Reviewerly/exHarmony huggingface dataset:
Research on the Implementation of Informatization Teaching Based on Network Teaching Platform and flipped classroom

In order to realize the student-centered teaching model based on informatization teaching, this paper mainly discusses the implementation of informatization teaching based on network teaching platform and flipped classroom. Firstly the paper expounds the meaning of informatization teaching and its characteristics compared with traditional teaching; then introduces the implementation process based on network teaching platform and flipped classroom in detail from three phases-before class, during the class and after class; at last presents five strategies in the implementation process. It displays that the informatization teaching based on network teaching platform and flipped classroom can improve learning effect and realize the student-centered teaching model.
Outlook and Direction of AI Tour Guide Services - from the Lifelong Machine Learning View

Artificial intelligence (AI) has entered tourism and become a new service in a tour guide. AI technology can help tourism by providing customized services and attracting visitors to fight with the crisis of the COVID-19 epidemic. This paper introduces how AI tour guide services contribute to tourism and its main issues. The future development of AI tour guides also was discussed at the end and the authors believe lifelong machine learning is the key to developing AI tour guides.
Understanding the Mechanism of Deep Learning Frameworks in Lesion Detection for Pathological Images with Breast Cancer

With the advances of scanning sensors and deep learning algorithms, computational pathology has drawn much attention in recent years and started to play an important role in the clinical workflow.Computer-aided detection (CADe) systems have been developed to assist pathologists in slide assessment, increasing diagnosis efficiency and reducing misdetections.In this study, we conducted four experiments to demonstrate that the features learned by deep learning models are interpretable from a pathological perspective.In addition, classifiers such as the support vector machine (SVM) and random forests (RF) were used in experiments to replace the fully connected layers and decompose the end-to-end framework, verifying the validity of feature extraction in the convolutional layers.The experimental results reveal that the features learned from the convolutional layers work as morphological descriptors for specific cells or tissues, in agreement with the diagnostic rules in practice.Most of the properties learned by the deep learning models summarized detection rules that agree with those of experienced pathologists.The interpretability of deep features from a clinical viewpoint not only enhances the reliability of AI systems, enabling them to gain acceptance from medical experts, but also facilitates the development of deep learning frameworks for different tasks in pathological analytics.
Joint Re-Detection and Re-Identification for Multi-Object Tracking

Within the tracking-by-detection framework, multi-object tracking (MOT) has always been plagued by missing detection. To address this problem, existing methods usually predict new positions of the trajectories first to provide more candidate bounding boxes (BBoxes), and then use non-maximum suppression (NMS) to eliminate the redundant BBoxes. However, when two BBoxes belonging to different objects have a significant intersection over union (IoU) due to occlusion, NMS will mistakenly filter out the one with lower confidence score, and these methods ignore the missing detection caused by NMS. We propose a joint re-detection and re-identification tracker (JDI) for MOT, consisting of two components, trajectory re-detection and NMS with re-identification (ReID). Specifically, the trajectory re-detection could predict new position of the trajectory in detection, a more reliable way than motion model (MM), based on feature matching. Furthermore, we propose to embed ReID features into NMS and take the similarity of the ReID features as an additional necessary condition to determine whether two BBoxes are the same object. Based on the “overlap degree” calculated by IoU and the similarity of ReID features, accurate filtering can be achieved through double-checking. We demonstrate the effectiveness of our tracking components with ablative experiments and surpass the state-of-the-art methods on the three tracking benchmarks MOT16, MOT17, and MOT20.
Question Answering Survey: Directions, Challenges, Datasets, Evaluation Matrices

The usage and amount of information available on the internet increase over the past decade. This digitization leads to the need for automated answering system to extract fruitful information from redundant and transitional knowledge sources. Such systems are designed to cater the most prominent answer from this giant knowledge source to the user query using natural language understanding (NLU) and thus eminently depends on the Question-answering(QA) field. Question answering involves but not limited to the steps like mapping of user question to pertinent query, retrieval of relevant information, finding the best suitable answer from the retrieved information etc. The current improvement of deep learning models evince compelling performance improvement in all these tasks. In this review work, the research directions of QA field are analyzed based on the type of question, answer type, source of evidence-answer, and modeling approach. This detailing followed by open challenges of the field like automatic question generation, similarity detection and, low resource availability for a language. In the end, a survey of available datasets and evaluation measures is presented.
An Empirical User-Study of Text-Based Nonverbal Annotation Systems for Human-Human Conversations

With the substantial increase in the number of online human-human conversations and the usefulness of multimodal transcripts, there is a rising need for automated multimodal transcription systems to help us better understand the conversations. In this paper, we evaluated our automated system (MONAH and its enhanced version MONAH+) against the prevalent Jefferson manual system to perform multimodal transcription, which is widely used by the linguistics community but also very labour intensive. Our previous work, MONAH (Multimodal Narratives for Human) aims to make multimodal transcripts accessible and automated, whilst MONAH+ expands MONAH to visualize machine attention – in other words, what words are deemed to be important to a machine classifier and draw the users’ attention towards them. Based on 104 participants responses, we found that (1) all text-based methods significantly reduced the amount of information for the human users, (2) MONAH was found to be more usable than Jefferson, (3) Jefferson’s relative strength was in chronemics (pace / delay) and paralinguistics (pitch / volume) annotations, whilst MONAH’s relative strength was in kinesics (body language) annotations, (4) enlarging words’ font-size based on machine attention was confusing human users as loudness. These results pose considerations for researchers designing a multimodal annotation system for the masses, or who would like a fully-automated or human-augmented conversational analysis system.
Prediction of Ballistic trajectories based on Gaussian Mixture Model

Existing trajectory prediction methods have some problems like low accuracy and poor real-time performance. The ballistic trajectory sampled by radar is essentially a continuous sequence, and the Gaussian Mixture Model (GMM) performs well in time-series prediction. To predict the trajectory more accurately, we construct a GMM with two different kernel functions weighted together. We build datasets of exterior trajectories under different initial conditions and train a GMM with optimal hyperparameters. Experimental results show that the GMM has higher prediction accuracy in the short term with about three times faster speed than the traditional Ballistic Differential Equations(BDE) method.
Distributed Ledger Technology in Autonomous Driving: A Security Protection Layer

Cybersecurity in autonomous driving is of utmost importance since a hacked self-driving car could turn into a remote-controlled weapon. Appropriate measures must be taken and implemented to ensure future road safety. The substantially shorter renewal cycles in the hardware (e.g., sensors, computer hardware) and especially software domain compared to the current service life of a vehicle represent a further challenge The use of blockchain technology could enhance security in autonomous driving and thus reduce cybersecurity risks. This paper studies current developments of Swiss pilot projects in autonomous driving and discusses existing solutions for increasing safety of autonomous driving through blockchain.
Psychology of personal success of a modern lawyer

The issues of achieving personal success and its impact on the effectiveness of professional activity, including legal activity, are of particular public interest. The proposed article is devoted to the analysis of scientific research on the issue of personal success, various approaches to understanding this term are considered. The relevance of the article is determined by the fact that the effectiveness of the lawyer’s work largely depends not only on professionalism, but also on the personality characteristics. Based on the proposed research model, the article briefly considers the manifestations of external and internal forms of personal success, the main psychological mechanisms of its regulation, factors and conditions for achieving it, evaluation criteria; positive and negative results and consequences are indicated.
Developing and deploying an integrated workshop curriculum teaching computational skills for reproducible research

Inspired by well-established material and pedagogy provided by The Carpentries (Wilson, 2016), we developed a two-day workshop curriculum that teaches introductory R programming for managing, analyzing, plotting and reporting data using packages from the tidyverse (Wickham et al., 2019), the Unix shell, version control with git, and GitHub. While the official Software Carpentry curriculum is comprehensive, we found that it contains too much content for a two-day workshop. We also felt that the independent nature of the lessons left learners confused about how to integrate the newly acquired programming skills in their own work. Thus, we developed a new curriculum that aims to teach novices how to implement reproducible research principles in their own data analysis. The curriculum integrates live coding lessons with individual-level and group-based practice exercises, and also serves as a succinct resource that learners can reference both during and after the workshop. Moreover, it lowers the entry barrier for new instructors as they do not have to develop their own teaching materials or sift through extensive content. We developed this curriculum during a two-day sprint, successfully used it to host a two-day virtual workshop with almost 40 participants, and updated the material based on instructor and learner feedback. We hope that our new curriculum will prove useful to future instructors interested in teaching workshops with similar learning objectives.
Perceptions from PINE Aspiring Students about their Virtual Teacher’s Profile

E-learning represented a true revolution that generated training needs at educational institutional levels all around the world. As a result of COVID-19 pandemic, face-to-face educational institutions were forced to change drastically from their regular teaching practice into virtual education. Consequently, the virtual teacher had to master skills, technological knowledge, and attitudes that would promote e-learning. With this premise, the objective of this research focused to analyze if the teachers of the Pedagogy of National and Foreign Languages (PINE, as per its initials in Spanish) Major possess sufficient skills, technological knowledge, and attitudes to teach in virtual mode. This study is quantitative-descriptive in nature and the study of the hypothesis is descriptive. The participants correspond to a sample of 87 aspirant students to Pedagogy of National and Foreign Languages Major (PINE). The evaluation instrument was a survey with a Likert scale. The percentages of the responses were considered for the analysis and comparison of the Ideal and Real Profile of the virtual teacher. Both, the statistical results, and the comparison of the Ideal Profile versus the Real Profile of the teachers, presented data above the estimated 60%. Finally, this work contributes to virtual teaching practice in emerging times. The results show that, despite the urgency and the drastic form of the change in modality, teachers knew how to adapt themselves to this change and were able to work successfully in this new modality.
LANGUAGE ANXIETY AMONG KUPTM’s TESL UNDERGRADUATES IN AN ONLINE LEARNING ENVIRONMENT

2020 started off with the hit of Covid-19 which has halted various sectors including the educational sector. The mode of learning has abruptly changed to online learning as the virus limits any physical contact and almost everyone was affected. Language anxiety is not a newly coined term and has been around for a long time. However, research that studies second language anxiety in online mode classes is still very limited. Therefore, this research intended to investigate the contributing factors towards KUPTM’s TESL Undergraduates language anxiety and discover the coping strategies in dealing with such anxiety in an online learning environment. A mixed method study involving a survey and four (4) interviews was done. All 105 participants of this research were the students who were involved in online language learning. The data from the survey was analysed using IBM SPSS Statistic 25, while the interviews would support the quantitative findings through thematic analysis. The findings showed that the biggest factor that leads to language anxiety is the fear of negative evaluation, followed by the factor of distractions and the lack of a productive environment, and the third factor discovered from the data is communication apprehension. The coping strategies were divided into three domains; cognitive, affective and behavioural. It was found that the most used coping strategies by the participants of this research was behavioural. The results highlight a number of implications and recommendations for future researchers.
New Records of Pre-image Search of Reduced SHA-1 Using SAT Solvers

Although the first SHA-1 collision attack has been carried out in 2017, the cryptographic hash function seems still robust against pre-image attacks. The aim of this work is to measure how many rounds of SHA-1 can be inverted in practice using SAT solvers. To do so, we have first modeled the problem of finding a pre-image of SHA-1 as a system of Boolean formulas, explicitly describing the procedure to obtain such model. Then, we configured the model based on different combinations of number of rounds and number of free bits in our target pre-image. Finally, to find a solution of the model, we used a SAT solver on our server, testing several combinations of restart policies and polarity modes. We analyze and report the number and positions of the pre-image bits that can be fixed to influence the ability of the SAT solver to find the remaining free bits of the pre-image in a shorter time. In particular, we execute partial pre-image attacks on 64-, 80- 96-, 112- and 128-bit messages, outperforming the current state-of-the-art records.
Pixel Attack for CAPTCHA Construction

Recent research has revealed that adversarial examples can prove themselves useful in the generation of more robust Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) systems and decrease their vulnerability to deep neural networks (DNNs) recognition . In this paper, we study how the One Pixel Attack can be used for CAPTCHA creation. By proposing a new framework and small redesign to the attack we also have been able to increase the attack robustness to filtering and increase its transferability to different neural networks and successfully reduce the accuracy state of the art deep neural networks to $$0\%$$ .
Prospectus, Investor Attention and Stock Performance of the First Listed Companies on the Beijing Stock Exchange

Beijing Stock Exchange is China's first corporate stock exchange established with the approval of the State Council incorporated on September 3, 2021.In this paper, we studied the first 74 listed companies on the Beijing Stock Exchange on November 15, 2021 and constructed relevant textual indicators from the dimensions of prospectus and investor attention.We found that the value figures in the prospectus showed a significant positive correlation with the rise and fall of listed companies on the early days, and there is a significant negative correlation between daily stock attention and stock price.In addition, whether a company is listed in stock market for the first time has a significant positive correlation with stock price fluctuations.At the same time, it can be seen from the text linear discriminant analysis that most of the stocks that rose on the first day belonged to the software and pharmaceutical high-tech industries, and they were more inclined to disclose their own risks, employees and other basic information of the company in the prospectus.However, most of the stocks that fell on the first day belong to the image and material industries, and their financial situation is more disclosed in the prospectus.
Tensor Products of Entangled Subspaces and Their Properties from Diagrammatic Constructions

Construction of genuinely entangled multipartite subspaces with certain characteristics has become a relevant task in various branches of quantum information. Here we show that such subspaces can be obtained from an arbitrary collection of bipartite entangled subspaces under joining of their adjacent subsystems. In addition, it is shown that direct sums of such constructions under certain conditions are genuinely entangled. These facts are then used in detecting entanglement of tensor products of mixed states and constructing subspaces that are distillable across every bipartite cut, where for the former application we include example with the analysis of genuine entanglement of a tripartite state obtained from two Werner states.
Quantifying Changes in Predictions of Classification Models for Data Streams

Evaluation methods for data stream classification have frequently been focused on how available data are used for learning a model and for its performance assessment, with major emphasis on the difference between predicted and true labels. More recently, growing interest in delayed labelling evaluation has resulted in the evaluation of multiple predictions made by an evolving model for an instance before its true label arrival. Still, under this setting predictions are also compared with true labels rather than changes in predictions focused on. In this study, we aim to provide an intuitive evaluation framework to quantify changes in predictions made over time for the same input instances by evolving classification models. The primary motivation is to gain insight into the impact of the evolution of a classification model on the changes in decision boundaries, which may effectively re-assign the instances to other classes. The prediction change measures proposed in this study make it possible to reveal the scale of such changes. Furthermore, the notions of volatility of predictions and productive volatility are proposed and quantified. Results for a number of real and synthetic data streams show that similar accuracy of the models can be accompanied by significantly different volatility of predictions made by these models.
An Analytical Study of Juvenile Delinquency in India with Reference to 2021

Teenagers are the valuable heritage of any nation and its golden future, but adolescence is the only stage when this future of the country needs to be handled the most. Adolescence is a stage of sensitivity, passion, excitement, struggle, labor, learning, personality change. This age is the most important factor which decides the personality of the individual and from this personality the condition of nation-building or destruction is created. For the last many decades, adolescent is on the path of delinquency. The facts are shocking if the NCRB reports are studied. Crimes committed by juveniles are increasing. Their involvement is increasing in crimes like murder, robbery, dacoity, theft, rebellion, kidnapping, which is a matter of crisis. Concerned about this, India adopted the Convention on the Rights of Children adopted by the United Nations Assembly on 11 December 1992 and, showing its activeness, enacted the Juvenile Justice and Protection of Children Act 2000 so that Work can be done for the improvement of delinquent juveniles, their protection and rehabilitation.
Feature Selection Based on Genetic Algorithm With Stochastic Disturbance Local Optimization

The paper proposes a feature selection method based on genetic algorithm with stochastic disturbance local optimization (GASD) for data dimension reduction problem. In this algorithm, a local search module is introduced into every search iteration under the global search framework of genetic algorithm. In the local search, a stochastic disturbance mechanism is utilized to update the current optimal feature subset. The optimal feature subset is obtained by using global search and optimized local search. Experimental results show that GASD can effectively delete redundant features, reduce data dimensions, and improve the generalization ability of classification model, especially in high-dimensional data.
Research on Blockchain Technology and Know-How in Cryptographic Exploration

Building a common manufacturing unit into a clever manufacturing unit is one of the dreams of "Industry 4.0". As factories pass in the direction of clever development, the current community safety structures can no longer meet the wishes of organisations and users. Aiming at the hidden risks of records leakage and unlawful get right of entry to to the facts of cryptographic manufacturing amenities and merchandise in the clever factory, the article combines the core science of the Internet of Things radio frequency identification (RFID) science and blockchain technology, and proposes a blockchain-based technology, the light- weight password safety authentication mechanism of the clever manufacturing facility RFID system, which has the traits of lightweight, anti-data leakage, and low administration cost. It can make sure the protected and dependable get entry to of industrial information whilst stopping the utility of RFID in clever factories. Security troubles such as replay attacks, manin-the center attacks, and server spoofing assaults additionally grant new thoughts for the lookup on statistics protection safety for clever factories..