Reviewerly/exHarmony huggingface dataset:
Uses and Considerations for Cinematic Virtual Reality in Health Care

Through the combination of virtual reality (VR) technology with techniques from theater, filmmaking, and gaming, individuals from the Game Research and Immersive Design Laboratory (GRID Lab) at Ohio University have developed an approach to train soft skills such as communication, problem-solving, teamwork, and interpersonal skills which shows great promise.The purpose of this article is to provide an overview of VR and cinematic-VR (cine-VR). This article serves as a preface to the VR research included in this special issue.In this article, we define VR, review key terminology, present a case study, and offer future directions.Prior research with cine-VR has demonstrated the effectiveness in improving provider attitudes and cultural self-efficacy. While cine-VR may differ from other types of VR applications, we have been able to leverage the strengths of cine-VR to create training programs which are user friendly and highly effective. Early projects on diabetes care and opioid use disorder were sufficiently successful that the team received additional funding to pursue series addressing elder abuse/neglect and intimate partner violence. Their work has gone beyond health care and is currently being leveraged for law enforcement training as well. While this article will explore Ohio University's approach to cine-VR training, details of their research including efficacy can be found in McCalla et al, Wardian et al, and Beverly et al.When produced correctly, cine-VR has the potential to become a mainstay component of training for soft skill applications across a multitude of industries.
Hashes are not suitable to verify fixity of the public archived web

Web archives, such as the Internet Archive, preserve the web and allow access to prior states of web pages. We implicitly trust their versions of archived pages, but as their role moves from preserving curios of the past to facilitating present day adjudication, we are concerned with verifying the fixity of archived web pages, or mementos, to ensure they have always remained unaltered. A widely used technique in digital preservation to verify the fixity of an archived resource is to periodically compute a cryptographic hash value on a resource and then compare it with a previous hash value. If the hash values generated on the same resource are identical, then the fixity of the resource is verified. We tested this process by conducting a study on 16,627 mementos from 17 public web archives. We replayed and downloaded the mementos 39 times using a headless browser over a period of 442 days and generated a hash for each memento after each download, resulting in 39 hashes per memento. The hash is calculated by including not only the content of the base HTML of a memento but also all embedded resources, such as images and style sheets. We expected to always observe the same hash for a memento regardless of the number of downloads. However, our results indicate that 88.45% of mementos produce more than one unique hash value, and about 16% (or one in six) of those mementos always produce different hash values. We identify and quantify the types of changes that cause the same memento to produce different hashes. These results point to the need for defining an archive-aware hashing function, as conventional hashing functions are not suitable for replayed archived web pages.
3D Multi-Views Object Classification Based on a Fully Generalized Dirichlet Allocation Model

Comparing 3D objects based on their local features necessitates a substantial amount of computational resources. Recent studies have demonstrated that the combination of topic modeling and the Bag of Visual Words (BoVWs) approach can effectively capture useful and distinguishable object rep-resentations in a consistent way. One of the most common topic modeling approaches is LDA which is based on Dirichlet distribution priors, but it is limited by its incapacity in modeling topic correlations. Consequently, several extensions of LDA were proposed to solve this problem including GD-LDA, LGDA, and CVB-LGDA which have shown good results in discovering the semantic relationships between topics but are either suffering from incomplete generative processes assumptions that impact their inference efficiency or require high-computational power due to their complexity. In this paper, we introduce F-GDA, a fully Generalized Dirichlet Allocation model that is mainly derived from Generalized Dirichlet distributions for 3D objects recognition. Unlike GD-LDA which generalizes only the topics parameter, F-GDA generalizes all the model priors parameters, ensuring complete flexibility in the priors. Extensive experimental results have demonstrated the ability of our model to learn high-quality data representations of a real-world 3D Multi-views dataset ETH80 and also N15.
Application Research of Computer Artificial Intelligence Face Recognition Technology in Student File Management Information System

Based on artificial intelligence method, this paper proposes and designs a face recognition technology scheme of file management system. The system can quickly find the user's file information or store the file information into the file database. The system includes six modules: acquisition equipment, face recognition module, management server, archive database, comparison module and mobile terminal. This paper proposes a face recognition file management system architecture based on deep learning, and uses Kera’s deep learning framework and VGG16 network model to successfully realize the photo file face recognition experiment. At the same time, SVM multi-class classifier is used to find the optimal segmentation hyperplane between face samples, and the feature face is trained and classified. Two dimensional PCA feature extraction was performed on untrained faces. Then SVM multi-class classifier is used to identify and match the faces in the trained face library to determine the face identity. Finally, the experimental simulation shows that the face recognition system has faster recognition speed and higher accuracy.
Binary Code Vulnerability Location Identification with Fine-grained Slicing

Precise binary code vulnerability detection is an important research topic in software security. Currently, most software is released in binary form, which requires corresponding binary code vulnerability detection methods. Existing deep learning-based detection techniques can only detect binary code vulnerabilities but cannot accurately identify the location of vulnerabilities. Meanwhile, binary vulnerability detection is usually performed at the program or function level, which requires a large amount of markup data and cannot better focus on the vulnerable code. In order to mine the syntactic and semantic information of binary vulnerability code in a more fine-grained way, this paper proposes BVLIFS, a binary code vulnerability location identification system based on program slice. BVLIFS generates binary slices based on control dependencies and data dependencies of library/API function calls, and then uses hierarchical attention neural networks to extract the semantic information at the slice and basic block levels. After that, vulnerability locations are identified by combining the attention mechanism and vulnerability location matrix. We mark different codes with different colors depending on the level of contribution to vulnerability detection, which can help developers to locate and fix vulnerabilities. The experimental results show that our proposed method performs better and can locate vulnerabilities quickly compared to existing methods.
Comparative Analysis of China's Pilot Carbon Market Based on Knowledge Graph

In order to solve the problem of carbon market data integration and management, and to explore the reference significance of the information contained in different regional carbon markets to improve the national carbon market mechanism. In this paper, we propose a knowledge graph construction method in the carbon market domain, which is based on bidirectional encoder representation from transformers and conditional random field (BERT-CRF) and dependency parsing model for entity and relationship extraction. Taking Guangdong and Tianjin as examples, the knowledge graph of carbon market in each region is constructed, and the Neo4j database is used for storage and analysis. The experimental results show that this method can effectively construct a knowledge graph in the carbon market domain. Through visual analysis, it is found that each graph covers the main knowledge entities related to the entire carbon market, but there is also regional heterogeneity, that is, different carbon markets have their own unique information content. Specifically, the Guangdong carbon market focuses on local information, and its content is slightly monotonous; while the Tianjin carbon market focuses on synchronization with national and global information, and its coverage is more abundant.
Identification of Unsafe Behaviors of Electrician Based on Attention Path Clustering

Being able to reduce accidents is facilitated by the recognition of unsafe behaviors through computer vision. However, the recognition accuracy is affected by the large amount of temporal redundancy present in task videos. Therefore, a keyframe extraction method based on attention path clustering is proposed in this paper to eliminate temporal redundancy information in videos and utilize it for unsafe behavior recognition. The spatial geometric features of each saliency map are calculated using an attention point transfer model to quantify the video frame information. The spatial geometric features are then mapped to the polar coordinate system for clustering to obtain several frame clusters. Select the center frame of a different frame cluster as the keyframes. A behavior-safe video dataset was constructed, and the proposed method was validated by inputting the keyframe-reconstructed video into the behavior recognition method. The results indicate that the accuracy of behavior recognition can be improved by the keyframe extraction method based on attention path clustering, with an accuracy rate of 86.61%, compared to 80.42% with traditional methods.
Application of Knowledge Distillation in Generative Adversarial Networks

In the area of computer vision, image translation attracts more and more attention with the development of generative adversarial networks. However, deep generative adversarial networks(GANs) always consume much computation resource as well as deep classification models. Directly applying previous model compression methods will lead to poor performance in that image generation quietly differs from classification tasks and the training process of GANs is unstable. Our work uses knowledge distillation methods to solve the above problem which will shrink the size of the model and reduce the inference time while maintain the performance. First, with the help of knowledge distillation, we can transfer the knowledge from the teacher network to the student network which will improve the student performance. Second, we propose a small but effective strategy that can stabilize the process of training. Last but not least, our methods can be easily applied to other GANs such as GauGAN in that the design of our knowledge distillation methods only change the number of channels instead of changing the model. The results reveal the effectiveness of our proposed methods that will reduce the model size without losing generation quality.
Research on text sentiment analysis method based on knowledge enhancement

This paper suggests a sentiment analysis model based on knowledge enhancement to address the issues of inadequate mining of semantic information and incomplete extraction of deep-level features when traditional algorithms perform sentiment analysis on text. To obtain the word embedding representation of the text in this study, static word vectors GloVe and ELMo are used, followed by CNN and Self- attention, to obtain the contextual relationship features of the text. Afterwards, an attention mechanism is used to interact with the text and obtain the text’s deep semantic information. The experimental findings demonstrate that the model in this work is more accurate than other models, with a 51.6% accuracy on the SST dataset.
Image Super-Resolution Based on Residual Attention and Multi-Scale Feature Fusion

At present, deep residual network has been widely used in image super-resolution and proved to be able to achieve good reconstruction results. However, the existing super-resolution algorithms based on deep residual network have the problems of indiscriminately learning feature information of different regions and low utilization rate of feature information, which make them difficult to further improve the reconstruction effect. In view of the above problems, a novel super-resolution reconstruction network based on residual attention and multi-scale feature fusion (RAMF) is proposed in this paper. Firstly, a lightweight multi-scale residual module (LMRM) is proposed in the deep feature extraction stage, by which the multi-scale features are extracted and further cross-connected to enrich the information of different receptive fields. Then, to fully improve the utilization rate of feature information, a dense feature fusion structure is designed to fuse the output feature of each LMRM. Finally, a residual spatial attention module (RSAM) is proposed to specifically learn and better retain high-frequency feature information, so as to improve the reconstruction effect. Experimental tests and comparisons are conducted with the current advanced methods on four baseline databases, and the results demonstrate that the proposed RAMF can achieve better reconstruction effect with fewer parameters, low computational complexity, fast processing speed and high objective evaluation index. Especially, the peak signal-to-noise ratio measured on Urban100 data set increases by 0.13dB on average, and the reconstructed image has better visual effect and richer texture detail features.
Experimental Study on Impedance Characteristics of Trees under 10kV Voltage

The tree direct contact line is the fault type that has the greatest impact on the power system in the tree-line contradiction. At present, most of the existing studies are aimed at the monitoring and disposal of tree-line faults, but there are few studies on the impedance characteristics of trees when tree- line faults occur. Based on the 10k V simulated test line, this paper experimentally studies the change process, development trend and change characteristics of tree impedance when trees of different types, lengths and moisture contents have tree-line faults, as well as the influence of different system capacitive currents on fault characteristics. The experiment took three different species of pine, eucalyptus and bamboo as the test objects. The results showed that the impedance characteristics of the three different species of trees during tree line contact discharge were different, among which pine and eucalyptus had larger impedance and bamboo had smaller impedance. And the correlation between tree length and tree impedance is not strong. Under the premise of the same tree species, the impedance of the part with high moisture content is small, and the impedance of the part with low moisture content is large. In the time range of 0~ 20s, the impedance of trees decreases with the increase of discharge time.
Radar and Infra-Red array Sensor Fusion in a Robotized Environment: An Experimental Study

The paper describes a sensor fusion architecture and develops a detection/localization algorithm that fuses the sensor data obtained from several environmental sources, namely a network of 122 GHz frequency modulated continuous wave (FMCW) radar, a 100 GHz imaging camera, and a network of Infra-Red (IR) array sensors. The capabilities of the proposed system are addressed based on several measurements and use cases tailored for a robotized environment and human-robot fenceless cooperation scenarios in manufacturing. The paper proposes methods for verification of safety procedures, analyzes localization, human-robot distance estimation accuracy, and end-to-end latency introduced by fusion and machine learning algorithms. It also quantifies the interference/noise sources introduced by moving robots/machinery, for different collaborative setups. The proposed platform is shown to effectively exploit heterogeneous sensors and data fusion techniques to improve performance. Lessons learned from the experiments are also discussed as well as the relative strengths/limitations of the proposed system.
Attention Enhanced Multi-Scale Feature Map Fusion Few Shot Learning

Deep learning has made great contributions to the few-shot learning object detecting and counting. In this paper, we propose a novel network framework for few-shot learning in density object counting. Firstly, several exemplar image patches with different scales were convoluted with the feature map individually, then the results were fused to enhance the detection ability of multi-scale targets in the dataset. Secondly, the attention module was inserted into the density map blending procedure. The attention module can suppress the false response of the convolution and enhance the true response efficiently. Experiment results showed that the proposed method could improve the accuracy of the density map and adaption of multi-scale targets. It achieved a 15.3% improvement on average in the object counting task with 147 categories of objects. Code for this research can be found at https://github.com/1578630119/ASF-Few-Shot-Learning.
Bounds and Constructions of Parent Identifying Schemes via the Algorithmic Version of the Lovász Local Lemma

The usefulness of Identifiable Parent Property (IPP) schemes in diverse scenarios has led to several distinct but related concepts. This work focuses on three of these concepts: “classical” IPP codes, Multimedia IPP codes, and IPP set systems. Although several existence bounds for all of the above schemes are known, constructions are scarce. In this paper, we present explicit constructions of all mentioned IPP notions, in the form of combinatorial objects. Our discussion follows a systematic procedure. First, we use the Lovász Local Lemma (LLL) to obtain existence bounds for the object to be constructed. The bounds derived essentially match the previously best-known ones. Additionally, our proof strategy enables for further development. It allows us to use the Moser-Tardos algorithmic version of the LLL in order to construct, with polynomial complexity, the actual objects. Moreover, we extend the results of Giotis et al. to precisely establish the computational complexity of the proposed algorithms.
Cybersecurity in Lesotho: Current Challenges and Future Opportunities

The rapid growth of digital technologies has transformed the world, enabling new opportunities while also introducing new security risks. Lesotho, like many other countries, faces a range of cyber security challenges that threaten the integrity of its information systems, data, and critical infrastructure. This paper provides an overview of the current state of cybersecurity in Lesotho, including the challenges faced by the country and the opportunities for improvement. The paper reviews the regulatory framework for cybersecurity in Lesotho, the state of cybercrime and cyber threats, and the existing efforts to address these challenges. The paper also proposes a set of recommendations for enhancing cybersecurity in Lesotho.
Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation

Deep learning (DL) techniques are highly effective for defect detection from images. Training DL classification models, however, requires vast amounts of labeled data which is often expensive to collect. In many cases, not only the available training data is limited but may also imbalanced. In this paper, we propose a novel domain adaptation (DA) approach to address the problem of labeled training data scarcity for a target learning task by transferring knowledge gained from an existing source dataset used for a similar learning task. Our approach works for scenarios where the source dataset and the dataset available for the target learning task have same or different feature spaces. We combine our DA approach with an autoencoder-based data augmentation approach to address the problem of imbalanced target datasets. We evaluate our combined approach using image data for wafer defect prediction. The experiments show its superior performance against other algorithms when the number of labeled samples in the target dataset is significantly small and the target dataset is imbalanced.
High-level optimization of industrial big data of DCC device based on neural network

The use of industrial big data high-level optimization to diagnose and analyze DCC devices has been applied in the petrochemical production process, promoting the revolutionary breakthrough of production process control. Combined with the company’s idea of enhancing its core competitiveness through information technology, it then elaborates the existing data modeling methods, builds a software model, and tries to optimize its process big data and models through neural network algorithms to improve the potential of process control, so as to achieve the purpose of reducing costs and increasing efficiency. Finally, the characteristics of high-level optimization of industrial big data and the problems and challenges that may be faced in the process of realizing this are discussed, in order to achieve a breakthrough in the company’s overall business value.
Few-shot Image Classification Algorithm Based on Multi-scale Attention and Residual Network

Current few-shot image classification algorithm cannot sufficiently extract features, the generalization ability of the model is weak, and the classification accuracy is low. In response to this problem, a few-shot image classification algorithm was proposed with multi-scale attention and residual connection based on Relation Network in this paper. The introduction of multi-scale attention can extract more important image features, and the residual connection in the model can transfer the shallow feature information to the deep, thereby enhancing the generalization ability of the model. Compared with Relation Network, the image classification accuracy of our method was significantly improved on the MiniImageNet and Omniglot dataset. Experimental results show that the introduction of multi-scale attention and residual connection can effectively improve the accuracy of few-shot image classification.
Kalman Filter-Based Adversarial Patch Attack Defense for Autonomous Driving Multi-Target Tracking

The visual perception module in autonomous vehicles is based on deep learning technology to realize environmental perception, including object detection, semantic segmentation, depth estimation, etc. While deep neural networks have achieved state-of-the-art performance on various computer vision tasks, researchers have found that deep learning models drop significantly in accuracy when they add carefully crafted small perturbations to their inputs. Such an attack is called adversarial attack. The vulnerability of deep neural networks to adversarial attacks brings potential security risks to autonomous driving perception systems. As the first step in autonomous driving, any erroneous output from the perception module can lead to serious consequences. In the existing literature, there are few studies on the impact of the adversarial attack on the perception module on the subsequent modules and the corresponding coping strategies of the subsequent modules. In this paper we addresses this gap by investigating the impact of adversarial attacks on the object detection algorithm on the multi-object tracking module and we proposes a solution that uses the Kalman filter with packet loss compensation to mitigate the reduction of multi-object tracking accuracy under adversarial attack.