Reviewerly/exHarmony huggingface dataset:
Robust Template Matching Using Multiple-Layered Absent Color Indexing

Color is an essential feature in histogram-based matching. This can be extracted as statistical data during the comparison process. Although the applicability of color features in histogram-based techniques has been proven, position information is lacking during the matching process. We present a conceptually simple and effective method called multiple-layered absent color indexing (ABC-ML) for template matching. Apparent and absent color histograms are obtained from the original color histogram, where the absent colors belong to low-frequency or vacant bins. To determine the color range of compared images, we propose a total color space (TCS) that can determine the operating range of the histogram bins. Furthermore, we invert the absent colors to obtain the properties of these colors using threshold hT. Then, we compute the similarity using the intersection. A multiple-layered structure is proposed against the shift issue in histogram-based approaches. Each layer is constructed using the isotonic principle. Thus, absent color indexing and multiple-layered structure are combined to solve the precision problem. Our experiments on real-world images and open data demonstrated that they have produced state-of-the-art results. Moreover, they retained the histogram merits of robustness in cases of deformation and scaling.
An Effective Approach of IIoT for Anomaly Detection Using Unsupervised Machine Learning Approach

The Industrial Internet of Things (IIoT) has the potential to boost the growth of industrial intelligence, increase production effectiveness, and lower manufacturing costs. The automatic monitoring and identification of anomalous events, changes, and drifts on the acquired data constitutes one of the Industrial IoT's primary objectives. All methods for identifying data patterns that differ from expected behaviour are categorised as anomaly detection methods. Accurately and promptly detecting anomalies is becoming more and more crucial since Industrial IoT device failures have a significant impact on the production of industrial goods. Also, anomalies which are needed to be identified could be used for better data analysis. So to address these problems, in this paper work is carried out using unsupervised learning with clustering approach in which sensors data from a prototype embedded system are used for findings anomaly in the data and the concept is to recognise clusters for interpretation of sensed data under different working circumstances to evaluate if new inspections fall in to any of these clusters. The approach is to find faulty or normal working conditions based on the anomaly findings methods applied to sensor data for analysis. There are three methods used in this proposed approach include fuzzy c-means-k-means clustering, and density-based clustering. The complete approach is found to be favourable for anomaly finding and situation control using cluster-based methods for industry related equipment’s systems rely on sensor data. The experimental results indicates that proposed method is accurate and effective based on sensor for different operating conditions of industrial scenario and assist in predictive maintenance.
Problems and Countermeasures of Coal Mine Engineering Management under the Background of Big Data and IoT

In the context of the era of big data and the Internet of Things, there are certain engineering management problems in the development of my country’s coal mine enterprise engineering management. Based on the research content of domestic coal enterprises in the construction and engineering management of production and engineering, comprehensive research and analysis of problems at different stages of different stages, optimizing the improvement of coal mine engineering management levels, were done. In the process of various factors that affect the safety of coal mine safety production and the high-quality development of enterprises, this article focuses on the problems and hidden dangers affecting the management of coal mine engineering management and focuses on improving measures to improve the safety of coal mines and improve corporate safety benefits. After conducting research, an evaluation and analysis model, a comprehensive research on the various factors that affect the safety of coal mine, conducted a comprehensive research and conducted research on factors and measures affecting engineering management. A comprehensive data analysis model and factor management model based on coal mine engineering management are constructed. In the context of big data and the Internet of Things, targeted evaluation and analysis based on different security risks and engineering management issues were proposed with feasible opinions and measures.
EEG-based evaluation of motion sickness and reducing sensory conflict in a simulated autonomous driving environment

Autonomous driving offers significant potential for changes in the automotive industry. However, sensory conflict during autonomous driving can lead to motion sickness. Quantitative evaluation and effective preventions to predict and reduce motion sickness are needed. The goal of this study is to verify the objective indicator of motion sickness level based on encephalography (EEG) that we proposed before and investigate the influence of attenuating sensory conflict on motion sickness. A 6-degree of freedom (DOF) driving simulator platform was used to provide an autonomous driving environment to the subjects, and the subjective motion sickness level (MSL), as well as the EEG signals of 15 healthy subjects, were collected simultaneously during 3 conditions, i) autonomous driving, ii) autonomous driving with eyes blindfolded and iii) active driving. The MSLs were reported by the subjects every two minutes, providing a reference to the recorded EEG signals. The EEG signals were analyzed and compared among different conditions. Average MSLs were higher in autonomous driving than in autonomous driving with eyes blindfolded and active driving, together with the increase of the mean EEG frequency of theta band in the central, parietal and occipital areas (FC5, Cz, CP5, P3, and POz). These findings validated that EEG mean frequency of theta band could be an indicator of motion sickness, besides an attenuated visual input or active control of the vehicle can effectively reduce the generation of motion sickness.
Automatic Error Detection Method of Embedded English Speech Teaching Recognition System under the Background of Artificial Intelligence

The embedded English speech teaching recognition system is a technology that writes the English speech recognition device control program into the chip and embeds the chip into the device, so that the human-like chip controls the English speech device to complete the speech recognition operation. Applying the embedded technology to the English speech recognition system can improve the recognition accuracy of the system to a higher level in terms of recognizing the English speech of a specific person. The purpose of this paper is to research and design an automatic error detection method for embedded English speech teaching recognition system in the context of artificial intelligence. This paper first gives a general introduction to the overview of artificial intelligence, then analyzes the speech recognition algorithm, uses MatLab software to obtain the correct number of recognition system words and the correct rate, and then implements the embedded English teaching recognition system in different environments. The experimental results of comparison through multiple test analysis show that in a quiet environment, the error rate of the embedded English speech teaching recognition system is very low, and the correct recognition rate can reach more than 90%. In a noisy environment affected by various noises, the correct recognition rate of the embedded English speech teaching recognition system is basically above 60%.
Complex Hand Interaction Authoring Tool for User Selective Media

Nowadays, with the advancement of the Internet and personal mobile devices, many interactive media are prevailing, where viewers make their own decisions on the story of the media based on their interactions. The interaction that the user can make is usually pre-programmed by a programmer. Therefore, interactions that users can make are limited to programmable areas. In comparison, in this paper, we propose an Interactive media authoring tool which can compose diverse two-hand interactions from several one-hand interactive components. The aim is to provide content creators with a tool to produce multiple hand motions so that they can design a variety of user interactions to stimulate the interest of content viewers and increase their sense of immersion. Using the proposed system, the content creator can gain greater freedom to create more diverse and complex interactions than programmable ones. The system is composed of a complex motion editor that edits one-hand motions into complex two-hand motions, a touchless sensor that senses the hand motion and a metadata manager that handles the metadata, which specify the settings for the interactive functions. To our knowledge, the proposed system is the first web-based authoring tool that can authorize complex two-hand motions from single hand motions, and which can also control a touchless motion control device.
Classification and Identification Method of Radio Fuze Target and Sweep Jamming Signals Based on Third-Order Spectrum Features

To overcome the problem of insufficiency of linear frequency modulation (LFM) radio fuzes against sweep-type jamming, a method is proposed to classify and identify radio fuze targets and interfering signals based on third-order spectrum features. Using the measured data of an LFM radio fuze, the third-order spectral transform is applied to the output signals of the detector end under the action of the target and several amplitude modulated sweeping interfering signals, and the amplitude mean value, third-order spectral amplitude entropy, and third-order spectral singular value entropy based on the third-order spectrum are extracted as three-dimensional features. The experimental results show that the classification and identification of targets and AM sweep-type interference using the third-order spectral features of the signal at the detector end has a high success rate, with a comprehensive identification accuracy of 98.33%.
Prevention of Kernel Memory Corruption Using Kernel Page Restriction Mechanism

An adversary's user process can compromise the security of the operating system (OS) kernel, and subsequent invocation of the vulnerable kernel code can cause kernel memory corruption. The vulnerable kernel code could overwrite the kernel data containing the privilege information of user processes or the kernel data related to security features (i.e., mandatory access control). As a means of kernel protection, OS researchers have proposed the multiple kernel address space approach that partitions the kernel address space to protect the kernel memory from memory corruption (e.g., process-local memory and system call isolation). However, in the previous approach, the vulnerable kernel code and the kernel data targeted for attack still reside in the same kernel memory. Consequently, to compromise the kernel, adversaries simply focus on calling the latest vulnerable kernel code, which relies on the starting points of the kernel attack process. With the aim of preventing such subversion attacks, this paper proposes the kernel page restriction mechanism (KPRM), which employs an alternative design and method to obviate kernel memory corruption. The objective of the KPRM is to prohibit vulnerable kernel code execution and prevent writing to the kernel data from an adversary's user process. KPRM ensures the unmapping of vulnerable kernel code or kernel data to prevent the exploitation of the kernel due to kernel vulnerability. Therefore, an adversary's user process is obstructed from executing vulnerable kernel code and overwriting kernel data on the running kernel. Evaluation results indicate that actual proof-of-concept attacks on vulnerable kernel code resulting in kernel memory corruption can successfully be prevented by KPRM. Moreover, the implementations of KPRM indicate that the maximum latency for system calls is 0.703µs, while the overhead for 100,000 Hypertext Transfer Protocol (HTTP) downloads via a web client program ranged from 1.188% to 4.093% of the access overhead. In addition, KPRM implementations achieved acceptable overheads of 2.459% and 2.193% for the kernel compile time.
A Peek Into the Memory of T5: Investigating the Factual Knowledge Memory in a Closed-Book QA Setting and Finding Responsible Parts

Recent research shows that Transformer-based language models (LMs) store considerable factual knowledge from the unstructured text datasets on which they are pre-trained. The existence and amount of such knowledge have been investigated by probing pre-trained Transformers to answer questions without accessing any external context or knowledge (also called closed-book question answering (QA)). However, this factual knowledge is spread over the parameters inexplicably. The parts of the model most responsible for finding an answer only from a question are unclear. This study aims to understand which parts are responsible for the Transformer-based T5 reaching an answer in a closed-book QA setting. Furthermore, we introduce a head importance scoring method and compare it with other methods on three datasets. We investigate important parts by looking inside the attention heads in a novel manner. We also investigate why some heads are more critical than others and suggest a good identification approach. We demonstrate that some model parts are more important than others in retaining knowledge through a series of pruning experiments. We also investigate the roles of encoder and decoder in a closed-book setting.
Light-shift-induced behaviors observed in momentum-space quantum walks

Over the last decade there have been many advances in studies of quantum walks (QWs) including a momentum-space QW recently realized in our spinor Bose-Einstein condensate system. This QW possessed behaviors that generally agreed with theoretical predictions; however, it also showed momentum distributions that were not adequately explained by the theory. We present a theoretical model which proves that the coherent dynamics of the spinor condensate is sufficient to explain the experimental data without invoking the presence of a thermal cloud of atoms as in the original theory. Our numerical findings are supported by an analytical prediction for the momentum distributions in the limit of zero-temperature condensates. This current model provides more complete explanations to the momentum-space QWs that can be applied to study quantum search algorithms and topological phases in Floquet-driven systems.
CommuNety: deep learning-based face recognition system for the prediction of cohesive communities

Abstract Effective mining of social media, which consists of a large number of users is a challenging task. Traditional approaches rely on the analysis of text data related to users to accomplish this task. However, text data lacks significant information about the social users and their associated groups. In this paper, we propose CommuNety, a deep learning system for the prediction of cohesive networks using face images from photo albums. The proposed deep learning model consists of hierarchical CNN architecture to learn descriptive features related to each cohesive network. The paper also proposes a novel Face Co-occurrence Frequency algorithm to quantify existence of people in images, and a novel photo ranking method to analyze the strength of relationship between different individuals in a predicted social network. We extensively evaluate the proposed technique on PIPA dataset and compare with state-of-the-art methods. Our experimental results demonstrate the superior performance of the proposed technique for the prediction of relationship between different individuals and the cohesiveness of communities.
A Machine Learning Supervised Model to Detect Cyber-Begging in Social Media

The misuse of social media leads many of the users to fall into crimes that they are not aware of it especially financial ones. Recently, cyber-begging has grown significantly on social platforms, impacting users and actual beneficiaries. The effects of this threat are serious and harmful that could lead to financial crime charges. With the spread of social media in recent times, this threat has become a globally important issue due to the economic and security implications. Most of the researchers discussed different types of cybercrimes to discover them and develop solutions to them. However, the number of research that focuses on cyber-begging in social media is insufficient. In this paper, we used machine learning to automatically identify cyber-begging in social media. The proposed model identifies cyber-begging using the Naive Bayes (NB) classification algorithm by training and testing the classifier using a real dataset collected from Twitter.
A Preliminary Study of Bots Usage in Open Source Community

Bots are seen as a promising approach in software development, which help to deal with the ever-increasing complexity of modern software engineering and development. The number of bots in open source community, such as GitHub, has expanded substantially over the last three years. Due to its increasing popularity, it is essential to characterize the current usage of bots in practices. In this paper, we present an empirical study of bots usage in GitHub community. By analyzing 7,399 projects from GitHub, we find that 4,148 (56%) projects have used bots. Through automatic identification and manual detection, we collect a total of 196 bots. We then analyze and classify them into 4 categories and 14 topics. Finally, we discuss some raised implications for bots in current GitHub community.
In the pixel zone: Perception of digital design

Abstract Since the beginning of the millennium, the internet has undeniably influenced everyday life as well as the creative fields, in countless ways that have already been exhaustively discussed. In this paper, we discuss the term postdigital, which is relevant to anchoring the perception of digital design. Numerous theoretical works are dealing with the terminology of postdigital, with conceptualisations differing from one another. Post-digitality offers a set of speculative strategies with the intention of building a complex architecture for thinking and creating under contemporary conditions: how to critically consider, contextualize, and shift the perception of new technologies as part of the existing culture. Digital design has become an integral part of everyday reality: websites, mobile devices, tablets, but also products and services that use digital interfaces as interactive communication channels between a human and a machine. These interfaces require a specific approach to design. The term digital design covers the design of the entire range of digital products and services and is understood as a complex set of many disciplines: user interface, interaction design, information architecture, user experience design, visual design, web design, app design, or game design. The boundaries between the different areas of design are blurred and permeable, and although their mutual interaction can be beneficial, it is necessary to clearly differentiate between graphic and digital design. Digital design grows out of the principles of graphic (visual) design and introduces additional knowledge and very specific principles based on the nature of the digital medium and the transdisciplinary field of cognitive ergonomics.
Research on Subway Operation Safety Platform Model Based on Blockchain and Internet of Things

With the continuous and rapid increase in the scale of subway operation, the security situation faced by the operating units is becoming increasingly severe. Based on the analysis of the characteristics of subway operation and the current situation of information construction, this paper deeply analyzes the main problems of subway operation safety information construction. Aiming at the problems of poor data circulation, difficult supervision, distrust, fear of leakage, poor timeliness, poor quality and insufficient certificates, this paper puts forward the construction requirements of subway operation safety platform. The platform model is designed from three aspects of block chain platform, platform logic architecture and Internet of Things forensics, and the new mode of safe and efficient management of subway operation is explored.
Topic Modeling with Transformers for Sentence-Level Using Coronavirus Corpus

A Topic Model is a class of generative probabilistic models which has gained widespread use in computer science in recent years, especially in the field of text mining and information retrieval. Since it was first proposed, it has received a large amount of attention and general interest among scientists in many research areas. It allows us to discover the mix of hidden or "latent" subjects that differs from one document to another in a given corpus. But since topic modeling usually requires the prior definition of some parameters - above all the number of topics k to be discovered -, model evaluation is decisive to identify an "optimal" set of parameters for the specific data. Latent Dirichlet allocation (LDA) and Bidirectional Encoder Representations from Transformers Topic (BerTopic) are the two most popular topic modeling techniques. LDA uses a probabilistic approach whereas BerTopic uses transformers (BERT embeddings) and class-based TF-IDF to create dense clusters.
Self-learning and One-Shot Learning Based Single-Slice Annotation for 3D Medical Image Segmentation

As deep learning methods continue to improve medical image segmentation performance, data annotation is still a big bottleneck due to the labor-intensive and time-consuming burden on medical experts, especially for 3D images. To significantly reduce annotation efforts while attaining competitive segmentation accuracy, we propose a self-learning and one-shot learning based framework for 3D medical image segmentation by annotating only one slice of each 3D image. Our approach takes two steps: (1) self-learning of a reconstruction network to learn semantic correspondence among 2D slices within 3D images, and (2) representative selection of single slices for one-shot manual annotation and propagating the annotated data with the well-trained reconstruction network. Extensive experiments verify that our new framework achieves comparable performance with less than $$1\%$$ annotated data compared with fully supervised methods and generalizes well on several out-of-distribution testing sets.