborative large language model (LLM) modeling, while retaining the proprietary nature of code, by using federated learning (FL). The study explores five simple techniques for multi-task training, including two sequential methods, one parallel method, and two cumulative methods. The results indicate that sequentially training a federated LLM (FedLLM) for our code review multi-task use case is less efficient in terms of time, computation, and performance metrics, compared to training separate models for each task. Because sequential training demonstrates catastrophic forgetting, alternatively cumulative fine-tuning for multi-task training performs better than training models for individual tasks. This study highlights the need for research focused on effective fine-tuning of multi-task FedLLMs for SE tasks. © 2024, CC BY.

关键词： Coding errors

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Proposed Different Signal Processing Tools for Efficient Optical Wireless Communications

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computers, Materials & Continua 2022年第5期71卷 3293-3318页

作者： Hend Ibrahim Abeer D.Algarni Mahmoud Abdalla Walid El-Shafai Fathi E.Abd El-Samie Naglaa F.Soliman Department of Electronics and Communications Engineering Faculty of EngineeringZagazig UniversityZagazig44519Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh 11586Saudi Arabia

Optical Wireless Communication(OWC)is a new trend in communication systems to achieve large bandwidth,high bit rate,high security,fast deployment,and low *** basic idea of the OWC is to transmit data on unguided media with *** system requires multi-carrier modulation such as Orthogonal Frequency Division Multiplexing(OFDM).This paper studies optical OFDM performance based on Intensity Modulation with Direct Detection(IM/DD)*** system requires a non-negativity *** paper presents a framework for wireless optical OFDM system that comprises(IM/DD)with different forms,Direct Current biased Optical OFDM(DCO-OFDM),Asymmetrically Clipped Optical OFDM(ACO-OFDM),Asymmetrically DC-biased Optical OFDM(ADO-OFDM),and *** also considers channel coding as a tool for error control,channel equalization for reducing deterioration due to channel effects,and investigation of the turbulence *** evaluation results of the proposed framework reveal enhancement of *** performance of the IM/DD-OFDM system is investigated over different channel models such as AWGN,log-normal turbulence channel model,and ceiling bounce channel *** simulation results show that the BER performance of the IM/DD-OFDM communication system is enhanced while the fading strength is *** results reveal also that Hamming codes,BCH codes,and convolutional codes achieve better BER ***,two algorithms of channel estimation and equalization are considered and *** algorithms include the Least Squares(LS)and the Minimum Mean Square Error(MMSE).The simulation results show that the MMSE algorithm gives better BER performance than the LS algorithm.

关键词： Optical communication systems OWC IM/DD OFDM MMSE LS ADO-OFDM DCO-OFDM ACO-OFDM

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What Do Developers Feel About Fast-Growing programming Languages? An Exploratory Study

What Do Developers Feel About Fast-Growing Programming Langu...

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IEEE/ACM International Conference on Program Comprehension (ICPC)

作者： Jahnavi Kumar Sridhar Chimalakonda Department of Computer Science and Engineering Research in Intelligent Software & Human Analytics Lab Indian Institute of Technology Tirupati India

ISBN: (数字)9798400705861

ISBN: (纸本)9798350352276

The developer community has witnessed an unprecedented surge in recent years, with over 100 million active developers on the GitHub platform in 2023. Along with it, there is a significant rise and adoption of new programming languages, frameworks and tools. The study aims to comprehend how developers perceive these fastgrowing programming languages by performing emotion analysis of developer’s comments posted in various software artifacts such as pull requests, issues and commits of GitHub repositories. In this regard, we employed a fine-tuned small ‘Large Language Model’ (sLLM) to detect emotions, leveraging a balanced dataset from existing literature complemented with additional manual annotations from our collected data. We have analyzed 10 fast-growing programming languages, examining 1.8 million comments from 4.1 million non-code artifacts. To further validate our findings, we have performed a qualitative survey and analysis with 28 developers. Our study reveals insights into the developers emotion associated with these fast-growing languages. Notably, “Surprise’’ is the predominant emotion associated with these languages. CCS CONCEPTS • Software and its engineering $\rightarrow$ General programming languages.

关键词： Surveys computer languages Annotations Software Data models Surges Software development management

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LEARNING DOMAIN-AGNOSTIC REPRESENTATION FOR DISEASE DIAGNOSIS 11

LEARNING DOMAIN-AGNOSTIC REPRESENTATION FOR DISEASE DIAGNOSI...

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11th International Conference on Learning Representations, ICLR 2023

作者： Wang, Churan Li, Jing Sun, Xinwei Zhang, Fandong Yu, Yizhou Wang, Yizhou School of Computer Science Peking University China CFCS School of CS Inst. for AI Peking University China China Nat'l Eng. Research Center of Visual Technology China AI lab Deepwise Beijing China Department of Computer Science The University of Hong Kong Hong Kong School of Data Science Fudan University China

In clinical environments, image-based diagnosis is desired to achieve robustness on multi-center samples. Toward this goal, a natural way is to capture only clinically disease-related features. However, such disease-related features are often entangled with center-effect, disabling robust transferring to unseen centers/domains. To disentangle disease-related features, we first leverage structural causal modeling to explicitly model disease-related and center-effects that are provable to be disentangled from each other. Guided by this, we propose a novel Domain Agnostic Representation Model (DarMo) based on variational Auto-Encoder. To facilitate disentanglement, we design domain-agnostic and domain-aware encoders to respectively capture disease-related features and varied center effects by incorporating a domain-aware batch normalization layer. Besides, we constrain the disease-related features to well predict the disease label as well as clinical attributes, by leveraging Graph Convolutional Network (GCN) into our decoder. The effectiveness and utility of our method are demonstrated by the superior performance over others on both public datasets and in-house datasets. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Diagnosis

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Design and Implementation of a Smart Human Posture Monitoring Device Using a Wearable Vest 7

Design and Implementation of a Smart Human Posture Monitorin...

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7th International Conference on Advances in Biomedical Engineering, ICABME 2023

作者： Daou, Roy Abi Zeid Geha, Elio Aoun, Youssef Hayek, Ali Boercsoek, Josef Olmedo, Jose Javier Serrano Université la Sagesse Biomedical Engineering Department Furn El Chebbak Beirut Lebanon Mart Learning Education and Research Center Chananir Lebanon Lebanese German University Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon University of Applied Science Hamm-Lippstadt Department Lippstadt 2 Hardware Engineering Marker Allee 76-78 Hamm59063 Germany University of Kassel Faculty of Electrical Engineering /Computer Science Department of Computer Architecture and System Programming Germany Universidad Politecnica de Madrid Lab of Bioinstrumentation and Nanomedicine Centro de Tecnologia Biomedica Spain

ISBN: (纸本)9798350325850

Chronic back pain can present a serious health concern, with symptoms that can significantly affect an individual's well-being, mobility, and overall quality of life over an extended period. While chronic back pain may manifest suddenly in some cases, it often develops gradually and persists for weeks, and in untreated cases, it can linger for years. Hence, the utilization of assistive devices such as wearable posture-monitoring vests can offer valuable assistance and guidance to users. This research paper is dedicated to the development of a system for detecting, diagnosing, and correcting poor posture, specifically leaning posture. The vest is designed to provide users with visual, auditory, and tactile cues to help them address this issue, thereby reducing the risk associated with leaning. Additionally, an integrated electrical box has been designed to consolidate all components directly onto the main board in a secure enclosure. This box also displays the daily count of instances where the user has leaned. This system is characterized by its electrical safety, portability, compactness, comfort, and affordability. A comprehensive analysis of the system's performance has been conducted with a meticulous evaluation of accuracy. Each component of the system has undergone successful testing, and the system as a whole is currently in the testing phase. The results of these tests have indicated a lack of faulty errors and have demonstrated outstanding accuracy and detection rates. Over 100 individuals of varying ages, genders, and BMI categories were involved in testing, with each person wearing the device for an average of six hours. The accuracy rate achieved was 98.85%, with an average of 54.35 instances of poor posture detected per participant. © 2023 IEEE.

关键词： Wearable technology

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