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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Elie Saad Joost Broekens Mark A. Neerincx Department of Intelligent Systems Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands School of Engineering Holy Spirit University of Kaslik Jounieh Lebanon The Leiden Institute of Advanced Computer Science Leiden University Leiden The Netherlands The Netherlands Organization for Applied Scientific Research (TNO) Soesterberg The Netherlands

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Whereas the reception task is a promising application domain for social robots, knowledge is lacking about how to design the appropriate re-usable communication styles for a reception robot. This paper presents the use and evaluation of an iterative interaction-design (ID) method with which task- and person-oriented multi-modal communication styles have been designed for such a robot. First, we report on an evaluation study of the ID-method with Industrial Design students (N =13) who designed these two communication styles for a Pepper robot. This provided a set of distinct designs of the two styles, for which the differences in design parameters were in line with social science theory. The task-oriented style showed a more formal, shorter and less chatty communication. Second, we present findings from a Mechanical Turk study conducted to evaluate the perception of these style designs. Participants (N =301) were presented with videos showing the robot acting as a receptionist and were asked to rate their perception of the robot, the service experience and the orientation of the designs. Overall, the interaction with the robot was appreciated well. The robot with a person-oriented style was perceived to be more animate and likeable. Analysis showed that chit-chat was the main contributor to the perceived difference between the person-oriented and task-oriented styles. This is an important finding as it gives interaction designers a validated best-practice approach to make interaction style more or less personal.

关键词： Service robots Social robots Design tools Behavioral sciences Iterative methods Robots Videos

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Repairing Schemes for Tamo-Barg Codes

Repairing Schemes for Tamo-Barg Codes

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IEEE International Symposium on Information Theory

作者： Han Cai Ying Miao Moshe Schwartz Xiaohu Tang School of Information Science and Technology Southwest Jiaotong University Chengdu China Faculty of Engineering Information and Systems University of Tsukuba Tsukuba Japan Department of Electrical and Computer Engineering McMaster University Hamilton Ontario Canada School of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer Sheva Israel

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

We study the problem of repairing erasures in locally repairable codes beyond the code locality under the rack-aware model. We devise two repair schemes to reduce the repair bandwidth for Tamo-Barg codes under the rack-aware model, by setting each repair set as a rack. The first repair scheme provides optimal repair bandwidth for one rack erasure. We then establish a cut-set bound for locally repairable codes under the rack-aware model. Using this bound we show that our second repair scheme is optimal. Furthermore, we consider the partial-repair problem for locally repairable codes under the rack-aware model, and introduce both repair schemes and bounds for this scenario.

关键词： Codes Bandwidth Maintenance engineering

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Efficient UAV Routing Strategies for Wireless Sensor Network Data Retrieval

Efficient UAV Routing Strategies for Wireless Sensor Network...

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Decision Aid sciences and Application (DASA), International Conference on

作者： Amjed Abbas Ahmed Kareem Ali Malalah Al-sharhanee Ali Hamza Najim Khattab M. Ali Alheeti Nurhizam Safie Mohd Satar Aisha Hassan Abdallah Hashim Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Imam AlKadhum College (IKC) Baghdad Iraq Department of Communication Technical Engineering College of Engineering Techniques Al-Farahidi University Baghdad Iraq Department of Computer Technical Engineering Imam Al-Kadhum College (IKC) Al-Diwaniyah Iraq Computer Networking Systems Department College of Computer and Information Technology University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Malaysia Department of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

WSNs are applied in many disciplines where certain conditions require the ability to adapt to network sink mobility and changes in the dynamics of the area coverage. To meet these needs, it is necessary to develop integrated systems where the “‘terrestrial’ systems and the Unmanned Aerial Vehicles (UAVs)” collect data from sensors efficiently. Planning of UAV trajectories is another challenging issue if many WSN nodes are randomly deployed. This challenge has a big impact on the overall effectiveness of the system. This paper presents a novel approach for reducing the traveled distance of unscrewed aerial vehicles (UAVs) to improve the data collection efficiency in WSNs with poor node density. Yet the method that has been developed is aimed at increasing “the efficiency and efficacy of data collection… with the assistance of Unmanned Aerial Vehicles (UAVs)” which means this work reveals practical application solutions.

关键词： Wireless sensor networks Trajectory planning Autonomous aerial vehicles Turning Routing Rendering (computer graphics) Sensor systems Trajectory Sensors Vehicle dynamics

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Identifying Audio-Visual Familiarity Effects on Emotion via Directed Functional Connectivity 6

Identifying Audio-Visual Familiarity Effects on Emotion via ...

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6th International Conference on Intelligent Informatics and Biomedical sciences, ICIIBMS 2021

作者： Iddas, Hadriana Lahuddin, Harlinda Bahri, Syamsul Iramina, Keiji Graduate School of Systems Life Sciences Kyushu University Makassar Indonesia University of Muslim Indonesia Faculty of Computer Science Makassar Indonesia Kyushu University Graduate School of Systems Life Sciences Faculty of Information Science and Electrical Engineering Fukuoka Japan

ISBN: (纸本)9781728167145

Some studies have reported that audiovisual stimuli have a significant influence on generating human emotions. In this paper, we propose to adopt directed functional connectivity to identify how emotions, in terms of high valence (HV) and low valence (LV), are affected by familiar/unfamiliar music videos. The current study applied Directed Transfer Function (DTF) to estimate connectivity between each pair of channels, followed by theoretical graph analysis (e.g., degree and strength). Our results showed that watching familiar/unfamiliar musical videos had a significant impact on emotions. First, HV showed greater functional connectivity during familiar conditions, while LV revealed stronger functional connectivity for unfamiliar tasks within the alpha and beta bands. Second, in the beta band during unfamiliar conditions, increased outflows of LV were identified at the right central, and enhanced inflows at the left frontal. © 2021 IEEE.

关键词： Transfer functions

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***:Hybrid Deep Learning for Enhancing IoT Network Intrusion Detection

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computers, Materials & Continua 2021年第12期69卷 3945-3966页

作者： Ziadoon K.Maseer Robiah Yusof Salama A.Mostafa Nazrulazhar Bahaman Omar Musa Bander Ali Saleh Al-rimy Faculty of Information and Communication Technology Universiti Teknikal MalaysiaMelaka76100Malaysia Center of Intelligent and Autonomous Systems Faculty of Computer Science and Information TechnologyUniversiti Tun Hussein Onn MalaysiaJohor86400Malaysia Faculty of Business and Technology UNITAR International UniversitySelangor47301Malaysia School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaJohor81310Malaysia

With an increasing number of services connected to the internet,including cloud computing and Internet of Things(IoT)systems,the prevention of cyberattacks has become more challenging due to the high dimensionality of the network traffic data and access ***,researchers have suggested deep learning(DL)algorithms to define intrusion features through training empirical data and learning anomaly patterns of ***,due to the high dynamics and imbalanced nature of the data,the existing DL classifiers are not completely effective at distinguishing between abnormal and normal behavior line connections for modern ***,it is important to design a self-adaptive model for an intrusion detection system(IDS)to improve the detection of ***,in this paper,a novel hybrid weighted deep belief network(HW-DBN)algorithm is proposed for building an efficient and reliable IDS(***)model to detect existing and novel *** HW-DBN algorithm integrates an improved Gaussian–Bernoulli restricted Boltzmann machine(Deep GB-RBM)feature learning operator with a weighted deep neural networks(WDNN)*** CICIDS2017 dataset is selected to evaluate the *** model as it contains multiple types of attacks,complex data patterns,noise values,and imbalanced *** have compared the performance of the *** model with three recent *** results show the *** model outperforms the three other models by achieving a higher detection accuracy of 99.38%and 99.99%for web attack and bot attack scenarios,***,it can detect the occurrence of low-frequency attacks that are undetectable by other models.

关键词： Cyberattacks internet of things intrusion detection system deep learning neural network supervised and unsupervised deep learning

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Brain Function Assessment of EEG in Mild Cognitive Impairment using Timelag Analysis

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and systems 2025年第4期145卷 467-473页

作者： Tanabe, Akifumi Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Graduate School of Information Science University of Hyogo 7-1-28 Minatojima Minami Chuo-ku Hyogo Kobe650-0047 Japan Department of Medical Engineering Aino University 4-5-4 Higashi-Ohda Ibaraki Osaka567-0012 Japan D3 Center The University of Osaka 2-8 Yamadaoka Osaka Suita565-0871 Japan D3 Center The University of Osaka 5-1 Mihogaoka Ibaraki Osaka567-0047 Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan Faculty of Software and Information Technology Aomori University 2-3-1 Shachihata Aomori-shi Aomori030-0943 Japan

This research aimed to reveal the information process in the brain of the patients who suffered from mild cognitive impairment (MCI) using the power spectra and timelag analysis in electroencephalogram (EEG). The patients with MCI have memory or thinking problems, and are at a great risk of developing Alzheimer's disease or a related dementia. Ten patients with MCI and 13 healthy adults (non-MCI) were examined. The EEGs (122.88 seconds/subject) during a state of relaxation with the eyes closed, were analyzed using the Fourier analysis (power spectrum) and the timelag analysis. The results showed that the mean value of the power spectra in MCI was significantly higher than the mean value in non-MCI in the theta band. The mean value of the absolute timelag values (ATL) in MCI was significantly lower than the mean value in non-MCI in the frontal and left temporal areas. Our findings suggest that the features from EEG of MCI were abstracted in the theta band, and the propagation of information related to memory in brain would becoming fast in MCI. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Electroencephalography

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Leveraging Artificial Intelligence for Advancements in Mental Disorders: A Short Review

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Procedia computer science 2025年 257卷 676-683页

作者： Selain K. Kasereka Kenny N. Tshibangu Myriam M. Nyembo Lionel K. Tshitenge Marina K. Muzindusi Godwill W.K. Ilunga Soufian Ben Amor Christian D. Bope Tasho Tashev Kyandoghere Kyamakya Mathematics Statistics and Computer Science Department University of Kinshasa Kinshasa DR. Congo Faculty of English Engineering Studies Technical University of Sofia Sofia Bulgaria ABIL Research Center Kinshasa DR. Congo Faculty of Computer Science Université Nouveaux Horizons Lubumbashi DR Congo LI-PaRAD Université de Versailles-Saint-Quentin-en-Yvelines France Institute of Smart Systems Technologies University of Klagenfurt Klagenfurt Austria Polytechnic Faculty University of Kinshasa Kinshasa Democratic Republic of the Congo

This study explores the expanding influence of artificial intelligence (AI) on the comprehension and management of mental disorders conditions. By analyzing recent advancements, this article highlights how AI technologies are being applied to diagnose, model, predict, and treat conditions, such as depression, anxiety, and bipolar disorder. It examines innovative approaches, including AI-powered mental wellness apps, that enable users to monitor their symptoms and access personalized support. Additionally, it discusses research on advanced algorithms designed to analyze online social interactions to identify early signs of psychological distress and to facilitate timely interventions. The paper also addresses the challenges involved in integrating AI into mental healthcare and highlights ethical and legal concerns that should be addressed when developing AI mental health models.

关键词： Mental Health Predictive Analytics Mental Health Interventions Machine Learning AI in Healthcare

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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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Runtime Verification for Trustworthy Computing

arXiv

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arXiv 2023年

作者： Abela, Robert Colombo, Christian Curmi, Axel Fenech, Mattea Vella, Mark Ferrando, Angelo Department of Computer Science Faculty of ICT University of Malta Msida Malta Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genova16145 Italy

Autonomous and robotic systems are increasingly being trusted with sensitive activities with potentially serious consequences if that trust is broken. Runtime verification techniques present a natural source of inspiration for monitoring and enforcing the desirable properties of the communication protocols in place, providing a formal basis and ways to limit intrusiveness. A recently proposed approach, RV-TEE, shows how runtime verification can enhance the level of trust to the Rich Execution Environment (REE), consequently adding a further layer of protection around the Trusted Execution Environment (TEE). By reflecting on the implication of deploying RV in the context of trustworthy computing, we propose practical solutions to two threat models for the RV-TEE monitoring process: one where the adversary has gained access to the system without elevated privileges, and another where the adversary gains all privileges to the host system but fails to steal secrets from the TEE. © 2023, CC BY.

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Multi-view Semantic Information Guidance for Light Field Image Segmentation

Multi-view Semantic Information Guidance for Light Field Ima...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yiming Cong, Ruixuan Wang, Sizhe Zhao, Mingyuan Zhang, Yang Li, Fangping Sheng, Hao Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang Hangzhou Innovation Institute Yuhang Xixi Octagon City Hangzhou310023 China Beijing University of Chemical Technology College of Information Science and Technology Beijing100029 China Macao Polytechnic University Faculty of Applied Sciences 999078 China

ISBN: (纸本)9798350302493

One of the great important fields of computer vision is semantic segmentation. As for single image semantic segmentation, due to limited available information, it appears poor performance when the occlusion and similar color interference occur, and has difficulty exploiting the rich scene information. In comparison, the special micro-len array structure of light field camera can record multi-view information of the scene, which provides us with a new solution to solve this issue. In this paper, we propose a multi-view semantic information guidance network (MSIGNet) for light field semantic segmentation. It can effectively utilize semantic information from multi-view images to guide pixel feature of center view image. First, we extract feature of each view image and further obtain semantic probability. Then all probabilities are aggregated through a self-adaptive multi-view probability fusion module. Last, the resulting coarse fusion representation interacts with center view feature to obtain the refined segmentation result. The proposed method shows excellent performance on both real-world and synthetic light field datasets. © 2023 IEEE.

关键词： Semantic Segmentation

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