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29th Annual Americas Conference on Information Systems: Diving into Uncharted Waters, AMCIS 2023

作者： Nawaz, Saqib Department of Computing Technologies School of Science Computing and Engineering Technologies Swinburne University of Technology Australia

ISBN: (纸本)9781713893592

The increasing prevalence of smartphones and social media has sparked growing concerns about anxiety related to digital disconnection, particularly in the context of problematic smartphone use and dependence (PSUD). Fear of missing out (FOMO) is a widely recognised form of anxiety associated with frequent smartphone use, especially on social media apps, and it has been primarily observed among younger adults. However, beyond FOMO, other technological concerns, such as nomophobia (no-mobile-phone phobia) and technoference (the interference of technology in interpersonal relationships), have also become more prevalent, highlighting the broader impact of smartphone use on individuals' mental wellbeing. My ongoing PhD research into the identification and management of PSUD aimed to delve deeper into understanding the complexities of PSUD and its effects on individuals' lives. FOMO is one of the critical aspects examined within the broader framework of PSUD, as it can have significant implications for mental health and wellbeing. It also explored how FOMO may arise both from PSUD and effectual smartphone use, where individuals may feel concerned about missing important meetings, notifications, calls, or other responsibilities related to their social and professional roles. The multifaceted cause of FOMO was one of the focal points of my investigation, encompassing factors such as the need for connectedness for various reasons and constant exposure to media. I have investigated how FOMO can lead to feelings of inadequacy, loneliness, and anxiety, impacting individuals emotionally and socially. This could include further examining how FOMO may negatively impact self-esteem, increase stress, and contribute to depression and its influence on developing unhealthy coping mechanisms and impulsive behaviours. Addressing FOMO and digital disconnection anxiety within the context of PSUD requires a holistic approach that involves self-care, mindfulness practices, and boundary setti

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Predicting NIRF Ranking via Novel K-Star Ensembles 15

Predicting NIRF Ranking via Novel K-Star Ensembles

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Kumar, Praveen Gupta, Manika Priyanka, Priyanka Dubey, Anuj Kumar Dutt, Varun Indian Institute of Technology School of Computing and Electrical Engineering Mandi India Indian Institute of Technology Mandi India

ISBN: (纸本)9798350370249

The National Institutional Ranking Framework (NIRF) was established by the Indian Ministry of Education (MoE), which ranks higher education institutions across the country. The institutions being ranked may benefit if their rankings or performance could be predicted given their parameters, thereby, improving their Institute's attributes. However, less attention has been given to the problem of predicting rank from the parameters collected for the rankings. The main objective of this research is to forecast the ranking of the Institutions using a four-year dataset of NIRF collected for 16 Indian Institute of Technologies (IITs). The change in parameter data was collected across 16 parameters to predict the change in ranks using two consecutive years. A novel K-Star 2 -stage ensemble algorithm was developed. In the first stage, a number of lazy learning algorithms, K-Star, Instance-Based Learner (IBk) and Locally Weighted Learning (LWL), were trained using 10 -fold crossvalidation. The predictions of these algorithms from stage 1 were taken into stage 2, where the K-Star predicted the change in rank. The results revealed that the K-Star Ensemble had an RMSE of 0.009 compared to other algorithms including IBk (RMSE: 5.76), K-Star (RMSE: 5.85), and LWL (RMSE: 5.91). We highlight the main implications of our work for rank prediction in the real world. © 2024 IEEE.

关键词： IBk K-Star Lazy Learning LWL Machine Learning Ranking

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Smart Health Diagnosis:Harnessing Data Mining for Predictive Healthcare 1

Smart Health Diagnosis:Harnessing Data Mining for Predictive...

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1st IEEE International Conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Indrakumari, R. Pandey, Vishal Sinha, Ashish Kumar Kumar, Ankesh Galgotias University School of Computer Science and Engineering India Galgotias University School of Computing Application and Technology India

ISBN: (纸本)9798350356816

The use of Smart Health Diagnosis: Data Mining for Predictive Healthcare is to provide a user- friendly and easily accessible platform where individuals can get instant health advice through an intelligent online healthcare system. By the analysis of the reported symptoms, the system can predict potential future diseases and provide relevant recommendations to healthcare providers on courses of action. Intelligent methods accurately determine potential interferences based on patient symptoms. Medical record database was used and symptoms were assigned to underlying diseases using Naive Bayesian calculations. Fast, reliable access to health care is a cornerstone of individual and social health but many people face barriers in receiving timely care. We are trying to solve these problems by introducing Health Prediction System, a revolutionary online healthcare solution that uses data mining for quick support and customized recommendations. © 2024 IEEE.

关键词： Electronic health record

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Parameter-efficient fine-tuning of pre-trained code models for just-in-time defect prediction

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Neural computing and Applications 2024年第27期36卷 16911-16940页

作者： Abu Talib, Manar Bou Nassif, Ali Azzeh, Mohammad Alesh, Yaser Afadar, Yaman Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computing and Informatics Sharjah United Arab Emirates King Hussein School for Computing Sciences Princess Sumaya University for Technology Amman Jordan

Software engineering workflows use version control systems to track changes and handle merge cases from multiple contributors. This has introduced challenges to testing because it is impractical to test whole codebases to ensure each change is defect-free, and it is not enough to test changed files alone. Just-in-time software defect prediction (JIT-SDP) systems have been proposed to solve this by predicting the likelihood that a code change is defective. Numerous techniques have been studied to build such JIT software defect prediction models, but the power of pre-trained code transformer language models in this task has been underexplored. These models have achieved human-level performance in code understanding and software engineering tasks. Inspired by that, we modeled the problem of change defect prediction as a text classification task utilizing these pre-trained models. We have investigated this idea on a recently published dataset, ApacheJIT, consisting of 44k commits. We concatenated the changed lines in each commit as one string and augmented it with the commit message and static code metrics. Parameter-efficient fine-tuning was performed for 4 chosen pre-trained models, JavaBERT, CodeBERT, CodeT5, and CodeReviewer, with either partially frozen layers or low-rank adaptation (LoRA). Additionally, experiments with the Local, Sparse, and Global (LSG) attention variants were conducted to handle long commits efficiently, which reduces memory consumption. As far as the authors are aware, this is the first investigation into the abilities of pre-trained code models to detect defective changes in the ApacheJIT dataset. Our results show that proper fine-tuning improves the defect prediction performance of the chosen models in the F1 scores. CodeBERT and CodeReviewer achieved a 10% and 12% increase in the F1 score over the best baseline models, JITGNN and JITLine, when commit messages and code metrics are included. Our approach sheds more light on the abilities of l

关键词： Forecasting

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Revolutionizing Potato Farming: A CNN-Powered Approach for Early Blight and Late Blight Detection to Ensure Global Food Security

Revolutionizing Potato Farming: A CNN-Powered Approach for E...

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2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2024

作者： Ponnuru, Mahesh Datta Sai Amasala, Likhitha Srm Institute of Science and Technology School of Computing Chennai India Vellore Institute of Technology School of Computer Science and Engineering Amaravathi India

ISBN: (纸本)9798350348460

Potato, a crucial global food crop, faces persistent threats from diseases like early blight and late blight, jeopardizing both yields and economic stability. In response, we present an innovative approach using CNN for early disease detection in potato crops. Our objective is to empower farmers with rapid and accurate disease identification, facilitating timely treatments to prevent economic losses and reduce agricultural waste. Our CNN model achieved a remarkable validation accuracy of 99.59%, outperforming conventional methods. With 183,747 trainable parameters, our efficient and accurate model is set to revolutionize agricultural practices. Furthermore, we are deploying our model to Google Cloud and developing a React Native mobile app for potato prediction. This integration aims to provide farmers with real-time disease insights, preventing economic losses and ensuring global food security, thereby enhancing economic stability in the potato farming sector. © 2024 IEEE.

关键词： Agriculture Convolutional Neural Networks (CNNs) Crop protection Food security Early blight Economic stability Late blight Potato disease detection

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Utilizing Proxy Re-Encryption for Enhanced Security in Data Sharing based on Blockchain 7

Utilizing Proxy Re-Encryption for Enhanced Security in Data ...

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7th International Conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Saisanthiya, D. Saxena, Naman Saini, Pratham SRM Institute of Science and Technology Networking and Communications School of Computing Faculty of Engineering and Technology Chennai603203 India

ISBN: (纸本)9798350372816

The rapid growth of the Internet of Things (IoT) has underscored the critical need for robust information exchange mechanisms, especially within cloud services. However, this advancement also introduces significant data security risks due to the potential for misuse. To address these concerns, this research proposes an innovative method to bolster data security in cloud environments using proxy re-encryption. Our method employs identity-based encryption, enabling data owners to securely upload encrypted data to the cloud. Authorized users can then access this data through a proxy re-encryption process. To handle computationally demanding tasks, an edge device functions as a proxy, thereby alleviating the limitations of typical IoT devices. A key feature of our system is the integration of blockchain technology, which decentralizes data sharing and alleviates the bottlenecks inherent in centralized systems, while also enabling fine-grained access control. A thorough data security analysis and review indicate that our solution effectively ensures data confidentiality, integrity, and public *** of Form, Bottom of Form. © 2024 IEEE.

关键词： Cloud security

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S-MGHSTN: Towards An Effective Streaming Traffic Accident Risk Prediction Framework

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IEEE Transactions on Knowledge and Data engineering 2025年第7期37卷 4285-4298页

作者： Chen, Minxiao Yuan, Haitao Jiang, Nan Zheng, Zhihan Bao, Zhifeng Zhou, Ao Jiang, Jiaxin Wang, Shangguang Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore RMIT University School of Computing Technologies MelbourneVIC3000 Australia National University of Singapore School of Computing 117417 Singapore

Traffic accidents pose a significant risk to human health and property safety. To address this issue, predicting their risks has garnered growing interest. We argue that a desired prediction solution should demonstrate resilience to the complexity of traffic accidents. In particular, it should adequately consider the streaming nature of data and key related aspects, such as regional background, accurately capture both proximity and similarity while bridging the disparities, and effectively address the sparsity. However, these factors are often overlooked or difficult to incorporate. In this paper, we propose a novel streaming multi-granularity hierarchical spatio-temporal network. Initially, we innovate by incorporating remote sensing data, facilitating the creation of hierarchical multi-granularity structure and the comprehension of regional background. We construct multiple high-level risk prediction tasks to enhance model's ability to cope with sparsity. Subsequently, to capture and bridge spatial proximity and semantic similarity, region features and multi-view graph undergo encoding processes to distill effective representations, followed by a graph-enhanced representation alignment module that reconciles their disparities. At last, an alternating experience replay with a dual-memory buffer is employed to accommodate streaming data scenarios. Extensive experiments on two real datasets verify the superiority of our model against the state-of-the-art methods. © 1989-2012 IEEE.

关键词： Accidents Semantics Remote Sensing Correlation Urban Areas Streaming Media Roads Predictive Models Encoding Data Models Traffic Accident Risk Prediction Streaming Data Spatio Temporal Data Remote Sensing Car Accident Risk Of Traffic Accidents Hierarchical Structure Sparsity Regional Characteristics Remote Sensing Data Streams Semantic Similarity Spatial Proximity Background Regions Similarity Graph Experience Replay Short Term Memory Point Of Interest Semantic Features Incremental Learning Spatiotemporal Characteristics Weight Training Node Features Self Supervised Learning Traffic Risk Traffic Prediction Remote Sensing Images Point Of Interest Data Semantic Graph Feature Encoder Geographical Correlation Graph Features Temporal Modulation Concept Drift

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AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage Cost KV-Separated LSM Store with SmartSSD-based GC Offloading 23

AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage...

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23rd USENIX Conference on File and Storage Technologies, FAST 2025

作者： Duan, Zhuohui Feng, Hao Liu, Haikun Liao, Xiaofei Jin, Hai Li, Bangyu National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781939133458

The key-value separation is renowned for its significant mitigation of the write amplification inherent in traditional LSM trees. However, KV separation potentially increases performance overhead in the management of Value region, especially for garbage collection (GC) operation that is used to reduce the redundant space occupation. In response, many efforts have been made to optimize the GC mechanism for KV separation. However, our analysis indicates that such solution based on trade-offs between CPU and I/O overheads cannot simultaneously satisfy the three requirements of KV separated systems in terms of throughput, tail latency, and space usage. This limitation hinders their real-world application. In this paper, we introduce AegonKV, a "three-birds-one-stone" solution that comprehensively enhances the throughput, tail latency, and space usage of KV separated systems. AegonKV first proposes a SmartSSD-based GC offloading mechanism to enable asynchronous GC operations without competing with LSM read/write for bandwidth or CPU. AegonKV leverages offload-friendly data structures and hardware/software execution logic to address the challenges of GC offloading. Experiments demonstrate that AegonKV achieves the largest throughput improvement of 1.28-3.3 times, a significant reduction of 37%-66% in tail latency, and 15%-85% in space overhead compared to existing KV separated systems. © 2025 FAST. All Rights Reserved.

关键词： Digital storage

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Short Circuit Fault Diagnosis for a Parallel Lithium-Ion Battery Pack in Electric Vehicles 3rd

Short Circuit Fault Diagnosis for a Parallel Lithium-Ion Bat...

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3rd International Joint Conference on Energy, Electrical and Power engineering, CoEEPE 2023

作者： Xu, Yiming Ge, Xiaohua Guo, Ruohan Hu, Cungang Shen, Weixiang School of Science Computing and Engineering Swinburne University of Technology Melbourne Australia School of Electrical Engineering and Automation Anhui University Hefei China

ISBN: (纸本)9789819739394

Lithium-ion battery packs are widely deployed as power sources in transportation electrification solutions. To ensure safe and reliable operation of battery packs, it is of critical importance to monitor battery operation status and diagnose battery faults early. This paper proposes a soft short circuit (SC) fault detection method for a parallel battery pack. Since it is impractical to equip current sensors for all the cells in a parallel pack, a reconstructed state-space equation combining the electrical dynamic of a battery cell and the electrical characteristics of a parallel battery pack is designed for the cell current estimation. Then, the occurrence of an SC fault can be timely detected based on the difference between the measured and estimated branch current flowing through the first interconnected resistance. Simulations on a parallel-connected battery pack under a 10 Ω SC resistance are conducted to verify the effectiveness of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fault detection

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A survey on textual emotion cause extraction in social networks

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Digital Communications and Networks 2025年第2期11卷 524-536页

作者： Sancheng Peng Lihong Cao Guojun Wang Zhouhao Ouyang Yongmei Zhou Shui Yu Laboratory of Language Engineering and Computing Center for Linguistics and Applied LinguisticsGuangdong University of Foreign StudiesGuangzhou510006China School of English Education Guangdong University of Foreign StudiesGuangzhou510006China School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510006China South China University of Technology Guangzhou510006China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou510006China School of Computer Science University of Technology SydneySydneyNSW 2007Australia

With the rapid development of web technology,Social Networks(SNs)have become one of the most popular platforms for users to exchange views and to express their *** and more people are used to commenting on a certain hot spot in SNs,resulting in a large amount of texts containing *** Emotion Cause Extraction(TECE)aims to automatically extract causes for a certain emotion in texts,which is an important research issue in natural language *** is different from the previous tasks of emotion recognition and emotion *** addition,it is not limited to the shallow-level emotion classification of text,but to trace the emotion *** this paper,we provide a survey for ***,we introduce the development process and classification of ***,we discuss the existing methods and key factors for ***,we enumerate the challenges and developing trend for TECE.

关键词： Text Emotion Emotion cause Machine learning Deep learning

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