Reviewerly/exHarmony huggingface dataset:
Feature Extraction in Arabic Sign Language using Hand and Wrist Localization Techniques

Recognition of hand and wrist gestures is the vital and challenging problems in human computer interaction (HCI) community. In this paper, an essential phase in the process of identifying hand gestures is presented using innovative hand detection and wrist localization method. There has not been much focused to localizing the wrist, and available work are very few which cover a wide range of circumstances. On a publicly available dataset, the suggested approach was implemented, tested and the findings obtained support its effectiveness. By contrasting this method with earlier research, the effectiveness of this method and the significance of the localization with wrist phase has been presented. In this work, we suggest to use proposed method which is applicable for general sign language recognition (SLR) and Arabic digit SLR. The proposed technique (having error = 125 when maximum detection (E) = 1 and error 175 when E = 0.5) exhibits the better results as compeered to the existing method (having error = 131.8 when E = 1 and error 323.3 when E = 0.5). Using this paper, the researchers will explore to investigate novel creation of a real-time Arabic SL application using hand characteristics in order to propose other classifiers to boost the effectiveness of the detection of hands and wrists method.
Image Forgery Detection Model using CNN Architecture with SVM Classifier

This Due to rapid advancement in computer vision system and easy availability of digital camera’s acquisition of images and sharing the content on social media platform with falsify content is challenging task to the researcher community. For the legal purpose to provide the evidence in court, it’s a difficult task identify the forgery in image that why various traditional artefacts of capturing device and internal footprints of image used to check the forgery in original image. Now a day’s easy availability of software editing tools the captured image modify with wrong content to defame the person or misleading the people on social media with fake content. In this article, proposed a CNN based architecture to classify the forgery in given image. CNN architecture is capable to detect the unseen forgeries based on features extracted through various convolution layers and used SVM classifier to classify the label of forged image with high accuracy.
A Secure Data Sharing Scheme Based on Differential Privacy and Blockchain

When analyzing power consumption data, it will be faced the risk of leakage of privacy of trade secrets. Because this data involves users' privacy information or trade secrets data. Based on the unique characteristics of the Blockchain and the differential privacy theory, which is almost immutable and traceable, this paper proposed a multi-leveled, multi-source, and multi-target data-sharing scheme. This scheme is proposed to meet the requirements of researching power consumption data. The results of the case study showed efficiency for secure power data sharing.
Research on Application Method of Object Detection in Video

Object detection is an important problem in the field of computer vision, which is the basis of image segmentation, object tracking, action recognition and action judgment. With the continuous efforts of many scholars, object detection develops from the traditional object detection to the deep learning-based object detection. This paper firstly summarizes the commonly used target detection methods, then summarizes and compares the mainstream detection methods, and points out the advantages and disadvantages of some main methods. Finally, the development of target detection is predicted and prospected.
Research on 3D hand information acquisition based on binocular camera

It is important to acquire hand spatial position information in a non-contact form when hand-impaired patients are undergoing assisted rehabilitation training. In this paper, we build a 3D reconstruction platform based on binocular vision camera, and adopt the Zhang Zhengyou calibration method to calibrate the binocular camera with high accuracy; combined with SGM(semi-global matching) algorithm, a fast and efficient matching method is given to obtain the parallax map; and transform it into point cloud information. The experimental results show that: the average error of reconstruction within 2m of the method is within 5%, and the error gradually increases with the increase of distance; the average time of single reconstruction is 3.566s, which meets the requirement of rapid acquisition of hand 3D information.
Recent Development on Extractive Rationale for Model Interpretability: A Survey

Interpretability is an important yet difficult issue for natural language understanding, which aims to understand the behaviour mode of deep neural models, and hence to obtain corresponding explanation for model prediction. For interpretability, extractive rationales are some highlights in the original input, which can sufficiently and comprehensively decide the label, while generative explanations are free texts, which describe the inference process with natural language. In this paper, we focus on extractive rationale, and give a detailed review on recent development. Specifically, firstly we will introduce the task formulation, evaluation metrics and datasets. Then we will introduce several kinds of rationale extraction models, including vanilla attribution algorithm, AA-based training, and select-predict pipeline. And we next list the diverse utilizations of rationale. Finally, we summarize the main challenges of this field.
Research on the abnormal state of train brake chains based on target detection

Railway transport is the main mode of transport in China, and timely detection of abnormalities in trains during railway operation can effectively avert disasters. Holding brake is a type of anomaly detection. The brake chain, as an indicator of the brake, is the key to the study of the state of the train in motion. Based on the object detection algorithm, this paper detects the moving train brake chain from the aspects of one-stage and two-stage, and determines the abnormal situation of the train through its tightness. Firstly, the railway brake chain data set is constructed, and the target categories of the data are defined as slack brake chain, taut brake chain and duct, considering the overall nature of the brake chain and duct; then the detection frame, category and confidence level of the target are obtained by YOLOv3 and Faster RCNN algorithm models respectively; finally, based on the detection results of the previous step, if there is a taut brake chain, it is judged as anomalous, i.e. output "Anomalous!". The results show that the accuracy of YOLOv3 and Faster RCNN algorithm models for brake chain anomaly detection is 91.324187% and 93.108396% respectively, which effectively warn the brake chain anomalous state.
Online Teacher-Student Interpersonal Management under Crisis Conditions: A Sentiment Analysis through Machine Learning

Online teacher-student communication is an indispensable part to sustain education in crisis conditions. However, the teacher-student interpersonal relationship is more fragile in crisis, and thus interpersonal management becomes vital. This paper employed social media data and largescale questionnaire data to examine how to manage the interpersonal relationship through the subtle manipulation of text, picture, emoji, and voice messages in a teacher-student community under crisis conditions. This paper used python to automatically traced, extracted, and counted the use of text, picture, emoji, and voice messages in one year’s teacher-student online communication. The messages were processed through sentiment analysis through machine learning to explore the students’ emotions. Having a fuller understanding of students’ emotions, the interpersonal functions of these messages were further examined through a questionnaire to reach a solution of interpersonal management. The results show that the students' emotions were not overwhelmingly negative under crisis conditions. With the proper use of text, picture, emoji and voice messages, interpersonal management can be successfully achieved. The findings indicate that the careful choice of message types can facilitate the maintenance of teacher-student interpersonal relationships and teachers need to make fuller use of technology-mediated communication in a time of crisis.
Word-level Textual Adversarial Attack Method Based On Differential Evolution Algorithm

Deep neural networks are now widely used, but adversarial attack methods against deep neural networks are also proliferating, posing a significant security risk to deep learning. Adversarial attacks expose shortcomings of neural network, and for text it is also more challenging. Because text is discrete and cannot be optimized directly using gradients like images. Some existing word-level textual adversarial attack methods have achieved good performance, but there are still problems that the generated text is not of high enough quality or semantic bias. In this paper, we propose a model that combine the sememe-based word substitution method with the improved differential evolution algorithm to solve above problems. We have made a series of improvements to the differential evolution algorithm, including the use of dynamic cross-rates and scaling factors to enhance the search capability of the algorithm. The Metropolis criterion is also introduced to enhance the algorithm’s ability to explore the adversarial examples in the potential space. As a black-box attack method, the algorithm can attack without knowing the model’s interior. We tested the attack effectiveness of the model on benchmark datasets by attacking two victim models (BERT and BiLSTM). The experimental results shows that our model has higher attack success rate and higher quality of the adversarial examples than the baseline methods.
Research on the end-game of DouDizhu: A method to improve AI of imperfect information game

The imperfect information game has too much hidden information in the game process, which leads to the huge information set in the game process. It is difficult to solve the problem through the mature game search algorithm. This paper takes the game guessing as the carrier, imitates the game behavior of human beings in the game remnant state, and gives a method to transform the imperfect information game into the perfect information game. Through this method, the other players' hands in the game are guessed, and the imperfect information is transformed into perfect information, so as to solve the problem that the data dimension brought by the hidden information of the game is too large Questions. As for the game of DouDizhu, in the end of game, there are 133 kinds of ending states. In this paper, the game information generated in the process of DouDizhu is studied, the appropriate representation method of game information is given, and a reasonable 3D time series convolution model is designed to extract features, and then a classification model with high accuracy of guessing cards is obtained. The model has a high accuracy of 92.1% for the final game. The perfect game search algorithm is used to solve the game's hand out strategy to improve the game AI's playing strength.
Concurrency Control Program Generation in Genetic Programming Considering Depth of the Program Tree

This paper tries to generate an appropriate concurrency control program by using genetic programming (GP). In GP, a program is represented with a tree. It has been pointed out that there is a large variability in the generated programs. This paper proposes the operations considering the depth of the program tree in selecting the target node of genetic operations for improving the diversity of the generated programs and generating more stable and suitable concurrency control programs. It is shown that the proposed operations can generate better concurrency control programs than the conventional ones.
Facial Expression Intensity Estimation Considering Change Characteristic of Facial Feature Values for Each Facial Expression

Facial expression intensity, which quantifies the degree of facial expression, has been proposed. It is calculated based on how much facial feature values change compared to an expressionless face. The estimation has two aspects. One is to classify facial expressions, and the other is to estimate their intensity. However, it is difficult to do them at the same time. There- fore, in this work, the estimation of intensity and the classification of expression are separated. We suggest an explicit method and an implicit method. In the explicit one, a classifier determines which types of expression the inputs are, and each regressor determines its intensity. On the other hand, in the implicit one, we give zero values or non-zero values to regressors for each type of facial expression as ground truth, depending on whether or not an input image is the correct facial expression. We evaluated the two methods and, as a result, found that they are effective for facial expression recognition.
Tetris: Automatic UAF Exploit Generation by Manipulating Layout based on Reactivated Paths

Automatic exploit generation (AEG) for Use-After-Free (UAF) vulnerabilities is a challenging work. Both complex dependencies between program semantics and unpredictable behaviors of the heap allocator increase the difficulty. Existing AEG solutions for UAF vulnerabilities rely on fuzzer to search exploitable states in the whole program space, which shows low efficiency. In this paper, we proposed Tetris, a system that automatically generates exploits for UAF vulnerabilities in user space. Tetris employs a memory collision strategy to automatically infer target memory layout for exploiting after vulnerability triggers. To construct the target layout, we proposed a conception of Reactivated Path (RAP) to represent program semantics for layout manipulation, and built a novel Fast Layout Exploration technique on top of that. Our method works without any knowledge of heap allocators’ internal mechanism and can be easily deployed to other heap memory corruption vulnerabilities by replacing the target layout inference part. We benchmarked Tetris on a set of 19 CTF (capture the flag) programs. The results show that Tetris can generate exploits for 15 of them, and generate inputs for satisfying layout requirements on 2 of them. The evaluation compared with other state-of-the-art AEG tools show that Tetris can generate UAF exploits more efficient.
Argo: Projects’ Time-Series Data Fetching and Visualizing Tool for GitHub

GitHub, which has more than 10 million repositories, attracts the targets of mining software repositories. Analyzing and investigating GitHub repositories provide clues to improve activities in software development. However, there is no standard method to access source code change histories and communication histories. This paper aims to implement a tool (Argo) to fetch communication histories from GitHub. Argo accepts users’ GraphQL queries to fetch the communication histories recorded on GitHub and draws line charts of their transition. As a case study, we implement three types of GraphQL queries for fetching data of stargazers, issues, and pull requests from 11 repositories of famous programming languages managed on GitHub. Argo can fetch the number of entries per second of stargazers, issues, and pull requests data are 145.19, 81.27, and 139.90, respectively. Also, stargazers of golang/go required the most consumed time for fetching; the number of entries was 96,425, and they spent 716 seconds. Finally, we showed that our tool could draw the line charts from fetched data.
Analysis of Student Difficulties in Online Learning During the COVID-19 Pandemic Class VIII MTsS Khudrinnur In Toari District

Learning in schools before the pandemic was carried out directly in schools, but because of the pandemic, it required teachers and students to carry out the online learning process. Teachers and students experience many obstacles in the online learning process, especially for students new to online learning. This study aims to describe students' difficulties in online learning during the Coronavirus disease (COVID-19) pandemic and to find out the causes of students' problems in online learning during the Coronavirus disease (COVID-19) pandemic class VIII Madrasah Tsanawiyah Khudrinnur. This research is a qualitative descriptive study. The samples in this study were students and teachers of class VIII MTsS Khudrinnur. The research instrument is a questionnaire or questionnaire, interviews, and documentation. From the results of the study, it is known that the factors that affect the difficulties in online learning of students are network difficulties of 69.09% of the total number of students. So it can be concluded that the factors that influence students' difficulties in online learning are network difficulties, about 69.09% of the total number of students. Factors do not have a quota of about 68.75 % of the total number of students. The factor of not having a cellphone (HP) is about 66.40% of the total number of students. The aspect of not understanding the material is about 58.85% of the students who do not understand the learning material will ask the teacher when they do not understand. The cool factor for playing games is around 71.35% of students prefer to play games compared to participating in online learning. Keywords : Difficulty, Online (Online), MTSS Khudrinnur
Treating Speech as Personally Identifiable Information and Its Impact in Machine Translation

Speech is the most natural and immediate form of communication. It is ubiquitous. The tremendous progress in language technologies that we have witnessed in the past few years has led to the use of speech as input/output modality in a panoply of applications which have been mostly reserved for text until recently. Machine translation is one of the technologies which traditionally has dealt with text input and output. However, speech-to-speech translation is no longer a research-only topic, and one can only anticipate its growing use in our multilingual world. Many of these applications run on cloud-based platforms that provide remote access to powerful models, enabling the automation of time-consuming tasks such as document translation, or transcribing speech, and helping users to perform everyday tasks (e.g. voice-based virtual assistants). When a biometric signal such as speech is sent to a remote server for processing, however, this input signal can be used to determine information about the user, including his/her preferences, personality traits, mood, health, political opinions, among other data such as gender, age range, height, accent, etc. Moreover, information can also be extracted about the recording environment. Although there is a growing society awareness about user data protection (the GDPR in Europe is an example), most users of such remote servers are unaware of the amount of information that can be extracted from a handful of their sentences. In fact, most users are unaware of the potential for misuse allowed by this new generation of speech technology systems. For instance, most users do not know how many sentences in their own voice are necessary for cloning it, nor have they heard about spoofing speaker recognition systems, or understand to what extent their recordings can be anonymised. Moreover, most users do not realize that adversarial techniques now enable the effective injection of hidden commands in spoken messages without being audible. The recent progress in speech and language technologies is also reflected in speech-to-speech translation systems, where the traditional cascade of "speech recognition—machine translation—speech synthesis" is being replaced by end-to-end systems that allow the sentences in the target language to sound as the voice of the speaker in the source language, opening a world of possibilities. All these privacy and security issues are becoming more and more pressing in an era where speech must be legally regarded as PII (Personally Identifiable Information).
Application of 2-gram to English Q&amp;A Statements to Obtain Factor Scores

In order to solve the issues of mismatches between the intentions of questioners and respondents of Question and Answer (Q&A) sites, impression evaluation experiments have resulted in obtaining nine factors of impressions for Japanese Q&A statements. The feature values of syntactic information were initially extracted through morphological analysis and utilized for multiple regression analysis to obtain factor scores for any Q&A statements. The calculation of factor scores has also been realized by extracting syntactic information through N-gram applied to Part-of-Speech extracted by way of morphological analysis. However, most of our methods so far have been dependent on Japanese, and so we need to validate if our method can be extended to other languages as well. So far, in a similar way as in Japanese, the nine factors have also experimentally been obtained from English Q&A statements. It was confirmed as well that the factor scores were calculated using the feature values of English statements extracted through morphological analysis. Therefore, in this paper, 2-gram as well as morphological analysis was applied to the feature values of English Q&A statements. As a result of multiple regression analysis, it has been shown that the application of 2-gram would result in as good an accuracy as morphological analysis for all nine factors. Therefore, it could be concluded that 2-gram would be applicable to our method in English as well.
Comparison of reinforcement learning in game AI

In recent years, various reinforcement learning algorithms have been developed, and research is being conducted to apply them to game AI. We aim to develop a game AI that is strong against any game. To achieve this, we need to investigate existing reinforcement learning algorithms, to evaluate the maximum score, average score and standard deviations, and clarify their characteristics.This paper compares 19 typical games of Atari video games with on-policy algorithm PPO and off-policy algorithms DQN and ACER with average, maximum score, and standard deviation. As a result of the experiment, we find that it can be roughly divided into an algorithm (DQN) having maximum score and large standard deviation value and an algorithm (PPO) having low score and small standard deviation. In addition, ACER stops learning while the score is low for many games. We also find that DQN can achieve higher score than ACER and PPO in total. In addition, there are games with maximum score and large standard deviation value, and games with low maximum score but a low standard deviation. From the experimental results, we investigate how to realize general-purpose strong reinforcement learning game AI for the general public by using the rule of getting high score only once or Always get a high score.
What are they Talking About? A Systematic Exploration of Theme Identification Methods for Character Speech in Dramatic Texts

Zusammenfassung This article systematically explores different methods to describe and compare the thematic content of character speech in drama. The problems here are two-fold: The determination of the theme of a text segment is challenging, as it might be subtle or deliberately hidden. Secondly, the evaluation of this determination is difficult, because gold standards do not exist. We approach these problems by comparing the output of different systems to previously stated expectations (postulates) that are partially derived from drama theory and history. Results show that all systems currently have difficulties reaching high performance numbers. Topic modeling and systems based on a general-purpose dictionary ( GermaNet ) achieve comparable performance.