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Design, Automation and Test in Europe Conference and Exhibition

作者： Shaoqi Li Tianyu Wang Yongbiao Zhu Chenlin Ma Yi Wang Zhaoyan Shen Zili Shao College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Science and Technology Shandong University Qingdao China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Gray code, a voltage-level-to-data-bit translation scheme, is widely used in QLC SSDs. However, it causes the four data bits in QLC to exhibit significantly different read and write performance with up to 8 × latency variation, severely impacting the worst-case performance of QLC SSDs. This paper presents BDP, a novel Bi-Directional Programming scheme. Based on a fixed Gray code, BDP combines both the normal (forward) and reverse programming directions to enable runtime programming direction arbitration. Experimental results show that BDP can effectively improve the read and write performance of SSD compared to representative schemes.

关键词： Translation Runtime Europe Bidirectional control Programming Reflective binary codes

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Protein Residue Contact Prediction Based on Deep Learning and Massive Statistical Features from Multi-Sequence Alignment

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Tsinghua science and Technology 2022年第5期27卷 843-854页

作者： Huiling Zhang Min Hao Hao Wu Hing-Fung Ting Yihong Tang Wenhui Xi Yanjie Wei Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China College of Electronic and Information Engineering Southwest UniversityChongqing 400715China School of Software Engineering University of Science and Technology of ChinaHefei 230051China Department of Computer Science The University of Hong KongHong Kong 999077China School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China

Sequence-based protein tertiary structure prediction is of fundamental importance because the function of a protein ultimately depends on its 3 D *** accurate residue-residue contact map is one of the essential elements for current ab initio prediction protocols of 3 D structure ***,with the combination of deep learning and direct coupling techniques,the performance of residue contact prediction has achieved significant ***,a considerable number of current Deep-Learning(DL)-based prediction methods are usually time-consuming,mainly because they rely on different categories of data types and third-party *** this research,we transformed the complex biological problem into a pure computational problem through statistics and artificial *** have accordingly proposed a feature extraction method to obtain various categories of statistical information from only the multi-sequence alignment,followed by training a DL model for residue-residue contact prediction based on the massive statistical *** proposed method is robust in terms of different test sets,showed high reliability on model confidence score,could obtain high computational efficiency and achieve comparable prediction precisions with DL methods that relying on multi-source inputs.

关键词： multi-sequence alignment residue-residue contact prediction feature extraction statistical information Deep Learning(DL) high computational efficiency

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Efficient Zero-Shot Voice Cloning for Bengali Speech Synthesis

Efficient Zero-Shot Voice Cloning for Bengali Speech Synthes...

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International Conference on computer and Information Technology (ICCIT)

作者： Abir Ahmed Prapti Roy Durjoy Chandra Paul M. Shahidur Rahman Department of Software Engineering Shahjalal University of Science & Technology Sylhet Bangladesh Department of Computer Science & Engineering Shahjalal University of Science & Technology Sylhet Bangladesh Department of Computer Science & Engineering Sylhet Engineering College Sylhet Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Voice cloning has numerous useful applications, including assisting individuals who have lost their ability to speak, movie dubbing, and translating voices into different languages. However, voice cloning in Bangla is still in its early stages. In this work, we explore two standard approaches for zero-shot Bengali voice cloning, which can generate cloned audio from short samples, even for Bengali speakers not included in the training data. Despite using limited datasets, our models achieve cloning quality comparable to state-of-the-art models trained on much larger datasets, maximizing potential benefits for the Bengali community. We use a neural network technique called the speaker encoder (SE) voice cloning method, which achieved a Mean Opinion Score (MOS) of 3.8 and 83% cosine similarity between the original and cloned voice. This model was trained on a custom multi-speaker dataset. Additionally, we employ a speaker converter (SC) approach trained on a single-speaker dataset, which achieved a MOS of 4.0 and 82.64% cosine similarity. By combining the two models in an ensemble, we further improved performance, reaching an MOS of 4.4 and a cosine similarity of 85.21%. Our proposed approach can be an important step toward developing voice cloning technology for the Bengali language, potentially positively impacting the community. The demos can be accessed here.

关键词： Training Translation Neural networks Cloning Training data Motion pictures Ensemble learning Speech synthesis Information technology Standards

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Chest Radiographs Based Pneumothorax Detection Using Federated Learning

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computer Systems science & engineering 2023年第11期47卷 1775-1791页

作者： Ahmad Almadhor Arfat Ahmad Khan Chitapong Wechtaisong Iqra Yousaf Natalia Kryvinska Usman Tariq Haithem Ben Chikha Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand Department of Computer Science and Software Engineering International Islamic UniversityIslamabadPakistan Information Systems Department Faculty of ManagementComenius University in BratislavaOdbojárovBratislava440Slovakia Prince Sattam bin Abdulaziz University AlKharjSaudi Arabia

Pneumothorax is a thoracic condition that occurs when a person’s lungs collapse,causing air to enter the pleural cavity,the area close to the lungs and chest *** most persistent disease,as well as one that necessitates particular patient care and the privacy of their health *** radiologists find it challenging to diagnose pneumothorax due to the variations in *** learning-based techniques are commonly employed to solve image categorization and segmentation ***,it is challenging to employ it in the medical field due to privacy issues and a lack of *** address this issue,a federated learning framework based on an Xception neural network model is proposed in this *** pneumothorax medical image dataset is obtained from the Kaggle *** preprocessing is performed on the used dataset to convert unstructured data into structured information to improve the model’s ***-max normalization technique is used to normalize the data,and the features are extracted from chest Xray *** dataset converts into two windows to make two clients for local model *** neural network model is trained on the dataset individually and aggregates model updates from two clients on the server *** decrease the over-fitting effect,every client analyses the results three *** 1 performed better in round 2 with a 79.0%accuracy,and client 2 performed better in round 2 with a 77.0%*** experimental result shows the effectiveness of the federated learning-based technique on a deep neural network,reaching a 79.28%accuracy while also providing privacy to the patient’s data.

关键词： Privacy preserving pneumothorax disease federated learning chest x-ray images healthcare machine learning deep learning

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Partial Permutations Comparison, Maintenance and Applications 33

Partial Permutations Comparison, Maintenance and Application...

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33rd Annual Symposium on Combinatorial Pattern Matching, CPM 2022

作者： Levy, Avivit Porat, Ely Shalom, B. Riva Department of Software Engineering Shenkar College Ramat-Gan Israel Department of Computer Science Bar-Ilan University Ramat-Gan Israel

ISBN: (纸本)9783959772341

This paper focuses on the concept of partial permutations and their use in algorithmic tasks. A partial permutation over Σ is a bijection πpar : Σ1 7→ Σ2 mapping a subset Σ1 ⊂ Σ to a subset Σ2 ⊂ Σ, where |Σ1| = |Σ2| (|Σ| denotes the size of a set Σ). Intuitively, two partial permutations agree if their mapping pairs do not form conflicts. This notion, which is formally defined in this paper, enables a consistent as well as informatively rich comparison between partial permutations. We formalize the Partial Permutations Agreement problem (PPA), as follows. Given two sets A1, A2 of partial permutations over alphabet Σ, each of size n, output all pairs (πi, πj), where πi ∈ A1, πj ∈ A2 and πi agrees with πj. The possibility of having a data structure for efficiently maintaining a dynamic set of partial permutations enabling to retrieve agreement of partial permutations is then studied, giving both negative and positive results. Applying our study enables to point out fruitful versus futile methods for efficient genes sequences comparison in database or automatic color transformation data augmentation technique for image processing through neural networks. It also shows that an efficient solution of strict Parameterized Dictionary Matching with One Gap (PDMOG) over general dictionary alphabets is not likely, unless the Strong Exponential Time Hypothesis (SETH) fails, thus negatively answering an open question posed lately. © Avivit Levy, Ely Porat, and B. Riva Shalom;licensed under Creative Commons License CC-BY 4.0

关键词： Mapping

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Analysis of Non-Fungible Token Pricing Factors with Machine Learning

Analysis of Non-Fungible Token Pricing Factors with Machine ...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Ho, Kin-Hon Hou, Yun Chan, Tse-Tin Pan, Haoyuan The Hang Seng University of Hong Kong Department of Computing Hong Kong Shenzhen University College of Computer Science and Software Engineering China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Rarity is known to be a factor in the price of non-fungible tokens (NFTs). Most investors make their purchasing decisions based on the rarity score or rarity rank of NFTs. However, not all rare NFTs are associated with a higher price, especially for play-to-earn gaming NFTs. In this paper, we studied the top-ranked play-to-earn gaming NFTs on Axie Infinity. We found that, in addition to rarity, utility is also a significant factor influencing the price. Furthermore, we use utility as a predictor to predict the price of Axies using the XGBoost regressor. Our results reveal that, compared to using rarity-based predictors only, leveraging utility-based predictors can improve the prediction accuracy, thus highlighting utility as a price determinant for play-to-earn gaming NFTs. © 2022 IEEE.

关键词： Machine learning

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Automating the Diagnosis of Cucumber Plant Diseases Using Machine Learning 25

Automating the Diagnosis of Cucumber Plant Diseases Using Ma...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Banimustafa, Ahmed Qattouz, Hazem Almatarneh, Sattam Mansour, Khalid Alazaidah, Raed Bulkrock, Olla Higher Colleges of Technology Computer Information Science Dept Abu Dhabi United Arab Emirates Princess Sumaya University for Technology Software Engineering Dept Amman Jordan Zarqa University Data Science & Artificial Intelligence Dept Zarqa Jordan University of Business and Technology College of Engineering Jeddah Saudi Arabia Arab-Open University Bahrain Faculty of Computer Studies Bahrain

ISBN: (纸本)9798331540012

Farmers all over the world struggle to fight plant diseases. However, the incorrect use of pesticides can waste time, effort, and money and can also be very harmful to the environment and human health. In this work, we present an empirical study that involves identifying two pathogens related to Cucumbers plant diseases: Powdery Mildew and Alternaria Cucumerina. The study aims to develop a computer-based solution based on deep learning and machine learning algorithms to detect these diseases. To achieve this, a dataset of hundreds of samples was collected from ordinary farms in Beqaa, an agricultural region 15 KM north of Amman, Jordan. Three machine learning models were created in this study using Support Vector Machines (SVM), Logistic Regression (LR), and Multi-layer perceptron algorithms. The constructed models were applied to the dataset after embedding the dataset images using the Convolutional Neural Networks (CNN) InceptionV3 pre-trained model. The model was tested and evaluated using cross-evaluation and F1-Score, CA, AUC, Precision, Recall, and Specificity, which all showed excellent results, particularly using the SVM algorithm. The constructed methods are intended to be deployed to farms in their field condition environments using a CCTV system that can capture images of plants during growth, which can help identify plant diseases at that right stage, which would save time, effort, and money and reduce the impact of wrong and overuse of pesticides on consumers and the environment. © 2024 IEEE.

关键词： Convolutional neural networks

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Combatting the TrickBot Threat: Analysis, Impact, and Defensive Strategies in Cybersecurity

Combatting the TrickBot Threat: Analysis, Impact, and Defens...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Jintao Cao Allie Null Marissa Stewart Suman Bhunia Mohammad Salman Department of Computer Science and Software Engineering Miami University Oxford Ohio USA College of Computer University of Anbar Anbar Iraq

ISBN: (数字)9798350351637

ISBN: (纸本)9798350351644

The TrickBot Botnet, emerging in late 2016, has been a significant cybersecurity threat, leveraging sophisticated attack vectors such as phishing emails, network vulnerabilities, and secondary payloads. This paper provides a comprehensive analysis of TrickBot’s evolution, attack methodologies, and widespread impact on individual users, businesses, and government entities. Despite some disruptions to its infrastructure, TrickBot has demonstrated remarkable resilience, continually adapting its tactics to evade detection and enhance its malicious capabilities. Our research offers detailed insights into the operational mechanisms of TrickBot, supported by extensive data on its propagation and impact. Furthermore, we evaluate various defense strategies, including advanced technical measures and human-centric approaches, to mitigate the ongoing threat posed by TrickBot and similar botnets. The findings underscore the critical need for continuous vigilance and innovation in cybersecurity practices to effectively counter such persistent threats.

关键词： Training Technological innovation Social computing Phishing Botnet Telecommunication traffic Vectors Electronic mail Trojan horses Monitoring

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Detection of IoT Botnet Based on Convolutional Neural Network and Linear Support Vector Machine 23

Detection of IoT Botnet Based on Convolutional Neural Networ...

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Proceedings of the 2023 13th International Conference on Communication and Network Security

作者： Changping Lai Yinan Yao Yuzhong Chen Xintian Liang Tianqi Cai Yiqing Shi College of Computer and Data Science/College of Software Fuzhou University China College of Photonic and Electronic Engineering Fujian Normal University China

ISBN: (纸本)9798400707964

The development of machine learning has brought new methods for botnet detection. Traditional machine learning methods and deep learning methods are used in botnet detection, but the former requires prior knowledge to select features. And deep learning methods solve this problem. This paper proposes a new botnet detection model that combines Convolutional Neural Network (CNN) with Support Vector Machine (SVM). This approach directly acquires network traffic data, preprocesses the data to obtain input suitable for CNN, utilizes CNN for feature extraction after preprocessing, and feeds the extracted features through two convolutional layers. The obtained features are then input to a SVM for classification. This method leverages the powerful feature extraction capabilities of CNN and the faster computational speed of linear SVM, resulting in faster training and excellent classification performance. Experiments show that the method has good performance on botnet detection and reduces training time compared to the CNN model.

关键词： Botnet Detection Convolutional Neural Network Deep Learning Support Vector Machine

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A high-resolution numerical investigation of unsteady wake vortices for coaxial rotors in hover

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Chinese Journal of Aeronautics 2024年第11期37卷 278-299页

作者： Shaoqiang HAN Wenping SONG Zhonghua HAN Jianhua XU Tianfu Engineering-oriented Numerical&Software Innovation Center College of Computer ScienceSichuan UniversityChengdu 610065China National Key Laboratory of Science and Technology on Aerodynamic Design and Research School of AeronauticsNorthwestern Polytechnical UniversityXi’an 710072China

High-resolution numerical simulations for wake vortical flows have long been a challenge in rotor aerodynamics.A novel spectrum-optimized sixth-order Weighted Essentially NonOscillatory(WENO)scheme is proposed to discretize inviscid fluxes on moving overset grids,and the Improved Delayed Detached Eddy Simulation(IDDES)is employed to resolve turbulent *** integration of these methods facilitates a comprehensive numerical investigation into the unsteady vortical flows over coaxial rotors in *** results highlight the substantial improvement in numerical resolution,in terms of both spatial structure and temporal evolution of unsteady multiscale wake *** rotors in hover manifest three primary scales of wake vortex structures:(A)the helical evolution of primary blade tip vortices and the periodic occurrence of strong Blade-Vortex Interactions(BVI);(B)the continuous shedding of small-scale horseshoeshaped vortices from the trailing edges of rotor blades,forming the vortex sheets;(C)the emergence of small-scale secondary vortex braids induced by interactions between rotor tip vortices and the vortex *** vortex structures and their interactions cause high-frequency oscillations in rotor disk loads and induce unsteady perturbations in the local flow *** among these primary vortices,coupled with the generation of secondary vortices,result in the dissipation,distortion,and breakup of the rotor tip vortices,ultimately forming a vortex ***,a substantial quantity of seemingly weak small-scale secondary vortex braids significantly contribute to energy dissipation during the evolution of wake vortices for coaxial rotors in hover.ó2024 Chinese Society of Aeronautics and *** and hosting by Elsevier *** is an open access article under the CC BY-NC-ND license(http://***/licenses/by-nc-nd/4.0/).

关键词： Coaxial rotor Blade tip vortex Secondary vortex High-order scheme IDDES Moving overset grid

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