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Humanitarian Technology Conference (KHI-HTC), IEEE Karachi Section

作者： Muhammad Rashid Imran Rasool Nazir Ahmad Zafar Hamra Afzaal Dept. of Computer Science Comsats University Islamabad Sahiwal Campus Pakistan Dept. of Computer Science Information Technology University Lahore Pakistan

New to the Ethereum platform with version 2.0 is the Beacon chain. Validator status, attestation information, and many more are maintained via the proof-of-stake (PoS) consensus protocol, which is relied upon. The Ethereum 2.0 beacon chain relies on the validation and completion of checkpoints to validate and finish all the blocks associated with those checkpoints. By formally verifying it using the SPIN model checker, this research tackles the issue of the dependability and security of the Beacon Chain’s justification and finalization operations. Due of its novelty (launched in 2020), there is little any literature on the subject. Additionally, no previous study has formally verified the beacon chain using the SPIN model checker. The study makes use of PROMELA, a formal specification language, to formally outline the reasoning and finalization method of the Ethereum 2.0 Beacon Chain. Utilizing the SPIN Model Checker, a program graph is generated for this procedure, which formulaically expresses safety features via the use of linear temporal logic (LTL). To make sure everything is in order, we run the SPIN model checker with the program graph and LTL formulae as inputs to see whether the program graph satisfies the properties. This is the formal verification process.

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Quantitative Analysis of Error Propagation in Hindi-English Cascaded Speech-to-Speech Translation Models

Quantitative Analysis of Error Propagation in Hindi-English ...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Mahendra Gupta Maitreyee Dutta Chandresh Kumar Maurya Dept. of Computer Science and Engineering NITTTR Chandigarh Chandigarh India Dept. of IMEE NITTTR Chandigarh Chandigarh India Dept. of Computer Science and Engineering IIT Indore Indore India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Speech-to-Speech Translation (S2ST) plays a crucial role in reducing language barriers and enabling seamless communication between people from different linguistic backgrounds. Traditional S2ST systems typically rely on a cascaded design, where components like Automatic Speech Recognition (ASR), Machine Translation (MT), and Text-to-Speech (TTS) work together in sequence to complete the translation process. Cascaded models are widely used S2ST models and are prone to error propagation (EP) across the pipeline, significantly impacting translation quality. EP is discussed in various literature. However, a comprehensive quantitative analysis is not available, particularly for low-resource languages like Hindi and English. This work presents a detailed quantitative study of EP in Hindi-English cascaded S2ST models, bridging this critical research gap. This study utilizes both text-based and textless evaluation metrics such as BLEU, Translation Edit Rate (TER), and BLASER score for translation accuracy to quantify the impact of EP at various stages of the pipeline on the FLEURES dataset. The result analysis shows that due to EP, the translation quality of the S2ST model decreases in BLEU score of 11.55 for English→Hindi and BLEU score of 12.16 for Hindi→English. Similarly, reference-based BLASER decreases by 0.61 and 0.45 for English→Hindi and Hindi→English, respectively.

关键词： Measurement Analytical models Translation Accuracy Statistical analysis Computational modeling Pipelines Linguistics Text to speech Machine translation

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SynBioTrace: Integrating Safety and Security Artifacts to Build Assurance Cases for Synthetic Biology Applications

SynBioTrace: Integrating Safety and Security Artifacts to Bu...

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IEEE International Conference on Software Reliability Engineering Workshops (ISSRE Wksp)

作者： Justin Firestone Myra B. Cohen Robyn R. Lutz Dept. of Computer Science & Engineering University of Nebraska–Lincoln Lincoln NE Dept. of Computer Science Iowa State University Ames IA

ISBN: (数字)9798350367041

ISBN: (纸本)9798350367058

The rapidly advancing cyber-physical domain of synthetic biology modifies the functionality of micro-organisms which can act as living computational devices. Applications include intelligent drug delivery, customized cancer therapies, and pollution detection and mitigation. While many synthetic biology applications have been proposed, prototyped, and even deployed, these systems often lack standard approaches to verify their safety and security. One approach, the assurance case, provides evidence demonstrating proper implementation in the target application, and is often used in other safety-critical domains. However, synthetic biologists lack guidance in developing assurance arguments and tracing safety and security requirements to evidence as required for building assurance cases. Although there has been some research combining safety and security artifacts, such techniques often require extensive expertise from different domains and may not be accessible to synthetic biologists. In this paper we propose SynBioTrace, an assistive process to help propagate information from risk-based analyses of such systems to preliminary assurance cases. SynBioTrace preserves traceability among its steps so that the assurance case can be further refined. We apply and evaluate it through a case study based on a real-world synthetic biology application. Our case study suggests this approach could aid synthetic biologists in identifying, documenting, and structuring safety and security artifacts, as well as linking evidence to support traceability for a complete, integrated assurance case.

关键词： Pollution Prevention and mitigation Conferences Medical treatment Synthetic biology Drug delivery Safety Software reliability Security Standards

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XGBClassifier and Explainable AI for Disease Classification from Patient Symptom and Demographic data with Late Fusion

XGBClassifier and Explainable AI for Disease Classification ...

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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Md. Ismiel Hossen Abir Nurul Bashar Md. Shaek Ahmed Hasibul Islam Peyal Dept. of Computer Science & Engineering International Standard University Dhaka Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

The accurate identification of diseases based on patient symptoms and demographic data is a critical area of healthcare research, with the potential to significantly improve patient outcomes. In this study, we employed machine learning algorithms, specifically the XGBClassifier, to classify diseases based on a dataset containing patient symptoms and demographic information such as age, gender, fever, cough, fatigue, and other health indicators. To ensure model interpretability and transparency, we incorporated Explainable AI (XAI) techniques like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), which allow us to understand and interpret the feature contributions to the model’s predictions. The results demonstrated that the XGBClassifier achieved an accuracy of 81.42%, outperforming other machine learning models tested. This study emphasizes the importance of combining XAI with machine learning for disease classification, offering greater transparency in the decision-making process, which is vital in healthcare settings.

关键词： Support vector machines Electric potential Accuracy Machine learning algorithms Explainable AI Medical services Predictive models Fatigue Convolutional neural networks Diseases

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Real Time Modeling and Control Algorithm of a Grid-Connected Battery Energy Storage System

Real Time Modeling and Control Algorithm of a Grid-Connected...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Karimi, Sepehr Nazaralizadeh, Solmaz Srivastava, Anurag Salem, Aboubakr Famouri, Parviz West Virginia University Lane Department of Computer Science and Electrical Engineering Morgantown United States WVU-IT Electrical and Computer Engineering Dept. Beckley United States PCS Eaton PittsburghPA United States

ISBN: (纸本)9798331541125

This work develops real time model and associate control for a grid-tied battery energy storage system (BESS), based on the real industrial system specifications, 362kW/1499kWh lithium-ion BESS. An active and reactive power controller based on the dq reference frame is designed for the grid-tied inverter. To ensure system performance, an LCL filter with passive damping is also developed and analyzed. The system is tested using the Controller Hardware-in-the-Loop (CHIL) platform: Typhoon. The results demonstrate the controller's effective reference tracking across varying load conditions, with the filter design achieving a 3.3% output current THD, emphasizing the inverter's optimized performance. © 2025 IEEE.

关键词： Battery storage

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Smart Web Application for Efficient Management of Hospital Appointments

Smart Web Application for Efficient Management of Hospital A...

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2022 IEEE International Conference on Current Development in Engineering and Technology, CCET 2022

作者： Patil, Siddheshwar V. Patil, Shobha B. Terdalkar, Omarsharif A. Yelure, Bhushan S. D. Y. Patil College of Engineering and Technology Dept. of Computer Science and Engineering Kolhapur India Government College of Engineering Dept. of Computer Science and Engineering Karad India

ISBN: (纸本)9781665454155

The interest in medical care administrations is expanding as the populace develops, and the number of patients looking for care at clinics, clinical foundations, all-encompassing gatherings, and doctors' workplaces has significantly expanded. These present new hindrances for the office's laborers and directors. The most pervasive trouble that clinics, their administration, and their staff face are bringing down functional costs and accomplishing productivity objectives. In the interim, most clinics are working on a careful financial plan. Patients have generally been not able to move openly all through their visits and return because of their endeavors to smooth out the association. Due to the shortcomings of existing appointment monitoring and management systems, this paper proposes an alternative and efficient approach for appointment monitoring. In this work, a smart-appointment booking system for patients to book appointments with storage facilities for medical reports and emergency service is proposed. A Health-Buddy BooknMeet system with online taking appointments to a specific hospital enables patients to check directly in without interacting with staff. © 2022 IEEE.

关键词： Emergency services

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ROBOT PATH PLANNING WITH A HYBRID ARITHMETIC OPTIMIZATION ALGORITHM

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical Engineering and computer science 2024年第3期86卷 197-210页

作者： Song, Qiang Dept. of Computer Science and Software Engineering Zhaoqing University Zhaoqing China

To address the premature convergence and search stagnation of arithmetic optimization algorithm (AOA), the paper proposes a hybrid arithmetic optimization algorithm (HAOA) and applies it to the practical robot path planning (RPP) problem. In initialization phase, the chaotic mechanism is used to create high-quality individuals. In evolution phase, the stochastic disturbance method is applied to enhance the exchange of information between different individuals. The solution space information around the candidate solution was integrated by Levy flight to enhance gene diversity and enhance search performance. In order to better solver the RPP problem, the Spline interpolation method is adapted to the HAOA algorithm for path optimization, so as to smooth the path curve and ensure the planning accuracy. Finally, numeric experiments of benchmark functions and RPP instances verify HAOA’s excellent performance in accuracy and robustness. This research shows arithmetic optimization algorithm can quickly find the global optimal solution, and has fast convergence speed and accuracy. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Stochastic systems

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Utilization of Decentralized Finance (DeFi) and Distributed Ledger Technology (DLT) in Banking operations

Utilization of Decentralized Finance (DeFi) and Distributed ...

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Intelligent Computing and Sustainable Innovations in Technology (IC-SIT), International Conference on

作者： Ch Sree Kumar Akhilendra Pratap Singh K Hemant K Reddy Dept. of Computer Science National Institute of Technology Meghalaya Meghalaya India Dept. of Computer Science and Engineering VIT-AP Vijayawada India

ISBN: (数字)9798350369175

ISBN: (纸本)9798350369182

Distributed Ledger Technology (DLT) is a decentralized database system where transactions are recorded and verified across multiple nodes. Its key features include immutability, time-stamping, and consensus-based validation. Numerous DLT applications are in supply chain management, intellectual property, cross border payments, energy trading, real estate, and online donations. DeFi, a combination of cryptocurrency and blockchain technology, offers financial services without intermediaries. Hence transactions with various digital assets based on cryptocurrency price feeds needs an automated framework. Smart contracts, self-executing contracts with terms directly written into code, automate processes and reduce manual intervention. This paper proposes a decentralized financial trading model using the AAVE protocol. AAVE is a decentralized platform that allows for transactions with various digital assets based on cryptocurrency price feeds. Proposed model uses Chainlink, a decentralized oracle network, to provide accurate and reliable price feeds. IPFS is used for data storage, while Graph is employed for indexing and querying blockchain data. The paper presents an example of a DeFi protocol to simulate banking operations, showcasing the potential of DeFi and DLT in revolutionizing traditional banking processes.

关键词： Accuracy Distributed ledger Finance Banking Decentralized applications Blockchains Cryptocurrency Feeds Reliability Indexing

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Time series forecasting using transformer neural network

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International Journal of computers and Applications 2024年第10期46卷 880-888页

作者： Bhogade, Vaibhav Nithya, B. Dept. of Computer Science and Engineering National Institute of Technology Tiruchirappalli India

Time series forecasting is a vital component of data science, giving essential insights that help decision-makers to predict future trends across a number of sectors. This paper focuses on projecting complicated stock market price dynamics, weather data variations, and hourly traffic occupancy rates. The Informer-Based Time Series Forecasting (IBTSF) model, which employs a transformer-based architecture to capture long-term relationships and give probabilistic predictions, is *** model is assessed against Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) model to benchmark its performance. Simulation findings demonstrate that the IBTSF model considerably outperforms other models across all datasets. For traffic data, the IBTSF model achieves a Mean Squared Error (MSE) of 10.1 and a Mean Absolute Scaled Error (MASE) of 1.69, compared to other model's larger mistakes. In stock data forecasting, the IBTSF model reports an MSE of 8.04 and a MASE of 1.53, again outperforming other models. Similarly, using meteorological data, the IBTSF model obtains an MSE of 86.12 and a MASE of 0.7, whereas other models fall short. These results underline the IBTSF model's resilience and accuracy in capturing subtle patterns and relationships within time series data, giving precise insights that benefit decision-making processes. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Prediction models

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Survey Paper: Study of Natural Language Processing and its Recent Applications 2

Survey Paper: Study of Natural Language Processing and its R...

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2nd International Conference on Innovative Sustainable Computational Technologies, CISCT 2022

作者： Shivahare, Basu Dev Singh, Arun Kumar Uppal, Nilesh Rizwan, Ashar Vaathsav, Vangala Sri Suman, Shashikant Amity School of Engineering and Technology Dept. of Computer Science Greater Noida India Png University of Technology Dept. of Mathematics and Computer Science Port Morobe Lae Papua New Guinea

ISBN: (纸本)9781665474160

Natural Language Processing (NLP) is a field of research related to developing computer systems that can process native language intelligently. Considering natural language such as English has always been one of the key issues of the practical feasibility of research, both because of the key language that works with human ingenuity and because of the richness of potential applications. The main functions of natural language processing are machine translation, speech analysis, automatic summarization, conference resolution, speech recognition, etc. NLP is used to make the virtual computer interface easier for humans. This paper gives you an overview of NLP from scratch and discusses some of the key applications. © 2022 IEEE.

关键词： Speech recognition

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