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An Enhanced Authentication Protocol Suitable for Constrained RFID Systems

IEEE Access 2024年 12卷 61610-61628页

作者： Hosseinzadeh, Mehdi Servati, Mohammad Reza Rahmani, Amir Masoud Safkhani, Masoumeh Lansky, Jan Janoscova, Renata Ahmed, Omed Hassan Tanveer, Jawad Lee, Sang-Woong Duy Tan University Institute of Research and Development Da Nang550000 Viet Nam Duy Tan University School of Medicine and Pharmacy Da Nang550000 Viet Nam Shahid Rajaee Teacher Training University Department of Computer Engineering Tehran16788-15811 Iran National Yunlin University of Science and Technology Future Technology Research Center Douliou Yunlin64002 Taiwan School of Computer Science Tehran19395-5746 Iran University of Finance and Administration Faculty of Economic Studies Department of Computer Science and Mathematics Prague101 00 Czech Republic University of Human Development Department of Information Technology Sulaymaniyah0778-6 Iraq Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of Gachon University Pattern Recognition and Machine Learning Laboratory Department of Ai Software Seongnam-si13557 Korea Republic of

RFID technology offers an affordable and user-friendly solution for contactless identification of objects and individuals. However, the widespread adoption of RFID systems raises concerns regarding security and privacy. Vulnerabilities such as message tampering, interception, and eavesdropping pose significant risks to the integrity of the system. This study examines the effectiveness of two recently proposed ultra-lightweight RFID authentication protocols, URASP and KUAJB, in addressing these security challenges. Surprisingly, our findings reveal that both URASP and KUAJB protocols are susceptible to secret disclosure attacks, despite their claims of providing robust security. Consequently, the development of a truly ultra-lightweight protocol that ensures adequate security becomes a formidable task. As a potential solution, the enhancement of the URASP protocol through the integration of a simple cryptographic primitive is suggested to bolster its security measures. © 2013 IEEE.

关键词： Hash functions

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Post-Training Quantization in Brain-computer Interfaces Based on Event-Related Potential Detection

Post-Training Quantization in Brain-Computer Interfaces Base...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Hubert Cecotti Dalvir Dhaliwal Hardip Singh Yohesh Kumar Meena Department of Computer Science California State University Fresno USA Human-AI Interaction (HAIx) Lab IIT Gandhinagar India

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Post-training quantization (PTQ) is a technique used to optimize and reduce the memory footprint and computational requirements of machine learning models. It has been used primarily for neural networks. For Brain-computer Interfaces (BCI) that are fully portable and usable in various situations, it is necessary to provide approaches that are lightweight for storage and computation. In this paper, we propose the evaluation of post-training quantization on state-of-the-art approaches in brain-computer interfaces and assess their impact on accuracy. We evaluate the performance of the single-trial detection of event-related potentials representing one major BCI paradigm. The area under the receiver operating characteristic curve drops from 0.861 to 0.825 with PTQ when applied on both spatial filters and the classifier, while reducing the size of the model by about x 15. The results support the conclusion that PTQ can substantially reduce the memory footprint of the models while keeping roughly the same level of accuracy.

关键词： Quantization (signal) Accuracy Memory management Deep architecture Receivers Machine learning Brain modeling Spatial filters Brain-computer interfaces Hardware

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Enhanced Fine-Tuning of Lightweight Domain-Specific Q&A Model Based on Large Language Models 35

Enhanced Fine-Tuning of Lightweight Domain-Specific Q&A Mode...

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35th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2024

作者： Zhang, Shenglin Zhu, Pengtian Ma, Minghua Wang, Jiagang Sun, Yongqian Li, Dongwen Wang, Jingyu Guo, Qianying Hua, Xiaolei Zhu, Lin Pei, Dan Nankai University China Haihe Laboratory of Information Technology Application Innovation China Microsoft Tsinghua University China Tianjin Key Laboratory of Software Experience and Human Computer Interaction China China Mobile Research Institute China Beijing National Research Center for Information Science and Technology China

ISBN: (纸本)9798350367041

Large language models (LLMs) excel at general question-answering (Q&A) but often fall short in specialized domains due to a lack of domain-specific knowledge. Commercial companies face the dual challenges of privacy protection and resource constraints when involving LLMs for fine-tuning. This paper propose a novel framework, Self-Evolution, designed to address these issues by leveraging lightweight open-source LLMs through multiple iterative fine-tuning rounds. To enhance the efficiency of iterative fine-tuning, Self-Evolution employ a strategy that filters and reinforces the knowledge with higher value during the iterative process. We employed Self-Evolution on Qwen1.5-7B-Chat using 4,000 documents containing rich domain knowledge from China Mobile, achieving a performance score 174% higher on domain-specific question-answering evaluations than Qwen1.5-7B-Chat and even 22% higher than Qwen1.5-72B-Chat. Self-Evolution has been deployed in China Mobile's daily operation and maintenance for 117 days, and it improves the efficiency of locating alarms, fixing problems, and finding related reports, with an average efficiency improvement of over 18.6%. In addition, we release Self-Evolution framework code in https://***/Zero-Pointer/Self-Evolution. © 2024 IEEE.

关键词： Data mining

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Quantifying Spatial Domain Explanations in BCI using Earth Mover’s Distance

Quantifying Spatial Domain Explanations in BCI using Earth M...

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International Joint Conference on Neural Networks (IJCNN)

作者： Param Rajpura Hubert Cecotti Yogesh Kumar Meena Human-AI Interaction (HAIx) Lab IIT Gandhinagar India Department of Computer Science California State University Fresno USA

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Brain-computer interface (BCI) systems facilitate unique communication between humans and computers, benefiting severely disabled individuals. Despite decades of research, BCIs are not fully integrated into clinical and commercial settings. It’s crucial to assess and explain BCI performance, offering clear explanations for potential users to avoid frustration when it doesn’t work as expected. This work investigates the efficacy of different deep learning and Riemannian geometry-based classification models in the context of motor imagery (MI) based BCI using electroencephalography (EEG). We then propose an optimal transport theory-based approach using earth mover’s distance (EMD) to quantify the comparison of the feature relevance map with the domain knowledge of neuroscience. For this, we utilized explainable AI (XAI) techniques for generating feature relevance in the spatial domain to identify important channels for model outcomes. Three state-of-the-art models are implemented - 1) Riemannian geometry-based classifier, 2) EEGNet, and 3) EEG Conformer, and the observed trend in the model’s accuracy across different architectures on the dataset correlates with the proposed feature relevance metrics. The models with diverse architectures perform significantly better when trained on channels relevant to motor imagery than data-driven channel selection. This work focuses attention on the necessity for interpretability and incorporating metrics beyond accuracy, underscores the value of combining domain knowledge and quantifying model interpretations with data-driven approaches in creating reliable and robust Brain-computer Interfaces (BCIs).

关键词： Measurement Earth Accuracy computer architecture Brain modeling Motors Feature extraction

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Critically Examining the Broader Implications of Methodological Design in Cross-Cultural, Multi-sited Case Studies of Engineering Ethics Education [Work in Progress]

Critically Examining the Broader Implications of Methodologi...

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2023 ASEE Annual Conference and Exposition - The Harbor of Engineering: Education for 130 Years, ASEE 2023

作者： Clancy, Rockwell Franklin Zhu, Qin Streiner, Scott Thorpe, Ryan The Department of Humanities Arts and Social Sciences The Colorado School of Mines The Department of Values Technology and Innovation Delft University of Technology Netherlands The Department of Engineering Education and Affiliate Faculty The Department of Science Technology & Society The Center for Human-Computer Interaction Virginia Tech United States School of Engineering the University of Pittsburgh United States

Ethics has long been recognized as crucial to responsible engineering, but the increasingly globalized environments present challenges to effective engineering ethics training. This paper is part of a larger research project that aims to examine the effects of culture and education on ethics training in undergraduate engineering students at universities in the United States, China, and the Netherlands. We are interested in how students’ curricular and extra-curricular (e.g., internships, service projects) experiences and training impact their ethical reasoning and moral dispositions, and how this differs cross-culturally. To understand this, we are conducting mixed methods research longitudinally over four years to engineering students at our participating universities to gauge their moral dispositions and ethical reasoning skills and to measure any change in these. This work-in-progress paper, however, is not about the direct outcomes of this research project. Rather, it critically examines our own practices and methods in doing this research. We begin the paper by briefly introducing the larger research project and motivating the use of comparative, multi-institutional case studies as necessary for contextualizing, complementing, and interpreting quantitative data on ethical reasoning and moral dispositions. Because the conditions related to engineering ethics education differ widely per participating institution for institutional (and also likely cultural) reasons, interpreting and analyzing quantitative survey data will require understanding contextual conditions of education at each institution. Comparative case studies can supply missing contextual information to provide a more complete picture of the engineering ethics educational contexts, strategies, and practices at each of the participating universities. However, in considering how to design and conduct these case studies, we realized we were operating under certain assumptions such as ethics in engineeri

关键词： Students

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Measuring Social Solidarity During Crisis:The Role of Design Choices

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Journal of Social Computing 2022年第2期3卷 139-157页

作者： Steffen Eger Dan Liu Daniela Grunow the Center for Cognitive Interaction Technology(CITEC) Bielefeld UniversityBielefeld 33615Germany the Computer Science Department Technical University DarmstadtDarmstadt 64289Germany the Faculty of Social Sciences Goethe University FrankfurtFrankfurt 60629Germany

Building on our previous work,we assess how social solidarity towards migrants and refugees has changed before and after the onset of the COVID-19 pandemic,by collecting and analyzing a large,novel,and longitudinal dataset of migration-related *** this end,we first annotate above 2000 tweets for(anti-)solidarity expressions towards immigrants,utilizing two annotation approaches(experts ***).On these annotations,we train a BERT model with multiple data augmentation strategies,which performs close to the human upper *** use this high-quality model to automatically label over 240000 tweets between September 2019 and June *** then assess the automatically labeled data for how statements related to migrant(anti-)solidarity developed over time,before and during the COVID-19 *** findings show that migrant solidarity became increasingly salient and contested during the early stages of the pandemic but declined in importance since late 2020,with tweet numbers falling slightly below pre-pandemic levels in summer *** the same period,the share of anti-solidarity tweets increased in a sub-sample of COVID-19-related *** findings highlight the importance of long-term observation,pre-and post-crisis comparison,and sampling in research interested in crisis related *** one of our main contributions,we outline potential pitfalls of an analysis of social solidarity trends:for example,the ratio of solidarity and anti-solidarity statements depends on the sampling design,i.e.,tweet language,Twitter-user accounts’national identification(country known or unknown)and selection of relevant *** our sample,the share of anti-solidarity tweets is higher in native(German)language tweets and among“anonymous”Twitter users writing in German compared to English-language tweets of users located in Germany.

关键词： social solidarity crises COVID-19 natural language processing

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Gaze Behaviour in Adolescents with Obsessive-Compulsive Disorder During Exposure Within Cognitive-Behavioural Therapy 17th

Gaze Behaviour in Adolescents with Obsessive-Compulsive Di...

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17th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2023

作者： Thierfelder, Annika Severitt, Björn Klein, Carolin S. Alt, Annika K. Hollmann, Karsten Bulling, Andreas Ilg, Winfried Hertie Institute for Clinical Brain Research Section for Computational Sensomotorics University Hospital Tübingen Tübingen Germany Department of Computer Science Human-Computer Interaction University of Tübingen Tübingen Germany Department of Child and Adolescent Psychiatry Psychosomatics and Psychotherapy University Hospital Tübingen Tübingen Germany Institute for Visualisation and Interactive Systems University Stuttgart Stuttgart Germany

ISBN: (纸本)9783031597169

Digital health interventions that involve monitoring patient behaviour increasingly benefit from improvements in sensor technology. Eye tracking in particular can provide useful information for psychotherapy but an effective method to extract this information is currently missing. We propose a method to analyse natural gaze behaviour during exposure exercises for obsessive-compulsive disorder (OCD). At the core of our method is a neural network to detect fixations based on gaze patch similarities. Detected fixations are clustered into exposure-relevant, therapist, and other locations and corresponding eye movement metrics are correlated with subjective stress reported during exposure. We evaluate our method on gaze and stress data recorded during video-based psychotherapy of four adolescents with OCD. We found that fixation duration onto exposure-relevant locations consistently increases with the perceived stress level as opposed to fixations onto other locations. Fixation behaviour towards the therapist varied largely between patients. Taken together, our results not only demonstrate the effectiveness of our method for analysing natural gaze behaviour during exposure sessions. The fixation analysis shows that patients allocate more attention towards exposure-related objects under higher stress levels, suggesting higher mental load. As such, providing feedback on fixation behaviour holds significant promise to support therapists in monitoring intensity of exposure exercises. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Location

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Don’t You Worry ’bout a Thing? Identification and Quantification of Relevant Privacy Parameters Within the Acceptance of AAL Technology 7th

Don’t You Worry ’bout a Thing? Identification and Quanti...

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7th and 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2021 and ICT4AWE 2022

作者： Maidhof, Caterina Offermann, Julia Ziefle, Martina Chair of Communication Science Human Computer Interaction Center RWTH Aachen University Campus Boulevard Aachen Germany

ISBN: (纸本)9783031374951

To combat healthcare challenges Ambient Assisted Living (AAL) technologies offer the opportunity to support an independent life in older age. Although the potential and advantages of such technology are acknowledged, perceived barriers in particular referred to privacy still represent a hurdle for their acceptance. Applying a two-step empirical approach, this study aimed at the identification and quantification of relevant privacy parameters for the acceptance of visual AAL technology. First, a qualitative study investigated the participants’ cognitive privacy representations in a scenario in which AAL was installed in their own home to support life in older age. 12 participants (age range: 23–81 years) shared their opinions within semi-structured interviews. Based on these findings, an online survey (N = 134) study was applied in a second step in order to quantify the identified privacy parameters. Overall, the paper presents the qualitative key findings as well as the descriptive results of the quantitative study. The results show the usefulness of both methods to a) identify and understand thought processes of potential users regarding privacy requirements as well as b) to get insights into the relevance and meaning of the identified privacy parameters. The qualitative and quantitative findings are useful to inform technical designers as well as lawmakers to consider the privacy parameters during technology or law development. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Parameter estimation

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A 2D Geometry Based Grasping Pose Generation Algorithm for a Two-Finger Robot Hand

A 2D Geometry Based Grasping Pose Generation Algorithm for a...

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Iranian Conference on Electrical Engineering (ICEE)

作者： Arash Akbari Arman Akbari Mehdi Tale Masouleh Department of Computer Science University of Tehran Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran

Robot grasping is of paramount importance in industrial and service robotics. In recent years, various data-driven algorithms have been proposed to solve the problem of grasp detection and a part of them are based on reinforcement learning (RL) approaches. In a variety of proposed algorithms, random key points are being employed which will make the learning process inefficient and time-consuming. In this paper, a geometry-based algorithm is presented which can find grasp poses based on the geometry of the unknown object and propose the ones which may lead to successful grasping. For the grasp contacts computation part, the presented algorithm produces a finite number of key points based on the 2D shape of the object from a specific point of view. Afterward, it will narrow down the candidate points and output a finite number of successful grasp poses based on three grasp quality metrics for various unknown objects. Three approaches are proposed in order to achieve center points which can describe different parts of a 2D shape. Then, the obtained points are used as the center of circles which are tangent to the 2D shape contour. Also, a new grasp quality metric is proposed. The time of the grasp and the amount of object disorientation after grasping are considered as a metric to evaluate the successfulness of the grasp. Simulation results demonstrate that the proposed algorithm for unknown object grasping can find a finite number of successful grasp poses for different seen or unseen objects without using any random point,

关键词：

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TouchPose: Hand Pose Prediction, Depth Estimation, and Touch Classification from Capacitive Images 21

TouchPose: Hand Pose Prediction, Depth Estimation, and Touch...

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34th Annual ACM Symposium on User Interface Software and Technology, UIST 2021

作者： Ahuja, Karan Streli, Paul Holz, Christian Human-Computer Interaction Institute Carnegie Mellon University United States Department of Computer Science ETH Zürich Switzerland

ISBN: (纸本)9781450386357

Today's touchscreen devices commonly detect the coordinates of user input through capacitive sensing. Yet, these coordinates are the mere 2D manifestations of the more complex 3D configuration of the whole hand - a sensation that touchscreen devices so far remain oblivious to. In this work, we introduce the problem of reconstructing a 3D hand skeleton from capacitive images, which encode the sparse observations captured by touch sensors. These low-resolution images represent intensity mappings that are proportional to the distance to the user's fingers and hands. We present the first dataset of capacitive images with corresponding depth maps and 3D hand pose coordinates, comprising 65,374 aligned records from 10 participants. We introduce our supervised method TouchPose, which learns a 3D hand model and a corresponding depth map using a cross-modal trained embedding from capacitive images in our dataset. We quantitatively evaluate TouchPose's accuracy in touch classification, depth estimation, and 3D joint reconstruction, showing that our model generalizes to hand poses it has never seen during training and can infer joints that lie outside the touch sensor's volume. Enabled by TouchPose, we demonstrate a series of interactive apps and novel interactions on multitouch devices. These applications show TouchPose's versatile capability to serve as a general-purpose model, operating independent of use-case, and establishing 3D hand pose as an integral part of the input dictionary for application designers and developers. We also release our dataset, code, and model to enable future work in this domain. © 2021 Owner/Author.

关键词： Tactile sensors

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