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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Hussain, Shahid Wang, Wen-Li Khan, Saif Ur Rahman Rahim, Muhammad Abdul Basit Ur Ibrahim, Naseem Zhao, Jie Department of Computer Science and Software Engineering Penn State University Erie United States Department of Software Engineering Shifa Tameer-e-Millat University Islamabad Pakistan Department of Computer Science California State University Long Beach United States

ISBN: (纸本)9789819757985

The long-term participation of trained and competent employees is required to improve the morale, productivity, safety, value, and autonomy of an organization. In small and medium-sized software industries, the long-term involvement of developers will not only boost the efficiency of software development but also help train their team members. Although software industries have managed to stimulate developers’ productivity and reduce their turnover with a better compensation plan, the incentive through money has caused contradictory arguments. Hence, there is a need to employ a more systematic way to explore, analyze, and calibrate the developer’s needs, so that the managerial roles can make informed decisions and incorporate influential motivators to increase a developer’s job satisfaction level. In this respect, we are conducting an empirical study using the basics of Maslow's theory, a motivational theory in psychology, to (1) explore the developer’s motivation level in software industries, (2) benchmark the developer’s needs for job satisfaction at each level, and (3) analyze the job-hunting factors for developers’ turnover. We conducted a questionnaire-based study to gather feedback from 128 developers in 10 software industries with four types of analyses being conducted. The results indicate that satisfaction levels can vary across software industries, but the management can anticipate developers’ high-level needs from their low-level needs to determine influencing and motivating factors for job satisfaction improvements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Job satisfaction

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Ground Source Heat Pump System Based on Residential Building

Ground Source Heat Pump System Based on Residential Building

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2023 IEEE PES Innovative Smart Grid Technologies - Asia, ISGT Asia 2023

作者： Hu, Pengcheng Ukil, Abhisek Nair, Nirmal University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand

ISBN: (纸本)9798350327748

Based on the climate conditions in the Gisborne region of New Zealand, this paper analyzes the economy and environmental protection of ground source heat pumps (GSHP) by simulation. The results show that GSHP can effectively achieve indoor heating function in winter and has a high coefficient of performance. That means more heat output per unit of electricity. This highly efficient energy conversion reduces energy consumption;more importantly, no combustion process is involved, so no harmful emissions such as combustion exhaust gas are produced. This helps to reduce carbon emissions and mitigate climate change. © 2023 IEEE.

关键词： Energy utilization

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Evaluating Transparency: A Cross-Model Exploration of Explainable AI in Financial Forecasting

Evaluating Transparency: A Cross-Model Exploration of Explai...

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2023 IEEE Asia-Pacific Conference on computer Science and Data engineering, CSDE 2023

作者： Valensky, Dylan Mohaghegh, Mahsa Auckland University of Technology Computer Science and Software Engineering Auckland New Zealand

ISBN: (纸本)9798350341072

Advances in machine learning and deep learning have assisted progress in stock market predictions, which have presented unique opportunities for investors and traders to benefit from predictions. However, understanding how these models work is crucial to trusting their predictions. This research applies three prominent Explainable AI (XAI) techniques - SHAP, LIME, and Permutation Importance - to three distinct forecasting models: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Decision Tree. This work fills a gap in the literature by comparing XAI methods across a combination of 'black-box' and 'white-box' models. Two key research questions guide this research: the performance of these XAI methods across the chosen models and the consistency between global and local explanations across these models. Findings reveal interesting model-specific behaviors, like CNN's emphasis on slightly older data and LSTM's focus on immediate past data. In the S&P 500 dataset context, features such as 'Close' and 'Adj Close' prices emerged as significant across models, while 'Volume' remained insignificant. This study offers a broader perspective on applying XAI in financial time series forecasting and helps enhance trust and comprehension among stakeholders relying on these model predictions. © 2023 IEEE.

关键词： Forecasting

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APT Detector: Detect and Identify APT Malware Based on Deep Learning Framework 23

APT Detector: Detect and Identify APT Malware Based on Deep ...

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9th International Conference on Computing and Artificial Intelligence, ICCAI 2023

作者： Tang, Binhui Yang, Jialin Li, Xin Cao, Yang Wang, Jie School of Computer and software Chengdu Jincheng College School of Cyber Science and Engineering Sichuan University China School of Computer and software Chengdu Jincheng College China

ISBN: (纸本)9781450399029

Advanced persistent threat (APT) attacks use sophisticated attack techniques and covert command and control (C&C) channels to conduct long-term sustained cyber attacks on specific targets as unobtrusively as possible. Over the past ten years, APT attacks have become increasingly frequent, gradually taking on a highly organized, nationalized, and militarized nature. Because APT malware is highly covert, has a long latency period, and presents characteristics such as diversification, high frequency, and sophistication. In the face of existing detection methods, it is easy for attackers to bypass detection by customizing malware and adapting and configuring tools to the target network. The continuous improvement of network technologies and tools and the complex and diverse attack process make it very difficult to detect unknown APT malware. To address the problems, this paper takes APT malware as the detection object and collects 6777 malware from 20 types of APT attack actors. Through the improved Convolutional Neural network (CNN) model combined with an attention mechanism to complete automatic extraction and weighting of static malware features, which can detect and discover APT malware. Meanwhile, the recognition effect for APT malware is better than other existing methods, which realize the attribution of APT attack groups. © 2023 Copyright held by the owner/author(s).

关键词： Malware

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Identifying Athletics Tracks using Keypoint Detection 38

Identifying Athletics Tracks using Keypoint Detection

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38th International Conference on Image and Vision Computing New Zealand, IVCNZ 2023

作者： Harcombe, Gareth Green, Richard Neshatian, Kourosh University of Canterbury Department of Computer Science and Software Engineering Christchurch New Zealand

ISBN: (纸本)9798350370515

This paper proposes the first known approaches to locating an athletics track from GPS data of a running workout. This is done by mapping the GPS points onto an image and applying computer Vision methods, including: performing keypoint detection using a Keypoint Region-Based Convolutional Neural Network (R-CNN) model with a Residual Neural Network backbone;and applying Convolutional Kernels over the image. The Keypoint R-CNN model achieved a Root Mean Square Error (RMSE) of 1.29 on synthetic data, which is lower than the error of other keypoint detection models of 2.23, but performed poorly on real data. The Convolutional Method achieved an RMSE of 4.20. The methods and results of this paper can be used in future applications for error correction and feature extraction of GPS data for running workout analysis. © 2023 IEEE.

关键词： Neural network models

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CE-HigherHRNet: Enhancing Channel Information for Small Persons Bottom-Up Human Pose Estimation

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2022年第1期49卷 260-269页

作者： Li, M.Y. Zhao, J. Master’s Student of School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

Accurately detecting the keypoints of small persons in an image using bottom-up multi-person pose estimation algorithms is exceptionally difficult owing to scale variation challenges. HigherHRNet initially solved the challenge of multi-player scale change pose estimation. However, because it uses repeated cross-scale fusion, owing to inherent defects in channel reduction, semantic information is lost. Furthermore, the aliasing effects produced by the miscellaneous feature maps formed after cross-scale fusion have a significant impact on the detection accuracy of small persons. In this paper, we propose a novel bottom-up human pose estimation algorithm based on HigherHRNet, called Channel-Enhanced HigherHRNet (CE-HigherHRNet). CE-HigherHRNet comprises three main components: a multiscale sub_pixel skip fusion module, a lightweight attention mechanism (with channel attention enhanced and spatial attention modules), and a high-resolution feature pyramid with an added Dupsampling module. The lightweight attention mechanism optimizes the feature map after each fusion. Deconvolution is replaced with Dupsampling, which strengthens the network’s scale awareness and makes it more sensitive to robust scale changes. The average precision (AP) of CE-HigherHRNet on the COCO test-dev dataset was 71.9% (an improvement of 1.4% compared with HigherHRNet). Furthermore, the average detection accuracy of small persons was 68.1% AP (an improvement of 1.5% AP). These results verify that the proposed CE-HigherHRNet is more robust in processing scale changes and has a stronger ability to handle crowded environments. Thus, it is more accurate in positioning small persons in images and human bodies in crowded environments. © 2022. All Rights Reserved.

关键词： Semantics

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Enhanced Loan Approval Prediction System using Ensemble Machine Learning Techniques 3

Enhanced Loan Approval Prediction System using Ensemble Mach...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Gothai, E. Rajalaxmi, R.R. Thamilselvan, R. Sridhar, K. Department of Computer Technology Kongu Engineering College Tamilnadu Perundurai India Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Perundurai India MSc Software Systems Kongu Engineering College Erode India

ISBN: (纸本)9798331532420

Banks play a pivotal role in generating significant profits through loan operations. However, the challenge lies in accurately identifying genuine loan applicants who are likely to repay their loans. Manual processes are prone to misconceptions, prompting the development of a loan prediction system using deep learning. This system aims to automate the selection of eligible candidates, benefiting both bank staff and applicants by drastically reducing the loan approval timeframe. Two key machine learning algorithms such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) are employed in this work for applicant selection. SVM, a popular supervised learning algorithm, creates a decision boundary or hyperplane to segregate classes in n-dimensional space. Conversely, ANN is inspired by biological neural networks, which mimic the organization of neurons in the input layer, hidden and output layers of the human brain. The integration of ANN and SVM in an ensemble model enhances accuracy compared to traditional methods. This collaborative approach ensures precise predictions of loan repayment likelihood based on applicant data and past loan eligibility records. The model's performance is measured using accuracy. The outcomes of the research were promising and the loan prediction system defines the minimum and maximum loan amounts that can be sanctioned to an applicant. By leveraging the strengths of both ANN and SVM, the ensemble model serves as a robust tool for accurately identifying worthy loan applicants, streamlining the loan approval process, and minimizing the risk of default. The accuracy of the algorithm is improved by this method to 91%. © 2024 IEEE.

关键词： Support vector machines

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KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration

KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driv...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Chengyuan Zhou, Suyang Kong, Jieping Qi, Lei Xue, Hui College of Software Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9798350368741

Zero-shot anomaly detection (ZSAD) identifies anomalies without needing training samples from the target dataset, essential for scenarios with privacy concerns or limited data. Vision-language models like CLIP show potential in ZSAD but have limitations: relying on manually crafted fixed textual descriptions or anomaly prompts is time-consuming and prone to semantic ambiguity, and CLIP struggles with pixel-level anomaly segmentation, focusing more on global semantics than local details. To address these limitations, We introduce KAnoCLIP, a novel ZSAD framework that leverages vision-language models. KAnoCLIP combines general knowledge from a Large Language Model (GPT-3.5) and fine-grained, image-specific knowledge from a Visual Question Answering system (Llama3) via Knowledge-Driven Prompt Learning (KnPL). KnPL uses a knowledge-driven (KD) loss function to create learnable anomaly prompts, removing the need for fixed text prompts and enhancing generalization. KAnoCLIP includes the CLIP visual encoder with V-V attention (CLIP-VV), Bi-Directional Cross-Attention for Multi-Level Cross-Modal Interaction (Bi-CMCI), and Conv-Adapter. These components preserve local visual semantics, improve local cross-modal fusion, and align global visual features with textual information, enhancing pixel-level anomaly detection. KAnoCLIP achieves state-of-the-art performance in ZSAD across 12 industrial and medical datasets, demonstrating superior generalization compared to existing methods. © 2025 IEEE.

关键词： Prompt Learning Vision-Language Models Zero-shot Anomaly Detection

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Binary Decomposition for Multi-Class Classification Problems: Development and Applications

Binary Decomposition for Multi-Class Classification Problems...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Li, Peiwei Liu, Han College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9798350303780

Binary decomposition of a multi-class classification problem is a widely used method in the field of machine learning, which involves using an ensemble of binary classifiers to undertake multi-class classification tasks. The motivation of binary decomposition lies in two main aspects. Firstly, some learning algorithms such as support vector machine can not achieve directly learning a multi-class classifier. Secondly, in some cases, the adoption of binary decomposition for training an ensemble of binary classifiers can even achieve better performance than the direct learning of a multi-class classifier. There are some existing methods that can be applied in achieving binary decomposition and have been proven to be effective. Some of these methods may also be applied in other tasks beyond classification. This paper presents a comprehensive review of the development and applications of binary decomposition methods. Specifically, these methods are put into two categories, namely, ordered and unordered decomposition, which each is introduced in terms of the essence and strategies of decomposition. These methods are also compared through theoretical analysis of their effectiveness and efficiency. On the basis of the comparative analysis, we suggest some future directions towards achieving further advances in binary decomposition. © 2023 IEEE.

关键词： Classification (of information)

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Synthetic/virtual Inertia in Renewable Energy Sourced Grid: A Review 5

Synthetic/virtual Inertia in Renewable Energy Sourced Grid: ...

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5th International Conference on Energy, Power, and Environment, ICEPE 2023

作者： Jadeja, Prachal Sood, Vijay K. Ontario Tech University Department of Electrical Computer and Software Engineering Oshawa Canada

ISBN: (纸本)9798350313123

Integration of renewable energy sources (RES) (i.e., solar or wind power plant) has introduced significant changes in conventional power systems. RESs use power electronic converters at the grid interface and are electrically decoupled from the grid. The reliability and stability of such systems has become significantfactors and requires advanced controllers for their frequency stability. The inertia of the traditional power systems originally depended on the rotating inertia but with the introduction of RESs, the power system's inertia decreased considerably with subsequent consequences. Thus, new approaches such as the use of synthetic or virtual inertia are required to add RESs to the grid. This paper reviews the significance of virtual inertia and suitable control strategies to provide inertia stability under the high penetration of RESs in power systems. © 2023 IEEE.

关键词： Wind power

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