Reviewerly/exHarmony huggingface dataset:
Clustering Algorithm for Running Test Data Sequence of Computer Integrated System

This paper mainly studies the clustering algorithm classification of computer integrated system running test data sequence. For the test system that requires a faster acquisition rate, the oscilloscope has the advantages of acquisition frequency bandwidth, high sensitivity, and strong anti-interference. The data is collected through an oscilloscope with a GPIB interface, and then the OPTICS density clustering algorithm is used to test the data generated by the user. The sequence SQL operation log is clustered, and the abnormal behavior of users is extracted by analyzing each attribute in the SQL statement to form prior knowledge; Bagging, Boosting and Stacking are combined to form an integrated learning model, and related experimental data are used for testing the results show that this scheme can detect abnormal database behaviors with higher efficiency, and is better than similar schemes in terms of accuracy.
Performance Analysis and Optimization Techniques in Unity 3D

Unity3d is a powerful toolset that can be used to build 3D interactive applications and technology like games, simulations, augmented reality, immersive reality, etc. This game engine can execute the graphics, simulate physics, play audio, provide interactions, facilitate photorealistic rendering, and is excellent for cross-platform development. Although everything that users perceive is super-optimized, still an application suffers from poor performance, because of inefficient or poorly organized code, disorganized memory management, unnecessary use of overly complex physics simulations, etc. Poor performance of the application can break the user experience. Another side of an application properly optimized provides a smooth and responsive experience. To provide a good experience to the users, advanced knowledge of engines is required. Even if each application required a unique approach for optimization, Still there are key techniques we can adopt in most situations. We are aiming at filling this gap, by evaluating the best techniques while performing tests and analyses with Unity3d tools. We started by explaining game engine architecture. Then we performed tests while comparing different techniques and came up with observations and findings. As Unity3d, aggressively contribute to developing innovative applications related with 3D computer graphics to promote and support industry and Manufacturing. This work is contribution to the Unity3D community to develop good performance applications to provide a good user experience.
Fraud Detection and Management for Telecommunication Systems using Artificial Intelligence (AI)

Fraud is on the rise worldwide, which can cost businesses billions of dollars and cause significant financial damage. Researchers from different fields of application have proposed different approaches. Investigating these ideas will enable us to view the issues more clearly. This paper objective is to examine numerous directions of fraud detection and prevention in the communications sector. This paper provides an overview of the different classifications of telecom fraud, issues that impede the process of detection, and a few solutions suggested to overcome it. The performance of the current approaches is reported at, followed by recommendations and recommendations for picking the best fit performance metrics.
Heart Disease Prediction using an optimized Extreme Learning Machine with Bacterial Colony optimization

Heart disease (HD) is often regarded most complex and life-threatening human infection. Recently, the extreme learning machine (ELM) method is used to handle a variety of health disease detection and prediction problems. The ELM algorithm, on the other hand, determines the input weights and biases at random. As a result, the random selection of connection biases and weight provides an unexpected result, resulting in an error and lower accuracy. In this paper, bacterial colony optimization (BCO) uses to optimize the weights and bias to address the above-mentioned drawbacks of ELM. Three separate metrics are used to calculate the performance of the BCO+ELM which is applied to heart disease prediction. The experimental effects show that the BCO+ELM produces better results when compared to other methods.
Detection and Tracking the Criminal Activity using Network of CCTV cameras

In recent years, the number of troublesome people with provocative behaviour has been increased. This necessitates the need to increase the public security. CCTV cameras are gradually being deployed on streets, banks, and other areas to protect people’s safety in public areas like shopping malls. Since it is practically impossible to continuously monitor the security cameras, the crowd monitoring system must be computerized with high accuracy. Moreover, if a criminal is running, it is difficult to detect and track down by only using the capture CCTV image is a time-consuming process. To solve these challenges, the proposed system will scan real-time CCTV surveillance footage and use criminal reference information to identify the criminals. To recognize the uniqueness, it is highly required to precisely automate this activity. It is also necessary to show which frames and sections of the recording contain the unexpected behaviour.
Learning Dual-view User Representations for Enhanced Sequential Recommendation

Sequential recommendation (SR) aims to predict a user’s next interacted item given his/her historical interactions. Most existing sequential recommendation systems model user preferences only with item-level representations, where a user’s interaction sequence are often modeled with sequential or graph-based method to infer the user’s sequential interaction pattern. However, since a user’s preference factors may vary over time, the user modeling on item-level could hardly represent the user’s preference precisely and sufficiently, resulting in suboptimal recommendation performance. In addition, the recommendation results based on the item-level user representations lack the interpretability of preference factors. To address these problems, we propose a novel SR model with dual-view user representations in this paper, namely DUVRec, where a user’s preference is learned based on the representations of two distinct views, i.e., item view and factor view . Specifically, the item-view user representation is learned as the previous SR models to encode the user preference of item level, while the factor-view user representation is learned by an coarse-grained graph embedding method to explicitly represent the user in terms of preference factors. As a result, such dual-view user representations are more comprehensive than that in the previous SR models, leading to enhanced SR performance. Furthermore, we design a contrastive learning strategy to achieve mutual complementation between these two views. Our extensive experiments upon three benchmark datasets justify DUVRec’s superior performance over the state-of-the-art SR models, including the advantage of the dual-view contrastive learning. In addition, DUVRec’s capability of providing explanations on recommendation results is also demonstrated through some specific case studies.
Automated Generation of Security Assertions for RTL Models

System-on-Chip (SoC) security is vital in designing trustworthy systems. Detecting and fixing a vulnerability in the early stages is easier and cost-effective. Assertion-based verification is widely used for functional validation of Register-Transfer Level (RTL) designs. Assertions can improve the controllability and observability that can lead to faster error detection and localization. Although assertions are widely used for functional validation of RTL models, there is limited effort in applying assertions to detect SoC security vulnerabilities. Specifically, a fundamental challenge in SoC security and trust validation is how to develop high-quality security assertions. In this article, we perform automated vulnerability analysis of RTL models to generate security assertions for six classes of vulnerabilities. Experimental results show that the generated security assertions can detect a wide variety of vulnerabilities. Our automated framework can drastically reduce the overall security validation effort compared to the manual development of security assertions. Automated generation of security assertions will enable assertion-based verification to be one of the most promising pre-silicon security sign-off solutions.
Explanatory machine learning for justified trust in human-AI collaboration: Experiments on file deletion recommendations

In the digital age, saving and accumulating large amounts of digital data is a common phenomenon. However, saving does not only consume energy, but may also cause information overload and prevent people from staying focused and working effectively. We present and systematically examine an explanatory AI system (Dare2Del), which supports individuals to delete irrelevant digital objects. To give recommendations for the optimization of related human-computer interactions, we vary different design features (explanations, familiarity, verifiability) within and across three experiments (N1 = 61, N2 = 33, N3= 73). Moreover, building on the concept of distributed cognition, we check possible cross-connections between external (digital) and internal (human) memory. Specifically, we examine whether deleting external files also contributes to human forgetting of the related mental representations. Multilevel modeling results show the importance of presenting explanations for the acceptance of deleting suggestions in all three experiments, but also point to the need of their verifiability to generate trust in the system. However, we did not find clear evidence that deleting computer files contributes to human forgetting of the related memories. Based on our findings, we provide basic recommendations for the design of AI systems that can help to reduce the burden on people and the digital environment, and suggest directions for future research.
Assessing Advances in Real Noise Image Denoisers

Recently image denoiser networks have made a number of advances to go beyond additive Gaussian white noise and deal with real noise, such as produced by digital cameras. We note that some of the performance gains reported in the state of the art could potentially be explained by an increase of network sizes. In this paper we propose to revisit some of these advances, including the synthetic noise generator and noise maps proposed in CBDNet, and re-assess them using a simple DnCNN baseline network and thus attempt at measuring how much gains can be attributed to using more modern architectures. In this work, we observe an increase of over +2 dB in denoising performance over our baseline network on the DND real world benchmark. Through this observation, we demonstrate that a smaller networks can offer competitive denoising results when correctly optimised for real world denoising.
Visual focus groups: Stimulating reflexive conversations with collective drawing

In this methodological article, I introduce a qualitative research method, called the visual focus group (VFG), which incorporates a collective drawing task within the structure of a focus group. The VFG was specifically developed to support engaged research about how activists conceptualize the political role of technology, by stimulating participants to reflect on their unspoken assumptions about digital technologies. After reviewing the relevant literature on focus groups and graphic elicitation techniques, the article presents two types of VFGs: diagnostic and speculative. While diagnostic VFGs are primarily a research tool meant to enable researchers to assess how people envision technology, speculative VFGs encourage participants to imagine better digital technologies. I describe the structure of both types of VFGs and offer examples of their outputs; I then discuss the limitations of this method and propose other research topics for which it might be used.
Fast intensive crowd counting model of Internet of Things based on multi‐scale attention mechanism

Abstract Object detection based on deep learning plays an important role in the application of the Internet of Things (IoT). Traditional methods consume a lot of computing resources and cannot be well deployed in the IoT environment. A lightweight object detection method based on attention mechanism is proposed and applied to crowd counting. In view of the low accuracy and poor real‐time performance of multi‐scale crowd detection, we design a crowd counting model based on YOLO v5, and apply it to the IoT environment. It is proposed to insert the transformer into the YOLO v5 backbone network. Based on the multi‐head attention mechanism in the transformer encoder, the global dependency is modelled to make full use of the context information. The CNN is used to realize the fusion of multi‐scale feature maps, and the feature enhancement modules concerned by the attention network are further counted. Experiments show that it can not only detect multi‐scale targets, but also achieve real‐time performance in video surveillance scenes.
Compatibility of Arguments from Different Functional Groups in Scientific Texts

The study aims to determine the compatibility of arguments from different functional groups in a collection of scientific texts. The study is novel in that it develops a functional classification of argumentation schemes and identifies the features of using arguments from different functional groups in the collection of Russian-language scientific texts (it is the first time that such an analysis of functional compatibility of arguments has been carried out both for texts of the scientific genre and for texts in Russian). Based on a comparative analysis of the semantics of arguments and the functional features of their use, a classifi-cation of argumentation schemes has been developed differentiating four methods of proof (from authority, from practical value, through elaboration or causal analysis). The use of arguments from four groups has been investigated using a set of 1030 reasoning sequences extracted from expertly annotated scientific papers on linguistics and computer technology. It has been shown that the analysed papers are characterised by an active combination of arguments from different functional groups with their uneven positional arrangement in some sequences, depending on the emphasis in the proof. The work includes the following parts: argumentation modelling, a functional comparison of argumentation schemes, presentation of reasoning through functional blocks, a compatibility analysis of such arguments.
SOCRAT: A Dynamic Web Toolbox for Interactive Data Processing, Analysis and Visualization

Many systems for exploratory and visual data analytics require platform-dependent software installation, coding skills, and analytical expertise. The rapid advances in data-acquisition, web-based information, and communication and computation technologies promoted the explosive growth of online services and tools implementing novel solutions for interactive data exploration and visualization. However, web-based solutions for visual analytics remain scattered and relatively problem-specific. This leads to per-case re-implementations of common components, system architectures, and user interfaces, rather than focusing on innovation and building sophisticated applications for visual analytics. In this paper, we present the Statistics Online Computational Resource Analytical Toolbox (SOCRAT), a dynamic, flexible, and extensible web-based visual analytics framework. The SOCRAT platform is designed and implemented using multi-level modularity and declarative specifications. This enables easy integration of a number of components for data management, analysis, and visualization. SOCRAT benefits from the diverse landscape of existing in-browser solutions by combining them with flexible template modules into a unique, powerful, and feature-rich visual analytics toolbox. The platform integrates a number of independently developed tools for data import, display, storage, interactive visualization, statistical analysis, and machine learning. Various use cases demonstrate the unique features of SOCRAT for visual and statistical analysis of heterogeneous types of data.
Impact of the Digitalization Level on the Assessment of Virtual Reality in Higher Education

This paper conducts quantitative research on the assessment made by university professors of the didactic use of virtual reality technologies according to the level of technological development and digitization of their country. For this purpose, a survey was used, the responses to which were statistically analyzed, and the level of digitalization was differentiated through the country's global innovation index. The results show that the valuations of virtual reality as a teaching tool are high, but the competence for its use of university professors is intermediate. On the other hand, it was found that the higher the country's level of digital development, the more pronounced the gender gap in this study. Similarly, the higher the country's level of digital development, the smaller the age gap.
A Generic Preprocessing Architecture for Multi-Modal IoT Sensor Data in Artificial General Intelligence

A main barrier for autonomous and general learning systems is their inability to understand and adapt to new environments—that is, to apply previously learned abstract solutions to new problems. Supervised learning system functions such as classification require data labeling from an external source and do not have the ability to learn feature representation autonomously. This research details an unsupervised learning method for multi-modal feature detection and evaluation to be used for preprocessing in general learning systems. The learning method details a clustering algorithm that can be applied to any generic IoT sensor data, and a seeded stimulus labeling algorithm impacted and evolved by cross-modal input. The method is implemented and tested in two agents consuming audio and image data, each with varying innate stimulus criteria. Their run-time stimulus changes over time depending on their experiences, while newly experienced features become meaningful without preprogrammed labeling of distinct attributes. The architecture provides interfaces for higher-order cognitive processes to be built on top of the unsupervised preprocessor. This method is unsupervised and modular, in contrast to the highly constrained and pretrained learning systems that exist, making it extendable and well-disposed for use in artificial general intelligence.
3D-Guided Frontal Face Generation for Pose-Invariant Recognition

Although deep learning techniques have achieved extraordinary accuracy in recognizing human faces, the pose variances of images captured in real-world scenarios still hinder reliable model appliance. To mitigate this gap, we propose to recognize faces via generation frontal face images with a 3D -Guided Deep P ose- I nvariant Face Recognition M odel (3D-PIM) consisted of a simulator and a refiner module. The simulator employs a 3D Morphable Model (3D MM) to fit the shape and appearance features and recover primary frontal images with less training data. The refiner further enhances the image realism on both global facial structure and local details with adversarial training, while keeping the discriminative identity information consistent with original images. An Adaptive Weighting (AW) metric is then adopted to leverage the complimentary information from recovered frontal faces and original profile faces and to obtain credible similarity scores for recognition. Extended experiments verify the superiority of the proposed “recognition via generation” framework over state-of-the-art.
Code DARK: Leveraging the Human Firewall

<h2>Abstract</h2> Health care organizations can no longer afford to "bolt on" cybersecurity elements to projects and initiatives in an ad hoc style. To best prepare our health care system to address cyber risks, it's important to consider both the technical and human aspects of a robust cybersecurity program. Cybersecurity works best when it employs a "layered defense"—multiple layers of security built into the organizational infrastructure that provide additional coverage should a threat bypass a single layer of protection. Code DARK is a user-centered strategy that leverages the human firewall, empowering our first responders to be active participants in our cyber defense efforts.
Water hazard detection: A 20-year review

Obstacle detection is a complex task involving detection of obstacle features, identification of appropriate sensor and environmental conditions. The development of water hazard detection in the past two decades can be regarded as a microcosm of the history of typical obstacle detection. This review provides an extensive study of water hazard detection papers spanning a quarter-century (the 1990 s to 2021). The review mainly focuses on the width of water hazards, features of water hazards, sensor types for the detection of water hazards, and environmental light. This paper analyses and summarizes the research overview and status of some research institutions in the field over the past 20 years, hoping to provide some reference for UGV water hazard detection. In addition, the existing water hazard detection problems are summarized, and future development trends are proposed.
Transition-based semantic role labeling with pointer networks

Semantic role labeling (SRL) focuses on recognizing the predicate–argument structure of a sentence and plays a critical role in many natural language processing tasks such as machine translation and question answering. Practically all available methods do not perform full SRL, since they rely on pre-identified predicates, and most of them follow a pipeline strategy, using specific models for undertaking one or several SRL subtasks. In addition, previous approaches have a strong dependence on syntactic information to achieve state-of-the-art performance, despite being syntactic trees equally hard to produce. These simplifications and requirements make the majority of SRL systems impractical for real-world applications. In this article, we propose the first transition-based SRL approach that is capable of completely processing an input sentence in a single left-to-right pass, with neither leveraging syntactic information nor resorting to additional modules. Thanks to our implementation based on Pointer Networks, full SRL can be accurately and efficiently done in O(n2), achieving the best performance to date on the majority of languages from the CoNLL-2009 shared task.
Structure-aware Video Style Transfer with Map Art

Changing the style of an image/video while preserving its content is a crucial criterion to access a new neural style transfer algorithm. However, it is very challenging to transfer a new map art style to a certain video in which “content” comprises a map background and animation objects. In this article, we present a novel comprehensive system that solves the problems in transferring map art style in such video. Our system takes as input an arbitrary video, a map image, and an off-the-shelf map art image. It then generates an artistic video without damaging the functionality of the map and the consistency in details. To solve this challenge, we propose a novel network, Map Art Video Network (MAViNet), the tailored objective functions, and a rich training set with rich animation contents and different map structures. We have evaluated our method on various challenging cases and many comparisons with those of the related works. Our method substantially outperforms state-of-the-art methods in terms of visual quality and meets the mentioned criteria in this research domain.