Reviewerly/exHarmony huggingface dataset:
Improved Multisignature Scheme for Authenticity of Digital Document in Digital Forensics Using Edward-Curve Digital Signature Algorithm

At the moment, digital documents are just as important as paper documents. As a result, authenticity is essential, especially in legal situations and digital forensics. As technology advances, these digital signature algorithms become weaker, necessitating the development of digital authentication schemes capable of withstanding current security threats. This study proposed a scheme based on an asymmetric key cryptosystem and the user’s biometric credentials to generate keys for digital signatures. A single document can be signed by multiple signatories at the same time under this scheme. The primary goal of this article is to create a safe and cost-effective multiignature scheme. To create keys for document signing and verification, the Edwards-curve Digital Signature Algorithm (EdDSA), especially Ed25519, is employed. The Edwards-curve Digital Signature Algorithm is used with blockchain technology to sign crypto wallets. The Python implementation of a scheme that enables platform independence. We performed performance, security, and comparative analysis to ensure maximum usability. The article’s main findings are that the Ed25519 algorithm can be used in blockchain.
Methodologies of building synergetic learning systems

In this paper, methodologies of building synergetic learning systems (SLS), which refer to artificial intelligence (AI) systems, are presented. From our viewpoint, an AI system might not only be constructed with deep learning but also with multi-discipline knowledge. To build the SLS, we need to develop the methodologies which should draw on the mathematical methods of neurocognitive mechanisms and machine learning, knowledge across a wide range of disciplines including cognitive neuroscience, physics, psychology, medicine, automation, computer science, life science, systems science and social science, and so on. It is dependent on the integration of statistical physics and the systematic view of complexity science. Our methodologies of building SLS are investigated systematically and analyzed on multi-scale, multi-level and multi-perspective. With our proposed methodologies, an AI system can be constructed with the properties of interpretability, extensibility and evolvability.
Improve the Robot Path Planning Based on the Integration of A* and DWA

ABSTARCT: Aiming at the problems of poor search efficiency, unsmooth path and real-time obstacle avoidance of traditional A* algorithm, a robot path planning algorithm integrating improved A* algorithm and DWA algorithm is proposed. Firstly, the traditional two-way A* algorithm is used to optimize the evaluation function. Secondly, the search strategy is simplified to reduce the number of search nodes and improve the search efficiency. Then, the unnecessary inflection points on the optimal path are deleted through the redundant point elimination strategy for three times. Finally, the local planning between adjacent two points is combined with DWA algorithm to solve the problems of path unsmooth and real-time obstacle avoidance. The experimental results show that the improved A* algorithm has a great improvement in search efficiency compared with the traditional A* algorithm. By integrating DWA algorithm, the smoothness of the path can be increased and the real-time obstacle avoidance of unknown obstacles can be realized
Dense crowd detection algorithm for YOLOv5 based on Coordinate attention mechanism

A dense crowd detection algorithm for YOLOv5 based on CA (Coordinate attention) mechanism is proposed for the problem of low recognition accuracy and high miss detection rate caused by many targets and serious occlusion in dense crowd detection. Firstly, the collected images are processed for data enhancement; secondly, the depth separable convolution be used instead of normal convolution of backbone network. It effectively reduces the complexity and number of participants of the model, while CA (Coordinate attention) attention mechanism with location information is used to obtain the effective feature layer image width and height for effective feature fusion so that the model can more accurately. Finally, the GIoU loss is replaced by the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{SIoU}$</tex> loss function towards raise training speed and accuracy of inference. Experimental results show that compared to traditional YOLOv5 network, the average accuracy AP of the improved network model is improved by 3.9%, effectively improving the recognition accuracy of dense crowds.
Efficient-ViT: A Light-Weight Classification Model Based on CNN and ViT

In view of the following problems of the Vision Transformer (ViT) model: a large number of parameters, the lack of global modeling ability and sensitivity to data enhancement. Inspired by MobileViT, based on Convolutional Neural Networks (CNN) and Vision Transformer (ViT), propose a light-weight classification model: Efficient-ViT. By introducing the following modules: Squeeze-and-Excitation Block (SE-Block), Overlapping Patch Embedding (OPE), Linear Spatial Reduction Attention (Linear SRA). While keeping the model compact, the local and global information of the input feature map can be effectively encoded and integrated. The local information of the feature map is processed by CNN, while the global information is processed by ViT, and then simply fused the captured information. The proposed model has both the inductive bias ability of the CNN model and the good properties of the global modeling of the ViT model, and can learn better feature representations. Classification experiments swere carried out on three datasets such as CIFAR10, CIFAR100, and Stanford Cars. The experimental results show that the proposed method achieves better results, and the accuracy of Top1 is improved by 3.32% (86.55% to 89.87%).
Implementing Eye Tracking Technology in Experimental Design Studies in Food and Beverage Advertising

This chapter focuses on the use of eye-tracking technology in experimental studies addressing advertising effectiveness in the food and beverage sector. Experimental design prevails in advertising research, and it significantly contributes to the disclosure of the factors and conditions that direct consumers’ attitudes and behaviors. According to most hierarchical models (i.e., A.I.D.A) in advertising, attention is a fundamental variable for any subsequent cognitive processing that shapes consumers’ attitudes and behaviors. Traditionally, attention is recorded through self-report measures, such as recall and recognition. However, the increasingly popular technology on tracking and neuroimaging enables researchers and companies to accurately and objectively detect consumers’ attention. Taking a head start among these approaches, eye tracking technology records eye movement and fixations, thus detecting specific areas of (consumers’) interest (AOI’s) in advertising. The present chapter aims to provide a useful guide for both academics and practitioners wishing to engage in eye-tracking experimental studies. It further provides clear guidance on the settings of the experimental process.
A Systematic Literature Review on Volatility Memory Forensics

Memory forensics is a valuable tool for investigating digital crimes. Memory forensics can provide investigators with critical information about what happened on a computer during an incident, even when other evidence has been destroyed or removed. In addition, memory forensics is non-destructive and can be used to supplement other forensic techniques. This review aims to provide an overview of the recent developments in memory forensics, focussing on tools and techniques used in operating systems and memory analysis.
Black-Box Dataset Ownership Verification via Backdoor Watermarking

Deep learning, especially deep neural networks (DNNs), has been widely and successfully adopted in many critical applications for its high effectiveness and efficiency. The rapid development of DNNs has benefited from the existence of some high-quality datasets ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., ImageNet), which allow researchers and developers to easily verify the performance of their methods. Currently, almost all existing released datasets require that they can only be adopted for academic or educational purposes rather than commercial purposes without permission. However, there is still no good way to ensure that. In this paper, we formulate the protection of released datasets as verifying whether they are adopted for training a (suspicious) third-party model, where defenders can only query the model while having no information about its parameters and training details. Based on this formulation, we propose to embed external patterns via backdoor watermarking for the ownership verification to protect them. Our method contains two main parts, including dataset watermarking and dataset verification. Specifically, we exploit poison-only backdoor attacks ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., BadNets) for dataset watermarking and design a hypothesis-test-guided method for dataset verification. We also provide some theoretical analyses of our methods. Experiments on multiple benchmark datasets of different tasks are conducted, which verify the effectiveness of our method. The code for reproducing main experiments is available at https://github.com/THUYimingLi/DVBW.
Research proposal content extraction using natural language processing and semi-supervised clustering: A demonstration and comparative analysis

Funding institutions often solicit text-based research proposals to evaluate potential recipients. Leveraging the information contained in these documents could help institutions understand the supply of research within their domain. In this work, an end-to-end methodology for semi-supervised document clustering is introduced to partially automate classification of research proposals based on thematic areas of interest. The methodology consists of three stages: (1) manual annotation of a document sample; (2) semi-supervised clustering of documents; (3) evaluation of cluster results using quantitative metrics and qualitative ratings (coherence, relevance, distinctiveness) by experts. The methodology is described in detail to encourage replication and is demonstrated on a real-world data set. This demonstration sought to categorize proposals submitted to the US Army Telemedicine and Advanced Technology Research Center (TATRC) related to technological innovations in military medicine. A comparative analysis of method features was performed, including unsupervised vs. semi-supervised clustering, several document vectorization techniques, and several cluster result selection strategies. Outcomes suggest that pretrained Bidirectional Encoder Representations from Transformers (BERT) embeddings were better suited for the task than older text embedding techniques. When comparing expert ratings between algorithms, semi-supervised clustering produced coherence ratings ~ 25% better on average compared to standard unsupervised clustering with negligible differences in cluster distinctiveness. Last, it was shown that a cluster result selection strategy that balances internal and external validity produced ideal results. With further refinement, this methodological framework shows promise as a useful analytical tool for institutions to unlock hidden insights from untapped archives and similar administrative document repositories.The online version contains supplementary material available at 10.1007/s11192-023-04689-3.
Chatbot-based training on logical fallacy in EFL argumentative writing

ABSTRACTABSTRACTEFL learners generally have the problem of logical fallacies in EFL argumentative writings. Logical fallacies are errors in reasoning that can undermine EFL argumentative writing quality. Explicit training on logical fallacies may help learners deal with the problem and enhance their self-efficacy and proficiency in EFL argumentative writing, which educational chatbots may further enhance. However, few studies have been conducted in this direction. To fill the gap, we developed a chatbot for out-of-class, self-regulated training on logical fallacies in EFL argumentative writing. Fifteen Chinese EFL undergraduate and graduate students engaged in the training for five weeks. Semi-structured interviews and transcript analysis were conducted to investigate the participants' perceived impacts of the training on self-efficacy and proficiency in EFL argumentative writing. To triangulate the interview results, we conducted pre–post questionnaires on the participants' writing self-efficacy and analysed their pre–post argumentative writings based on the Illinois Critical Thinking Essay Scoring Rubric. The results showed that the training on logical fallacies might improve EFL argumentative writing proficiency, although it might reduce writing self-efficacy. Based on the results, we called for more implementation and investigation of chatbot-based training on logical fallacies, especially the long-term training integrated with practice in writing tasks.KEYWORDS: ChatbotEnglish writinglogical fallacysecond language writingtechnology-enhanced language learning Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsRuofei ZhangRuofei Zhang is a PhD student at the Education University of Hong Kong. Her research interests include technology-enhanced language learning, game-based language learning, and self-regulated language learning.Di ZouDr. Di Zou is an Assistant Professor at The Education University of Hong Kong. Her research interests include second language acquisition, technology-enhanced language learning, game-based language learning, and AI in language education. She has published over 150 research papers in international journals, conferences and books, including Computers & Education, Computer Assisted Language Learning, Language Teaching Research, and British Journal of Educational Technology.Gary ChengDr. Gary Cheng is an Associate Professor of the Department of Mathematics and Information Technology at The Education University of Hong Kong (EdUHK). He has been involved in research projects funded by EdUHK and the Research Grant Council of Hong Kong on a range of topics mainly related to technology enhanced learning. His research interests include Information Technology Supported L2 Learning, ePortfolio-mediated Learning, Computer Programming Education, Online Learning Management System, e-Assessment, and Learning Analytics.
EdgeAuth: An intelligent token‐based collaborative authentication scheme

Abstract Edge computing is regarded as an extension of cloud computing that brings computing and storage resources to the network edge. For some Industrial Internet of Things (IIoT) applications such as supply‐chain supervision and collaboration, Internet of Vehicles, real‐time video analysis and so forth, users should be authenticated before visiting the geographically distributed edge servers. Limited by the considerable latency between the cloud and edge servers, and the limited capacity of edge servers, it is infeasible to copy the authentication method from cloud servers when users are authenticated in edge servers. In view of this challenge, this paper proposes a novel token‐based authentication scheme, named EdgeAuth, that enables fast edge user authentication through collaboration among cloud servers and edge servers. Under the EdgeAuth scheme, edge servers can rapidly verify the credentials of users who have been authenticated by the cloud server. EdgeAuth can also protect users from a series of authentication attacks, for example, the replay attack, DoS attack and man‐in‐the‐middle attack. The results of experiments conducted on a simulated edge computing environment validate the usefulness of EdgeAuth through a comparison in latency and throughput against two baseline schemes.
Analyzing and comparing the security of self-sovereign identity management systems through threat modeling

Abstract The concept of Self-Sovereign Identity (SSI) promises to strengthen the security and user-centricity of identity management. Since any secure online service relies on secure identity management, we comparatively analyze the intrinsic security of SSI. Thus, we adopt a hybrid threat modeling approach comprising STRIDE, attack trees, and ratings towards this unique context. Data flow diagrams of the isolated, centralized and the SSI model serve as the foundation for the assessment. The evolution of the paradigms shows an increasing complexity in security zones and communication paths between the components. We identified 35 threats to all SSI components and 15 protection measures that reduce the threats’ criticality. As a result, our research shows that the SSI paradigm’s threat surface is significantly higher compared to the traditional models. Besides the threat assessment on model level, the adapted methodology can evaluate a specific implementation. We analyzed uPort with a restricted scope to its user agent. Thus, 2 out of 10 threats were not properly addressed, leading to potential spoofing, denial, or repudiation of identity actions.
Artificial intelligence facial recognition and voice anomaly detection in the application of English MOOC teaching system

As a direct and effective biometric technology that follows human life habits, facial recognition has gradually become a mainstream, stable and reliable recognition method in the process of further development of science and technology. Facial recognition is a kind of biometric authentication based on recognition technology that is an original biological characteristic. After collecting the biometric functions, use a computer for digital image processing and template matching to complete the process of facial recognition. In the face of MOOC, establish a high-quality resource sharing mechanism, use this opportunity to explore innovative education models, and truly feel the strong momentum of traditional Chinese higher education, which has greatly improved the quality of education in Chinese universities. In addition, in order to vigorously promote the internationalization of education in China, education scholars need to conduct a lot of in-depth research on MOOC. In this article, a demand-based testing method is used to establish a skin color distribution model for color image preprocessing, and then develop and construct according to the comprehensive analysis of the university English class in the MOOC platform implementation mechanism (including the establishment of basic principles of operation mechanism), thereby become a guarantee and support for curriculum and education quality evaluation system. This article combines the characteristics of educational practice and MOOC education, takes college English courses as an example, studies its application mechanism, builds a MOOC platform, and continuously enhances students' interest in learning English, aiming to provide a practical reference for the reform of Chinese college English education.
CNNParted: An open source framework for efficient Convolutional Neural Network inference partitioning in embedded systems

Applications such as autonomous driving or assistive robotics heavily rely on the usage of Deep Neural Networks. In particular, Convolutional Neural Networks (CNNs) provide precise and reliable results in image processing tasks like camera-based object detection or semantic segmentation. However, to achieve even better results, CNNs are becoming more and more complex. Deploying these networks in distributed embedded systems thereby imposes new challenges, due to additional constraints regarding performance and energy consumption in the near-sensor compute platforms, i.e. the sensor nodes. Processing all data in the central node, however, is disadvantageous since raw data of camera consumes large bandwidth and running CNN inference of multiple tasks requires certain performance. Moreover, sending raw data over the interconnect is not advisable for privacy reasons. Hence, offloading CNN workload to the sensor nodes in the system can lead to reduced traffic on the link and a higher level of data security. However, due to the limited hardware-resources on the sensor nodes, partitioning CNNs has to be done carefully to meet overall latency requirements and energy constraints. Therefore, we present CNNParted, an open-source framework for efficient, hardware-aware CNN inference partitioning targeting embedded AI applications. It automatically searches for potential partitioning points in the CNN to find a beneficial workload distribution between sensor nodes and a central edge node. Thereby, CNNParted not only analyzes the CNN architecture but also takes hardware components, such as dedicated hardware accelerators and memories, into consideration to evaluate inference partitioning regarding latency and energy consumption. Exemplary, we apply CNNParted to three commonly used feed forward CNNs in embedded systems. Thereby, the framework first searches for several potential partitioning points and then evaluates the latter regarding inference latency and energy consumption. Based on the results, beneficial partitioning points can be identified depending on the system constraints. Using the framework, we are able to find and evaluate 10 potential partitioning points for FCN ResNet-50, 13 partitioning points for GoogLeNet, and 8 partitioning points for SqueezeNet V1.1 within 520 s, 330 s, and 140 s, respectively, on an AMD EPYC 7702P running 8 concurrent threads. For GoogLeNet, we determine two partitioning points that provide a good trade-off between required bandwidth, latency and energy consumption. We also provide insights into further interesting findings that can be derived from the evaluation results.
TOD-Net: An end-to-end transformer-based object detection network

Various object detection approaches using a learning model intends to learn the semantic and multi-scaling information to attain superior object saliency. This research employs a transformer-based network framework for object detection (TOD − Net) for object detection. It is composed of encoders, decoders, and transformer and predictor module. The predictor model bridges the connectivity between the encoder and the transformer module and offers better insight into the transformer module's local measures. Here, feature extraction is performed to measure the local features and establishes dense modeling by analyzing local features. The model gives broader knowledge of local and global features. Python programming was used to experiment with the MS COCO dataset (Microsoft Common Objects in Context) where the experimentation gives better results over existing models. In contrast to existing methods, the proposed method achieves 68.7% precision and 4% accuracy. The proposed model outperforms different prevailing approaches and establishes a better trade-off.