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5th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2022

作者： Yu, Yue She, Kun Kang, Jian Jackson, Jehoiada Shaukat, Hayat Li, Yang University of Electronic Science and Technology of China Information and Software Engineering Chengdu China Sichuan Provincial Big Data Technology Services Center Chengdu China Iqra National University Department of Computer Science Peshawar Pakistan

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

In intelligent transportation systems, generated high-resolution images promote high-level version tasks compared to captured low-resolution vehicle logo images, which lack adequate details. However, the resolution of vehicle logo images is restricted by factors like hardware. Few attempts have been made with the Laplacian pyramid structure, while images can be represented on multi-scale in the wavelet domain. This paper proposes a progressive reconstruction network with a Laplacian pyramid (WLapsrn) structure to predict the sub-bands of wavelet transformed images. Firstly, we adopted a suitable network architecture for feature extraction using residual blocks for the advanced reconstruction network in the wavelet domain. Then, the Texture-Charbonnier loss function is used to achieve highfrequency sub-bands reconstruction. Ultimately, the inverse 2D discrete stationary wavelet transform is applied to generate predicting HR images from mapped coefficients. Extensive objective and subjective evaluations using benchmark datasets demonstrate that the proposed algorithm achieves superior visual effects compared with the state-of-the-art Laplacian-based algorithms. © 2022 IEEE.

关键词： Wavelet transforms

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DIFFERENTIABLE FOLDING FOR NEAREST NEIGHBOR MODEL OPTIMIZATION

arXiv

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arXiv 2025年

作者： Krueger, Ryan K. Aviran, Sharon Mathews, David H. Zuber, Jeffrey Ward, Max School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Biomedical Engineering and Genome Center University of California Davis DavisCA95616 United States Department of Biochemistry and Biophysics and Center for RNA Biology University of the Rochester Medical Center RochesterNY14642 United States Department of Computer Science and Software Engineering The University of Western Australia PerthWA Australia

The Nearest Neighbor model is the de facto thermodynamic model of RNA secondary structure formation and is a cornerstone of RNA structure prediction and sequence design. The current functional form (Turner 2004) contains ≈ 13, 000 underlying thermodynamic parameters, and fitting these to both experimental and structural data is computationally challenging. Here, we leverage recent advances in differentiable folding, a method for directly computing gradients of the RNA folding algorithms, to devise an efficient, scalable, and flexible means of parameter optimization that uses known RNA structures and thermodynamic experiments. Our method yields a significantly improved parameter set that outperforms existing baselines on all metrics, including an increase in the average predicted probability of ground-truth sequence-structure pairs for a single RNA family by over 23 orders of magnitude. Our framework provides a path towards drastically improved RNA models, enabling the flexible incorporation of new experimental data, definition of novel loss terms, large training sets, and even treatment as a module in larger deep learning pipelines. We make available a new database, RNAometer, with experimentally-determined stabilities for small RNA model systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Nearest neighbor search

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Positive Sample Augmentation with Graph Mixup for DDI Prediction

Positive Sample Augmentation with Graph Mixup for DDI Predic...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Hailong Yu Xinyu Ma Xu Chu Yasha Wang Rengyu Wu Qiang Zhou School of Computer Science Peking University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China Shenzhen Center for Prehospital Care Shenzhen China

Unexpected drug-drug interactions (DDIs) may occur when drugs are taken at the same time. Harmful DDIs can reduce drug efficacy and even increase unintended toxicity, putting patients treated with different drugs at risk. Previous works on DDI prediction mainly rely on hand-engineered domain knowledge, which is laborious to obtain and cannot handle the problem of sparse positive samples in DDI prediction. Even though some works attempt to handle data sparsity from the perspective of data distribution to improve the generalization ability of the model, they are limited to negative samples distribution. To address these limitations, we propose a novel method, Graph Mixup for Positive Sample Augmentation (GMPSA), to effectively combine positive drug molecule graphs and extend the training distribution to alleviate the problem of sparse positive samples. To model the graph signals and graph structures jointly, we map source graphs to the fused gromov-wasserstein metric space, then minimizes the weighted sum of transportation distances between the distributions of the source graphs and the objective graph in this metric space. By doing so, interpolated positive samples improve the the generalization error of the model. Experiments results on a real-word dataset show that our method effectively improve the perfomermance in DDI prediction on a state-of-the-art architecture. Further studies also verify the robustness and generalizability of GMPSA under different GNN backbones and parameter settings.

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Challenges and Limitations in Speech Recognition Technology:A Critical Review of Speech Signal Processing Algorithms,Tools and Systems

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computer Modeling in engineering & Sciences 2023年第5期135卷 1053-1089页

作者： Sneha Basak Himanshi Agrawal Shreya Jena Shilpa Gite Mrinal Bachute Biswajeet Pradhan Mazen Assiri Computer Science and Information Technology Department Symbiosis Institute of TechnologySymbiosis International(Deemed University)Pune412115India Symbiosis Centre for Applied Artificial Intelligence Symbiosis International(Deemed University)Pune412115India Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) School of Civil and Environmental EngineeringFaculty of Engineering and Information TechnologyUniversity of Technology SydneyNew South Wales2007Australia Center of Excellence for Climate Change Research King Abdulaziz UniversityJeddah21589Saudi Arabia Earth Observation Centre Institute of Climate ChangeUniversity Kebangsaan MalaysiaBangiSelangor43600Malaysia

Speech recognition systems have become a unique human-computer interaction(HCI)*** is one of the most naturally developed human abilities;speech signal processing opens up a transparent and hand-free computation *** paper aims to present a retrospective yet modern approach to the world of speech recognition *** development journey of ASR(Automatic Speech Recognition)has seen quite a few milestones and breakthrough technologies that have been highlighted in this paper.A step-by-step rundown of the fundamental stages in developing speech recognition systems has been presented,along with a brief discussion of various modern-day developments and applications in this *** review paper aims to summarize and provide a beginning point for those starting in the vast field of speech signal *** speech recognition has a vast potential in various industries like telecommunication,emotion recognition,healthcare,etc.,this review would be helpful to researchers who aim at exploring more applications that society can quickly adopt in future years of evolution.

关键词： Speech recognition automatic speech recognition(ASR) mel-frequency cepstral coefficients(MFCC) hidden Markov model(HMM) artificial neural network(ANN)

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LanCOPE: Language-Guided Category-Level Object Pose Estimation from a Single RGB Image

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IEEE Robotics and Automation Letters 2025年第7期10卷 7555-7562页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Dai, Zhenqi Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha 410082 China Central South University School of Architecture and Art Changsha 410082 China The University of Western Australia Department of Computer Science and Software Engineering 6009 WA Australia

Monocular RGB-based category-level object pose estimation is more practical and cost-effective for robotics. However, existing methods do not fully exploit the rich semantic and contextual information in multimodal data (e.g. language) that provides additional object attributes to guide the model in extracting category features more reliably. We propose a language-guided category-level object pose estimation method (LanCOPE), taking a single RGB image as input. Our method uses DINOv2 to recover depth from a single RGB image and converts it into point cloud to perceive the object's geometry. We then introduce language descriptions for the RGB image, estimated point cloud and overall scene to better guide the point cloud encoder and image encoder in learning category features. We develop a cross-modal differential perception feature fusion network to fuse multimodal features. This network employs a differential perception module to eliminate redundant information across different modalities, highlighting signifcant semantic differences and similarities. Furthermore, it uses a cross-attention mechanism to fuse the semantic information of the language and vision features, improving the overall perception. Finally, we design a denoising network based on the skip fusion transformer to recover the object pose accurately. Extensive experiments on REAL275 and Wild6D datasets show that LanCOPE achieves state-of-the-art performance. Our code is available at LanCOPE. © 2016 IEEE.

关键词： category-level object pose estimation languag RGB image

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Comprehensive Analysis of Blackhole Attack on RPL in Static, Mobile and Hybrid Network Environment

Comprehensive Analysis of Blackhole Attack on RPL in Static,...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Faisal Ali Garba Oluwakemi Oduwole Abubakar Balarabe Isa Abdulkadir Sarki Aliyu Rosemary Mrumun Dima Department of Computer Science Education Sa’Adatu Rimi University of Education Kano Nigeria Computer Science Programme National Mathematical Center Kwali-Sheda Abuja Nigeria Department of Software Engineering Federal University Dutsinma Katsina Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

The Routing Protocol for Low-Power and Lossy Networks (RPL), extensively applied in IoT applications, is susceptible to the Blackhole attack, wherein hostile nodes discard packets without transmission, resulting in considerable loss of network performance. This attack jeopardizes the reliability and usefulness of RPL-based networks, particularly in crucial applications like environmental monitoring and healthcare. Understanding the effects of Blackhole attacks in diverse network environments—static, mobile, and hybrid—is vital for creating personalized security solutions. Every scenario provides various issues, including the stability of static networks and the dynamic nature of mobile and hybrid setups, which determine the severity of the attack. This study performs a comparative investigation of the impact of Blackhole attacks on RPL-based networks in static, mobile, and hybrid scenarios. Simulations were done with varied node densities (10, 20, and 30 nodes) and different percentages of malicious nodes (10%, 20%, and 30%). Key performance indicators, such as Packet Delivery Ratio (PDR), Average End-to-End Delay (AE2ED), overhead packets, energy utilization, and throughput, were investigated across these conditions. The analysis suggests that static networks, however more robust to random attacks, are susceptible to disruption when Blackhole nodes interfere with important links. Mobile networks incur the highest performance deterioration due to frequent route changes and increasing energy demand. Hybrid networks, having both static and mobile nodes, demonstrate intermediate susceptibility but are more affected by mobile attackers. Overall, the statistics imply that mobility improves the impact of Blackhole attacks. The findings underscore the need for environment-specific security solutions in RPL-based networks. Static networks demand stronger detection systems for targeted attacks, while mobile and hybrid environments need adaptable, low-overhead security soluti

关键词： Energy consumption Network topology Throughput Propagation losses Hybrid power systems Stability analysis Routing protocols Topology Delays Reliability

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Enhancing Encrypted Internet Traffic Classification Through Advanced Data Augmentation Techniques

arXiv

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arXiv 2024年

作者： Zion, Yehonatan Aharon, Porat Dubin, Ran Dvir, Amit Hajaj, Chen Department of Computer Science Ariel Cyber Innovation Center Ariel University Israel Dept. of Computer and Software Eng. Ariel Cyber Innovation Center Ariel University Israel Department of Industrial Engineering and Management Data Science Artificial Intelligence Research Center Ariel University Israel

The increasing popularity of online services has made Internet Traffic Classification a critical field of study. However, the rapid development of internet protocols and encryption limits usable data availability. This paper addresses the challenges of classifying encrypted internet traffic, focusing on the scarcity of open-source datasets and limitations of existing ones. We propose two Data Augmentation (DA) techniques to synthetically generate data based on real samples: Average augmentation and MTU augmentation. Both augmentations are aimed to improve the performance of the classifier, each from a different perspective: The Average augmentation aims to increase dataset size by generating new synthetic samples, while the MTU augmentation enhances classifier robustness to varying Maximum Transmission Units (MTUs). Our experiments, conducted on two well-known academic datasets and a commercial dataset, demonstrate the effectiveness of these approaches in improving model performance and mitigating constraints associated with limited and homogeneous datasets. Our findings underscore the potential of data augmentation in addressing the challenges of modern internet traffic classification. Specifically, we show that our augmentation techniques significantly enhance encrypted traffic classification models. This improvement can positively impact user Quality of Experience (QoE) by more accurately classifying traffic as video streaming (e.g., YouTube) or chat (e.g., Google Chat). Additionally, it can enhance Quality of Service (QoS) for file downloading activities (e.g., Google Docs). © 2024, CC BY-NC-ND.

关键词： Quality of service

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Physical Layer Security of Wireless Communications: When Moving Target Defense Meets Unmanned Aerial Vehicles

SSRN

引用

SSRN 2024年

作者： Ghourab, Esraa M. Jaafar, Wael Naser, Shimaa Muhaidat, Sami Al-Qutayri, Mahmoud Damiani, Ernesto Center for Cyber-Physical Systems Department of Computer and Communication Engineering Khalifa University United Arab Emirates Department of Software and Information Technology Engineering École de Technologie Supérieure MontrealQCH3C 1K3 Canada 6G Research Center Khalifa University United Arab Emirates Center Department of Computer and Communication Engineering Khalifa University United Arab Emirates

In the realm of unmanned aerial vehicle (UAV) communication, the utilization of UAVs as aerial relays for ground nodes (GNs) introduces strategic flexibility, especially in scenarios where ground base stations may experience unforeseen impairments. However, this form of communication is vulnerable to eavesdropping by malicious entities due to the broadcast nature of wireless channels. In this paper, we tackle this problem by introducing a spatiotemporal diversification-based artificial noise (AN) injection strategy, known as moving target defense (MTD), aiming to confuse potential attackers without compromising legitimate communications. The proposed approach targets maximizing the average secrecy rate (ASR) by jointly optimizing the UAV's trajectory and transmit power, aligned with optimizing the MTD transmit power splitting factor between legitimate and AN signals at the GN source. The formulated problem is a non-convex mixed integer nonlinear programming (MINLP) problem due to the non-convexity of the secrecy rate. To solve it, we formulate our system as a Markov decision process, and then we propose a novel deep reinforcement learning (DRL)-based approach to enhance the ASR under various system *** results demonstrate the superiority of the proposed algorithm over benchmarks in terms of ASR and intercept probability, showcasing its effectiveness in enhancing the security of UAV-assisted communications. © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Convexification Approaches for Regional Route Guidance and Demand Management with Generalized Mfds

SSRN

引用

SSRN 2022年

作者： Menelaou, Charalambos Timotheou, Stelios Kolios, Panayiotis Panayiotou, Christos G. KIOS Research and Innovation Center of Excellence The Department of Electrical and Computer Engineering University of Cyprus Cyprus

The joint demand management with route guidance problem is formulated as a nonlinear and non-convex Model Predictive Control problem. To tackle this challenging problem, we propose two different solution approaches: (i) a successive convexification approach that constructs convex bounding sets for all nonlinear terms, and (ii) a linear approximation approach that solves the problem using approximations of the generalized with triangular macroscopic fundamental diagrams. The proposed approaches offer a different tradeoff between execution speed and solution quality, as the linear approximation approach runs faster while the successive convexification approach yields better quality solutions. Lower bounds on the optimal solution are also obtained through the successive convexification approach. Macroscopic simulation results illustrate the efficiency of the successive convexification and linear approximation approaches yielding an optimality gap of less than 5.5% and 10% in all considered cases, respectively. Furthermore, both approaches outperform a state-of-practice nonlinear solver in terms of solution quality and execution time. Finally, substantial gains are also obtained regarding travel time and traffic flow efficiency in a realistic microsimulation environment. © 2022, The Authors. All rights reserved.

关键词： Travel time

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Low Complexity Heuristics for Multi-Objective Sensor Placement in Traffic Networks

Low Complexity Heuristics for Multi-Objective Sensor Placeme...

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International Conference on Intelligent Transportation

作者： Marina Sofokleous Yiolanda Englezou Aristotelis Savva Stelios Timotheou Christos G. Panayiotou Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Public Works Department of the Ministry of Transport Communications & Works Nicosia Cyprus

Monitoring the traffic state of the road network is very important for a plethora of reasons, such as the prevention of traffic congestion and the development of estimation and control policies. In order to efficiently obtain high-quality information on traffic, the sensors must be installed at optimal locations in the road network under study. This problem is known as the Network Sensor Location Problem (NSLP). In this work, a multi-objective NSLP is proposed for the installation of a pre-defined number of sensors to maximise (i) the covered traffic flow volume and (ii) the minimum distance between candidate links for sensor installation, while taking into account pre-installed sensors. We reformulate the problem into a single-objective mixed-integer linear program (MILP) that yields the optimal sensor locations. In addition, we propose four low-complexity heuristics for the solution of the problem. The performance of the proposed algorithms is evaluated for the traffic network of the Republic of Cyprus under real-life conditions and traffic data. Results show that the four low-complexity approaches yield a different trade-off between execution speed and solution quality.

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