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A Likelihood Ratio Test of Genetic Relationship among Languages

A Likelihood Ratio Test of Genetic Relationship among Langua...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Akavarapu, V.S.D.S. Mahesh Bhattacharya, Arnab Dept. of Computer Science and Engineering Indian Institute of Technology Kanpur India

ISBN: (纸本)9798891761148

Lexical resemblances among a group of languages indicate that the languages could be genetically related, i.e., they could have descended from a common ancestral language. However, such resemblances can arise by chance and, hence, need not always imply an underlying genetic relationship. Many tests of significance based on permutation of wordlists and word similarity measures appeared in the past to determine the statistical significance of such relationships. We demonstrate that although existing tests may work well for bilateral comparisons, i.e., on pairs of languages, they are either infeasible by design or are prone to yield false positives when applied to groups of languages or language families. To this end, inspired by molecular phylogenetics, we propose a likelihood ratio test to determine if given languages are related based on the proportion of invariant character sites in the aligned wordlists applied during tree inference. Further, we evaluate some language families and show that the proposed test solves the problem of false positives. Finally, we demonstrate that the test supports the existence of macro language families such as Nostratic and Macro-Mayan. © 2024 Association for Computational Linguistics.

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DynaCache: A Checkpoint Aware Reconfigurable Cache for Intermittently Powered Computing Systems 32

DynaCache: A Checkpoint Aware Reconfigurable Cache for Inter...

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32nd IFIP/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2024

作者： Mahanta, Rishabh Kapoor, Hemangee K. Indian Institute of Technology Dept. of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331539672

Energy harvesting devices are rapidly evolving to rival battery-backed technologies. Batteries have a shorter life- time and need maintenance compared to capacitors. Moreover, the usage of batteries comes with undeniable environmental costs. Distinct challenges have emerged such as performance enhancement, crash consistency of data, energy management, etc. To tackle these challenges we propose DynaCache: A checkpoint-aware reconfigurable cache for Intermittent powered computing systems. It is an intermittent computing architecture consisting of a reconfigurable L1 cache that dynamically trans- forms its write policy to strike a favorable trade-off between per- formance, data consistency, and forward progress of a program. Our proposal achieves a speedup in performance of 1.85x and 1.5x compared to non-volatile write-back cache and volatile write- through cache respectively. It also achieves a 99% reduction in dirty data compared to a volatile write-back cache guaranteeing a near absolute data consistency in a scenario of a power failure. © 2024 IEEE.

关键词： Cache memory

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PraaKrum: A Practical Byzantine-Resilient Federated Learning Algorithm 16

PraaKrum: A Practical Byzantine-Resilient Federated Learning...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Kukreja, Prabhleen Mahendran, V. Indian Institute of Technology Tirupati Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350383119

Federated Learning (FL) is considered as a suitable paradigm for intelligent data analytics over Internet of Thing (IoT) devices. While the data-privacy preserving feature of FL is useful, the lack of data auditing abilities of the participating IoTs creates a potential attack surface. To this end, a potential attacker can perform data/model poisoning attacks on the FL. To protect from such attacks, a popular theoretically proven algorithm for FL namely, Krum [1] was proposed. From an implementation perspective, the Krum algorithm needs to be provided with apriori information such as the total number of attackers in each round, which is impractical. It is not feasible to know, in advance, which of the participating IoT clients are attackers. Furthermore, in the presence of mobile Parameter Server (PS) which stays in the WiFi coverage area consisting of stationary clients for an unknown time, the implementation of Krum algorithm in such practical setting is not investigated, to the best of our knowledge. With an aim to provide a practical implementation of a resilient FL framework, for the first time, this paper investigates the sensitivity of the attackers information towards the robustness offered by the Krum-based FL algorithm. The insights gained from the sensitivity analysis are used to develop a novel and Practical algorithm of Krum (PraaKrum) for FL for a WiFi network with mobile PS. The developed analysis is helpful in both understanding the key resilient characteristics offered by the Krum algorithm and providing the research community with a guideline towards the practical implementation of the attacker-resilient Krum-based FL algorithm in a mobile wireless setting. © 2024 IEEE.

关键词： Byzantine-resilience Federated learning insensitivity result Krum algorithm WiFi wireless IoT

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Latency-Aware Cell Deletion in Link Scheduling for 6TiSCH Networks 16

Latency-Aware Cell Deletion in Link Scheduling for 6TiSCH Ne...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Tapadar, Karnish N A Khatua, Manas Tamarapalli, Venkatesh Indian Institute of Technology Guwahati Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350383119

The 6TiSCH standard uses IEEE 802.15.4 Time Slotted Channel Hopping (TSCH) as a MAC layer protocol. TSCH cell scheduling determines packet transmission schedules in terms of timing and channel allocation. The Minimal Scheduling Function (MSF) and the 6P protocol facilitate schedule management dynamically. MSF involves a decision-making process to add/delete one cell at a time from the current schedule to adapt the schedule to modified traffic rate. The cell deletion is accomplished by randomly selecting one of the scheduled cells between two communicating nodes. However, random cell deletion can elevate end-to-end latency and energy consumption. Therefore, this paper proposes a Latency-aware Cell Deletion (LCD) mechanism to minimize associated average end-to-end latency and energy consumption. Simulations using the 6TiSCH simulator verified that in an 80-node network, compared to the standard MSF and another benchmark scheme IMSF, LCD achieved a substantial 44.40% and 42.39% reduction in end-to-end latency, respectively. © 2024 IEEE.

关键词： 6TiSCH Network Adaptive Link Scheduling Link Scheduling Minimal Scheduling Function

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AI-Driven Power Gating for Enhanced Energy Efficiency in Superscalar Processors 31

AI-Driven Power Gating for Enhanced Energy Efficiency in Sup...

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31st IEEE International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

作者： Kalra, Naman Tudu, Jaynarayan T. Dept. of Electrical Engineering Indian Institute of Technology Tirupati India Dept. of Computer Science Engineering Indian Institute of Technology Tirupati India

ISBN: (纸本)9798331509118

Amid the rising demand for efficient processors, the challenge has always been to reduce power consumption without compromising performance. FinFET technology has significantly reduced leakage power issues, but dynamic power consumption at lower nodes has reemerged as a concern. Methodologies such as power/clock gating, DVFS, and voltage biasing have been proposed in the past. However, next-generation complex SoC designs at © 2024 IEEE

关键词： System-on-chip

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Ensemble Model Classifier in Hybrid CNN to Predict Diabetic Foot Ulcers 15

Ensemble Model Classifier in Hybrid CNN to Predict Diabetic ...

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

作者： Athira, Anil Jyothi Varshepally, Anjana Manogna, Akula Sridhar, Alakunta Vivek, D. B V Raju Institute of Technology Dept.of Computer Science Engineering Medak Telangana Narsapur India

ISBN: (纸本)9798350370249

Diabetes arises due to decrease in the level of sufficient insulin produced in the human body. The prevalent issue faced by the individuals suffering from diabetes is Diabetic foot ulcer (DFU). Diabetic Feet affected around 1-4% individuals within 6.1% of diabetic population in 2023 globally. In this developing world, computer vision technologies are playing a vital role even in the field of medicine, particularly it can be beneficial in detecting and monitoring diabetes, along with its difficulties such as foot ulcers. Various models in computer vision are used for detecting the foot ulcers by image processing. Here the proposed novel hybrid model detects and monitors the diabetic foot ulcer in a proficient way. The proposed model is a combination of multiple neural network frameworks including EfficientNet as a Convolutional neural network architecture along with InceptionNet, Stacked Sparse Autoencoder (SSAE) and Sparrow Search Optimization (SSO). Every framework helps in performing different tasks in contribution for an efficient and accurate output. By analyzing diabetic foot images, EfficientNet brings out the information related to visual features and Inception-ResNet-v2 is able to capture the specific features as well as patterns from the images. Stacked Sparse Autoencoder (SSAE) can learn about the features in detail. Sparrow Search Optimization (SSO) is an optimization algorithm which can make the models work together optimally moreover, can eliminate unnecessary parameters which results in less memory requirement and makes the model smaller. The proposed model can improve the detection and management of Diabetic Foot ulcers and is expected to be 2 percentage higher than any existing models. © 2024 IEEE.

关键词： Convolutional Neural Network (CNN) Diabetic Foot Ulcer (DFU) Digital image processing Frameworks Telemedicine application

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Face Recognition Using openCV- use case on student attendance system 15

Face Recognition Using openCV- use case on student attendanc...

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

作者： Kamalapur, Anjan Kumar Srikanth, Begari Yadav, Asari Shivadeep Yashwanth, Bachhanaboni Duraivelu, Vivek B V Raju Institute of Technology Dept.of Computer Science Engineering Telangana Narsapur502313 India

ISBN: (纸本)9798350370249

This is a modern approach to student attendance management using face recognition technology, powered by advanced techniques including Convolutional Neural Network (CNN) and Haar Cascade frontalface default algorithm. By leveraging OpenCV and these cutting-edge face recognition methods, the system offers a reliable and efficient solution for tracking attendance in educational institutions. Real-time face detection and recognition, support for multiple face recognition, and smooth interaction with current attendance management systems are some of the key characteristics. Additionally, the system incorporates deep learning algorithms to enhance its accuracy and robustness in identifying students' faces. Through practical implementation, the project demonstrates the effectiveness of using OpenCV and deep learning for automated student attendance management. By providing accurate and efficient attendance tracking, while improving operational efficiency in educational settings, this innovative solution marks a significant advancement in modernizing attendance management practices. © 2024 IEEE.

关键词： Convolutional Neural Network (CNN) Deep Learning Face Detection Haar Cascade Open CV

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Attention-Based AdaptSepCX Network for Effective Student Action Recognition in Online Learning 5

Attention-Based AdaptSepCX Network for Effective Student Act...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Dey, Arnab Anand, Anubhav Samanta, Subhajit Sah, Bijay Kumar Biswas, Samit Dept. of Computer Science and Technology Indian Institute of Engineering Science and Technology Shibpur Howrah711103 India

In the realm of online learning and distance education, the issue of inadequate supervision looms large, posing a significant obstacle. This paper delves into the challenges posed by the lack of supervision in online learning environments and proposes an innovative solution to understand and recognize students' behaviors. This study's primary objective is to detect and recognize students' actions in images captured through webcam. This task distinguishes itself from the well-established video-based student action recognition domain, which relies on temporal cues. Recognizing student actions from images intensifies the complexity of the problem. To meet this challenge, a novel deep learning model named AdaptSepCX Attention, specifically designed for student action recognition in online learning environments, is introduced. The proposed method exhibits exceptional performance with 92.73% validation accuracy on the Student Online Action Image dataset (SOAId), a carefully curated collection comprising 2029 student-centric images. The proposed model outperforms well-established models such as DenseNet121, NASNet Mobile, ConvXNet, DELVS1 and MobileNetV2 in student action recognition. Action recognition for students has broader implications beyond the online classroom. It has the potential to revolutionize educational technology, making online learning more interactive and engaging. Enabling machines to understand and respond to student actions enhances education, personalizes learning, and supports students' academic success and well-being. This research enhances the understanding of student involvement in online learning and offers an effective solution for recognizing actions from images. © 2024 The Authors. Published by Elsevier B.V.

关键词： Students

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Dynamic Charging on the Go: Optimizing Mobile Charging Stations for Electric Vehicle Infrastructure 11

Dynamic Charging on the Go: Optimizing Mobile Charging Stati...

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11th International Conference on Vehicle technology and Intelligent Transport Systems, VEHITS 2025

作者： Jain, Suhas Gupta, Arobinda Dept. of Computer Science & Engineering Indian Institute of Technology WB Kharagpur721302 India

ISBN: (纸本)9789897587450

With the growing adoption of electric vehicles (EVs) as an eco-friendly and sustainable means of transportation, availability of adequate EV charging infrastructure has become very important. While fixed charging stations are the primary means for public charging, they can be effectively augmented by mobile charging stations. In this paper, we address the problem of planning and operating a mobile charging station fleet by an operator. We propose an algorithm for the operator to use for route planning of the MCSs and for scheduling charging requests of EVs to them to try to maximize the number of charging requests served. Detailed simulation results are presented in different realistic scenarios to show that the proposed algorithm works well. Copyright © 2025 by SCITEPRESS - science and technology Publications, Lda.

关键词： Electric vehicles

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Routing and Charge Planning Strategies for Ridesharing EV Fleets 11

Routing and Charge Planning Strategies for Ridesharing EV Fl...

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11th International Conference on Vehicle technology and Intelligent Transport Systems, VEHITS 2025

作者： Singh, Ashutosh Gupta, Arobinda Dept. of Computer Science & Engineering Indian Institute of Technology KharagpurWB-721302 India

ISBN: (纸本)9789897587450

Ridesharing systems have become an important part of urban transportation. At the same time, electric vehicle (EV) adoption is also growing at a fast pace as an eco-friendly and sustainable transportation option. To operate a ridesharing system with EV fleets, scheduling an EV fleet to serve passenger requests requires consideration of both the requests, and the available charge and potential future charge requirements of the EVs. In this paper, we address the problem of scheduling EVs by a ridesharing operator, and propose four algorithms that schedule passenger requests while taking into consideration charging requirements of the EVs. Detailed simulation results are presented on a real world data set to show that the algorithms perform well. Copyright © 2025 by SCITEPRESS - science and technology Publications, Lda.

关键词： Urban transportation

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