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Proceedings of the 5th International Conference on Digital Technology in Education

作者： Max Angelo Dapitilla Perin Larmie Santos Feliscuzo Cherry Lyn Cando Sta. Romana Department of Computer Science Cebu Institute of Technology - University Philippines and College of Engineering Architecture and Industrial Design Bohol Island State University - Main Campus Philippines College of Computer Studies Cebu Institute of Technology - University Philippines

ISBN: (纸本)9781450384995

A language dies when the population that speaks it fails to pass it to their future generations. This study aims to address such problems for the Eskayas and Boholanos. The proposed EskayApp is an OCR Transliteration e-Learning Android Application that will serve as a tool to learn Eskaya. The app uses the k-Nearest Neighbors Algorithm (k-NN) for its business logic layer or backend and Supervised Machine Learning to create the application programming interface (API) for the said application. The Android application accesses the camera to capture an image of an Eskaya character and returns the Latin transliteration and the equivalent Latin character. The application got a 4.524-star rating through Google Play Store. After conducting testing, the machine learning model accuracy was 89.93% based on a 2x2 confusion matrix. A usability test was also conducted with members of the tribe as respondents. The usability review has 3 measurement items in which users can respond from 1 (Very Poor) to 5 (Excellent). The average for all the measurement items gathered from the users is 4.3-star and all the respondents want to use the e-Learning application again.

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Machine Learning based Cybersecurity Technique for Detection of Upcoming Cyber Attacks

Machine Learning based Cybersecurity Technique for Detection...

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International Conference on Communication and Electronics Systems (ICCES)

作者： Kanchi Lohitha Lakshmi S.Shobana Bathula Prasanna Kumar K.B.Glory Bonda Kiran Kumar Sandya Rani Department of Computer Science and Engineering Vasireddy Venkatadri Institute Of Technology Guntur Department of IT R.M.K Engineering College Department of Computer Science and Engineering KKR & KSR Institute of Technology and Sciences Guntur AP Department of Engineering English Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh State India Department of Architecture KL School of Architecture KL Deemed to be University Vaddeswaram Dept. of IT St. Martin's Engineering College Secunderabad Telangana India

Cyberattacks are prevalent in the age of the Internet. Each year, both the quantity and severity of cybercrimes increase. Protection against cyber-attacks has become a primary responsibility, Significant in the internet society of today. However, providing cyber security is a highly difficult task that requires experience in the field of attacks and the ability to evaluate the possibility of threats. The continual evolution of cyberattacks is the biggest challenge in this industry. This article describes the significance of cyber security and enumerates the hazards that exist in the present digital environment. The statistics and evaluation of cyberattacks demonstrate the seriousness of these occurrences. Several sorts of cyber security threats are outlined, along with the machine learning approaches that may be used to detect these attacks.

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Living Lab Long-Term Sustainability in Hybrid Access Positive Energy Districts - A Prosumager Smart Fog Computing Perspective

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IEEE Internet of Things Journal 2023年第21期10卷 18898-18908页

作者： Vohnout, Rudolf Bukovsky, Ivo Chou, Shuo-Yan Geyer, Jakub Budik, Ondrej Sharma, Rohit Prokysek, Milos Horvath, Tomas Wyckmans, Annemie University of South Bohemia in České Budějovice Faculty of Science Department of Computer Science České Budějovice37005 Czech Republic National Taiwan University of Science and Technology Department of Industrial Management Taipei106 Taiwan SRM Institute of Science and Technology NCR Campus Department of Electronics and Communication Engineering Ghaziabad201204 India Brno University of Technology Department of Telecommunications Brno61600 Czech Republic CESNET a.l.e Department of Optical Networks Prague16000 Czech Republic NTNU-Norwegian University of Science and Technology Department of Architecture and Planning Trondheim7491 Norway

Living lab, one of the recent emerging smart city concepts, faces long-term sustainability challenges associated with its complexity and breadth of use. To be efficient, it must rely on comprehensive set of information distributed appropriately among all stakeholders to unleash its full innovation potential. This is especially true in the case of positive energy districts (PEDs), where timely data dissemination is essential for prosumager decisions and their greedy behavior. This article interconnects intelligent information exchange, supported by ultralow latency hybrid access network infrastructure, with the clever use of available fog computing resources to properly disseminate complex energy details to all participating entities. As the optimal task offloading for effective information distribution constitutes the convergence problem, we reintroduced higher order neural units. These units contribute to sustaining both computational and energy efficiency, as well as the balance of the entire system. We have achieved a reliable hourly energy consumption prediction with a computationally very lightweight alternative to commonly used deep neural network approaches that can be deployed on available smart appliances with ease. The application and simulation were performed on the data set provided by one of Europe's smart city pioneers, where the prosumager PED transition has already started. © 2023 IEEE.

关键词： Decision making

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Multi-Target Classification Using Deep Learning Models for Automotive Applications

Multi-Target Classification Using Deep Learning Models for A...

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International Conference on Control, Mechatronics and Automation (ICCMA)

作者： Soumya. A Linga Reddy Cenkeramaddi Chalavadi Vishnu Yash Vinod Lanjewar Krishna Mohan C Department of Computer Science and Engineering Indian Institute of Technology Hyderabad India Department of Information and Communication Technology University of Agder Grimstad Norway Department of Architecture and Regional Planning Indian Institute of Technology Kharagpur India

This paper presents a multi-perspective convolutional neural network (CNN) that extracts the class of objects supporting intelligent transportation systems. The proposed model is the visual geometry group (VGG) backbone network with custom feature extraction blocks, that use multilayer prediction heads. The model addresses both multi-class and multi-object classification tasks utilizing the automotive object detection dataset. The model is designed with multiple prediction heads to classify different objects in an image and enable object count prediction. A publicly available automotive object detection dataset with 19800 images and labels has been utilized. The dataset consists of five primary types of objects: Persons, Trucks, Motorbikes, Cars, and Cyclists. On the dataset, pre-trained models such as VGG, Resenet, EfficientNet, and DenseNet were tested and their classification performance was evaluated. The experimental results illustrate the superiority of the proposed VGG backbone deep learning CNN model in comparison to other pre-trained models.

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Variation-Aware Defect Characterization at Cell Level

Variation-Aware Defect Characterization at Cell Level

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IEEE European Test Symposium (ETS)

作者： Zahra Paria Najafi-Haghi Marzieh Hashemipour-Nazari Hans-Joachim Wunderlich Institute of Computer Architecture and Computer Engineering University of Stuttgart Germany

ISBN: (数字)9781728143125

ISBN: (纸本)9781728143132

Small Delay Faults (SDFs) are an indicator of reliability threats even if they do not affect the behavior of a system at nominal speed. Various defects may evolve over time into a complete system failure, and defects have to be distinguished from delays due to process variations which also change the circuit timing but are benign. Based on Monte-Carlo electrical simulation at cell level, in this work it is shown that a few measurements at different operating points of voltage and frequency are sufficient to identify a defect cell even if its behavior is completely within the specification range. The developed classifier is based on statistical learning and can be annotated to each element of a cell library to support manufacturing test, diagnosis and optimizing the burn-in process or yield.

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Partitioned Reduction for Heterogeneous Environments

Partitioned Reduction for Heterogeneous Environments

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Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Alessio De Rango Gladys Utrera Marisa Gil Xavier Martorell Andrea Giordano Donato D'Ambrosio Giuseppe Mendicino Department of Environmental Engineering (DIAm) University of Calabria Rende CS Italy Computer Architecture Department BarcelonaTECH Universitat Politècnica de Catalunya Barcelona Spain National Research Counsil (CNR) Institute of High Performance Computing (ICAR) Rende CS Italy Department of Mathematics and Computer Science (DEMACS) University of Calabria Rende CS Italy

Nowadays, performance in HPC applications focuses on MPI efficiency as the de facto message-passing library to exploit parallelism. Features such as multithread and communication and processing overlap are continuously studied to adapt to new platforms and a more significant number of processing units like GPU platforms. In this sense, recently, the MPI-4.0 standard introduced the partitioned point-to-point communication primitives to potentiate computation and communication overlapping. This paper introduces an innovative extension to MPI, specifically addressing partitioned communication for MPI-reduction primitives. Traditional reduction tasks conventionally involve processing the complete input vector following the conclusion of GPU computations. In contrast, our proposed methodology exploits message partitioning to process reduction tasks in real-time incrementally. This approach allows the system to process individual partitions of the input vector as they become available, removing the necessity to await the full completion of GPU computations before initiating the reduction. Our results demonstrate promising benefits, particularly for large message sizes. However, it is essential to acknowledge that optimizations at synchronization points remain potential bottlenecks, requiring meticulous analysis and consideration.

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NOMA: A Novel Reliability Improvement Methodology for 3-D IC-based Neuromorphic Systems

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IEEE Transactions on Components, Packaging and Manufacturing Technology 2024年

作者： Nguyen, Ngo-Doanh Dang, Khanh N. Ahmed, Akram Ben Abdallah, Abderazek Ben Tran, Xuan-Tu The University of Aizu The Graduate School of Computer Science and Engineering Fukushima Aizu-Wakamatsu965-8580 Japan Vietnam National University The Information Technology Institute Hanoi10000 Viet Nam National Institute of Advanced Industrial Sciences and Technology The Digital Architecture Research Center 2-3-26 Aomi Koto-ku Tokyo Japan

Hardware is the foundation of trust for all real-time applications and is especially crucial in Artificial Intelligence (AI). The growing reliance on AI for various tasks has raised concerns about the reliability of the hardware. Extensive research has shown that hardware faults arising from the manufacturing process or device variation can significantly impact the precision and accuracy of AI applications. This also applies to 3-D IC (Three-Dimensional Integrated Circuit)-based Spiking Neural Networks (SNNs), despite their noise resilience, low-power, and low memory footprint advantages. This is because, in addition to the mentioned hardware faults, thermal dissipation on 3-D ICs can negatively affect the memory stacked on upper dies. Hence, this article proposes a methodology called NOMA (Network-of-Memory architecture) to enhance the reliability of 3-D IC-based SNNs by replacing the defective critical synaptic weights in high-priority layers with lower-priority ones. The method is assessed on multiple memory-on-logic architectures with various memory technologies such as SRAM, eDRAM, STT-RAM, and RRAM. As a result, for the 45nm SRAM library, the accuracy degradation improves by roughly 56% at a BER of 0.05 with a timing overhead of 3.538μs and a power overhead of 390.796nJ. © 2011-2012 IEEE.

关键词： Memory architecture

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Integrated Optimization of Charging Infrastructure, Renewable Energy and Battery Electric Bus Scheduling Subject to Power Distribution Network Constraints

SSRN

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SSRN 2024年

作者： Najafi, Arsalan Gao, Kun Parishwad, Omkar Tsaousoglou, Georgios Jin, Sheng Yi, Wen Department of Architecture and Civil Engineering Chalmers University of Technology GothenburgSE-412 96 Sweden Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Institute of Intelligent Transportation Systems College of Civil Engineering and Architecture Zhejiang University Hangzhou310058 China Department of Building and Real Estate The Hong Kong Polytechnic University Hung Hom Hong Kong

The adoption of Battery Electric Buses (BEBs) in electric public transit systems presents a significant opportunity for advancing sustainable transportation. However, it also poses challenges to the coupled electric transport and energy systems, particularly concerning the safety and efficiency of Power Distribution Networks (PDN). This study introduces a comprehensive BEB-PDN framework designed for the joint optimization of charging infrastructure, charging scheduling, and integration of renewable energy resources, with a specific focus on their impacts on PDN. The complexity of the proposed model is primarily due to its extensive decision variables and nonlinear constraints. To address this, a decomposition approach is employed, linearizing the problem and dividing it into master and subproblems for efficient resolution. A case study in Skövde, Sweden demonstrates that without renewable energy integration, high charging demands from BEBs can lead to PDN safety violations, such as significant voltage drops at certain nodes. The incorporation of renewable energy sources with battery energy storage in the BEB transit system is shown to enhance the PDN's stability and safety while catering to the system's spatiotemporal charging demands. This study offers an optimization framework for the integrated planning of charging infrastructure, BEB systems, and renewable energy, providing empirical insights into their potential benefits. © 2024, The Authors. All rights reserved.

关键词： Electric buses

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SAMOYE: ZERO-SHOT SINGING VOICE CONVERSION MODEL BASED ON FEATURE DISENTANGLEMENT AND ENHANCEMENT

arXiv

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arXiv 2024年

作者： Wang, Zihao Ma, Le Feng, Yongsheng Pan, Xin Jin, Yuhang Zhang, Kejun Department of Computer Science Zhejiang University Hangzhou China Department of AI Music and Music Information Technology Central Conservatory of Music Beijing China Institute of Remote Sensing and Applications Beijing University of Civil Engineering and Architecture Beijing China Department of Computer Science Zhejiang University Hangzhou310027 China Department of Computer Science Zhejiang Key Laboratory of Intelligent Systems and Equipment for Digital Creativity Innovation Center of Yangtze River Delta Zhejiang University Hangzhou China

Singing voice conversion (SVC) aims to convert a singer’s voice to another singer’s from a reference audio while keeping the original semantics. However, existing SVC methods can hardly perform zero-shot due to incomplete feature disentanglement or dependence on the speaker look-up table. We propose the first open-source high-quality zero-shot SVC model SaMoye that can convert singing to human and non-human timbre. SaMoye disentangles the singing voice’s features into content, timbre, and pitch features, where we combine multiple ASR models and compress the content features to reduce timbre leakages. Besides, we enhance the timbre features by unfreezing the speaker encoder and mixing the speaker embedding with top-3 similar speakers. We also establish an unparalleled large-scale dataset to guarantee zero-shot performance, which comprises more than 1,815 hours of pure singing voice and 6,367 speakers. We conduct objective and subjective experiments to find that SaMoye outperforms other models in zero-shot SVC tasks even under extreme conditions like converting singing to animals’ timbre. The weights, code, dataset, and documents of SaMoye are publicly available on https://***/CarlWangChina/*** Codes 68Txx(Primary)14F05, 91Fxx(Secondary) Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Dynamics and Control of Marine Mechatronic Oscillators Using Electromagnetic Coupling and Switching Power Electronics

SSRN

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SSRN 2024年

作者： Tsakyridis, Georgios Xiros, Nikolaos I. Litsardakis, George Rovithakis, George German Aerospace Center Institute of Space Systems Department of System Analysis Space Segment Bremen Germany Department of Naval Architecture & Marine Engineering University of New Orleans New OrleansLA United States Department of Electrical & Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece

Developing force control mechanisms employing electromagnetics is on the rise in active control applications for flexible mechanical systems like marine engines and shipboard machinery. Electromagnetic control devices offer superior performance indicators compared to traditional mechanical force actuators in terms of longevity, energy efficiency, maintenance requirements, rapid control response, and high operating speeds. This article investigates the use of magnetic actuation and switching power electronics in addressing the stabilization and tracking control challenges encountered in the dynamics of a mechanical system with a single degree of freedom, comprising mass, spring, and damper elements. Particularly, a linear mechanical oscillator is nonlinearly coupled with an electromagnet and its associated driving circuit via the magnetic field. The electromagnetically actuated mechanical system exhibits characteristics of a differentially flat nonlinear system. A control strategy is suggested for the purpose of tracking reference position trajectories using output feedback linearization. The synthetic linearized control signal is subsequently guided to a DC-DC buck converter, able to regulate the system’s input voltage in a wide range of operation, by switching the duty cycle. The converter is described using a precise electrical model of the system, accounting for parasitic resistances in the inductor, capacitor, and switches. An averaged state space approach is utilized to create a mathematical nonlinear model for the converter which is then linearized by employing the Exact Feedback Linearization technique. By applying optimal control theory, the controller's coefficients are fine-tuned for optimal performance. To assess the proposed method's performance, the dynamics of the compensated mechatronic system is simulated using MATLAB/Simulink. The simulation results demonstrate that the proposed control scheme choice for active control of vibrating mechanical system

关键词： Controllers

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