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computer science, information Technology and Engineering (ICCoSITE), International Conference on

作者： Yaya Heryadi Bambang Dwi Wijanarko Dina Fitria Murad Cuk Tho Kiyota Hashimoto Computer Science Department BINUS Graduate Program– Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Prince of Songkla University Phuket Thailand

The transformer model has become a state-of-the-art model in Natural Language Processing. The initial transformer model, known as the vanilla transformer model, is designed to improve some prominent models in sequence modeling and transduction problems such as language modeling and machine translation. The initial transformer model has 6 stacks of identical encoder-decoder layers with an attention mechanism whose aim is to push limitations of common recurrent language models and encoder-decoder architectures. Its outstanding performance has inspired many researchers to extend the architecture to improve its performance and computation efficiency. Despite many extensions to the vanilla transformer, there is no clear explanation of the encoder-decoder set out depth in the vanilla transformer model. This paper presents exploration results on the effect of combination encoder-decoder layer depth and activation function in the feed-forward layer of the vanilla transformer model on its performance. The model is tested to address a downstream task: text translation from Bahasa Indonesia to the Sundanese language. Although the value difference is not significantly large, the empirical results show that the combination of depth = 2 with Sigmoid, Tanh, and ReLU activation function; and d = 6 with ReLU activation shows the highest average training accuracy. Interestingly, d = 6 and ReLU show the lowest average training and validation loss. However, statistically, there is no significant difference between depth and activation functions.

关键词： Training computer science Computational modeling computer architecture Transformers Computational efficiency Machine translation

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DynamicLip: Shape-Independent Continuous Authentication via Lip Articulator Dynamics

arXiv

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arXiv 2025年

作者： Chen, Huashan Xu, Yifan Feng, Yue Jian, Ming Liu, Feng Hu, Pengfei Peng, Kebin He, Sen Wang, Zi Institute of Information Engineering Chinese Academy of Sciences Beijing100085 China School of Cyber Security University of Chinese Academy of Sciences Beijing101408 China School of Computer Science and Technology Shandong University Qingdao266237 China Department of Computer Science East Carolina University Greenville27858 United States Department of Systems and Industrial Engineering University of Arizona Tucson85718 United States Department of Computer & Cyber Sciences Augusta University Augusta30912 United States

Biometrics authentication has become increasingly popular due to its security and convenience;however, traditional biometrics are becoming less desirable in scenarios such as new mobile devices, Virtual Reality, and Smart Vehicles. For example, while face authentication is widely used, it suffers from significant privacy concerns. The collection of complete facial data makes it less desirable for privacy-sensitive applications. Lip authentication, on the other hand, has emerged as a promising biometrics method. However, existing lip-based authentication methods heavily depend on static lip shape when the mouth is closed, which can be less robust due to lip shape dynamic motion and can barely work when the user is speaking. In this paper, we revisit the nature of lip biometrics and extract shape-independent features from the lips. We study the dynamic characteristics of lip biometrics based on articulator motion. Building on the knowledge, we propose a system for shape-independent continuous authentication via lip articulator dynamics. This system enables robust, shape-independent and continuous authentication, making it particularly suitable for scenarios with high security and privacy requirements. We conducted comprehensive experiments in different environments and attack scenarios and collected a dataset of 50 subjects. The results indicate that our system achieves an overall accuracy of 99.06% and demonstrates robustness under advanced mimic attacks and AI deepfake attacks, making it a viable solution for continuous biometric authentication in various applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Biometrics

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SSRN

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SSRN 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nannig China Guangxi Key Laboratory of Multimedia Communications and Network Technology China China School of Systems Science Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China College of Systems Engineering National University of Defense Technology Changsha China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGA also uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a high-quality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data tra

关键词： Profitability

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Aspect-Based Sentiment Analysis on Amharic Text for Evaluating Ethio-Telecom Services

Aspect-Based Sentiment Analysis on Amharic Text for Evaluati...

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Emerging Trends in information Technology and Engineering (ic-ETITE), Conference on

作者： Tarikwa Tesfa Befikadu Belete Samuel Abera Sudhir Kumar Mohapatra Tapan Kumar Das Department of Software Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Ethio Telecom Services Addis Ababa Ethiopia Faculty of Emerging Technologies Sri Sri University Cuttack India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

People are increasingly expressing their views and opinions about a company's goods or service on social media these days. For all types of businesses and organizations, sentiment analysis in text can be used to find prospective customers and obtain quick feedback on the services or goods they provided. As a result, the organization has a better chance and opportunity to meet the needs of the consumer. Using sentiment analysis, we may extract important information from the thoughts and emotions of an organization's supporters and clients. It examines natural language content produced about services and goods. Detailed opinion mining at the aspect/feature level to serve consumer and organizational needs. In this study, three different machine learning algorithms, these are Naïve Bayes (NB), Support Vector Machine (SVM), and K- Nearest Neighbor (KNN) are used to build the proposed aspect-based sentiment analysis model from the customer comments in Amharic text which is collected from Ethio-telecom official Facebook page. The collected data is annotated into sentiment polarity and aspect-based sentiment classes by the help of five annotators. After data annotation, different preprocessing tasks were implemented. Bag of words and TF-IDF word representation techniques were used to represent each unique word to their vector representation. The experiment was conducted on those three models and the result was validated. The labeled aspects were not balanced so that a SMOTE class imbalance handling technique was employed to the dataset. Finally, SVM model trained with TF-IDF word representation technique with SMOTE oversampled dataset has achieved the highest accuracy result of 98% as compared to NB and KNN.

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Digital Twin for Smart Societies: A Catalyst for Inclusive and Accessible Healthcare

arXiv

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arXiv 2025年

作者： Mohanty, Joshit Alla, Sujatha Vaishali Bheesetty, Nagesh Chidipudi, Prasanthi Nandigam, Satya Prakash Chowdary Jmukhadze, Marisha Bheesetty, Puneeth Gowda, Narendra Lakshmana Old Dominion University VA United States Data Science and Information Systems Old Dominion University VA United States Department of Public Health Old Dominion University VA United States Department of Computer Sciences Eastern Illinois University United States Warsaw University of Technology Poland

With rapid digitization and digitalization, drawing a fine line between the digital and the physical world has become nearly impossible. It has become essential more than ever to integrate all spheres of life into a single Digital Thread to address pressing challenges of modern society – accessible and inclusive healthcare in terms of equality and equity. Techno-social advancements and mutual acceptance have enabled the infusion of digital models to simulate social settings with minimum resource utilization to make effective decisions. However, a significant gap exists in feeding back the models with appropriate real-time changes. In other words, active behavioral modeling of modern society is lacking, influencing community healthcare as a "whole." By creating virtual replicas of (physical) behavioral systems, digital twins can enable real-time monitoring, simulation, and optimization of urban dynamics. This paper explores the potential of digital twins to promote inclusive healthcare for evolving smart cities. We argue that digital twins can be used to: • Identify and address disparities in access to healthcare services • Facilitate community participation • Simulate the impact of urban policies and interventions on different groups of people • Aid policy-making bodies for better access to healthcare This paper proposes several ways to use digital twins to stitch the actual and virtual societies. Several discussed concepts within this framework envision an active, integrated, and synchronized community aware of data privacy and security. The proposal also provides high-level step-wise transitions that will enable this transformation. © 2025, CC BY.

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A Review on Energy Efficient Cooperative Routing Algorithm for Wireless Sensor Networks 1

A Review on Energy Efficient Cooperative Routing Algorithm f...

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1st IEEE International Conference on Automation, Computing and Renewable systems, ICACRS 2022

作者： Immanuvel Arokia James, K. Manjula, P. Mohana, M. Arthi, S. Department of Information Technology VEL TECH MULTI TECH Dr.Rangarajan Dr.Sakunthala Engineering College Chennai India Department of Computer Science and Business Systems Panimalar Engineering College Chennai India Department of Artificial Intelligence and Data Science VEL TECH MULTI TECH Dr.Rangarajan Dr.Sakunthala Engineering College Chennai India Department of Computer Science and Engineering VEL TECH MULTI TECH Dr.Rangarajan Dr.Sakunthala Engineering College Chennai India

ISBN: (纸本)9781665460842

Wireless communications have experienced speedy and technical development in this modern era. Significant improvements are required in terms of communication speed link, energy level, size of the device, network lifetime and applications for different generations of wireless devices. Due to the advancement in technical development, the researchers have started to build WNArchitectures the conventional method of data communication using peer-to-peer basis with a centralized (BS) controller. In Wireless Sensor Networks (WSNs), the conventional method of data communication in the network is instituted and this allows the nodes to forward data through other nodes in the communication network and create new communication paths that involve several wireless hops in the communication network. Recently, there has been an upraise in cooperative communication. This promising methodology plays an important role in relieving wireless channel fading, as well as, recuperating steadfastness in networks which permit nodes that allow cooperation among them. The nodes share data in cooperative communication by exploiting the communication in their broadcasting nature. Simulations were run for various adjustments in order to approve the Cooperative Power and Energy-efficient routing protocol (COPE) [14] convention's implementation alongside the proposed Minimum Power Least Cost Routing (MPLCR) Algorithm conventions. © 2022 IEEE

关键词： Energy efficiency

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Domain-Specific Pre-training Improves Confidence in Whole Slide Image Classification

arXiv

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arXiv 2023年

作者： Chitnis, Soham Rohit Liu, Sidong Dash, Tirtharaj Verlekar, Tanmay Tulsidas Ieva, Antonio Di Berkovsky, Shlomo Vig, Lovekesh Srinivasan, Ashwin Department of Computer Science & Information Systems Birla Institute of Technology and Science Pilani India Faculty of Medicine Health and Human Sciences Macquarie University Australia Boolean Lab University of California San Diego United States TCS Research India

Whole Slide Images (WSIs) or histopathology images are used in digital pathology. WSIs pose great challenges to deep learning models for clinical diagnosis, owing to their size and lack of pixel-level annotations. With the recent advancements in computational pathology, newer multiple-instance learning-based models have been proposed. Multiple-instance learning for WSIs necessitates creating patches and uses the encoding of these patches for diagnosis. These models use generic pre-trained models (ResNet-50 pre-trained on ImageNet) for patch encoding. The recently proposed KimiaNet, a DenseNet121 model pre-trained on TCGA slides, is a domain-specific pre-trained model. This paper shows the effect of domain-specific pre-training on WSI classification. To investigate the effect of domain-specific pre-training, we considered the current state-of-the-art multiple-instance learning models, 1) CLAM, an attention-based model, and 2) TransMIL, a self-attention-based model, and evaluated the models’ confidence and predictive performance in detecting primary brain tumors - gliomas. Domain-specific pre-training improves the confidence of the models and also achieves a new state-of-the-art performance of WSI-based glioma subtype classification, showing a high clinical applicability in assisting glioma diagnosis. We will publicly share our code and experimental results at https://***/ soham-chitnis10/WSI-domain-specific. Copyright © 2023, The Authors. All rights reserved.

关键词： Image classification

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Recurrent Neural Network-based Models as Bahasa Indonesia-Sundanese Language Neural Machine Translator

Recurrent Neural Network-based Models as Bahasa Indonesia-Su...

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computer science, information Technology and Engineering (ICCoSITE), International Conference on

作者： Bambang Dwi Wijanarko Yaya Heryadi Dina Fitria Murad Cuk Tho Kiyota Hashimoto Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Prince of Songkla University Phuket Thailand

Sequential data are available in many domains including weather observation data, stock price, gene sequence, and many others. Sequential data processing is very instrumental as a solution to a variety of downstream tasks in Natural Language Processing such as language translation. Translating a language to another language has become instrumental when peoples interact with other people who speak a different language. However, language translation is not an easy computation task when there is a language-resource gap. This paper presents some empirical results from the exploration of the Recurrent Neural Network (RNN), the Gated Recurrent Unit (GRU), the Long Short-term Memory (LSTM), and the Bidirectional Long Short-term Memory (Bi-LSTM) models as machine translation models from Bahasa Indonesia to the Sundanese language. These empirical results show that the Bi-LSTM achieved the highest performance (0.95 average training accuracy and 0.92 average testings BLEU score) compared to the RNN (0.93 average training accuracy and 0.91 average testings BLEU score), the GRU (0.88 average training accuracy and 0.89 average testings BLEU score), and the LSTM (0.92 average training accuracy and 0.91 average testings BLEU score) models. These results validate some previously reported studies that claim the Bi-LSTM model potentially outperforms the other recurrent neural network-based models when it is used to process sequence datasets.

关键词： Training Recurrent neural networks Computational modeling Instruments Data processing Machine translation Task analysis

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Near-optimal Approaches for Binary-Continuous Sum-of-ratios Optimization

arXiv

引用

arXiv 2022年

作者： Pham, Hoang Giang Duong, Ngan Ha Mai, Tien Ta, Thuy Anh School of Computing and Information Systems Singapore Management University Singapore Department of Engineering Computer Science and Mathematics University of L’Aquila Italy Orlab Faculty of Computer Science Phenikaa University Viet Nam

In this paper, we investigate a class of non-convex sum-of-ratios programs relevant to decision-making in key areas such as product assortment and pricing, facility location and cost planning, and security games. These optimization problems, characterized by both continuous and binary decision variables, are highly non-convex and challenging to solve. To the best of our knowledge, no existing methods can efficiently solve these problems to near-optimality with arbitrary precision. To address this challenge, we explore a piecewise linear approximation approach that enables the approximation of complex nonlinear components of the objective function as linear functions. We then demonstrate that the approximated problem can be reformulated as a mixed-integer linear program, a second-order cone program, or a bilinear program, all of which can be solved to optimality using off-the-shelf solvers like CPLEX or GUROBI. Additionally, we provide theoretical bounds on the approximation errors associated with the solutions derived from the approximated problem. We illustrate the applicability of our approach to competitive joint facility location and cost optimization, as well as product assortment and pricing problems. Extensive experiments on instances of varying sizes are conducted to assess the efficiency of our method. Copyright © 2022, The Authors. All rights reserved.

关键词： Mixed-integer linear programming

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Image Caption And Speech Generation Using LSTM And GTTS API

Image Caption And Speech Generation Using LSTM And GTTS API

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Augmented Intelligence and Sustainable systems (ICAISS), International Conference on

作者： K. Bhargav Ram Venkatesh B Sala Pooja Sai Sree Chunduru Anilkumar V. Surya Narayana Reddy Bhavya Kodumuri Department of Computer Science and Engineering BVRIT HYDERABAD College of Engineering for Women Hyderabad India Department of Computer Science and Engineering BVRIT HYDERABAD College of Engineering for Women Hydetabad India Systems Engineer Infosys Hyderabad India Dept of Information Technology GMR Institute Of Technology Rajam Andhra Pradesh Department of Computer Science and Engineering VNR VIGNANA JYOTHI INSTITUTE OF ENGINEERING AND TECHNOLOGY Hyderabad India Department of Computer Science and Engineering Vidya Jyothi Institute of Technology HYderabad India

Image Caption generation is one of the challenging tasks in the field of artificial intelligence. It is used to generate a textual description for a given picture. But due to, the recent advancement in deep learning techniques requires only one single end-to-end model to create a caption for the given image. This paper is intended to provide an image caption and speech generator used to generate a single-line description for a given image and audio/speech for the report generated. The researchers used a sophisticated pipeline of specifically designed models in the previous models. To achieve the proposed model, VGG16 and LSTM models are used to obtain descriptions for the image and GTTS API is used for the audio/speech generation.

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