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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Abiodun E. Onile Juri Belikov Eduard Petlenkov Yoash Levron Department of Software Science Tallinn University of Technology Akadeemia tee 15a Tallinn Department of Computer Systems Tallinn University of Technology Akadeemia tee 15a Tallinn Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa Israel

The next evolution of traditional energy systems towards smart grid will require end-consumers to actively participate and make informed decisions regarding their energy usage. Industry 4.0 facilitates such progress by allowing more advanced analytics and creating means for end-consumers and distributed grid assets to be modelled as their Digital twins (DT) equivalents, paving the way for asset-level analytics. Note-worthily, consumers’ comfort is crucial towards promotion of easy adoption of such models from consumers’ perspectives. This study presents the application of hybrid DT and multiagent reinforcement learning models for real-time estimation of end-consumers future energy behaviors while generating actionable recommendation feedback for improving their energy efficiency and enhancing end-user comfort.

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Machine Learning in Higher Education using RF and SVM for Pedagogical Enhancement 3

Machine Learning in Higher Education using RF and SVM for Pe...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Kunwar, Fateh Bahadur Srivastava, Satyajee Dhivya, R. Gulati, Monika Anand, Taruna Raj, D. Antony Arul Geetanjali Institute of Technical Studies Department of Computer Science and Engineering Udaipur India School of Engineering and Technology Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Faridabad India M.Kumarasamy College of Engineering Department of Information Technology Karur India Ambala India University Department of Humanities and Social Sciences Dehradun India Psg College of Arts & Science Department of Software Systems Coimbatore India

ISBN: (纸本)9798331508456

AI's revolutionary potential in higher education is examined in this proposal, including how it could revolutionize teaching, learning, administration, and research. Adaptive learning platforms, intelligent tutoring systems, and virtual learning assistants may change how students learn by improving engagement, providing quick feedback, and enabling tailored learning. The suggested process includes preprocessing, feature extraction, and model training. Preprocessing involves de-anonymizing, tokenizing, and removing punctuation, spaces, and numbers before analysis. The linear transformation method PCA extracts features. Training the systems uses the RF -SVM approach, which combines the advantages of RF and SVM. The suggested RF -SVM model beats two RF and SVM models with an average accuracy of 90.39 *** improves resource use, strategic decision-making, and research capacity in university management. The RF-SVM model's success in academia highlights the practicality of AI-driven solutions. © 2025 IEEE.

关键词： Teaching

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Good is Bad: Causality Inspired Cloth-debiasing for Cloth-changing Person Re-identification

Good is Bad: Causality Inspired Cloth-debiasing for Cloth-ch...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhengwei Yang Meng Lin Xian Zhong Yu Wu Zheng Wang National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science and Artificial Intelligence Wuhan University of Technology

Entangled representation of clothing and identity (ID)-intrinsic clues are potentially concomitant in conventional person Re- IDentification (ReID). Nevertheless, eliminating the negative impact of clothing on ID remains challenging due to the lack of theory and the difficulty of isolating the exact implications. In this paper, a causality-based Auto-Intervention Model, referred to as AIM 1 1 Codes will publicly available at https://***/BoomShakaY/AIM-CCReID., is first proposed to mitigate clothing bias for robust cloth-changing person ReID (CC-ReID). Specifically, we analyze the effect of clothing on the model inference and adopt a dual-branch model to simulate causal intervention. Progressively, clothing bias is eliminated automatically with model training. AIM is encouraged to learn more discriminative ID clues that are free from clothing bias. Extensive experiments on two standard CC-ReID datasets demonstrate the superiority of the proposed AIM over other state-of-the-art methods.

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MISA: Unveiling the Vulnerabilities in Split Federated Learning

MISA: Unveiling the Vulnerabilities in Split Federated Learn...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wei Wan Yuxuan Ning Shengshan Hu Lulu Xue Minghui Li Leo Yu Zhang Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Computer Science and Technology Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University Cluster and Grid Computing Lab

Federated learning (FL) and split learning (SL) are prevailing distributed paradigms in recent years. They both enable shared global model training while keeping data localized on users’ devices. The former excels in parallel execution capabilities, while the latter enjoys low dependence on edge computing resources and strong privacy protection. Split federated learning (SFL) combines the strengths of both FL and SL, making it one of the most popular distributed architectures. Furthermore, a recent study has claimed that SFL exhibits robustness against poisoning attacks, with a fivefold improvement compared to FL in terms of *** this paper, we present a novel poisoning attack known as $\color{Fuchsia} {{\text{MISA}}}$. It poisons both the top and bottom models, causing a misalignment in the global model, ultimately leading to a drastic accuracy collapse. This attack unveils the vulnerabilities in SFL, challenging the conventional belief that SFL is robust against poisoning attacks. Extensive experiments demonstrate that our proposed MISA poses a significant threat to the availability of SFL, underscoring the imperative for academia and industry to accord this matter due attention.

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A Cross modal Image and Text Retrieval Method for OpenKylin

A Cross modal Image and Text Retrieval Method for OpenKylin

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Frontiers of Robotics and software engineering (FRSE), International Conference on

作者： Jin Zhang Xin Xie Xiaodong Liu Jie Yu Long Peng Wenzhu Wang Pengfei Zhang Yue Lan Chao Zhang College of Computer and Communication Engineering Changsha University of Science and Technology Changsha China School of Computer Science National University of Defense Science and Technology Changsha China Haihe Lab of ITAI Tianjin China Kylin Software Tianjin China

With the rapid development of computer vision and natural language processing, there is an increasing demand for cross-modal retrieval (CMR) from users. The domestic operating system, openKylin, plays a key role in promoting domestic operating systems. It enriches the operating system functions with secure and trusted operation technology as the core. It is of great practical significance to integrate the field of computer vision into the domestic operating system. A graph retrieval method based on OpenKylin is proposed to enhance the functionality of searching graphs using text. This paper begins with a review of relevant domestic and international research on this technology. It then provides details on the design and implementation process, and concludes by demonstrating the final implementation results. The actual operational results demonstrate that the method is characterized by both high performance and high accuracy.

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Regression with cost-based rejection 23

Regression with cost-based rejection

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Xin Cheng Yuzhou Cao Haobo Wang Hongxin Wei Bo An Lei Feng College of Computer Science Chongqing University China School of Computer Science and Engineering Nanyang Technological University Singapore School of Software Technology Zhejiang University China Department of Statistics and Data Science Southern University of Science and Technology China

Learning with rejection is an important framework that can refrain from making predictions to avoid critical mispredictions by balancing between prediction and rejection. Previous studies on cost-based rejection only focused on the classification setting, which cannot handle the continuous and infinite target space in the regression setting. In this paper, we investigate a novel regression problem called regression with cost-based rejection, where the model can reject to make predictions on some examples given certain rejection costs. To solve this problem, we first formulate the expected risk for this problem and then derive the Bayes optimal solution, which shows that the optimal model should reject to make predictions on the examples whose variance is larger than the rejection cost when the mean squared error is used as the evaluation metric. Furthermore, we propose to train the model by a surrogate loss function that considers rejection as binary classification and we provide conditions for the model consistency, which implies that the Bayes optimal solution can be recovered by our proposed surrogate loss. Extensive experiments demonstrate the effectiveness of our proposed method.

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Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression

Expediting In-Network Federated Learning by Voting-Based Con...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Xiaoxin Su Yipeng Zhou Laizhong Cui Song Guo College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University Shenzhen China Department of Computer Science and Engineering The Hong Kong University of Science and Technology HKSAR

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Recently, federated learning (FL) has gained momentum because of its capability in preserving data privacy. To conduct model training by FL, multiple clients exchange model updates with a parameter server via Internet. To accelerate the communication speed, it has been explored to deploy a programmable switch (PS) in lieu of the parameter server to coordinate clients. The challenge to deploy the PS in FL lies in its scarce memory space, prohibiting running memory consuming aggregation algorithms on the PS. To overcome this challenge, we propose Federated Learning in-network Aggregation with Compression (FediAC) algorithm, consisting of two phases: client voting and model aggregating. In the former phase, clients report their significant model update indices to the PS to estimate global significant model updates. In the latter phase, clients upload global significant model updates to the PS for aggregation. FediAC consumes much less memory space and communication traffic than existing works because the first phase can guarantee consensus compression across clients. The PS easily aligns model update indices to swiftly complete aggregation in the second phase. Finally, we conduct extensive experiments by using public datasets to demonstrate that FediAC remarkably surpasses the state-of-the-art baselines in terms of model accuracy and communication traffic.

关键词： Training Data privacy Quantization (signal) Federated learning Computational modeling Memory management Collaboration

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SPAVIS: Mobile Application for Visually Impaired Based on Assistive software and Volunteerism

SPAVIS: Mobile Application for Visually Impaired Based on As...

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Advancements in Computing (ICAC), International Conference on

作者： M.A.M. Mahir M.N.M. Hussain R.D.D Perera Y.A.M Upendra C. J. Wickramarathne Dharshana Kasthurirathna Department of Computer Science & Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398106

Sri Lankan population accounts up to almost one million visually impaired individuals out of which are mostly students and young individuals. As the educational structure for the visually impaired improves with funds, blind schools, and free education, assistance with minute needs for most visually impaired individuals comes at a cost. There are many assistive technologies, such as audio books, screen magnifiers, braille books, and screen readers, prevalent around the island. However, there are several limitations to these technologies, mainly their availability and affordability. In Sri Lanka, many individuals, societies, clubs, and many more are willing to volunteer to help those in need, even those that require physical attention. As much as it is anticipated to aid those in need, there is very little attention to the ways it can be done. Hence, this research provides a way to develop a user-friendly mobile application with assistive software and volunteerism to aid visually impaired students with their daily needs.

关键词： Braille Costs Sociology Education Assistive technologies Chatbots Mobile applications

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Classifying Emotions in Roman Urdu Posts using Machine Learning 1

Classifying Emotions in Roman Urdu Posts using Machine Learn...

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1st Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021

作者： Nabeel, Zahra Mehmood, Maseeha Baqir, Anees Amjad, Anam Dept. of Software Engineering Sialkot Pakistan Ca'Foscari University of Venice Dept. of Environmental Sciences Informatics and Statistics Venice Italy National University of Sciences and Technology Dept. of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665424134

Emotion, a state of human mind has a significant impact on human behavior, social interactions and decision making. Few emotions from Carroll E. Izard model such as long-term fear, anger and sadness cause mental disorders which can lead to deterioration of mental and physical health. For the well-being of mental health, different psychological parameters such as voice, facial expressions and body movements were used to extract these emotions in earlier times. This paper focuses on verbal language for emotion classification. With the huge popularity of Web 3.0, people feel more comfortable in sharing their emotions over social media due to the anonymity of the web. Recently, researchers around the globe are interested to classify the emotions from social media posts (i.e., tweets, comments or micro-blogs etc.) using resource-rich languages e.g., English. However, less attention is paid towards the resource-poor language such as Urdu in Roman script. According to the Gallup survey in Pakistan, Urdu in Roman script is widely used to transmit text. To detect emotions in Roman Urdu text is a big challenge due to its scripting and morphology. In this paper, the following contributions are made;1) A corpus of Roman Urdu is developed for emotion classification. 2) We aim to identify user's mental states using textual posts on social media plat-forms;Instagram, Facebook, Twitter and YouTube. Particularly, Natural Language Processing methods incorporated with feature extraction methods (TF-IDF) and four supervised classifiers such as Support Vector Machine (SVM),Naïve Bayes, Random Forest and K-nearest Neighbor are used. By utilizing 80% training and 20% testing data, Naive Bayes secures 70.57% accuracy. 3) Moreover, suitability analysis is performed to identify the most suitable classifier in terms of accuracy, training and testing time. Results show that Naive Bayes is the most suitable classifier for emotion classification. © 2021 IEEE.

关键词： Deterioration

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Leveraging Bitcoin Mining Machines in Demand-Response Mechanisms to Mitigate Ramping-Induced Transients

SSRN

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SSRN 2025年

作者： Ginzburg-Ganz, Elinor Eyal, Ittay Baimel, Dmitry Santosh, Leena Belikov, Juri Levron, Yoash Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel Shamoon College of Engineering Beer-Sheva84100 Israel Pandit Deendayal Energy University India Department of Software Science Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia

Several previous works examine the use of cryptographic mining machines, for instance Bitcoin mining machines, in demand-response mechanisms, as part of the portfolio of assets managed by the grid operator. In this paper we extend this formulation by highlighting the effects of fast ramping transients, which may often occur in power grids rich with renewable energy sources. The resulting optimization problem is solved based on Pontryagin's minimum principle. We use this solution to examine the profitability and usage of these machines in a real-world settings, based on data from the California ISO and the ``Noga'' grid operator. In our analysis, we compare the effects of several key parameters, such as the electricity price, and the machines' price, hashrate and monetary revenue. These are examined for several different machine types that are available in the market today. Our main conclusion is that the profitability of the discussed mechanism is highly influenced by the cost of the mining machines, and the percentage of renewable sources within the energy mix, where some scenarios are more profitable then others. © 2025, The Authors. All rights reserved.

关键词： Demand response

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