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2nd International Conference on computer System, information Technology, and Electrical engineering, COSITE 2023

作者： Elwirehardja, Gregorius Natanael Isnan, Mahmud Perbangsa, Anzaludin Samsinga Muchtar, Kahlil Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mahmud Isnan Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350343069

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches. © 2023 IEEE.

关键词： Machine learning

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Exploring the Predictive Capabilities of AlphaFold Using Adversarial Protein Sequences

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3384-3392页

作者： Alkhouri, Ismail R. Jha, Sumit Beckus, Andre Atia, George Jha, Susmit Ewetz, Rickard Velasquez, Alvaro OrlandoFL32816 United States Michigan State University Computational Mathematics Science and Engineering Department East LansingMI48824 United States University of Michigan Electrical Engineering and Computer Science Department Ann ArborMI48109 United States Florida International University Knights Foundation School of Computing and Information Sciences MiamiFL33199 United States Air Force Research Laboratory Rome Information Directorate NY13441 United States Department Computer Science Department OrlandoFL32816 United States SRI International Computer Science Laboratory Menlo ParkCA94025 United States Electrical and Computer Engineering Department OrlandoFL32816 United States University of Colorado Computer Science Department BoulderCO80309 United States

Protein folding neural networks (PFNNs) such as AlphaFold predict remarkably accurate structures of proteins compared to other approaches. However, the robustness of such networks has heretofore not been fully explored. This is particularly relevant given the broad social implications of such technologies and the fact that biologically small perturbations to noncritical residues of a protein sequence do not typically lead to drastic changes in the protein structure. Our study demonstrates that, similar to adversarial methods in machine learning, small changes to protein sequences can result in significant differences in the predicted protein structures using AlphaFold as determined by large distance measures. Despite this, our findings using multiple protein sequences suggest that AlphaFold is able to accurately predict the domain structure and folding regions of a protein. To gauge structural differences, we employ two alignment-based measures [root-mean-square deviation (RMSD) and the global distance test (GDT) similarity], and one alignment-free measure, which is an effective graph-based structure representation (GraSR) method. We prove that the problem of minimally perturbing protein sequences is NP-complete. Based on the well-established block substitution matrices (BLOSUM62) sequence alignment scoring matrix, we generate adversarial sequences. In our experimental evaluation, we consider 111 proteins (including 29 COVID-19 sequences) in the Universal Protein resource (UniProt), a central resource for protein data. Our findings suggest that, despite the high RMSD values returned by AlphaFold, it is capable of handling the BLOSUM adversarial sequences considered in our analysis, as evidenced by the preservation of the folded regions and the GraSR results. © 2020 IEEE.

关键词： Neural networks

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Control and Data Integrity are Important Factors of Data Governance Technology 10

Control and Data Integrity are Important Factors of Data Gov...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Chandra, Yakob Utama Prabowo, Harjanto Gaol, Ford Lumban Purwandari, Betty School of Information Systems Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Information Systems Department Jakarta11480 Indonesia BINUS Business School Bina Nusantara University Doctor of Research Management Management Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Universitas Indonesia Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798350339543

The proliferation of data has become an inevitable consequence of the digital age as individuals across all domains increasingly rely on data for various purposes. Both companies and governments have a significant need for data. However, the sheer volume and complexity of this data, along with the potential risks associated with mishandling it, necessitate the establishment of a framework known as data governance. The primary objective of this research paper is to examine the factors that contribute to the effective implementation and utilization of Data governance. This study employs qualitative methods and adopts a systematic literature review approach to address the research inquiry: 'What are the important factors that determine the utilization of data governance technology?' Through the systematic literature review, a total of 41 key factors were identified from 20 research literatures that have been found to significantly impact the effective implementation of data governance. There are thirteen primary technological factors that hold significance in the realm of data governance. These factors encompass technology, application and use, storage of data, sharing, archiving, and preservation, big data algorithmic systems, data flow, deletion, diversity of data, mechanisms, size of data, control, and data integrity. © 2023 IEEE.

关键词： Digital storage

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Towards Utilizing Unlabeled Data for Flood Forecasting with Weakly Supervised Adaptive Learning

Towards Utilizing Unlabeled Data for Flood Forecasting with ...

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2023 Innovations in Intelligent systems and Applications Conference, ASYU 2023

作者： Abdulla, Nawaf Demirci, Mehmet Ozdemir, Suat Gazi University Graduate School of Informatics Department of Information Systems Ankara Turkey Gazi University Faculty of Engineering Department of Computer Engineering Ankara Turkey Hacettepe University Faculty of Engineering Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350306590

The importance of accurate flood forecasting is rising as climate change makes it more difficult to predict when and where floods will occur. Flood predictions use supervised machine learning models, and flood levels are being tracked via the Internet of Things (IoT). Despite advancements in data-driven hydrological forecasting, producing reliable flood forecasts remains challenging. This research paper explores the possibility of utilizing adaptive deep learning with weak supervision to predict flooding in coastal cities that are equipped with Internet of Things technology. This approach uses weakly supervised learning for data labeling and enhancement, a bidirectional long-short-term memory (LSTM) RNN for time-series prediction and classification, and adaptive learning to handle seasonality. The results of the proposed system show that flood forecasting accuracy could be improved by 40% compared to the baseline models. © 2023 IEEE.

关键词： Floods

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IRMIRS:Inception-ResNet-Based Network for MRI Image Super-Resolution

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computer Modeling in engineering & sciences 2023年第8期136卷 1121-1142页

作者： Wazir Muhammad Zuhaibuddin Bhutto Salman Masroor Murtaza Hussain Shaikh Jalal Shah Ayaz Hussain Department of Electrical Engineering Balochistan University of Engineering and TechnologyKhuzdar89100Pakistan Department of Computer Systems Engineering Balochistan University of Engineering and TechnologyKhuzdar89100Pakistan Department of Mechanical Engineering Balochistan University of Engineering and TechnologyKhuzdar89100Pakistan Department of Mechanical Engineering National Taiwan University of Science and Technology10607Taiwan Department of Information Systems Kyungsung UniversityBusan613010South Korea

Medical image super-resolution is a fundamental challenge due to absorption and scattering in *** challenges are increasing the interest in the quality of medical *** research has proven that the rapid progress in convolutional neural networks(CNNs)has achieved superior performance in the area of medical image ***,the traditional CNN approaches use interpolation techniques as a preprocessing stage to enlarge low-resolution magnetic resonance(MR)images,adding extra noise in the models and more memory ***,conventional deep CNN approaches used layers in series-wise connection to create the deeper mode,because this later end layer cannot receive complete information and work as a dead *** this paper,we propose Inception-ResNet-based Network for MRI Image Super-Resolution known as *** our proposed approach,a bicubic interpolation is replaced with a deconvolution layer to learn the upsampling ***,a residual skip connection with the Inception block is used to reconstruct a high-resolution output image from a low-quality input *** and qualitative evaluations of the proposed method are supported through extensive experiments in reconstructing sharper and clean texture details as compared to the state-of-the-art methods.

关键词： Super-resolution magnetic resonance imaging ResNet block inception block convolutional neural network deconvolution layer

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Randomized nonlinear two-dimensional principal component analysis network for object recognition

Randomized nonlinear two-dimensional principal component ana...

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作者： Sun, Zhijian Shao, Zhuhong Shang, Yuanyuan Li, Bicao Wu, Jiasong Bi, Hui College of Information Engineering Capital Normal University Beijing100048 China School of Electronic and Information Engineering Zhongyuan University of Technology Zhengzhou450007 China School of Computer Science and Technology Southeast University Nanjing210096 China School of Computer Science and Artificial Intelligence Changzhou University Changzhou213164 China

In order to capture nonlinear structures within data and more representational image features, this paper investigates a multi-stage convolutional neural network with predefined filters. The first two stages are the cascaded blocks consisted of random Fourier mapping, two-dimensional principal component analysis and activation operation. Among that, the approximate method based on Gaussian kernel is used to map the original image to random feature space. Subsequently, convolution filters are learned by two-dimensional principal component analysis. Next, the batch normalization and Gaussian linear error unit activation operation are followed. Afterward, the maximum pooling is utilized to further reduce dimensions of intermediate features. With binary hashing and encoding, the statistical histogram will be obtained and served as the higher-order feature of original image. Experiments have been carried out around the task of object recognition, and quantitative results demonstrate the proposed network has significantly advantageous both in terms of accuracy and computational time compared to the existed algorithms. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Mapping

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PBRL-TChain: A performance-enhanced permissioned blockchain for time-critical applications based on reinforcement learning

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Future Generation computer systems 2024年 154卷 301-313页

作者： Zhang, Yiguang Lin, Junxiong Lu, Zhihui Duan, Qiang Huang, Shih-Chia School of Computer Science Fudan University Shanghai200433 China Information Sciences & Technology Department Pennsylvania State University AbingtonPA United States Shanghai Blockchain Engineering Research Center Shanghai China Department of Electronic Engineering National Taipei University of Technology Taiwan

Permissioned blockchains can provide high security and reliability for various Internet of Things (IoT) systems, such as smart healthcare and vehicular networks. However, the performance issues of permissioned blockchains have been a constraint for fully supporting time-critical tasks with tight requirements of low latency and high throughput. This paper proposes PBRL-TChain, a performance-enhanced permissioned blockchain for time-critical applications based on deep reinforcement learning (DRL). First, we propose a priority ordering mechanism to minimize latency and maximize reliability. Then, we design a fast retransmission mechanism to alleviate the impact of transaction conflicts on the latency performance. Finally, we propose a DRL-based dynamic adjustment method in PBRL-TChain to achieve better performance and reliability. Experiments show that our method outperforms existing methods. Compared with Fabric++ and Athena, it can reduce the latency of time-critical transactions by 10 times, achieving a level of 10 ms, significantly improving the system's performance and reliability. © 2023 Elsevier B.V.

关键词： Blockchain

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Distributed Optimal Voltage Control for Multi-Terminal Direct Current System with Large-Scale Wind Farm Cluster Based on ADMM

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Journal of Modern Power systems and Clean Energy 2025年第3期13卷 1052-1063页

作者： Li, Xueping Qu, Yinpeng Deng, Jianxin Huang, Sheng Luo, Derong Wu, Qiuwei College of Electrical and Information Engineering Hunan University Changsha China Guangdong Power Grid Co. Ltd. Dongguan China School of Electronics Electrical Engineering and Computer Science Queen's University Belfast BelfastBT8 5BN United Kingdom

The power loss minimization and DC voltage stability of the multi-terminal direct current (MTDC) system with large-scale wind farm (WF) cluster affect the stability and power quality of the interconnected power grid. This paper proposes a distributed optimal voltage control (DOVC) strategy, which aims to optimize voltage distribution in MTDC and WF systems, reduce system power losses, and track power dispatch commands. The proposed DOVC strategy employs a bi-level distributed control architecture. At the upper level, the MTDC controller coordinates power flow, DC-side voltage of grid-side voltage source converters (GSVSCs), and WF-side voltage source converters (WFVSCs) for power loss minimization and DC voltage stabilization of the MTDC system. At the lower level, the WF controller coordinates the controlled bus voltage of WFVSC and the active and reactive power of wind turbines (WTs) to maintain WT terminal voltages within feasible range. Then, the WF controller minimizes the power loss of the WF system, while tracking the optimal command from the upper-level control strategy. Considering the computational tasks of multi-objective optimization with large-scale WF cluster, the proposed DOVC strategy is executed in a distributed manner based on the alternating direction method of multipliers (AD-MM). An MTDC system with large-scale WF cluster is established in MATLAB to validate the effectiveness of the proposed DOVC strategy. © 2013 State Grid Electric Power Research Institute.

关键词： Electric power transmission networks

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Creating Realities: An In-Depth Study of AI-Driven Image Generation with Generative Adversarial Networks 15

Creating Realities: An In-Depth Study of AI-Driven Image Gen...

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

作者： Dhand, Raman Sidana, Sagar Sinha, Anurag Kumar, Biresh Kumari, Shreya Madhukar Rao, G. Raj, Petheru Al-Khayyat, Ahmed Maheswaran, S. Guru Tegh Bahadur Institute of Technology Department of computer science and engineering India Maharshi Dayanand University Department of computer science and engineering Haryana Rohtak India IGNOU India School of Computing and information Science New Delhi India Amity University Department of computer science and It Jharkhand India Koneru Lakshmaiah Education Foundation Department of computer science and engineering India Reliance Jio Platforms Ltd Bangalore India Islamic University Department of computer science and engineering Iraq Kongu Engineering College Department of Electronics and Communication Engineering Erode Perundurai638060 India

ISBN: (纸本)9798350370249

The field of artificial intelligence has witnessed significant advancements with the advent of Generative Adversarial Networks, revolutionizing image generation techniques. The literature review covers seminal works and recent advancements, highlighting the evolution of image generation techniques. Taxonomy is developed to classify methods based on their underlying principles and applications across various domains including art, fashion, healthcare, and entertainment. Comparative analysis evaluates the effectiveness, efficiency, and applicability of different models, utilizing metrics corresponding Inception Score and Frechet Inception Distance. By conducting a thorough review of existing literature and technologies, we explore the evolution of GAN architectures, from their inception to the latest innovations. We examine various GAN models, including DCGAN, StyleGAN, and CycleGAN, assessing their effectiveness in generating high-fidelity images across diverse applications. In addition to this comprehensive analysis, we present our own implementation of a GAN model tailored for specific image generation tasks. Our model is tested on multiple datasets to evaluate its performance, highlighting improvements in training stability and image quality. We discuss the architectural choices, optimization techniques, and training procedures employed in our implementation. Furthermore, we address the challenges faced in the field, such as mode collapse, training instability, and the need for largescale datasets. Our findings offer valuable insights into optimizing GAN performance and propose potential directions for future research. This comprehensive analysis serves as a foundational reference for researchers and practitioners aiming to leverage GANs for advanced image generation tasks, pushing the boundaries of what is visually conceivable through artificial intelligence. © 2024 IEEE.

关键词： AI computer vision Deep Learning GANs Image generation ML

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OSAP‐Loss:Efficient optimization of average precision via involving samples after positive ones towards remote sensing image retrieval

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1191-1212页

作者： Xin Yuan Xin Xu Xiao Wang Kai Zhang Liang Liao Zheng Wang Chia‐Wen Lin School of Computer Science and Technology Wuhan University of Science and TechnologyWuhanChina Hubei Province Key Laboratory of Intelligent Information Processing and Real‐time Industrial System Wuhan University of Science and TechnologyWuhanChina Nanyang Technological University SingaporeSingapore School of Computer Science Wuhan UniversityWuhanChina Department of Electrical Engineering Industrial Technology Research InstituteNational Tsing Hua UniversityHsinchuChina

In existing remote sensing image retrieval(RSIR)datasets,the number of images among different classes varies dramatically,which leads to a severe class imbalance *** studies propose to train the model with the ranking‐based metric(e.g.,average precision[AP]),because AP is robust to class ***,current AP‐based methods overlook an important issue:only optimising samples ranking before each positive sample,which is limited by the definition of AP and is prone to local *** achieve global optimisation of AP,a novel method,namely Optimising Samples after positive ones&AP loss(OSAP‐Loss)is proposed in this ***,a novel superior ranking function is designed to make the AP loss differentiable while providing a tighter upper ***,a novel loss called Optimising Samples after Positive ones(OSP)loss is proposed to involve all positive and negative samples ranking after each positive one and to provide a more flexible optimisation strategy for each ***,a graphics processing unit memory‐free mechanism is developed to thoroughly address the non‐decomposability of AP *** experimental results on RSIR as well as conventional image retrieval datasets show the superiority and competitive performance of OSAP‐Loss compared to the state‐of‐the‐art.

关键词： computer vision image retrieval metric learning

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