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Rore: robust and efficient antioxidant protein classification via a novel dimensionality reduction strategy based on learning of fewer features

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Genomics and Informatics 2024年第1期22卷 1-10页

作者： Meng, Chaolu Hou, Yongqi Zou, Quan Shi, Lei Su, Xi Ju, Ying College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry Hohhot China School of Computer Science Inner Mongolia University Hohhot China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu China Department of Spine Surgery Changzheng Hospital Naval Medical University Huangpu District No. 415 Fengyang Road Shanghai China Foshan Women and Children Hospital Foshan China School of Informatics Xiamen University Xiamen China

In protein identification, researchers increasingly aim to achieve efficient classification using fewer features. While many feature selection methods effectively reduce the number of model features, they often cause information loss caused by merely selecting or discarding features, which limits classifier performance. To address this issue, we present Rore, an algorithm based on a feature-dimensionality reduction strategy. By mapping the original features to a latent space, Rore retains all relevant feature information while using fewer representations of the latent features. This approach significantly preserves the original information and overcomes the information loss problem associated with previous feature selection. Through extensive experimental validation and analysis, Rore demonstrated excellent performance on an antioxidant protein dataset, achieving an accuracy of 95.88% and MCC of 91.78%, using vectors including only 15 features. The Rore algorithm is available online at http://112.124.26.17:8021/Rore. © The Author(s) 2024.

关键词： Dimensionality reduction Protein classifier Protein sequence Robust feature

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Active Learning for Single-Stage Object Detection in UAV Images

Active Learning for Single-Stage Object Detection in UAV Ima...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Asma Yamani Albandari Alyami Hamzah Luqman Bernard Ghanem Silvio Giancola Information and Computer Science Department KFUPM SDAIA-KFUPM Joint Research Center for Artificial Intelligence KFUPM Image and Video Understanding Laboratory (IVUL) KAUST

Unmanned aerial vehicles (UAVs) are widely used for image acquisition in various applications, and object detection is a crucial task for UAV imagery analysis. However, training accurate object detectors requires a large amount of annotated data, which can be expensive and time-consuming. To address this issue, we propose an active learning framework for single-stage object detectors in UAV images. First, we introduce Diverse Uncertainty Aggregation (DUA), a novel uncertainty aggregation method that aims to select images with a more diverse variety of object classes with high uncertainties. Second, we address the problem of class imbalance by adjusting the uncertainty calculation based on the performance of each class. Third, we illustrate how reducing the number of images for labeling does not necessarily lead to a lower labeling cost. Evaluation of our approach on a common UAV dataset shows that we can perform similarly (within 0.02 0.5mAP) to using the whole dataset while using only 25% of the images and 32% of the labeled objects. It also outperforms Random Selection and some other aggregation methods. Evaluation on VOC2012 show also consistent results utilizing only 25% of the labeling cost to reach a performance within 0.1 0.5mAP of using the whole dataset. Our results suggest that our proposed active learning framework can effectively reduce the annotation cost while improving the performance of singlestage object detectors in UAV image settings. The code is available on: https://***/asmayamani/DUA

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NegCPARBP: Enhancing Privacy Protection for Cross-Project Aging-Related Bug Prediction Based on Negative Database

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IEEE Transactions on Emerging Topics in Computing 2025年

作者： Zhao, Dongdong Liu, Zhihui Zhang, Fengji Liu, Lei Keung, Jacky Wai Yu, Xiao Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan China Wuhan University of Technology Chongqing Research Institute Chongqing China City University of Hong Kong Department of Computer Science Hong Kong Xi'an Jiaotong University School of Electronic Science and Engineering Xi'an China Zhejiang University State Key Laboratory of Blockchain and Data Security Hangzhou China

The emergence of Aging-Related Bugs (ARBs) poses a significant challenge to software systems, resulting in performance degradation and increased error rates in resourceintensive systems. Consequently, numerous ARB prediction methods have been developed to mitigate these issues. However, in scenarios where training data is limited, the effectiveness of ARB prediction is often suboptimal. To address this problem, CrossProject Aging-Related Bug Prediction (CPARBP) is proposed, which utilizes data from other projects (i.e., source projects) to train a model aimed at predicting potential ARBs in a target project. However, the use of source-project data raises privacy concerns and discourages companies from sharing their data. Therefore, we propose a method called Cross-Project AgingRelated Bug Prediction based on Negative Database (NegCPARBP) for privacy protection. NegCPARBP first converts the feature vector of a software file into a binary string. Secondly, the corresponding Negative DataBase (NDB) is generated based on this binary string, containing data that is significantly more expressive from the original feature vector. Furthermore, to ensure more accurate prediction of ARB-prone and ARB-free files based on privacy-protected data (i.e., maintain the data utility), we propose a novel negative database generation algorithm that captures more information about important features, using information gain as a measure. Finally, NegCPARBP extracts a new feature vector from the NDB to represent the original feature vector, facilitating data sharing and ARB prediction objectives. Experimental results on Linux, MySQL, and NetBSD datasets demonstrate that NegCPARBP achieves a high defense against attacks (privacy protection performance reaching 0.97) and better data utility compared to existing privacy protection methods. © 2025 IEEE.

关键词： Differential privacy

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Enhancing Crop Disease Classification and Severity Assessment Using Fuzzy Rank-Based Ensemble Learning Approach

SSRN

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SSRN 2024年

作者： Medjadba, Yasmine Drid, Hamza Yu, XianChuan Batna 2 University LAMIE Laboratory Computer Science Department Batna05078 Algeria Beijing Normal University School of Artificial Intelligence Beijing China

Plant diseases can significantly affect crop yield, causing substantial production losses. Early disease detection is crucial for ensuring healthy plant growth and optimizing crop outcomes. Despite notable successes in previous research related to crop disease detection and classification, accurately localizing the infection and quantifying its severity poses challenges for the agricultural sector. Current approaches in computational intelligence and computer vision hold potential for advancements in disease diagnosis, resulting in substantial time and resource savings. This work introduces an end-to-end framework for crop disease detection, utilizing pre-trained convolutional neural networks (CNNs) alongside a fuzzy rank-based ensemble approach. Additionally, it incorporates the Analytic Hierarchy Process (AHP) to classify the severity levels of diseased plants. The initial phase determines various crop diseases from healthy images. Rather than relying on a single pre-trained model, we used three models and a fuzzy rank-based ensemble method to integrate the results. The proposed method differs from simple ensemble techniques, which generates final predictions for test samples based on evaluating the confidence levels of the predictions made by the base models. Then, the regions affected by the disease are identified, and the degree of disease is calculated upon classifying the image as diseased. Following this, a severity score ranging from 0 to 3 is determined based on the severity level of the disease. Based on the predicted scores, diseased images are categorized into three severity levels: mild, moderate, or severe. Finally, the model’s performance is evaluated using various metrics and demonstrates superior results compared to previous models. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Throughput Analysis of SIC-Based Two-Device Slotted ALOHA with Feedback Over Nakagami-m Fading Channels 18

Throughput Analysis of SIC-Based Two-Device Slotted ALOHA wi...

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18th International Symposium on Information Theory and Its Applications, ISITA 2024

作者： Fukui, Daiki Takahashi, Yuhei Song, Guanghui Kimura, Tomotaka Liu, Zilong Cheng, Jun Doshisha University Department of Intelligent Information Engineering and Sciences Kyoto610-0321 Japan Xidian University The State Key Laboratory of Integrated Services Networks Xi'an710071 China School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom

ISBN: (纸本)9784885523519

Throughput analysis for successive interference cancellation-based two-device slotted ALOHA with feedback is studied over Nakagami-m fading channels. Explicit expressions for the state transition probabilities are derived for a Markov process, thus facilitating the computation of the throughput. Through optimization of the transmission probability, it is shown that the maximum throughput is achieved for a given code rate. © 2024 The Institute of Electronics, Information and Communication Engineers.

关键词： Markov processes

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EXTREMAL AND CONTINUA SOLUTIONS FOR STOCHASTIC FRACTIONAL DIFFERENTIAL EQUATIONS

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Journal of Mathematical sciences (United States) 2025年 1-15页

作者： Benhamida, Ouafaa Dahmani, Zoubir Ibrahim, Rabha W. Laboratory of Pure and Applied Maths Faculty of Sciences UMAB Mostaganem Algeria Near East University Mathematics Research Center Department of Mathematics Near East Boulevard Nicosia /Mersin 10 99138 Türkiye Department of Computer Science and Mathematics Lebanese American University Beirut 13-5053 Lebanon Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq Laboratory of LAMDA-RO Faculty of Sciences University of Blida 1 Blida 09000 Algeria

The aim of this work is to study two classes of stochastic fractional differential equations via the application of the method of upper and lower solutions combined with the Arzela-Ascoli theorem. We begin by proving an auxiliary result for the integral representation of an Airy-type stochastic problem. The specific symmetry features of an Airy-type stochastic problem depend on the form of the stochastic differential equations (SDE) and the relevant coefficients, it is vital to note. To comprehend each issue’s unique symmetries and their effects, a thorough investigation is necessary. Then, we prove an existence result for extremal solutions for the same problem. Another class of stochastic equations of higher-order type is also studied. We also present some examples to show the validity of the obtained results. At the end, a conclusion follows. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Airy equation Extremal solutions Fractional calculus Fractional differential operator Stochastic fractional differential equation Upper and lower method

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On the Benefits of GPU Sample-Based Stochastic Predictive Controllers for Legged Locomotion

On the Benefits of GPU Sample-Based Stochastic Predictive Co...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Giulio Turrisi Valerio Modugno Lorenzo Amatucci Dimitrios Kanoulas Claudio Semini Dynamic Legged Systems Laboratory Istituto Italiano di Tecnologia (IIT) Genova Italy Department of Computer Science University College London London UK

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Quadrupedal robots excel in mobility, navigating complex terrains with agility. However, their complex control systems present challenges that are still far from being fully addressed. In this paper, we introduce the use of Sample-Based Stochastic control strategies for quadrupedal robots, as an alternative to traditional optimal control laws. We show that Sample-Based Stochastic methods, supported by GPU acceleration, can be effectively applied to real quadruped robots. In particular, in this work, we focus on achieving gait frequency adaptation, a notable challenge in quadrupedal locomotion for gradient-based methods. To validate the effectiveness of Sample-Based Stochastic controllers we test two distinct approaches for quadrupedal robots and compare them against a conventional gradientbased Model Predictive Control system. Our findings, validated both in simulation and on a real 21Kg Aliengo quadruped, demonstrate that our method is on par with a traditional Model Predictive Control strategy when the robot is subject to zero or moderate disturbance, while it surpasses gradient-based methods in handling sustained external disturbances, thanks to the straightforward gait adaptation strategy that is possible to achieve within their formulation.

关键词： Legged locomotion Visualization Torque control Stochastic processes graphics processing units Real-time systems Quadrupedal robots Frequency control Predictive control Testing

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Prophet: Conflict-Free Sharding Blockchain via Byzantine-Tolerant Deterministic Ordering

Prophet: Conflict-Free Sharding Blockchain via Byzantine-Tol...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Zicong Hong Song Guo Enyuan Zhou Jianting Zhang Wuhui Chen Jinwen Liang Jie Zhang Albert Zomaya Department of Computing The Hong Kong Polytechnic University Hong Kong The Hong Kong Polytechnic University Shenzhen Research Institute China Computer Science Department Purdue University USA School of Computer Science and Engineering Sun Yat-sen University and Pengcheng Laboratory Shenzhen China School of Computer Science The University of Sydney Australia

Sharding scales throughput by splitting blockchain nodes into parallel groups. However, different shards’ independent and random scheduling for cross-shard transactions results in numerous conflicts and aborts, since cross-shard transactions from different shards may access the same account. A deterministic ordering can eliminate conflicts by determining a global order for transactions before processing, as proved in the database field. Unfortunately, due to the intertwining of the Byzantine environment and information isolation among shards, there is no trusted party able to predetermine such an order for cross-shard transactions. To tackle this challenge, this paper proposes Prophet, a conflict-free sharding blockchain based on Byzantine-tolerant deterministic ordering. It first depends on untrusted self-organizing coalitions of nodes from different shards to pre-execute cross-shard transactions for prerequisite information about ordering. It then determines a trusted global order based on stateless ordering and post-verification for pre-executed results, through shard cooperation. Following the order, the shards thus orderly execute and commit transactions without conflicts. Prophet orchestrates the pre-execution, ordering, and execution processes in the sharding consensus for minimal overhead. We rigorously prove the determinism and serializability of transactions under the Byzantine and sharded environment. An evaluation of our prototype shows that Prophet improves the throughput by 3.11× and achieves nearly no aborts on 1 million Ethereum transactions compared with state-of-the-art sharding.

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A lightweight high-voltage boost circuit for soft-actuated micro-aerial-robots

A lightweight high-voltage boost circuit for soft-actuated m...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zhijian Ren Jiahui Yang Suhan Kim Yi-Hsuan Hsiao Jeffrey Lang Yufeng Chen Department of Electrical Engineering and Computer Science Research Laboratory of Electronics Massachusetts Institute of Technology (MIT) Cambridge MA USA

Flight is an energetically expensive task. While aerial insects can effortlessly fly through natural environments, achieving power autonomous flights in insect-scale robots remains a major challenge. In prior works, we developed soft-actuated insect-scale aerial robots that demonstrated unique capabilities such as in-flight collision recovery and somersaults. However, the soft dielectric elastomer actuators (DEAs) have low efficiency (< 20%) and require a high driving voltage (>600 V). These properties represent formidable obstacles for soft aerial robots to achieve power autonomous flights. In this work, we developed a 127 mg boost circuit that can convert a 7.7 V DC input into a 600 V and 400 Hz output for driving a 120 mg DEA. It has an equivalent capacitance and resistance of 20 nF and 5 $\mathbf{k}\Omega$ , respectively. The DEA is assembled into a 158 mg aerial robot, which can demonstrate liftoff while carrying the boost circuit as a payload. Although the robot remains tethered to an off-board power supply, this result represents a first step towards achieving power autonomy in soft aerial robots.

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Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks

Learning Active Subspaces for Effective and Scalable Uncerta...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Sanket Jantre Nathan M. Urban Xiaoning Qian Byung-Jun Yoon Computational Science Initiative Brookhaven National Laboratory Upton NY Department of Electrical & Computer Engineering Texas A&M University College Station TX

Bayesian inference for neural networks, or Bayesian deep learning, has the potential to provide well-calibrated predictions with quantified uncertainty and robustness. However, the main hurdle for Bayesian deep learning is its computational complexity due to the high dimensionality of the parameter space. In this work, we propose a novel scheme that addresses this limitation by constructing a low-dimensional subspace of the neural network parameters–referred to as an active subspace–by identifying the parameter directions that have the most significant influence on the output of the neural network. We demonstrate that the significantly reduced active subspace enables effective and scalable Bayesian inference via either Monte Carlo (MC) sampling methods, otherwise computationally intractable, or variational inference. Empirically, our approach provides reliable predictions with robust uncertainty estimates for various regression tasks.

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