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International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT)

作者： Akib Al Mahmud Auvy Rafiatul Zannah Mahbub-E-Elahi Shezhan Sharif Washik Al Mahmud Jannatun Noor Department of Computer Science and Engineering Computing for Sustainability and Social Good (C2SG) Research Group School of Data and Sciences BRAC University Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

Deep learning and digital image processing are crucial in medical imaging research. Lung segmentation is particularly challenging, demanding accurate differentiation of complex structures, sophisticated algorithms, and deep learning models for reliable results. In our research, We used the Shen-zhen chest X-ray dataset, comprising 566 frontal chest X-rays focused on pulmonary tuberculosis. This dataset, compiled by the Guangdong Medical College and Shenzhen No. 3 People's Hospital, was released by the United States National Library of Medicine. Images, captured using a Philips DR Digital Diagnostic system, were resized from $3000 \times 3000$ pixels to $512 \times 512$ pixels for computer-aided diagnosis research. We initially used the Attention Dense U-Net to extract lungs from chest X-rays, leveraging attention mechanisms to focus on relevant features and dense blocks to improve feature reuse. Our goal is to assess its performance for chest X-ray segmentation, comparing it with other deep learning models due to limited research in this area. So, the model is compared with four other variants of U-Net architectures to segment lung pixels from an X-ray image. After implementing the approach using a benchmark dataset and comparing it to other existing architectures, we present that Attention Dense U-Net gives the best accuracy for all given parameters, with a result of accuracy: 97.48%, Dice coefficient: 94.87%, and IoU: 93.87%.

关键词： Deep learning Tuberculosis Semantic segmentation Lung Computer architecture Feature extraction Libraries

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FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler

arXiv

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arXiv 2024年

作者： Peng, Hongyi Yu, Han Tang, Xiaoli Li, Xiaoxiao College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of British Columbia VancouverBC Canada Vector Institute Canada

Federated learning (FL) enables collaborative machine learning across distributed data owners. However, this approach poses a significant challenge for model calibration due to data heterogeneity. While prior work focused on improving accuracy for non-iid data, calibration remains under-explored. This study reveals existing FL aggregation approaches lead to sub-optimal calibration, and theoretical analysis shows despite constraining variance in clients’ label distributions, global calibration error is still asymptotically lower bounded. To address this, we propose a novel Federated Calibration (FedCal) approach, emphasizing both local and global calibration. It leverages client-specific scalers for local calibration to effectively correct output misalignment without sacrificing prediction accuracy. These scalers are then aggregated via weight averaging to generate a global scaler, minimizing the global calibration error. Extensive experiments demonstrate that FedCal significantly outperforms the best-performing baseline, reducing global calibration error by 47.66% on average. Copyright © 2024, The Authors. All rights reserved.

关键词： Errors

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On Zarankiewicz's Problem for Intersection Hypergraphs of Geometric Objects

arXiv

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arXiv 2024年

作者： Chan, Timothy M. Keller, Chaya Smorodinsky, Shakhar Siebel School of Computing and Data Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science Ariel University Israel Department of Computer Science Ben-Gurion University of the NEGEV Be'er Sheva84105 Israel

The hypergraph Zarankiewicz's problem, introduced by Erdős in 1964, asks for the maximum number of hyperedges in an r-partite hypergraph with n vertices in each part that does not contain a copy of Kt,t, ...,t. Erdős obtained a near optimal bound of O(nr−1/tr−1) for general hypergraphs. In recent years, several works obtained improved bounds under various algebraic assumptions - e.g., if the hypergraph is semialgebraic. In this paper we study the problem in a geometric setting - for r-partite intersection hypergraphs of families of geometric objects. Our main results are essentially sharp bounds for families of axis-parallel boxes in Rd and families of pseudo-discs. For axis-parallel boxes, we obtain the sharp bound Od,t(nr−1( log n/log log n)d−1). The best previous bound was larger by a factor of about (log n)d(2r−1−2). For pseudo-discs, we obtain the bound Ot(nr−1(log n)r−2), which is sharp up to logarithmic factors. As this hypergraph has no algebraic structure, no improvement of Erdős' 60-year-old O(nr−1/tr−1) bound was known for this setting. Futhermore, even in the special case of discs for which the semialgebraic structure can be used, our result improves the best known result by a factor of Ω˜(n2r-2/3r-2). To obtain our results, we use the recently improved results for the graph Zarankiewicz's problem in the corresponding settings, along with a variety of combinatorial and geometric techniques, including shallow cuttings, biclique covers, transversals, and *** Codes 05C35, 05C65 Copyright © 2024, The Authors. All rights reserved.

关键词： Algebra

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Enhancing the Felsenstein Method to Reinvigorate Phylogenetic Starting in the Big data 6

Enhancing the Felsenstein Method to Reinvigorate Phylogeneti...

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6th International Conference on Contemporary computing and Informatics, IC3I 2023

作者： Saxena, Ashendra Kumar Vishwkarma, Priyanka Kumar, Sandeep Thangarasu, N. Bhuvaneshwari, K.S. Ashok, Bhandari Mahesh College of Computing Science and Information Technology Teerthankar Magaveer University Uttar Pradesh Moradabad India Invertis University Department of Computer Science & Engineering Uttar Pradesh Bareilly India School of Commerce and Management IIMT University Uttar Pradesh Meerut India Karpagam Academy of Higher Education Department of Computer Science Tamil Nadu Coimbatore India Karpagam College of Engineering Department of Artificial Intelligence and Data Science Tamilnadu Coimbatore India Vishwakarma Institute of Information Technology Maharashtra Pune India

ISBN: (纸本)9798350304480

The present research examines how the Felsenstein approach might be improved in light of huge data and evolutionary inference. The Felsenstein approach, phylogeny starting points, huge data, statistical techniques, and testing hypotheses are among the important ideas and terms covered in the overview. It emphasizes the importance of repeatability in evolutionary inferences as well as the difficulties brought on by rogue genera and bootstrapping proportions. The article suggests a brand-new strategy to increase the accuracy of phylogenetic investigations that makes use of transfer separation and bipartition distance. The abstract's conclusion emphasizes the need of better methods for managing huge datasets and for recognizing instability taxonomic. © 2023 IEEE.

关键词： ABC Between trees Big data Bipartition distance Bootstrap Confidence intervals Felsenstein method Homogenization Hypothesis testing Phylogenetic estimation Phylogenetic starting Reboot method Rogue taxa Statistical approach Taxonomic units Transfer distance

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Defeasible-PROV: Conflict Resolution in Smart Building Devices

Defeasible-PROV: Conflict Resolution in Smart Building Devic...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Abdullah Al Farooq Zac Taylor Kyle Ruona Thomas Moyer School of Computing and Data Science Wentworth Institute of Technology Boston MA Department of Software and Information System The University of North Carolina Charlotte

Programmable Logic Controllers (PLCs) are an integral component for managing automation processes of smart buildings. PLCs use protocols which make these control systems vulnerable to many common attacks due to which it is possible to create conflicts on certain devices of smart buildings thereby disrupting functionality. In this paper, we propose DEFEASIBLE-PROV, a system for resolving conflicts in the system by detecting the conflict creating sensors and conflict impacted actuators. Our tool is capable of blocking conflict creating rules in the system. Our evaluation results show that our proposed methodology contributes significantly to conflict resolution in the system.

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Previs-Real:Interactive virtual previsualization system for news shooting rehearsal and evaluation

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虚拟现实与智能硬件（中英文） 2024年第6期6卷 527-549页

作者： Che QU Shaocong WANG Chao ZHOU Tongchen ZHAO Rui GUO Cheng Wa WONG Chi DENG Bin JI Yuhui WEN Yuanchun SHI Yong-Jin LIU Department of Film and Television Technology Beijing Film AcademyBeijing 100088China Department of Computer Science and Technology MOE-Key Laboratory of Pervasive ComputingTsinghua UniversityBeijing 100084China School of Social Sciences Tsinghua UniversityBeijing 100084China News Production and Broadcasting First Division China Media GroupBeijing 100038China School of Computer and Information Technology Bejing Jiaotong UniversityBejing 100044China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong UniversityBeijing 100044China

Background In the demanding field of live news broadcasting,the intricate studio production procedures and tight schedules pose significant challenges for physical rehearsals by *** paper explores the design and implementation of a lightweight virtual news previsualization system,leveraging virtual production technology and interaction design methods to address the lack of fidelity in presentations and manipulations,and the quantitative feedback of rehearsal effects in previous virtual *** Our system,Previs-Real,is informed by user investigation with professional cameramen and studio technicians,and adheres to principles of high fidelity,accurate replication of actual hardware operations,and real-time feedback on rehearsal *** system's software and hardware development are implemented based on Unreal Engine and accompanying toolsets,incorporating cutting-edge modeling and camera calibration *** We validated Previs-Real through a user study,demonstrating superior performance in previsualization shooting tasks using the virtual system compared to traditional camera *** findings,supported by both objective performance metrics and subjective responses,underline Previs-Real's effectiveness and potential in transforming news broadcasting *** Previs-Real eliminates the requirement for complex equipment interconnections and team coordination inherent in a physical studio by implementing methodologies complying the above principles,objectively resulting in a lightweight design of applicable version of virtual news previsualization *** offers a novel solution to the challenges in news studio previsualization by focusing on key operational features rather than full environment *** design approach is equally effective in the process of designing lightweight systems in other fields.

关键词： News broadcast Previsualization Human computer interaction design User-centered design Interface evaluation

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Interpretable Deep Learning for Solar Flare Prediction

Interpretable Deep Learning for Solar Flare Prediction

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Vinay Ram Gazula Katherine G. Herbert Yasser Abduallah Jason T. L. Wang Department of Data Science New Jersey Institute of Technology Newark NJ USA School of Computing Montclair State University Montclair NJ USA Department of Computer Science New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798331527235

ISBN: (纸本)9798331527242

We propose to incorporate three interpretable methods, namely SHAP (SHapley Additive exPlanations), PDP (partial dependence plots) and Anchors, into a deep learning-based model, called SolarFlareNet, for operational flare forecasting. SolarFlareNet takes as input a sample of SHARP (Space-weather HMI Active Region Patches) magnetic parameters and predicts as output whether a solar flare would occur within the next 24 hours. We analyze flare events that occurred from May 2010 to December 2022 using the Geostationary Operational Environmental Satellite's X-ray flare catalogs and construct a database of flares with identified active regions in the catalogs. This database, together with the SHARP magnetic parameters, is used to train and test the SolarFlareNet model. Our experimental results describe the use of the three proposed methods (SHAP, PDP, and Anchors) to interpret the SolarFlareNet model and demonstrate the effectiveness of the methods.

关键词： Deep learning Additives Accuracy databases Explainable AI Predictive models Forecasting

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R2V-PPI: Enhancing Prediction of Protein-Protein Interactions Using Word2Vec Embeddings and Deep Neural Networks

R2V-PPI: Enhancing Prediction of Protein-Protein Interaction...

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Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： I. R. Oviya Shanmukha Sravya N Kalpana Raja Department of Computer Science Engineering Amrita School Of Computing Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven Connecticut USA

Proteins play a crucial role in living organisms, and understanding protein-protein interactions is vital for comprehending their functions and aiding drug discovery. In recent years, advanced deep learning models have been developed to predict protein-protein interactions from sequences. However, these large-scale approaches often suffer from deficiencies, resulting in both false positive and false negative predictions. Traditional encoding methods struggle to effectively represent the semantic meaning of individual residues in protein sequences and their impact on neighbouring residues. To overcome these deficiencies, we propose a novel method named R2V-PPI (Residue-to-Vector embeddings based Protein-Protein Interaction Framework) that effectively represents protein sequences and predicts protein-protein interactions. Inspired by Word2Vec, R2V-PPI learns informative representations of individual amino acid residues, capturing the context and relationships between them in raw protein sequences. These representations provide valuable input features for a downstream deep learning model. Our methodology offers a robust and versatile approach for predicting protein-protein interactions, especially when structural information is limited or unavailable. Evaluation on the S. cerevisiae dataset demonstrates that R2V-PPI outperforms existing state-of-the-art machine learning models.

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Predicting Protein-Protein Interactions Using Self-Attention-Based Deep Neural Networks and FastText Embeddings

Predicting Protein-Protein Interactions Using Self-Attention...

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International Conference on computing and Networking Technology (ICCNT)

作者： I. R. Oviya N Shanmukha Sravya Kalpana Raja Department of Computer Science Engineering Amrita School Of Computing Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven Connecticut USA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Predicting Protein-protein interactions are central to understanding the intricate mechanisms governing cellular functions, as proteins seldom act in isolation. While current machine learning and deep learning models focus on encoding protein sequences based on residue composition or physicochemical properties, they often overlook the complex relationships between amino acid segments within lengthy sequences. To address these limitations, we propose a novel hybrid model that combines a self-attention to enhance Deep neural network feature extraction with an with FastText Protein embeddings for predicting PPI. Utilizing transfer learning via the unsupervised FastText embedding technique to derive statistical representations of protein sequences, offers advantages over traditional protein encoding techniques in the context of analysing k-Mer sequences. Our model’s performance is assessed through $\mathbf{5 - f o l d}$ cross-validation on datasets of *** and Human-Bacillus Anthracis protein interactions, resulting in accuracies of $\mathbf{9 2. 2 8 \%}$ and $\mathbf{9 4. 0 8 \%}$, respectively. Compared to existing machine learning models, our model demonstrates superior performance on both datasets.

关键词： Proteins Accuracy Transfer learning Pathogenesis Artificial neural networks Organizations Predictive models Feature extraction Encoding Vectors

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EMPATHIC: Emulating Human-Like Multimodal Personality Architecture Through Thoughtful Human-AI Conversation

EMPATHIC: Emulating Human-Like Multimodal Personality Archit...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Varshini Devi I I. R. Oviya Kalpana Raja Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven Connecticut USA

In the evolving landscape of human-computer interaction, this paper introduces an innovative framework poised to revolutionize chatbot systems. Our framework, meticulously designed for emotionally aware multimodal chatbots, seamlessly integrates real-time face and emotion recognition with natural language processing. Currently in the developmental phase, the framework exhibits a distinctive proficiency in executing a diverse array of human instructions. These instructions span tasks such as generating detailed captions, object counting, and responding to general queries. Leveraging parameter-efficient fine-tuning from OpenFlamingo, augmented by the inclusion of the Low-rank Adapter (LoRA), our framework strategically prioritizes versatility and operational efficiency. The core of our approach lies in the establishment of instruction templates that intricately merge both vision and language data. This strategic integration forms the backbone of our multi-modality instruction tuning methodology, enhancing the adaptability of the chatbot. Recognizing the pivotal role of high-quality training data, we emphasize its significance in shaping the dialogue performance of the chatbot. As the framework progresses through its developmental stages, this research lays a robust foundation for the creation of a versatile conversational agent. The envisioned applications of this agent range from enhancing customer service experiences to providing valuable support in mental health scenarios.

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