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7th International Conference on Informatics and Computing, ICIC 2022

作者： Syahidi, Aulia Akhrian Supianto, Ahmad Afif Hirashima, Tsukasa Watanobe, Yutaka Politeknik Negeri Banjarmasin Smart City Information System Applied Undergraduate Program Banjarmasin City Indonesia Research Center for Data and Information Sciences Bandung City Indonesia Graduate School of Advanced Science and Engineering Hiroshima University Kagamiyama Learning Engineering Laboratory Higashihiroshima Japan University of Aizu Division of Information Systems Department of Computer Science and Engineering Fukushima AizuWakamatsu Japan

ISBN: (纸本)9798350345711

Learning activities are an indicator of the learner's desire to learn during the learning process. The pattern of learner action is related to learning activities. In this case, in extracting the learning process, it is necessary to collect a lot of data through analysis of the learning process. The purpose of this study is to recommend and report the performance of an activity tracking system equipped with visual artifacts as an educational data mining approach to analyze action patterns when learners complete the arrangement of program code lines in each programming problem. In this study, data were taken from activity recordings when learners used interactive learning media for a basic programming subject called TOLSYASUPI-EduMed which had problem-solving learning models embedded in their interactions. Learning Analytics is used as a method of this research and data relationships from learner actions during the learning process using the 'if-then' rule. Log data is used for recording and detecting activities carried out by learners. We confirm that the results of the study are based on the findings of the data relationship that if the lines of program code are too long (more than five lines) then almost all learners experience a bottleneck condition. The completion time of each question and the number of iterations in completing the program code lines for each question have also been recorded and have a pattern of connection between the data. The information that has been obtained is then forwarded to teachers or stakeholders as key information to make appropriate feedback to learners based on the results as a means of analyzing and evaluating the programming learning process. © 2022 IEEE.

关键词： Data mining

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IM-Diff: Implicit Multi-Contrast Diffusion Model for Arbitrary Scale MRI Super-Resolution

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4200-4212页

作者： Liu, Lanqing Zou, Jing Xu, Cheng Wang, Kang Lyu, Jun Xu, Xuemiao Hu, Zhanli Qin, Jing The Hong Kong Polytechnic University Centre for Smart Health School of Nursing 999077 Hong Kong Nanyang Technological University School of Mechanical and Aerospace Engineering 639798 Singapore Harvard Medical School Department of Psychiatry Brigham and Women's Hospital BostonMA02115 United States South China University of Technology Guangdong Engineering Center for Large Model and GenAI Technology School of Computer Science and Engineering Guangzhou510640 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Lauterbur Research Center for Biomedical Imaging Shenzhen518172 China CAS SIAT-PolyU Multi-modal Medical Molecular Imaging Joint Laboratory 999077 Hong Kong

Diffusion models have garnered significant attention for MRI Super-Resolution (SR) and have achieved promising results. However, existing diffusion-based SR models face two formidable challenges: 1) insufficient exploitation of complementary information from multi-contrast images, which hinders the faithful reconstruction of texture details and anatomical structures;and 2) reliance on fixed magnification factors, such as 2× or 4×, which is impractical for clinical scenarios that require arbitrary scale magnification. To circumvent these issues, this paper introduces IM-Diff, an implicit multi-contrast diffusion model for arbitrary-scale MRI SR, leveraging the merits of both multi-contrast information and the continuous nature of implicit neural representation (INR). Firstly, we propose an innovative hierarchical multi-contrast fusion (HMF) module with reference-aware cross Mamba (RCM) to effectively incorporate target-relevant information from the reference image into the target image, while ensuring a substantial receptive field with computational efficiency. Secondly, we introduce multiple wavelet INR magnification (WINRM) modules into the denoising process by integrating the wavelet implicit neural non-linearity, enabling effective learning of continuous representations of MR images. The involved wavelet activation enhances space-frequency concentration, further bolstering representation accuracy and robustness in INR. Extensive experiments on three public datasets demonstrate the superiority of our method over existing state-of-the-art SR models across various magnification factors. © 2013 IEEE.

关键词： Image enhancement

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nmODE-MVC: Neural Memory ODE-Based Multi-View Clustering

nmODE-MVC: Neural Memory ODE-Based Multi-View Clustering

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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Yan Zheng Song Wu Yazhou Ren Xiaorong Pu Zenglin Xu Lifang He Zhang Yi School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China School of Computer Science and Technology Harbin Institute of Technology Shenzhen Shenzhen China Department of Computer Science and Engineering Lehigh University Bethlehem USA Intelligent Interdisciplinary Research Center and College of Computer Science Sichuan University Chengdu China

ISBN: (数字)9798350359145

ISBN: (纸本)9798350359152

Deep multi-view clustering (MVC) has recently gained significant interest for its capability to harness complementary information across multiple views through deep neural networks, enhancing clustering performance. However, existing deep MVC methods still have two issues: (1) They separate the feature learning objective and the clustering objective, potentially leading to suboptimal embedding features due to the neglect of the global clustering structure. (2) Most works utilize the target distribution obtained by the clustering objective to supervise feature learning. However, they simply sharpen the soft clustering distribution to obtain the target distribution, lacking the dynamics of the clustering process. To address these issues, in this paper we propose a novel end-to-end self-supervised multi-view clustering approach based on NeuralODEs, namely nmODE-MVC. Our approach operates in two stages: In the first stage, all view-embedded features are extracted, and then unified them into a global feature, facilitating global clustering assignments by exploiting multi-view data’s complementarity. The second stage leverages the dynamic attributes of NeuralODEs to dynamically refine the data features and generate a target distribution that actively drives the clustering decisions. We formulate these learning processes as a unified optimization problem, facilitating iterative training and refinement. The effectiveness of nmODE-MVC is validated through extensive experiments on multiple real-world multi-view datasets.

关键词： Representation learning Training Learning (artificial intelligence) Artificial neural networks Drives Feature extraction Iterative methods

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PipeEdge: A Trusted Pipelining Collaborative Edge Training based on Blockchain 23

PipeEdge: A Trusted Pipelining Collaborative Edge Training b...

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2023 World Wide Web Conference, WWW 2023

作者： Yuan, Liang He, Qiang Chen, Feifei Dou, Ruihan Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Mathematics University of Waterloo Canada National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Powered by the massive data generated by the blossom of mobile and Web-of-Things (WoT) devices, Deep Neural Networks (DNNs) have developed both in accuracy and size in recent years. Conventional cloud-based DNN training incurs rapidly-increasing data and model transmission overheads as well as privacy issues. Mobile edge computing (MEC) provides a promising solution by facilitating DNN model training on edge servers at the network edge. However, edge servers often suffer from constrained resources and need to collaborate on DNN training. Unfortunately, managed by different telecoms, edge servers cannot properly collaborate with each other without incentives and trust. In this paper, we introduce PipeEdge, a scheme that promotes collaborative edge training between edge servers by introducing incentives and trust based on blockchain. Under the PipeEdge scheme, edge servers can hire trustworthy workers for pipelined DNN training tasks based on model parallelism. We implement PipeEdge and evaluate it comprehensively with four different DNN models. The results show that it outperforms state-of-the-art schemes by up to 173.98% with negligible overheads. © 2023 ACM.

关键词： Deep neural networks

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Bioinformatics and systems biology approaches to identify molecular targets and pathways shared between Schizophrenia and bipolar disorder

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Informatics in Medicine Unlocked 2024年 49卷

作者： Khatun, Mst Tania Rana, Humayan Kabir Hossain, Md Arju Lakshmanna, Kuruva Rahman, Md Mafizur Parvin, Anzana Rahman, Md Habibur Department of Biotechnology and Genetic Engineering Islamic University Department of Computer Science and Engineering Green University of Bangladesh Department of Microbiology Primeasia University School of Computer Science Engineering and Information Systems VIT Department of Computer Science and Engineering Islamic University Center for Advanced Bioinformatics and Artificial Intelligence Research Islamic University

Schizophrenia is a heterogeneous spectrum disorder of unknown etiology that is going to be a public concern around the world. The molecular association between the pathophysiology of schizophrenia (SP) and bipolar disorder (BD) is still not clear enough. To address this gap, we developed an integrated bioinformatics approach to identify common molecular signatures and possible interactive pathways that are common in the molecular pathogenic process between SP and BD. We employed pluripotent stem cell (iPSC)-derived cortical interneuron transcriptomic data and post-mortem brain transcriptomic data from patients with SP and BD, respectively. Twenty common differentially expressed genes (DEGs) were identified between SP and BD. Positive regulation of cyclic AMP (cAMP)-mediated signaling and regulation of the metabolic process were found to be common biological pathways between these two disorders. Protein-protein interactions and transcriptional regulatory interactions identified with hub-bottleneck proteins (LY6G5B, DLGAP2, DXO, EGR3, BAG6, DL-GAP2, SKIV2L, CSNK2B and RXRB), regulatory transcription factors (FOXC1, GATA2, RELA and ELK4), and regulatory microRNAs (hsa-mir-34a-5p, hsa-mir23b-3p, hsa-mir-146a-5p, hsa-mir-27a-3p, and hsa-mir-1-3p) are as key molecular signatures between SP and BD. Protein-chemical and protein-drug interactions revealed valproic acid, antirheumatic agents, trichostatin A, aarsenic, and 4-(5-benzo (1,3) dioxol-5-yl-4-pyridin-2yl-1H-imidazole-2-yl) benzamide and isopropyl alcohol compounds could be used as a potential therapeutic target molecule against SP and BD. The research findings would be used in wet lab-based research to further clarify potential therapeutic targets for the treatment of SP and BP disorders. © 2024 The Authors

关键词： Bipolar Differentially expressed genes microRNAs Pathways Schizophrenia Transcription factors

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Optical nature of non-substituted triphenylmethyl cation:Crystalline state emission,thermochromism,and phosphorescence

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Aggregate 2021年第6期2卷 99-108页

作者： Tomohiko Nishiuchi Hikaru Sotome Risa Fukuuchi Kenji Kamada Hiroshi Miyasaka Takashi Kubo Department of Chemistry Graduate School of ScienceOsaka UniversityToyonakaOsakaJapan Innovative Catalysis Science Division Institute for Open and Transdisciplinary Research Initiatives(ICS-OTRI)Osaka UniversitySuitaOsakaJapan Division of Frontier Materials Science and Center for Advanced Interdisciplinary Research Graduate School of Engineering ScienceOsaka UniversityToyonakaOsakaJapan Nanomaterial Research Institute(NMRI) National Institute of Advanced Industrial Science and Technology(AIST)IkedaOsakaJapan Department of Chemistry Graduate School of Science and TechnologyKwansei Gakuin UniversitySandaHyogoJapan

Since the discovery of the triphenylmethyl(trityl)cation 120 years ago,a variety of aromatic cations having various colors and luminescence properties have been rigorously ***,differently substituted trityl cations have been synthesized,and their optical properties have been ***,the optical properties of the parent,non-substituted and highly reactive trityl cation,which was observed to be very weakly luminescent,have not been subjected to detailed *** the effort described herein,we explored the optical nature of non-substituted trityl hexafluorophosphate(PF_(6))in the crystalline *** PF_(6) was found to exist as two crystal polymorphs including a yellow(Y)and an orange(O)***,we observed that these crystalline forms display crystalline-state emission with different *** results of X-ray crystallographic analysis showed that the two polymorphs have totally different molecular packing ***,an investigation of their optical properties revealed that the O-crystal undergoes a distinct color change to yellow upon cooling as a consequence of a change in the nature of the charge transfer interaction between the cation and PF6 anion,and that both the Y-and O-crystal exhibit phosphorescence.

关键词： aggregation induced emission carbocation phosphorescence crystal phase transition thermochromism

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Imitation Game for Adversarial Disillusion With Chain-of-Thought Reasoning in Generative AI

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IEEE Access 2025年 13卷 95085-95093页

作者： Ching-Chun Chang Fan-Yun Chen Shih-Hong Gu Kai Gao Hanrui Wang Isao Echizen Information and Society Research Division National Institute of Informatics Tokyo Japan Department of Information Engineering and Computer Science Feng Chia University Taichung Taiwan School of Multidisciplinary Sciences Graduate Institute for Advanced Studies (SOKENDAI) Kanagawa Japan Graduate School of Information Science and Technology The University of Tokyo Tokyo Japan

As the cornerstone of artificial intelligence, machine perception confronts a fundamental threat posed by adversarial illusions. These adversarial attacks manifest in two primary forms: deductive illusion, where specific stimuli are crafted based on the victim model’s general decision logic, and inductive illusion, where the victim model’s general decision logic is shaped by specific stimuli. The former exploits the model’s decision boundaries to create a stimulus that, when applied, interferes with its decision-making process. The latter reinforces a conditioned reflex in the model, embedding a backdoor during its learning phase that, when triggered by a stimulus, causes aberrant behaviors. The multifaceted nature of adversarial illusions calls for a unified defence framework, addressing vulnerabilities across various forms of attack. In this study, we propose a disillusion paradigm based on the concept of an imitation game. At the heart of the imitation game lies a multimodal generative agent, steered by chain-of-thought reasoning, which observes, internalizes and reconstructs the semantic essence of a sample, liberated from the classic pursuit of reversing the sample to its original state. As a proof of concept, we conduct experimental simulations using a multimodal generative dialogue agent and evaluates the methodology under a variety of attack scenarios. Experimental results demonstrate that the proposed framework consistently neutralizes both deductive and inductive adversarial illusions across diverse white-box and black-box attack scenarios.

关键词： Games Predictive models Noise reduction Logic Cognition Adaptation models Semantics Object recognition Generative AI Data models

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Self-Propelled Capsule Endoscope and Tracking Control of Its Wireless Power Supply Coil

Self-Propelled Capsule Endoscope and Tracking Control of Its...

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Robotics, engineering, science, and Technology (RESTCON), International Conference on

作者： Takahiro Ito Kohei Fujita Kenji Miyauchi Sunao Murakami Hiroshi Ohtake Chihiro Katsuta Kaoru Karasawa Akane Tanaka Hiroshi Matsuda Toshihiro Kimura Hiromu Kutsumi Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Division of Animal Life Science Tokyo University of Agriculture and Technology Fuchu-shi Japan Savior Inc Yokohama-Sh Japan Center for Clinical Research and Advanced Medicine Establishment Shiga University of Medical Science Otsu-Shi Japan

We have developed the self-propelled capsule endoscope. Although it is the same size as a conventional capsule endoscope, with a diameter of 11 mm and a length of 31 mm, it can travel inside the gastrointestinal tract. A running mechanism that uses vibrations allows it to run without any protrusions on the outer shell. Therefore, there is no risk of harming the inside of the body. This is a noteworthy feature of this capsule. Experiments have confirmed that this capsule can travel in the intestines of living dogs. Because the power supply to this capsule needs to continue stably for over an hour, we developed a wireless power supply method to the internal capsule. Conventional wireless power supply with a fixed power transmission coil had the problem that power supply would be interrupted if the capsule moved. Therefore, we investigated stabilizing the power supply by moving the power transmission coil in X-Y to follow the capsule in response to increases and decreases in the power supply. We were able to confirm the feasibility through experiments. This technology can be expected to perform endoscopic examinations with less burden on both patients and doctors, and can also be applied to power supply to medical devices inside the body.

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Service Caching Based Collaborative Task Offloading Algorithm in Vehicular Edge Computing

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Jisuanji Xuebao/Chinese Journal of computers 2025年第4期48卷 864-876页

作者： Tang, Chao-Gang Li, Zhao Xiao, Shuo Wu, Hua-Ming School of Computer Science and Technology China University of Mining and Technology Jiangsu Xuzhou221116 China Mine Digitization Engineering Research Center of the Ministry of Education Jiangsu Xuzhou221116 China The Center for Applied Mathematics Tianjin University Tianjin300072 China

Vehicular Edge Computing (VEC) extends computing resources from the cloud to the network edge, such as Roadside Units (RSUs), providing nearby vehicles with computational support and enabling data processing and analysis at the edge. Consequently, offloading tasks to the network edge, such as roadside units (RSUs), for execution rather than processing them in the cloud or on vehicles themselves, can better meet the complex requirements of vehicular tasks. Particularly, this approach not only alleviates pressure on the backhaul network but also reduces the network latency. However, several objective factors still constrain the rapid development and performance enhancement of VEC systems. For instance, the computing power and storage resources at Edge Servers (ESs) are significantly lower than those in remote cloud centers. The mobility of vehicles results in a dynamic topology for vehicular ad-hoc networks (VANETs). The real-time requirements of vehicular tasks increase the complexity of allocating network bandwidth and computing resources at the network edge. To further enhance the performance of VEC systems, it is essential to consider the aforementioned factors and develop more efficient algorithms for task offloading and resource allocation in VEC. In this paper, to make full use of the limited resources of edge servers and improve the caching benefits of application services, a service caching based collaborative offloading model for vehicular edge computing was proposed. On this basis, the research on service caching and task offloading was conducted, with the purpose of minimizing the weighted sum of delay and energy consumption. Considering the reusability of application services, the application service can be cached at the edge server such as roadside units to serve the offloading requests. On the other hand, if the application service is not cached at the network edge, the vehicles can choose to offload the blocks of the required application services to th

关键词： Edge computing

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Retraction Note: A Systematic Survey on Implementation of Fuzzy Regression Models for Real Life Applications

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Archives of Computational Methods in engineering 2025年 1-2页

作者： Khan, Mufala Kumar, Rakesh Aledaily, Arwa N. Kariri, Elham Viriyasitavat, Wattana Yadav, Kusum Dhiman, Gaurav Kaur, Amandeep Sharma, Ashutosh Vimal, S. Department of Mathematics Lovely Professional University Phagwara India College of Computer Science and Engineering University of Ha’il Hail Saudi Arabia College of Computer Science and Engineering Prince Sattam Bin Abdulaziz University Al‑Kharj Saudi Arabia Business Information Technology Division Department of Statistics Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand Department of Computer Science Government Bikram College of Commerce Patiala India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Gharuan India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajaplayam India

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