Reviewerly/exHarmony huggingface dataset:
Single-board device individual authentication based on hardware performance and autoencoder transformer models

Authors:
raw_author_name: Pedro Miguel Sánchez Sánchez
raw_affiliation_string: Department of Information and Communications Engineering, University of Murcia, Murcia 30100, Spain
raw_author_name: Alberto Huertas Celdrán
raw_affiliation_string: Communication Systems Group (CSG), Department of Informatics (IfI), University of Zurich UZH, 8050 Zürich, Switzerland
raw_author_name: Gérôme Bovet
raw_affiliation_string: Cyber-Defence Campus, armasuisse Science & Technology, 3602 Thun, Switzerland
raw_author_name: Gregorio Martínez Pérez
raw_affiliation_string: Department of Information and Communications Engineering, University of Murcia, Murcia 30100, Spain

Abstract: The proliferation of the Internet of Things (IoT) has led to the emergence of crowdsensing applications, where a multitude of interconnected devices collaboratively collect and analyze data. Ensuring the authenticity and integrity of the data collected by these devices is crucial for reliable decision-making and maintaining trust in the system. Traditional authentication methods are often vulnerable to attacks or can be easily duplicated, posing challenges to securing crowdsensing applications. Besides, current solutions leveraging device behavior are mostly focused on device identification, which is a simpler task than authentication. To address these issues, an individual IoT device authentication framework based on hardware behavior fingerprinting and Transformer autoencoders is proposed in this work. To support the design, a threat model details the security problems faced when performing hardware-based authentication in IoT. This solution leverages the inherent imperfections and variations in IoT device hardware to differentiate between devices with identical specifications. By monitoring and analyzing the behavior of key hardware components, such as the CPU, GPU, RAM, and Storage on devices, unique fingerprints for each device are created. The performance samples are considered as time series data and used to train outlier detection transformer models, one per device and aiming to model its normal data distribution. Then, the framework is validated within a spectrum crowdsensing system leveraging Raspberry Pi devices. After a pool of experiments, the model from each device is able to individually authenticate it between the 45 devices employed for validation. An average True Positive Rate (TPR) of 0.74±0.13 and an average maximum False Positive Rate (FPR) of 0.06±0.09 demonstrate the effectiveness of this approach in enhancing authentication, security, and trust in crowdsensing applications.
A Thai-language chatbot analyzing mosquito-borne diseases using Jaccard similarity

Authors:
raw_author_name: Benjamin Chanakot
raw_affiliation_string: Rajamangala University of Technology Srivijaya
raw_author_name: Charun Sanrach
raw_affiliation_string: King Mongkut’s University of Technology North Bangkok

Abstract: The objective of this study is to create a Thai-language chatbot analyzing mosquito-borne diseases using Jaccard similarity with an aim to develop an artificial intelligence (AI)-based chatbot used to analyze Aedes-borne diseases through natural language processing. The analysis occurred when the symptoms provided by users on the chatbot were assessed to select relevant words as text attributes using the term frequency-inverse document frequency (TF-IDF) before the Jaccard similarity was used to measure the similarity of the information on the mosquito-borne disease database. The Line Messaging API was applied to facilitate communication between users and the chatbot through the Line application. The chatbot applied PHP 7.2.34 and MySQL 5.7.32 for database management, with Apache 2.2.29 serving as the bot server. The performance evaluation of the chatbot revealed that the chatbot accurately understood user intentions with an intent accuracy of 85.00%. Likewise, the usability of the chatbot was assessed using the system usability scale (SUS), and it received a score of 89.75, indicating a high level of user-friendliness. Furthermore, it has been found that appropriate tokenization enables accurate feature selection. This leads to improved accuracy in measuring Jaccard similarity. Consequently, the chatbot is capable of providing precise responses that align with the user's intent.
Learning feature contexts by transformer and CNN hybrid deep network for weakly supervised person search

Authors:
raw_author_name: Ning Lv
raw_affiliation_string: School of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
raw_author_name: Xuezhi Xiang
raw_affiliation_string: Key Laboratory of Advanced Marine Communication and Information Technology, Harbin 150001, China; School of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
raw_author_name: Xinyao Wang
raw_affiliation_string: School of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
raw_author_name: Yulong Qiao
raw_affiliation_string: Key Laboratory of Advanced Marine Communication and Information Technology, Harbin 150001, China; School of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
raw_author_name: Abdulmotaleb El Saddik
raw_affiliation_string: School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada

Abstract: Person search is a computer vision task that aims to locate and re-identify specific pedestrians in images captured by non-overlapping cameras. However, the identity annotation in person search is labor-intensive, especially as the amount of data increases. Therefore, more and more studies consider training person search models using weakly supervised learning with only location annotations. The context information is useful to improve feature representations in the absence of pedestrian identity as supervision. Existing weakly supervised person search methods focus on logic-driven contexts while ignoring feature contexts. In this paper, we propose a hybrid deep network for weakly supervised person search. The hybrid architecture consists of a Transformer-based feature extraction network and a fully convolution-based region recognition head network. The purpose is to enable the model to learn feature contexts at different levels. In our network, hierarchical vision Transformers are used to extract features in order to obtain discriminative representations of scene images. The context-enhanced head network is designed to integrate different features for candidate pedestrians. In addition, a pedestrian proposal network is proposed to improve the quality of predicted proposals. Experiments are conducted on the CUHK-SYSU and the PRW benchmarks to evaluate the effectiveness of the proposed method.
Systematic review on weapon detection in surveillance footage through deep learning

Authors:
raw_author_name: Hélder Oliveira
raw_affiliation_string: Faculty of Science, University of Porto, FCUP, Porto 4169-007, Portugal; Institute for Systems and Computer Engineering, Technology and Science, INESC TEC, 4200-465 Porto, Portugal
raw_author_name: António Cunha
raw_affiliation_string: Institute for Systems and Computer Engineering, Technology and Science, INESC TEC, 4200-465 Porto, Portugal; School of Science and Technology, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal

Abstract: In recent years, the number of crimes with weapons has grown on a large scale worldwide, mainly in locations where enforcement is lacking or possessing weapons is legal. It is necessary to combat this type of criminal activity to identify criminal behavior early and allow police and law enforcement agencies immediate action. Despite the human visual structure being highly evolved and able to process images quickly and accurately if an individual watches something very similar for a long time, there is a possibility of slowness and lack of attention. In addition, large surveillance systems with numerous equipment require a surveillance team, which increases the cost of operation. There are several solutions for automatic weapon detection based on computer vision; however, these have limited performance in challenging contexts. A systematic review of the current literature on deep learning-based weapon detection was conducted to identify the methods used, the main characteristics of the existing datasets, and the main problems in the area of automatic weapon detection. The most used models were the Faster R-CNN and the YOLO architecture. The use of realistic images and synthetic data showed improved performance. Several challenges were identified in weapon detection, such as poor lighting conditions and the difficulty of small weapon detection, the last being the most prominent. Finally, some future directions are outlined with a special focus on small weapon detection.
Determination of earthquake focal mechanism via multi-task learning

Authors:
raw_author_name: Pengyu Wang
raw_affiliation_string: Northeastern University, Software College, Institute of Network Science and Big Data Technology, Shenyang, China
raw_author_name: Tao Ren
raw_affiliation_string: Northeastern University, Software College, Institute of Network Science and Big Data Technology, Shenyang, China
raw_author_name: Hongfeng Chen
raw_affiliation_string: China Earthquake Networks Center, Beijing, China
raw_author_name: Xinliang Liu
raw_affiliation_string: Northeastern University, Software College, Institute of Network Science and Big Data Technology, Shenyang, China
raw_author_name: Fanchun Meng
raw_affiliation_string: Northeastern University, Software College, Institute of Network Science and Big Data Technology, Shenyang, China

Abstract: A multi-task learning-based focal mechanism network (MTFMN) is proposed for calculating parameters of the focal mechanism of earthquakes by regression with incorporating expert prior knowledge. The model automatically learns feature representations of seismic waveforms and transforms the inversion task of the focal mechanism into multi-task learning. Experimental results suggest that MTFMN outperforms traditional methods in the task of earthquake focal mechanism and improves the accuracy of parameter estimation of focal mechanism. In addition, comparative experiments demonstrate MTFMN's enhanced robustness and generalization capabilities compared to other methods. Our proposed methodology presents a more precise regression approach for focal mechanism inversion, with the potential to provide a better understanding of seismic events.
Multi-perspective thought navigation for source-free entity linking

Authors:
raw_author_name: Bohua Peng
raw_affiliation_string: Northwestern Polytechnical University, Xian, 710072, China; The University of Sheffield, Sheffield, S3 7HB, UK
raw_author_name: Wei He
raw_affiliation_string: The University of Sheffield, Sheffield, S3 7HB, UK
raw_author_name: Bin Chen
raw_affiliation_string: The University of Sheffield, Sheffield, S3 7HB, UK
raw_author_name: Aline Villavicencio
raw_affiliation_string: The University of Sheffield, Sheffield, S3 7HB, UK
raw_author_name: Chengfu Wu
raw_affiliation_string: Northwestern Polytechnical University, Xian, 710072, China

Abstract: Neural entity-linking models excel at bridging the lexical gap of multiple facets of facts, such as entity-related claims or evidence documents. Despite advancements in self-supervised learning and pretrained language models, challenges persist in entity linking, particularly in interpretability and transferability. Moreover, these models need many aligned documents to adapt to emerging entities, which may not be available due to data scarcity. In this work, we propose a novel Demonstrative Self-TrAining fRamework (D-STAR) that leverages multi-perspective thought navigation. D-STAR iteratively optimizes a question generator and an entity retriever by navigating thoughts on a dynamic graph reasoning across multiple perspectives for question generation. The generated question–answer pairs, along with hard negatives shared in the graph, enable adaptation with minimal computational overhead. Additionally, we introduce a new task, source-free entity linking, focusing on unsupervised transfer learning without direct access to original domain data. To demonstrate the feasibility of this task, we provide a generated question–answering dataset, FandomWiki, for novel entities. Our experiments show that D-STAR significantly improves baselines on SciFact, Zeshel, and FandomWiki.
EIGAN: An explicitly and implicitly feature-aligned GAN for degraded image classification

Authors:
raw_author_name: Jing Hu
raw_affiliation_string: National Key Laboratory of Science and Technology on Multi-Spectral Information Processing, Wuhan, China
raw_author_name: Meiqi Zhang
raw_affiliation_string: HuaZhong University of Science and Technology, Wuhan, China
raw_author_name: Susu Kang
raw_affiliation_string: HuaZhong University of Science and Technology, Wuhan, China

Abstract: The implementation of classification networks encounters a substantial decline in performance when subjected to degraded images due to factors such as blur, noise, and low resolution. Existing methods focus on addressing a specific kind of degraded images and thus cannot simultaneously adapt to multiple degradation scenarios. Besides, insufficient attention has been given to the causes of the decline. In this paper, we discuss about the reasons for the decrease and propose an Explicitly and Implicitly feature-aligned Generative Adversarial Network to guide the model to learn features that are more consistent with high-quality(HQ) images, named EIGAN. Initially, we introduce a feature matching loss to enable the model to focus on target regions as it does on high-quality images. Subsequently, we propose an adversarial loss intended to steer the model toward aligning with the feature distribution observed in high-quality images. As a result, our method demonstrates an enhancement in the classification accuracy of degraded images without introducing additional parameters. Extensive experiments across four types of degraded datasets indicate that as degradation intensifies, the advantages of our proposed method compared to other methods become notably more pronounced.
EffCause: Discover Dynamic Causal Relationships Efficiently from Time-Series

Authors:
raw_author_name: Yifan Zhang
raw_affiliation_string: Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
raw_author_name: Meng Ma
raw_affiliation_string: National Engineering Research Center for Software Engineering, Peking University, Beijing, China
raw_author_name: Ping Wang
raw_affiliation_string: National Engineering Research Center for Software Engineering, Peking University, Beijing, China

Abstract: Since the proposal of Granger causality, many researchers have followed the idea and developed extensions to the original algorithm. The classic Granger causality test aims to detect the existence of the static causal relationship. Notably, a fundamental assumption underlying most previous studies is the stationarity of causality, which requires the causality between variables to keep stable. However, this study argues that it is easy to break in real-world scenarios. Fortunately, our paper presents an essential observation: if we consider a sufficiently short window when discovering the rapidly changing causalities, they will keep approximately static and thus can be detected using the static way correctly. In light of this, we develop EffCause, bringing dynamics into classic Granger causality. Specifically, to efficiently examine the causalities on different sliding window lengths, we design two optimization schemes in EffCause and demonstrate the advantage of EffCause through extensive experiments on both simulated and real-world datasets. The results validate that EffCause achieves state-of-the-art accuracy in continuous causal discovery tasks while achieving faster computation. Case studies from cloud system failure analysis and traffic flow monitoring show that EffCause effectively helps us understand real-world time-series data and solve practical problems.
An Augmented Reality Hand-Eye Sensorimotor Impairment Assessment for Spaceflight Operations

Authors:
raw_author_name: Hannah Weiss
raw_author_name: Torin K. Clark
raw_author_name: Leia Stirling

Abstract: INTRODUCTION: Following a transition from microgravity to a gravity-rich environment (e.g., Earth, Moon, or Mars), astronauts experience sensorimotor impairment, primarily from a reinterpretation of vestibular cues, which can impact their ability to perform mission-critical tasks. To enable future exploration-class missions, the development of lightweight, space-conscious assessments for astronauts transitioning between gravity environments without expert assistance is needed. METHODS: We examined differences in performance during a two-dimensional (2D) hand–eye multidirectional tapping task, implemented in augmented reality in subjects ( N = 20) with and without the presence of a vestibular-dominated sensorimotor impairment paradigm: the binaural bipolar application of a pseudorandom galvanic vestibular stimulation (GVS) signal. Metrics associated with both the impairment paradigm and task performance were assessed. RESULTS: Medial-lateral sway during balance on an anterior-posterior sway-referenced platform with eyes closed was most affected by GVS (effect size: 1.2), in addition to anterior-posterior sway (effect size: 0.63) and the vestibular index (effect size: 0.65). During the augmented reality task, an increase in time to completion (effect size: 0.63), number of misses (effect size: 0.52), and head linear accelerations (effect size: 0.30) were found in the presence of the selected GVS waveform. DISCUSSION: Findings indicate that this multidirectional tapping task may detect emergent vestibular-dominated impairment (near landing day performance) in astronauts. Decrements in speed and accuracy indicate this impairment may hinder crews’ ability to acquire known target locations while in a static standing posture. The ability to track these decrements can support mission operations decisions. Allred AR, Weiss H, Clark TK, Stirling L. An augmented reality hand–eye sensorimotor impairment assessment for spaceflight operations . Aerosp Med Hum Perform. 2024; 95(2):69–78.
Mining significant local spatial association rules for multi-category point data

Authors:
raw_author_name: Fei Cai
raw_affiliation_string: College of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
raw_author_name: Jie Chen
raw_affiliation_string: Tong Yuan Design Group Co., Ltd, Jinan 250101, China
raw_author_name: Banghua Zhang
raw_affiliation_string: College of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
raw_author_name: Wenping Fan
raw_affiliation_string: College of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China

Abstract: Spatial association rule mining can reveal the inherent laws of spatial object interdependence and is an important part of spatial data mining. Most of the existing algorithms for mining local spatial association rules are oriented towards the spatial association between two categories of points and cannot fully reflect the spatial heterogeneity of complex spatial relations among multiple categories of points. In addition, the interactions between points in different categories are often asymmetrical. However, the existing algorithms ignore this asymmetry. To address the above problems, an algorithm for mining local spatial association rules for point data of multiple categories based on position quotients is proposed. First, the proximity relationship between points is determined by an adaptive filter, and the spatial weight value is given according to Gaussian kernel function. Then, the multivariate local colocation quotient of each point is calculated to measure the strength of the local regional spatial association rule. Finally, the Monte Carlo simulation function is used to generate a random sample distribution to test the significance of the results. The algorithm is verified on artificial simulation data and real Point of Interest (POI) data. The experimental results show that the algorithm can identify significant association regions of different spatial association rules for point sets.
Optimization-based trajectory planning for transport collaboration of heterogeneous systems

Authors:
raw_author_name: Jingshan Chen
raw_affiliation_string: Institute of Engineering and Computational Mechanics , University of Stuttgart , Pfaffenwaldring 9, 70569 Stuttgart , Germany
raw_author_name: Wei Luo
raw_affiliation_string: Institute of Engineering and Computational Mechanics , University of Stuttgart , Pfaffenwaldring 9, 70569 Stuttgart , Germany
raw_author_name: Henrik Ebel
raw_affiliation_string: Institute of Engineering and Computational Mechanics , University of Stuttgart , Pfaffenwaldring 9, 70569 Stuttgart , Germany
raw_author_name: Peter Eberhard
raw_affiliation_string: Institute of Engineering and Computational Mechanics , University of Stuttgart , Pfaffenwaldring 9, 70569 Stuttgart , Germany

Abstract: Abstract This paper describes an optimization-based trajectory planning scheme for handing over an object between a quadrotor and a wheeled robot in a transportation scenario. Concretely, a quadrotor should pick up an object from a moving ground mobile robot and deliver it to its destination. An optimization framework based on discrete mechanics and complementarity constraints is utilized here to jointly ensure dynamic feasibility and determine the position, timing, and coordination of the handover autonomously. Cooperative trajectories of the heterogeneous robot system can be generated simultaneously to satisfy different requirements by adjusting the objective function and constraints. The proposed planning scheme provides a novel paradigm combining trajectory planning and handover decision-making within an optimal control problem.
Physics bachelors’ outcomes

Authors:
raw_author_name: Susan C. White
raw_affiliation_string: American Institute of Physics, Statistical Research Center , College Park, MD 20740; swhite@aip.org

Abstract: Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Susan C. White; Physics bachelors’ outcomes. Phys. Teach. 1 February 2024; 62 (2): 149. https://doi.org/10.1119/5.0195446 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAmerican Association of Physics TeachersThe Physics Teacher Search Advanced Search |Citation Search