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International Conference on computer Science and engineering (UBMK)

作者： Kazım Kıvanç Eren Kerem Küçük Department of Computer Engineering Kocaeli University Kocaeli Turkey Department of Software Engineering Kocaeli University Kocaeli Turkey

The Internet of Things (IoT) has witnessed exponential growth in recent years, leading to a diverse and interconnected ecosystem of devices. However, this rapid expansion has also made IoT vulnerable to various security threats and attacks. The interconnected nature of IoT devices and their extensive integration into everyday life make them enticing targets for malicious actors. Consequently, the development and deployment of effective intrusion detection systems for IoT environments have become crucial. In the literature, it has been observed that feature engineering, feature extraction, and other preprocessing steps are problematic. The general trend has been to develop intrusion detection systems using complex models such as deep learning concepts, while reducing the effort spent on feature engineering. In this study, the importance of feature engineering is addressed, and it is demonstrated that effective results can be achieved with simple models when proper preprocessing and feature generation steps are applied. An intrusion detection system for IoT devices has been implemented in the ToN_IoT dataset by employing appropriate preprocessing steps and, additionally, utilizing mechanisms for automatic feature generation. In the experiments conducted on the ToN-IoT dataset, we propose a simple model that gives comparable results with the state-of-the-art deep learning models. This model utilizes a basic random forest algorithm and benefits f rom a different t raining scheme that take the benefits of grouping, stratification, re sampling, and automated feature generation strategies. We achieved 99.99% ROC-AUC values for both train and independent test sets. The proposed method shows mostly better performances for specifity, precision, recall, and F1-score than deep learning based models.

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Mittag–Leffler stability and synchronization of neutral-type fractional-order neural networks with leakage delay and mixed delays

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Journal of the Franklin Institute 2023年第1期360卷 327-355页

作者： Popa, Călin-Adrian Department of Mathematics West University of Timişoara Blvd. V. Pârvan No. 4 Timişoara300223 Romania Department of Computer and Software Engineering Polytechnic University Timişoara Blvd. V. Pârvan No. 2 Timişoara300223 Romania

In recent years, there have been a lot of studies focusing on the dynamics of fractional-order neural networks (FONNs). One problem is that the standard Lyapunov theory does not apply to fractional-order systems, so these studies rely on the use of Halanay-type lemmas to develop sufficient criteria for certain dynamic properties of FONNs, which frequently results in much more conservative criteria than the ones obtained for integer-order neural networks. The present research uses the Halanay-type lemma in a novel way, in order to obtain less conservative sufficient criteria than previous studies. On the other hand, the previous research only considers FONNs with some types of delays, thus limiting their applicability in real-life scenarios, where possibly many more types of delays are relevant. To address this issue, the paper discusses very general FONNs that include neutral-type delay, leakage delay, time-varying delays, and distributed delay. This is the first time such a general model for the fractional-order scenario has been developed, to our awareness. Three theorems, each based on a distinct Lyapunov-like function, are used to construct sufficient criteria, given in terms of linear matrix inequalities, which ensure, respectively, the Mittag–Leffler stability and synchronization, using two different and general state feedback control schemes, of the proposed networks. The usage of the free weighting matrix approach, among the first times in the context of FONNs, also significantly contributes to reducing the conservatism of the deduced sufficient conditions. One numerical example is provided to illustrate each of the three theorems. © 2022 The Franklin Institute

关键词： Linear matrix inequalities

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House price prediction using clustering and genetic programming along with conducting a comparative study 21

House price prediction using clustering and genetic programm...

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2021 Genetic and Evolutionary Computation Conference, GECCO 2021

作者： Azimlu, Fateme Rahnamayan, Shahryar Makrehchi, Masoud Ontario Tech University Electrical Computer and Software Engineering Department Oshawa Canada

ISBN: (纸本)9781450383516

One of the most important tasks in machine learning is prediction. Data scientists use different regression methods to find the most appropriate and accurate model for each type of datasets. This study proposes a method to improve accuracy in regression and prediction. In common methods, different models are applied to the whole data to find the best model with higher accuracy. In our proposed approach, first, we cluster data using different methods such as K-means, DBSCAN, and agglomerative hierarchical clustering algorithms. Then, for each clustering method and for each generated cluster we apply various regression models including linear and polynomial regressions, SVR, neural network, and symbolic regression in order to find the most accurate model and study the genetic programming potential in improving the prediction accuracy. This model is a combination of clustering and regression. After clustering, the number of samples in each created cluster, compared to the number of samples in the whole dataset is reduced, and consequently by decreasing the number of samples in each group, we lose accuracy. On the other hand, specifying data and setting similar samples in one group enhances the accuracy and decreases the computational cost. As a case study, we used real estate data with 20 features to improve house price estimation;however, this approach is applicable to other large datasets. © 2021 ACM.

关键词： Genetic programming

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Weak Network Oriented Mobile Distributed Storage: A Hybrid Fault-Tolerance Scheme Based on Potential Replicas 24

Weak Network Oriented Mobile Distributed Storage: A Hybrid F...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Wei, Xinlei Tan, Yujuan Liu, Duo Wu, Daitao Wu, Yu Chen, Xianzhang Li, Jian College of Computer Science Chongqing University Chongqing China School of Big Data and Software Engineering Chongqing University Chongqing China School of Electrical Engineering Chongqing University Chongqing China

ISBN: (纸本)9798350319934

Node failure is one of the most typical issues in distributed storage systems. The classic fault-tolerance methods can meet the fault tolerance needs of systems deployed in edge storage, 5G IoT, and other high-performance data centers. However, mobile distributed systems contend with inconsistent network signals and relatively low bandwidth in weak network environments. The traditional fault-tolerance schemes have difficulty ensuring distributed storage and data repair simultaneously, posing a considerable challenge to data storage reliability. To address this problem, we proposed a hybrid fault-tolerance scheme based on potential replicas, called HFPR, which trades off storage overhead and performance for weak network mobile distributed storage systems. HFPR first stores data with the erasure code redundancy and then gradually increases data redundancy by reserving potential replicas, enabling the system to disperse network transmission pressure and reduce network transmission. To maximize replica utilization and control the storage overhead of potential replicas, we design a node state prediction mechanism and a file lifecycle management mechanism. Evaluation results show that, compared with the traditional hybrid fault-tolerant scheme with the same storage overhead, HFPR achieves better replica-level repair efficiency in data repair and a reduced probability of degraded reads by approximately 50%. During data distribution, HFPR can minimize data write amplification by up to 1.1× and additional network transfers by up to 77.5%. © 2022 IEEE.

关键词： Fault tolerance

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AI-Driven Classification of Academic and Psychological Challenges in Higher Education

AI-Driven Classification of Academic and Psychological Chall...

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International Conference on New Trends in Computing Sciences (ICTCS)

作者： Seif Hshem Alazzam Mohammad Al-oudat Department of Software Engineering Philadelphia University of Jordan Amman Jordan Department of Computer Science Philadelphia University of Jordan Amman Jordan

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

Students enrolled in university experience academic along with mental stressors, which negatively impact their academic results. The research implements artificial intelligence methods for classifying and forecasting the difficulties which higher education students encounter within Jordanian universities. An electronic questionnaire was designed through structured procedures and received validation from eight academics before being distributed to 1020 students. Statistical analysis through Statistical Package for the Social Sciences (SPSS) validated the questionnaire data while testing the reliability of its findings. Students were classified into four categories—Academic Difficulties and Academic and Psychological Challenges and Psychological Distress alongside Normal—through the utilization of the GPT-4o mini API as a Large Language Model (LLM). Machine learning algorithms were applied to evaluate classification performance. Support Vector Machine (SVM) demonstrated the best result among classification models with an accuracy rate of 88.2% while Logistic Regression came second with 87.7% accuracy. A significant number of 54.7% students faced academic challenges while 60.8% of students reported psychological issues. The generated results will assist educational institutions by guiding their early prevention programs along with choosing appropriate assistance methods to enhance educational outcomes.

关键词： Support vector machines Accuracy Statistical analysis Large language models Computational modeling Education Psychology Nearest neighbor methods Reliability Testing

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3DS: An Efficient DPDK-based Data Distribution Service for Distributed Real-time Applications 24

3DS: An Efficient DPDK-based Data Distribution Service for D...

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作者： Xu, Tianyu Chen, Xianzhang Wu, Changze Wang, Jiapin Zheng, Rongwei Liu, Duo Tan, Yujuan Ren, Ao Li, Jian College of Computer Science Chongqing University Chongqing China School of Big Data and Software Engineering Chongqing University Chongqing China School of Electrical Engineering Chongqing University Chongqing China

ISBN: (纸本)9798350319934

Data Distribution Service (DDS) is a widely-used middleware for data transmission in distributed real-time applications, such as autonomous vehicles and robotics. However, the communication of existing DDS middlewares generally relies on the network stacks of the operating system, which brings long latency to the real-time applications. In this paper, we propose a DPDK-based DDS, dubbed 3DS, to improve the performance of communication of DDS middlewares. First, 3DS reduces the latency of communication between distributed real-time applications by avoiding the latency of kernel. Second, 3DS reduces the overhead of dynamic memory allocation and deallocation by a reserved memory pool. Finally, 3DS adopts a thread-exclusive CPU model to reduce the resource contention between threads. We implement and evaluate 3DS on real systems running Linux. The experimental results show that 3DS reduces 44% of the communication latency over FastDDS, which is the state-of-the-art DDS middleware used by ROS2. Moreover, the throughput of data transfer in 3DS can be 1.51 times that of FastDDS. © 2022 IEEE.

关键词： Data transfer

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A Multiobjective Multitask Evolutionary Algorithm Based on Decomposition and Multivariate Gaussian Distribution 7

A Multiobjective Multitask Evolutionary Algorithm Based on D...

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7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021

作者： Wu, Zhongjian Lin, Wu Tang, Huimei Lin, Qiuzhen Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

ISBN: (纸本)9781665441490

Evolutionary multiobjective multitask optimization (EMTO) has attracted widespread attention in recent years, which solves multiple tasks simultaneously in a single population. How to extract effective knowledge and recognize valuable transferred solutions is the key to enhance the performance of EMTO. However, few research studies consider these two issues at the same time. To fill this research gap, we propose a novel multiobjective multitask evolutionary algorithm based on decomposition and multivariate Gaussian distribution, called MTEA-DMG, in which a candidate transferred solution set is generated using resource allocation according to the priority of subproblems to multiple tasks. Then, one most valuable knowledge transfer carrier is selected by online statistical estimation of distribution density. The experimental results on a set of benchmark problems with different degrees of similarity show that MTEA-DMG is superior to other state-of-the-art EMTO algorithms. © 2021 IEEE.

关键词： Evolutionary algorithms

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SeaCap: Multi-Sight Embedding and Alignment for One-Stage Image Captioner

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IEEE Transactions on Multimedia 2025年

作者： Wang, Bo Zhang, Zhao Zhao, Mingbo Jin, Xiaojie Xu, Mingliang Wang, Meng Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Yunnan Key Laboratory of Software Engineering Yunan China Donghua University School of Computer Science and Engineering Shanghai201620 China ByteDance Culver CityCA90230 United States Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou450001 China

Recent mainstream image captioning methods usually adopt two-stage captioners, i.e., calculating the object features of the given image by a pre-trained detector and then feeding them into a language model to generate the descriptive sentences. However, such a two-stage procedure will lead to a task-based information gap that decreases the performance of the captioners, because the object features learned from the detection task are suboptimal representations and cannot provide all the necessary information for subsequent sentence generation. Besides, the object features are usually represented by the last pooling features of the detector that lose the local details of images. In this paper, we propose a novel One-Stage Image Captioner using dynamic multi-sight embedding and alignment, called SeaCap, which directly transforms input images into descriptive sentences in one stage to eliminate the information gap. Specifically, to obtain rich features, we use the Swin Transformer to capture multi-level features, followed by a sights alignment module to alleviate the vision confusion, and then feed them into a novel dynamic multi-sight embedding module to exploit both the global structure and local texture of input images. To enhance the global modeling capacity of the visual encoder, we propose a new dual-dimensional refining module to non-locally model the interaction of the embedded features. As a result, SeaCap can obtain rich and useful information to improve the performance of the captioner. Extensive comparisons on the benchmark MS-COCO, Flickr8K and Flickr30 K datasets verified the superior performance of our method. © 1999-2012 IEEE.

关键词： Embeddings

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Introduction to the Special Issue on Formal Methods and Models for System Design

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ACM Transactions on Embedded Computing Systems 2025年第3期24卷 1-3页

作者： Brandt, Jens Saha, Indranil Zhang, Lijun Hochschule Niederrhein Krefeld Germany Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kanpur India State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

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Generative Image Inpainting by Hybrid Contextual Attention Network 1

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27th International Conference on MultiMedia Modeling, MMM 2021

作者： Xiao, Zhijiao Li, Donglun College of Computer Science and Software Engineering Shenzhen University Shenzhen510680 China

ISBN: (数字)9783030678326

ISBN: (纸本)9783030678319

Image inpainting is a challenging task due to the loss of the image information. Recently, GAN-based approaches have shown promising performance in the field of image inpainting. For this task, a superior similarity measurement of extracted patches from known and missing regions is important. Existing approaches usually adopt cosine distance to measure this similarity for missing region reconstruction. However, from the semantic-level perspective, these methods often generate content with inconsistent color and disorder structure due to the ignorance of the magnitude distance of the attended patches. To resolve this problem, we propose a Hybrid Contextual Attention Network (HCA-Net) with a novel attention module called hybrid contextual attention module (HCAM). HCAM takes account of both cosine distance and Euclidean distance as the measurement of the extracted patches and gives a better prediction of missing features. Besides, a Spectral-Normalization patch discriminator and the cosine loss are added into the model for patch-level and pixel-level consistency enhancement. Extensive results on three public datasets (Paris Street View, Celeba-HQ, and Places2), have both validated that our approach significantly outperforms the state-of-the-art approaches. © 2021, Springer Nature Switzerland AG.

关键词： Semantics

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