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5th International Conference on Cybernetics and Intelligent systems, ICORIS 2023

作者： Sastrohartono, Hermantoro Krisdiarto, Andreas Wahyu Uktoro, Arief Ika Rahutomo, Reza Suparyanto, Teddy Pardamean, Bens Institute of Agriculture Stiper Faculty of Agricultural Technology Agricultural Engineering Department Yogyakarta Indonesia Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics & Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350369489

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results. © 2023 IEEE.

关键词： Internet of things

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WB-KNN for emotion recognition from physiological signals

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Optoelectronics Letters 2021年第7期17卷 444-448页

作者： XIE Weilun XUE Wanli School of Computer Science and Engineering Tianjin University of TechnologyTianjin 300384China Engineering Research Center of Learning-Based Intelligent System Ministry of EducationTianjin 300384China

K-nearest neighbor(KNN) has yielded excellent performance in physiological signals based on emotion recognition. But there are still some issues: the majority vote only by the nearest neighbors is too simple to deal with complex(like skewed) class distribution;features with the same contribution to the similarity will degrade the classification accuracy;samples in boundaries between classes are easily misclassified when k is larger. Therefore, we propose an improved KNN algorithm called WB-KNN, which takes into account the weight(both features and classification) and boundaries between classes. Firstly, a novel weighting method based on the distance and farthest neighbors named WDF is proposed to weight the classification, which improves the voting accuracy by making the nearer neighbors contribute more to the classification and using the farthest neighbors to reduce the weight of non-target class. Secondly, feature weight is introduced into the distance formula, so that the significant features contribute more to the similarity than noisy or irrelevant features. Thirdly, a voting classifier is adopted in order to overcome the weakness of KNN in boundaries between classes by combining different classifiers. Results of WB-KNN algorithm are encouraging compared with the traditional KNN and other classification algorithms on the physiological dataset with a skewed class distribution. Classification accuracy for 29 participants achieves 94.219 2% for the recognition of four emotions.

关键词： KNN neighbor classifier

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A Case Study of Big Data Processing in the Cloud Environments with Insights into Advantages, Tools, and Techniques

A Case Study of Big Data Processing in the Cloud Environment...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Pullela Harish Chowdary Puneet Thapar Arsalan Ahmed Alyas Urvashi Singh Department of Computer Science and Engineering Lovely Professional University Phagwara Punjab India Information System Technologies Wilmington University United States

Every single day, users are increasing as the world has explored technology where data is producing more and more big data and cloud technologies are the reason behind the scene because big data and cloud computing are the booming pieces of knowledge currently with AI helpful for users and for other professional and practical purposes. Nowadays integration of technologies also been an emerging topic it helps in the reduction of disadvantages of particular technology to provide better solutions. This paper talks about what, and why big data and cloud technology in daily lives and where big data in cloud technology is used. This paper examines recently proposed frameworks/models where big data and cloud technologies are utilized together and reviewed in a detailed way from the year 2020 to 2023. What Advantages can be there in utilizing both big data and cloud together is also discussed. This study explains the most used big data analytical tools and widely used cloud platforms and performed a simple case study on two datasets (world data and disease symptoms dataset) to draw some insights into which Rapid Miner and Power BI are used where RF and Gradient Boosted Trees got more accuracy than other classification algorithms with 0.7 accuracy while using disease symptoms dataset and some visualizations are drawn with use of world data.

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Edge computing hardware design for Cubesat camera 11

Edge computing hardware design for Cubesat camera

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Lee, Pei-Jun Yu, Chun-Hsien Shiu, Shiuan-Hal Weng, Chi Wei Huang, Shu-Chun Xu, Guo-Cheng Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Research and Development Department Liscotech System Co.Ltd Taipei Taiwan

ISBN: (纸本)9781665492324

In this paper, we designed and implemented an FPGA-based image system for satellite applications. The FPGA utilizes LVDS receivers to capture data up to eight 180MHz channels. The captured image data will be adjusted by the ADC offset compensator immediately, and after this adjustment, the histogram of each color channel will be booked. The summation of all pixels and the summation of pixels of four-color channels are calculated separately. Then, the control system decides whether the calculation of de-bayer, histogram equalization, and automatic white balance is needed. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Proposed National Artificial Intelligence Strategy

Proposed National Artificial Intelligence Strategy

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Bilal Zahran Belal Ayyoub Department of computer science University of Petra Jordan Department of computer information system Al-Balqa Applied University Jordan Department of Electrical Engineering Al-Balqa Applied University Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The rapid pace of development in Artificial Intelligence (AI) necessitates formulating effective national strategies for its maximization and minimizing its accompanying repercussions. In this article, a proposed National Artificial Intelligence Strategy for Jordan is formulated, with an orientation towards a systemic and organized national structure relevant to Jordan's socio-economic environment. What is new in this proposed National AI Strategy is its localized orientation, blending AI-facilitated economic development, governance, and ethics relevant to Jordan. It also formulates a governance model, with coordination between principal entities such as government, academia, and private sectors, for effective implementation. The proposed strategy sets specific digital transformation targets, in consonance with Jordan's Vision 2030 and National Strategy for Digital Transformation (2021–2025). AI use in critical sectors such as national security, energy, healthcare, and education takes a top position, with a view towards adhering to regulations, utilizing AI in an ethical manner, and developing technology in a sustainable manner. Outcomes include enhancing AI literacy, diversifying economies, enhancing employment in AI-related industries, and national competitiveness at a global level. This work forms part of an emerging portfolio of studies in national AI strategies and sets a country-specific model in consonance with Jordan's specific development aims, offering a model for AI strategy development in emerging economies.

关键词： Ethics Digital transformation Medical services Regulation Proposals Artificial intelligence Socioeconomics National security Portfolios Videos

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Identifying The Coconut Leaf Disease Using Deep Transfer Learning Approach

Identifying The Coconut Leaf Disease Using Deep Transfer Lea...

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Smart Information systems and Technologies (SIST), IEEE International Conference on

作者： Hemayet Hossain Tuhin Md Abdul Kayum Md Rahmatul Kabir Rasel Sarker Shah Md Tanvir Siddiquee Bishwajeet Pandey Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Intelligent System and Cyber Security Astana IT University Kazakhstan

ISBN: (数字)9798350374865

ISBN: (纸本)9798350374872

Coconut is one of the main economic crops in Bangladesh. It is a tree whose every part is useful in one way or another in public life. The leaves, flowers, fruits, stems, and roots of this tree are used as raw materials for various small and large industries, materials for making various delicious foods, delicious drinks, and food for patients. This is the world’s most beautiful tree and is well known and appreciated by all as the ’heavenly tree’. However, it has recently become known that most coconut trees suffer from illnesses that gradually weaken the trees’ health and coconut yield. Pest illnesses and nutrient deficits have an impact on the majority of the tree’s leaves. The main reason is that most of the farmers in our country are unaware. They do not know what kind of measures to take in the case of any disease. They are using pesticides, assuming ancient principles, sometimes benefiting and sometimes counterproductively, and making the farmers poor. As a result, farmers are showing disinterest in coconut cultivation. Our main objective is to increase the viability of coconut leaves and detect problems early on so that farmers can benefit more from the cultivation of coconuts. The study suggests examining diseases and detecting insect attacks and nutritional deficiencies in coconut leaves. This will help grow more coconuts. It is expected that this model will help the agricultural field on both an economic and ecological level. This study focuses on the three coconut diseases: WCLWD Yellowing, WCLWD Flaccidity, and CCI Caterpillars. Three neural networks have been chosen in this study to identify the best model. After evaluating the VGG19, MobileNet v2, Inception v3, and ResNet50 models, the accuracy for a collection of 3166 images was determined to be 96%, 96.02%, 97.77%, and 99.36%, respectively. As a result of this technology, farmers will be able to produce more coconuts, surely bringing about a revolution in the agricultural industry.

关键词： Industries Economics Biological system modeling Transfer learning Neural networks Pesticides Nutrients

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Multi-Scale Feature Extraction with Supervised Contrastive Learning for Vulnerability Detection 36

Multi-Scale Feature Extraction with Supervised Contrastive L...

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36th International Conference on Software engineering and Knowledge engineering, SEKE 2024

作者： Zhao, Yahui Lu, Lu Liang, Zhihong Suo, Siliang School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Pengcheng Laboratory Shenzhen China Electric Power Research Institute CSG Guangzhou China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China

ISBN: (纸本)1891706594

The capacity of Deep Learning to automatically learn features from source code has facilitated its extensive utilization in detecting software vulnerabilities. However, existing pre-trained models regard code snippets as token sequences, neglecting the inherent structure of the code. The utilization of graph-based code representations is constrained by the limitations of Graph Neural Networks, particularly the difficulty in capturing long-range dependencies, which renders it challenging to learn grammatical and semantic information from complex code graph representations. This paper proposes a novel multi-scale software vulnerability detection method based on supervised contrastive learning. The method integrates local features obtained from code paths with structural features extracted from the Code Property Graph by GraphTrans, thereby achieving a multi-scale feature representation of the source code. Additionally, a supervised contrastive loss function is employed during training in order to fully utilize label information and address the class imbalance problem. The experimental results demonstrate that the proposed method outperforms existing state-of-the-art methods, achieving the highest accuracy, precision, and F1 on the real-world benchmark dataset CodeXGLUE for software vulnerability detection. © 2024 Knowledge systems Institute Graduate School. All rights reserved.

关键词： Contrastive Learning

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A Scalable, Efficient, and Robust Dynamic Memory Management Library for HLS-based FPGAs

A Scalable, Efficient, and Robust Dynamic Memory Management ...

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IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Qinggang Wang Long Zheng Zhaozeng An Shuyi Xiong Runze Wang Yu Huang Pengcheng Yao Xiaofei Liao Hai Jin Jingling Xue National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Zhejiang Lab Hangzhou China School of Computer Science and Engineering University of New South Wales Sydney NSW Australia

ISBN: (数字)9798350350579

ISBN: (纸本)9798350350586

Nowadays, high-level synthesis (HLS) has gained prominence for FPGA-based architecture prototyping, enhancing productivity significantly. Despite this advancement, HLS tools are impeded by a critical drawback: they lack support for dynamic memory management (DMM), leading to static mem-ory allocation and suboptimal use of memory resources. In response, numerous efforts have been made to develop DMM solutions compatible with HLS. However, our analysis indicates that existing solutions fail to concurrently meet the desired trifecta of scalability (efficient management of memory of any size), efficiency (minimal latency in memory (de-)allocation), and robustness (low allocation failure rates). This limitation hampers their applicability in real-world scenarios. In this paper, we introduce GraDMM, a “three-birds-one- stone” solution that comprehensively enhances the scalability, efficiency, and robustness of DMM. The key insight is to formulate memory (de-)allocation as graph analytics and lever-age sophisticated FPGA-based graph processing techniques. To achieve scalability, GraDMM specializes a simplified pipeline that significantly suppresses resource utilization expansion caused by managed memory scaling. This is crucial for managing arbitrarily sized memory on resource-limited FPGA platforms. For efficiency, GraDMM implements a data-centric concurrent traversal scheme and a shortcut-assisted fast traversal policy to accelerate (de-)allocation-guided graph traversal, reducing mem-ory (de-)allocation latency. To enhance robustness, GraDMM incorporates an adaptive memory defragmenter that defragments managed memory to minimize fragmentation-induced allocation failures. GraDMM is encapsulated as a library, providing high- level interfaces for users and ensuring synthesizability with Vi- vado HLS. Experimental results demonstrate that GraDMM out-performs three state-of-the-art HLS-compatible DMM solutions by significant margins: 56.71 %-85.59% in resource consumption sa

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On Pipelined GCN with Communication-Efficient Sampling and Inclusion-Aware Caching

On Pipelined GCN with Communication-Efficient Sampling and I...

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

作者： Shulin Wang Qiang Yu Xiong Wang Yuqing Li Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Cyber Science and Engineering Wuhan University Wuhan China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Graph convolutional network (GCN) has achieved enormous success in learning structural information from unstructured data. As graphs become increasingly large, distributed training for GCNs is severely prolonged by frequent cross-worker communications. Existing efforts to improve the training efficiency often come at the expense of GCN performance, while the communication overhead persists. In this paper, we propose PSC-GCN, a holistic pipelined framework for distributed GCN training with communication-efficient sampling and inclusion-aware caching, to address the communication bottleneck while ensuring satisfactory model performance. Specifically, we devise an asynchronous pre-fetching scheme to retrieve stale statistics (features, embedding, gradient) of boundary nodes in advance, such that the embedding aggregation and model update are pipelined with statistics transmission. To alleviate communication volume and staleness effect, we introduce a variance-reduction based sampling policy, which prioritizes inner nodes over boundary ones for reducing the access frequency to remote neighbors, thus mitigating cross-worker statistics exchange. Complementing graph sampling, a feature caching module is co-designed to buffer hot nodes with high inclusion probability, ensuring that frequently sampled nodes will be available in local memory. Extensive evaluations on real-world datasets show the superiority of PSC-GCN over state-of-the-art methods, where we can reduce training time by 72%-80% without sacrificing model accuracy.

关键词： Training Estimation error Accuracy Graph convolutional networks Computational modeling Probability Benchmark testing

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Vulnerability Detection Based on Pre-trained Code Language Model and Convolutional Neural Network 36

Vulnerability Detection Based on Pre-trained Code Language M...

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36th International Conference on Software engineering and Knowledge engineering, SEKE 2024

作者： Liao, Tingfeng Lu, Lu Liang, Zhihong Suo, Siliang School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Pengcheng Laboratory Shenzhen China Electric Power Research Institute CSG Guangzhou China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China

ISBN: (纸本)1891706594

Software vulnerabilities damage the reliability of software systems. Recently, many methods based on deep learning have been proposed for vulnerability detection by learning features from code sequences or various property graphs. However, sequence-based methods frequently fail to consider the structural information of the code, whereas graph-based methods often encounter difficulties in capturing long-distance contextual semantic information. To overcome these limitations, we propose PTCN, a novel vulnerability detection method that integrates Pre-Trained code language model and Convolutional Neural Network (CNN). First, the code tokens sequence and the propagation chains are constructed from the source code in preparation for the subsequent feature extraction. Next, GraphCodeBERT, a graph-based pre-trained code language model, is introduced as a means of learning both the semantic and structural information contained in the code tokens sequence and the propagation chains. Then, a CNN architecture is designed to effectively extract the crucial vulnerability features from the hidden states of the last Transformer layer of GraphCodeBERT for vulnerability detection. The experiments on the public benchmark dataset REVEAL indicate that PTCN outperforms the state-of-the-art methods with respect to the Accuracy and F1-score metrics by 3.41%-15.80% and 22.68%-104.27%, respectively. © 2024 Knowledge systems Institute Graduate School. All rights reserved.

关键词： Convolutional neural networks

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