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International Conference on information Management and Technology (ICIMTech)

作者： Semi Yulianto Harco Leslie Hendric Spits Warnars Harjanto Prabowo Meyliana Achmad Nizar Hidayanto Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Faculty of Computer Science Universitas Indonesia Depok Indonesia

The rise of blockchain technology and smart contracts has brought widespread attention due to their capacity to transform multiple industrial sectors through decentralized, transparent, secure transactions. However, despite their promise to revolutionize various fields worldwide, lingering concerns regarding security risks impede their adoption rate. Addressing these concerns is crucial now more than ever; therefore, we conducted a comprehensive literature review within our study's scope that focused on published papers between 2014-2023 centered around security risks concerning blockchain and smart contracts. Our systematic approach using the PRISMA checklist analyzed nine categorized research model-based primary studies while recognizing vulnerabilities in smart contract development and providing best practices to mitigate such issues. These findings benefit both researchers and practitioners as they showcase how acknowledging these vulnerabilities can further develop into exploring more significant aspects of blockchain technology's security issues and smart contract development processes. Our study contributes significantly by expanding knowledge in this field while providing novel insights valuable for individuals involved in designing or implementing blockchain technologies.

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Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunities

arXiv

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arXiv 2024年

作者： Liu, Guangyuan Van Huynh, Nguyen Du, Hongyang Hoang, Dinh Thai Niyato, Dusit Zhu, Kun Kang, Jiawen Xiong, Zehui Jamalipour, Abbas In Kim, Dong The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The Department of Electrical Engineering and Electronics University of Liverpool Liverpool L69 3GJ United Kingdom The School of Electrical and Data Engineering University of Technology Sydney Australia The School of Computer Science and Engineering Nanyang Technological University Singapore College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Electrical and Information Engineering University of Sydney Australia The College of Information and Communication Engineering Sungkyunkwan University Korea Republic of

With recent advances in artificial intelligence (AI) and robotics, unmanned vehicle swarms have received great attention from both academia and industry due to their potential to provide services that are difficult and dangerous to perform by humans. However, learning and coordinating movements and actions for a large number of unmanned vehicles in complex and dynamic environments introduce significant challenges to conventional AI methods. Generative AI (GAI), with its capabilities in complex data feature extraction, transformation, and enhancement, offers great potential in solving these challenges of unmanned vehicle swarms. For that, this paper aims to provide a comprehensive survey on applications, challenges, and opportunities of GAI in unmanned vehicle swarms. Specifically, we first present an overview of unmanned vehicles and unmanned vehicle swarms as well as their use cases and existing issues. Then, an in-depth background of various GAI techniques together with their capabilities in enhancing unmanned vehicle swarms are provided. After that, we present a comprehensive review on the applications and challenges of GAI in unmanned vehicle swarms with various insights and discussions. Finally, we highlight open issues of GAI in unmanned vehicle swarms and discuss potential research directions. © 2024, CC BY.

关键词： Metadata

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A Feasibility-Preserved Quantum Approximate Solver for the Capacitated Vehicle Routing Problem

arXiv

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arXiv 2023年

作者： Xie, Ningyi Lee, Xinwei Cai, Dongsheng Saito, Yoshiyuki Asai, Nobuyoshi Lau, Hoong Chuin Graduate School of Science and Technology University of Tsukuba Ibaraki Prefecture Japan Faculty of Engineering Information and Systems University of Tsukuba Ibaraki Prefecture Japan School of Computer Science and Engineering University of Aizu Fukushima Prefecture Japan School of Computing and Information Systems Singapore Management University Ibaraki Prefecture Japan Graduate School of Computer Science and Engineering University of Aizu Fukushima Prefecture Japan School of Computing and Information Systems Singapore Management University 80 Stamford Road Singapore

The Capacitated Vehicle Routing Problem (CVRP) is an NP-optimization problem (NPO) that arises in various fields including transportation and logistics. The CVRP extends from the Vehicle Routing Problem (VRP), aiming to determine the most efficient plan for a fleet of vehicles to deliver goods to a set of customers, subject to the limited carrying capacity of each vehicle. As the number of possible solutions skyrockets when the number of customers increases, finding the optimal solution remains a significant challenge. Recently, the Quantum Approximate Optimization Algorithm (QAOA), a quantum-classical hybrid algorithm, has exhibited enhanced performance in certain combinatorial optimization problems compared to classical heuristics. However, its ability diminishes notably in solving constrained optimization problems including the CVRP. This limitation primarily arises from the typical approach of encoding the given problems as penalty-inclusive binary optimization problems. In this case, the QAOA faces challenges in sampling solutions satisfying all constraints. Addressing this, our work presents a new binary encoding for the CVRP, with an alternative objective function of minimizing the shortest path that bypasses the vehicle capacity constraint of the CVRP. The search space is further restricted by the constraint-preserving mixing operation. We examine and discuss the effectiveness of the proposed encoding under the framework of the variant of the QAOA, Quantum Alternating Operator Ansatz (AOA), through its application to several illustrative examples. Compared to the typical QAOA approach, the proposed method not only preserves the feasibility but also achieves a significant enhancement in the probability of measuring optimal solutions. © 2023, CC BY.

关键词： Vehicles

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Simulation Evaluation of Vehicle Movement Model Using Spacio-Temporal Grid Reservation for Automated Valet Parking

TechRxiv

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TechRxiv 2022年

作者： Yamamoto, Kotaro Teng, Rui Sato, Kenya Computer and Information Science Graduate School of Science and Engineering Doshisha University Kyoto610-0321 Japan

Automated valet parking (AVP) systems aim to use automated driving technology to park a vehicle from the passenger’s boarding/exiting location to a parking space in a parking lot and recall the vehicle from the parking space to the boarding/exiting location. For multiple vehicles to move efficiently through a parking lot, travel must be mediated between each vehicle. We have proposed a driving control method that improves the efficiency of AVP by using the Spatio-Temporal Grid Reservation mechanism. We previously compared it with an autonomous driving model, which showed that the proposed method improved the efficiency of vehicle movement when vehicles are entering small parking lots. In the present paper, we evaluate our proposed method by comparing it with a vehicle movement model based on a method proposed in a related study, in addition to an autonomous driving model. We performed simulations varying the percentage of vehicles arriving at the parking lot and showed that the proposed method improves the efficiency of vehicle movement when vehicles are entering and exiting the lot. © 2022-12-28, CC BY.

关键词： Autonomous vehicles

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Design of P Band Filter Antenna with High Out-of-Band Suppression

Design of P Band Filter Antenna with High Out-of-Band Suppre...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Jiangling Dou Yinsu Yuan Jian Song Qingwang Wang Tao Shen School of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China Graduate School Kunming University of Science and Technology Kunming China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

P-band antennas offer tremendous potential in various fields, including remote sensing, communication, and radar applications. However, It is crucial to enhance their effectiveness in these domains that the current challenges in the P-band, particularly wide bandwidth and exceptional out-of-band suppression be achieved. This paper aims to introduce an efficient design scheme for a high-efficiency, high out-of-band suppression P-band filter antenna. The antenna structure comprises a Y-shaped feeding network, upper and lower parallel dielectric plates, a vertical dielectric plate, a central metal plate, and a pair of microstrip patches. The bottom feeding network employs a Y-shaped feeding network. The coupling of the antenna was effectively controlled and the good bandwidth and impedance matching have been achieved. In order to achieve a wider bandwidth and lower return loss for the antenna, slotting technology was employed on the semi-circular radiation patch. The introduction of a vertical metal plate enhances filtering performance of antenna. Additionally, there are radiation zeros at 0.68 GHz and 1.43 GHz, high-frequency selectivity is imparted and signal reflection losses are effectively reduced. Key performance indicators include a filter antenna center frequency of 0.75 GHz, a −10 dB impedance bandwidth of 32.9% (0.89 GHz–1.24 GHz). In the upper stopband (0.5 GHz–0.8 GHz), the suppression level exceeds 20 dB, while in the lower stopband (1.1 GHz–1.8 GHz), suppression level exceeds 15 dB. Significant suppression characteristics are observed in the 0.5 GHz–0.8 GHz and 1.1 GHz–1.8 GHz frequency ranges. Design elements such as the semi-circular radiating patch, slotting technology, and rectangular slots on the substrate have been employed in the antenna. The suppression level, frequency selectivity had been enhance of antenna. Simulation results indicate a gain of 7.06 dB, overall efficiency beyond 95%, co-pol superior to cross-pol by more than 35 dB, and antenna

关键词： Slot antennas Filtering Simulation Bandwidth Microstrip antennas Directive antennas Dielectrics

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New Level-Set-Based Shape Recovery Method and its application to sparse-view shape tomography 21

New Level-Set-Based Shape Recovery Method and its applicatio...

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2021 4th International Conference on Digital Medicine and Image Processing

作者： Haytham A. Ali Hiroyuki Kudo Department of Computer Science Graduate School of Systems and Information Engineering University of Tsukuba Japan and Department of MathematicsFaculty of Science Sohag University Egypt Department of Computer Science Graduate School of Systems and Information Engineering University of Tsukuba Japan

ISBN: (纸本)9781450386487

The recovery of shapes from a few numbers of their projections is very important in Computed tomography. In this paper, we propose a novel scheme based on a collocation set of Gaussian functions to represent any object by using a limited number of projections. This approach provides a continuous representation of both the implicit function and its zero level set. We show that the appropriate choice of a basis function to represent the parametric level-set leads to an optimization problem with a modest number of parameters, which exceeds many difficulties with traditional level set methods, such as regularization, re-initialization, and use of signed distance function. For the purposes of this paper, we used a dictionary of Gaussian function to provide flexibility in the representation of shapes with few terms as a basis function located at lattice points to parameterize the level set function. We propose a convex program to recover the dictionary coefficients successfully so it works stably with only four projections by overcoming the issue of local-minimum of the cost function. Finally, the performance of the proposed approach in three examples of inverse problems shows that our method compares favorably to Sparse Shape Composition (SSC), Total Variation, and Dual Problem.

关键词： Compressed sensing Computed tomography Convex optimization Gaussian function Level set Reconstruction method Shape identification X-ray tomography

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An Efficient Antenna Optimization Method Based on a Parameter Dimensionality Reduction Approach

An Efficient Antenna Optimization Method Based on a Paramete...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Jiangling Dou Chenyang Zhao Jian Song Qingwang Wang Tao Shen School of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China Graduate School Kunming University of Science and Technology Kunming China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

As antenna structure grow in complexity, the problem of time-consuming in electromagnetic (EM) simulation have become more common. This problem undoubtedly brings a series of the challenges in raising the effectiveness of antenna optimization. In order to overcome the existing difficulties, an antenna optimization method based on RF-GWO method is proposed. This approach consists of three major components. Firstly, an artificial neural network (ANN) is used to build the dataset. Compared with the traditional sampling method, the data filtered by the artificial neural network (ANN) are closer to the optimization objective. Consequently, it can better responsive to the relationship between the antenna parameters and the optimization objective. Then, the Random Forest method (RF) is utilized to analyze the impact of the ten selected parameters based on the electromagnetic (EM) simulation results on the antenna performance. Random Forest Algorithm (RF) has strong interpretability, allowing for the assessment of the contribution of different parameters to the optimization objective, which can effectively reduce the number of design variables. Finally, the antenna optimization is performed utilizing the Grey Wolf Optimization (GWO) algorithm. Combined with the sensitivity analysis results, the Gray Wolf Algorithm (GWO) can effectively search for improved results in a faster manner. In order to evaluate the algorithm’s performance, the proposed method is employed to optimize a multiband miniaturized microstrip antenna. The result demonstrates that substantial improvement in impedance bandwidth is achieved by the proposed method, with an approximate 29% increase compared to conventional manual optimization methods. This outcome has proved potential and effectiveness of the proposed method in practical implications of antenna optimization.

关键词： Dimensionality reduction Sensitivity analysis Simulation Optimization methods Artificial neural networks Microstrip antennas Bandwidth

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Throughput-oriented associated transaction assignment in sharding blockchains for IoT social data storage

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Digital Communications and Networks 2022年第6期8卷 885-899页

作者： Liping Tao Yang Lu Xu Ding Yuqi Fan Jung Yoon Kim School of Computer Science and Information Engineering Hefei University of TechnologyHefei230009China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of EducationHefei University of TechnologyHefei230009China Institute of Industry&Equipment Technology Hefei University of TechnologyHefei230009China Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology) Ministry of EducationHefei230009China Graduate School of Game Gachon UniversitySeongnam-Si461-701South Korea

Blockchain is a viable solution to provide data integrity for the enormous volume of 5G IoT social data, while we need to break through the throughput bottleneck of blockchain. Sharding is a promising technology to solve the problem of low throughput in blockchains. However, cross-shard communication hinders the effective improvement of blockchain throughput. Therefore, it is critical to reasonably allocate transactions to different shards to improve blockchain throughput. Existing research on blockchain sharding mainly focuses on shards formation, configuration, and consensus, while ignoring the negative impact of cross-shard communication on blockchain throughput. Aiming to maximize the throughput of transaction processing, we study how to allocate blockchain transactions to shards in this paper. We propose an Associated Transaction assignment algorithm based on Closest Fit (ATCF). ATCF classifies associated transactions into transaction groups which are then assigned to different shards in the non-ascending order of transaction group sizes periodically. Within each epoch, ATCF tries to select a shard that can handle all the transactions for each transaction group. If there are multiple such shards, ATCF selects the shard with the remaining processing capacity closest to the number of transactions in the transaction group. When no such shard exists, ATCF chooses the shard with the largest remaining processing capacity for the transaction group. The transaction groups that cannot be completely processed within the current epoch will be allocated in the subsequent epochs. We prove that ATCF is a 2-approximation algorithm for the associated transaction assignment problem. Simulation results show that ATCF can effectively improve the blockchain throughput and reduce the number of cross-shard transactions.

关键词： IoT social data Blockchain Shar ding Associated transactions Transaction assi gnment

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Integration of the Use of Conventional Technology Towards Smart Home Technology for People in the City of Jakarta

Integration of the Use of Conventional Technology Towards Sm...

引用

International Conference on ICT For Smart Society (ICISS)

作者： Tanty Oktavia Febian Wijaya Lintang Gerald Christian Ongko Griselda Valerie Michelle Benedicta Vieri Aska Juneio Sembiring Zaldy Raditya Ananta Takaaki Hosoda Ford Lumban Gaol Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Decision Science Department Advanced Institute of Industrial Technology Tokyo Japan Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Smart Home technology offers technological concepts that can be controlled through smart devices to make it easier for the community. Therefore, smart home technology is starting to seize the home electronics market in Indonesia, especially in the city of Jakarta. This study aims to determine the integration of conventional technology into smart home technology for people in Jakarta. The research model in this study uses UTAUT2 which analyzes nine variables, namely Perceived Usefulness, Perceived Cost, Perceived Ease of Use, Social Influence, Perceived Compatibility, Trust, Perceived Pleasure, and Effort Expectations, Intention to Use Smart Home Technology. After the responses were received, data analysis was carried out using the PLS-SEM approach with the SmartPLS application. The results of this study show that there are many people who live in Jakarta who are helped by the presence of smart home technology. However, there are some people who don't really know how to use a smart home because of a lack of knowledge about this technology.

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Detection of Pre Shoplifting Suspicious Behavior Using Deep Learning

Detection of Pre Shoplifting Suspicious Behavior Using Deep ...

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Sujan Shrestha Yoji Taniguchi Tetsuo Tanaka Course of Information and computer sciences Graduate school of Engineering Kanagawa Institute of Tehnology Atsugi Japan Department of Information Artificial Intelligence and Data Science Daiichi Institute of Technology Tokyo Japan Dept. of Information and Computer Sciences Faculty of Information Technology Kanagawa Institute of Technology Atsugi Japan

ISBN: (数字)9798350377903

ISBN: (纸本)9798350377910

Shoplifting presents a significant challenge for retailers, leading to substantial financial losses and security concerns. Current surveillance systems effectively identify shoplifting incidents postoccurrence; however, the capability to detect and prevent such activities preemptively remains limited. This research focuses on identifying behaviors that likely precede shoplifting events. To this end, we introduce a novel benchmark dataset, specifically designed for the early detection of suspicious behavior that may lead to shoplifting. This dataset is curated by selecting and segmenting relevant sequences from the UCF101-Crime dataset, encompassing suspicious behavior, actual shoplifting incidents, and normal activities. Our approach addresses the limitations of existing datasets where the brief duration of shoplifting events may skew neural network performance. The dataset includes clips of only a few seconds each, for which we propose a robust neural network architecture combining the strengths of convolutional neural networks (CNN) for efficient feature extraction and Bidirectional long short-term memory (BiLSTM) networks for capturing temporal dependencies within action sequences. Our model achieves an impressive classification accuracy of 86.21 % on the newly created dataset.

关键词： Deep learning Recurrent neural networks Accuracy Surveillance Benchmark testing Feature extraction Convolutional neural networks Security Informatics Long short term memory

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