Reviewerly/exHarmony huggingface dataset:
Teaching and Learning foundation of the elective course "Cultural Geography"

At present, one of the urgent problems is the systematization of information on elective courses and the identification of methodological features of its implementation. The article presents the program of the elective course " Cultural geography" for classes of the natural-mathematical direction. In addition, the methodological structure of the course " Cultural geography " has been worked out. The article provides an analytical review of the latest sources on the cultural geagraphy in Russian and English. Keywords: elective course, geography of culture, program, annual plan, methodological structure.
Research on Face Recognition Algorithm Based on Multi-Class Support Vector Machine

Abstract Facial recognition is one of the main research directions in the field of artificial intelligence and image processing. It has been widely used in identity authentication, video surveillance and biological detection. Because it is non-contact, natural, convenient and reliable, facial recognition has become a popular choice for biometric systems. The accuracy of facial recognition still needs to be improved, the main goal of this paper is to improve the accuracy of face recognition. Based on the support vector machine method, the focus is on the feature extraction and feature matching of face images. In view of the particularity of face images, the pre-processing of face images is studied. In this paper, grayscale normalization and geometric normalization pre-processing methods are used. In order to reduce the interference factors of the image as much as possible, the features are high-lighted, and the non-featured parts are weakened, this paper adopts the Histogram of Oriented Gradient feature extraction method. Then we proposed a new method based on SVM, which uses a one-to-many method to construct multiple SVM classifiers, selects the optimal parameters through repeated experiments, and selects ORL face database for testing. The recognition rate can reach about 98.5%.
SPESC-Translator: Towards Automatically Smart Legal Contract Conversion for Blockchain-based Auction Services

Smart contract is a set of digital executable protocols intended to make contractual clauses partially or fully self-executing, self-enforcing, or both. As the second-generation blockchain technology, smart contracts have greatly enriched the functional expression of blockchain to make application development more convenient. All related data of smart contracts, including program codes, intermediate states, and executed results, will be stored in blockchain to ensure that these data are not tampered with. Also, the consensus protocols of blockchain verify the correctness of the running process by executing the smart contract with the same input at all nodes. Therefore, the blockchain’s security mechanism with tamper-proof and traceable makes it possible for smart contracts to be recognized by law.
Towards Interpretable Deep Neural Networks: An Exact Transformation to Multi-Class Multivariate Decision Trees.

Deep neural networks (DNNs) are commonly labelled as black-boxes lacking interpretability; thus, hindering human's understanding of DNNs' behaviors. A need exists to generate a meaningful sequential logic for the production of a specific output. Decision trees exhibit better interpretability and expressive power due to their representation language and the existence of efficient algorithms to generate rules. Growing a decision tree based on the available data could produce larger than necessary trees or trees that do not generalise well. In this paper, we introduce two novel multivariate decision tree (MDT) algorithms for rule extraction from a DNN: an Exact-Convertible Decision Tree (EC-DT) and a Deep C-Net algorithm to transform a neural network with Rectified Linear Unit activation functions into a representative tree which can be used to extract multivariate rules for reasoning. While the EC-DT translates the DNN in a layer-wise manner to represent exactly the decision boundaries implicitly learned by the hidden layers of the network, the Deep C-Net inherits the decompositional approach from EC-DT and combines with a C5 tree learning algorithm to construct the decision rules. The results suggest that while EC-DT is superior in preserving the structure and the accuracy of DNN, C-Net generates the most compact and highly effective trees from DNN. Both proposed MDT algorithms generate rules including combinations of multiple attributes for precise interpretation of decision-making processes.
Investigating the Helpfulness of Word-Level Quality Estimation for Post-Editing Machine Translation Output

Compared to fully manual translation, postediting (PE) machine translation (MT) output can save time and reduce errors.Automatic word-level quality estimation (QE) aims to predict the correctness of words in MT output and holds great promise to aid PE by flagging problematic output.Quality of QE is crucial, as incorrect QE might lead to translators missing errors or wasting time on already correct MT output.Achieving accurate automatic word-level QE is very hard, and it is currently not known (i) at what quality threshold QE is actually beginning to be useful for human PE, and (ii), how to best present wordlevel QE information to translators.In particular, should word-level QE visualization indicate uncertainty of the QE model or not?In this paper, we address both research questions with real and simulated word-level QE, visualizations, and user studies, where time, subjective ratings, and quality of the final translations are assessed.Results show that current wordlevel QE models are not yet good enough to support PE. Instead, quality levels of ≥ 80% F1 are required.For helpful quality levels, a visualization reflecting the uncertainty of the QE model is preferred.Our analysis further shows that speed gains achieved through QE are not merely a result of blindly trusting the QE system, but that the quality of the final translations also improves.The threshold results from the paper establish a quality goal for future wordlevel QE research.
Alternative learning media post-covid-19: uncertainty reduction theory perspective

This study aims to determine how users respond to the use of social media during online learning after the COVID-19 pandemic from the perspective of uncertainty reduction theory. This study uses a phenomenological research method, where data collection is carried out by means of Focus Group Discussion (FGD), which was conducted twice on 15 users of online learning applications. Through this research, it is known that the user's response from the compulsion in using the online learning model is that the subject tends to feel mostly bored, difficult to discipline, tendency to be lazy, and has the proclivity to be dishonest. In addition, in terms of emotional connection, teachers and students do not have a good bond, which is far different when using the offline learning modality. The majority of users prefer to utilize the offline learning system when the pandemic ends. One person prefers to use a hybrid system which is a combination of online and offline learning method.
Perbandingan model problem based learning (PBL) dan model pembelajaran konvensional terhadap hasil belajar IPS terpadu siswa SMP Negeri 6 Welak

This study focuses on discussing the results of the analysis of (1) the value of integrated social studies learning outcomes of students who are taught using the Problem based learning (PBL) model, (2) the value of integrated social studies learning outcomes of students who are taught using conventional learning models, (3) comparison of the results of Integrated Social Studies learning students who are taught using the Problem based learning (PBL) model with the value of Integrated Social Studies learning outcomes students who are taught using conventional learning models. This research method is quantitative with a quasi-experimental design. The population of this study was students of class VIII A and VIII B of SMP Negeri 6 Welak 2020/2021, with a total of 50 students (classes VIII A and VIII B each consisted of 25 students). Sampling uses total sampling, so that all members of the population are assigned to be the sample, where class VIII A is assigned to be the experimental class and class VIII B is assigned to be the control class. The treatment of the PBL model was only applied to the experimental class, while the control class was taught by the teacher using a conventional learning model. This research data collection using test technique. Data analysis techniques include normality test, homogeneity test, and Independent Sample T Test. The results of this study conclude: (1) the value of Integrated Social Studies learning outcomes for class VIII students of SMP Negeri 6 Welak who are taught using the Problem based learning (PBL) model is in the 'very good' category; (2) the value of Integrated Social Studies learning outcomes for grade VIII SMP Negeri 6 Welak students who are taught using conventional learning models are in the 'good' category; (3) the value of Integrated Social Studies learning outcomes for students of class VIII SMP Negeri 6 Welak who are taught using the Problem based learning (PBL) model is significantly different from the value of Integrated Social Studies learning outcomes of students who are taught using conventional learning models.
Design of Context Cluster and Load Balanced Video Recommendation System in Cloud Computing

Nowadays, in online social networks, there is an instantaneous extension of multimedia services and there are huge offers of video contents which has hindered users to acquire their interests. To solve these problem different personalized recommendation systems had been suggested. Although, all the personalized recommendation system which have been suggested are not efficient and they have significantly retarded the video recommendation process. So to solve this difficulty, context extractor based video recommendation system on cloud has been proposed in this paper. Further to this the system has server selection technique to handle the overload program and make it balanced. This paper explains the mechanism used to minimize network overhead and recommendation process is done by considering the context details of the users, it also uses rule based process and different algorithms used to achieve the objective. The videos will be stored in the cloud and through application videos will be dumped into cloud storage by reading, coping and storing process.
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data

Estimating personalized treatment effects from high-dimensional observational data is essential in situations where experimental designs are infeasible, unethical, or expensive. Existing approaches rely on fitting deep models on outcomes observed for treated and control populations. However, when measuring individual outcomes is costly, as is the case of a tumor biopsy, a sample-efficient strategy for acquiring each result is required. Deep Bayesian active learning provides a framework for efficient data acquisition by selecting points with high uncertainty. However, existing methods bias training data acquisition towards regions of non-overlapping support between the treated and control populations. These are not sample-efficient because the treatment effect is not identifiable in such regions. We introduce causal, Bayesian acquisition functions grounded in information theory that bias data acquisition towards regions with overlapping support to maximize sample efficiency for learning personalized treatment effects. We demonstrate the performance of the proposed acquisition strategies on synthetic and semi-synthetic datasets IHDP and CMNIST and their extensions, which aim to simulate common dataset biases and pathologies.
Decentralized Collaborative Learning with Probabilistic Data Protection

We discuss future directions of Blockchain as a collaborative value co-creation platform, in which network participants can gain extra insights that cannot be accessed when disconnected from the others. As such, we propose a decentralized machine learning framework that is carefully designed to respect the values of democracy, diversity, and privacy. Specifically, we propose a federated multi-task learning framework that integrates a privacy-preserving dynamic consensus algorithm. We show that a specific network topology called the expander graph dramatically improves the scalability of global consensus building. We conclude the paper by making some remarks on open problems.
A Novel Fast Template Matching Algorithm Based on Bounded Approach Using Block Partitioning Method

Exhaustive search template matching in real time object detection requires a large computational time. Reducing the number of computation for full search template matching is the current research interest. This paper presents a fast and efficient template matching method that uses difference of sum norms (L2 norm) as bounding measure that rapidly eliminates large number of mismatching candidates identifying only small number of locations on which the computation of normalized cross correlation (NCC) function is carried out. This method ensures high degree of detection accuracy and furnishes the same result as the full search NCC algorithm. Experimental comparison has been done with existing fast methods using number of real image data sets consisting of small to very large size images with number of template images varying in size. The results show that the proposed algorithm is significantly faster than the other fast matching algorithms.
Single Image Dehazing Method Based on Semi-Training Color Stripping

Abstract In this paper, a semi training color stripping dehaze-net (STCSDN) is proposed which is a new method of dehazing with strong adaptability. It mainly depends on two important properties of convolution neural network in the process of dehazing. One is that the learning speed of convolutional neural network for contour and shadow information is faster than that for color information. The other is that network is not sensitive to haze concentration. Based on property one, STCSDN extracts the haze free gray image from the hazy color image by using the semi trained generator as the sketch extraction module through the CycleGAN. The gray image only contains the image contour and shadow information, and discards the original color information interfered by haze information. This method has strong adaptability, visibility and authenticity, and can be applied to any scene. Based on property two, STCSDN can process hazy images with different concentrations and get better results. Through the simulation experiments on different types of dehazing data sets, it is proved that the STCSDN proposed in this paper can remove the influence of haze, restore the image details and enhance the visual effect.
Development of a Verbal Robot Hand Gesture Recognition System

This article analyzes the most famous sign languages, the correlation of sign languages, and also considers the development of a verbal robot hand gesture recognition system in relation to the Kazakh language. The proposed system contains a touch sensor, in which the contact of the electrical property of the user's skin is measured, which provides more accurate information for simulating and indicating the gestures of the robot hand. Within the framework of the system, the speed and accuracy of recognition of each gesture of the verbal robot are calculated. The average recognition accuracy was over 98%. The detection time was 3ms on a 1.9 GHz Jetson Nano processor, which is enough to create a robot showing natural language gestures. A complete fingerprint of the Kazakh sign language for a verbal robot is also proposed. To improve the quality of gesture recognition, a machine learning method was used. The operability of the developed technique for recognizing gestures by a verbal robot was tested, and on the basis of computational experiments, the effectiveness of algorithms and software for responding to a verbal robot to a voice command was evaluated based on automatic recognition of a multilingual human voice. Thus, we can assume that the authors have proposed an intelligent verbal complex implemented in Python with the CMUSphinx communication module and the PyOpenGL graphical command execution simulator. Robot manipulation module based on 3D modeling from ABB.
Designing a System of Monitoring for the Digital Learning Environment in Supplementary Education for Children

One of the most important tasks of education of the Russian Federation is the transformation of Supplementary education. The monitoring system is a tool for implementing the state policy in the field of Supplementary education. The urgency of creating a monitoring system that monitors the digital educational environment of Supplementary education of children has been determined. This is based on the analysis of modern trends and directions of modernization of Supplementary education of children. The Purpose of the article is the investigation of theoretical prerequisites for designing a system of monitoring for the digital educational environment in the Supplementary education of children in the Russian Federation. Research methodology and methods. The methods of theoretical research (analysis and synthesis) of special literature and regulatory documents in the field of Supplementary education and digital educational environment was used in this article. Conclusion. The prerequisites for the design of a system’s monitoring for the digital educational environment in the Supplementary education of children in the Russian Federation was investigated. One of the mechanisms for the updating of additional education for children can be the national system of monitoring of the digital educational environment. Significant indicators of the monitoring of the practices of a Supplementary education in the digital educational space was proposed.
Effective Fine-Tuning Methods for Cross-lingual Adaptation

Large scale multilingual pre-trained language models have shown promising results in zeroand few-shot cross-lingual tasks.However, recent studies have shown their lack of generalizability when the languages are structurally dissimilar.In this work, we propose a novel fine-tuning method based on co-training that aims to learn more generalized semantic equivalences as complementary to multilingual language modeling using the unlabeled data in the target language.We also propose an adaption method based on contrastive learning to better capture the semantic relationship in the parallel data, when a few translation pairs are available.To show our method's effectiveness, we conduct extensive experiments on cross-lingual inference and review classification tasks across various languages.We report significant gains compared to directly finetuning multilingual pre-trained models and other semi-supervised alternatives. 1
The impact of AR-HUD intelligent driving on the allocation of cognitive resources under the breakthrough of 5G technology

Abstract This paper focuses on the establishment of an ARHUD assisted driving test system based on a VR platform, which has the advantages of high security and immersion, repeatable experiments and the ability to perform eye-movement analysis. This paper first defines and designs the vehicle driving safety icons based on human-computer interaction principles and engineering psychology, supplemented by PS to define and design the AR-HUD interface while combining mental load and other factors, then uses 3Dsmax software to build the 3D model material required for driving, then builds the driving environment and designs various driving emergencies in Unity based on featured technologies such as multi-channel rendering and global illumination, and then combines HTC VIVE Pro eye display. Thirty drivers were then tested on a distraction task. Analysis of the subjects’ eye-movement data revealed that the AR-HUD system improved the cognitive efficiency of the drivers compared to the traditional driving method while allocating cognitive resources to the central driving area, speed module, navigation information, and hazard warnings in a balanced manner, thus improving the ability to react to unexpected driving events.
ANALISIS DETERMINASI DIRI REMAJA

Self-determination is the ability of individuals to direct themselves to achieve life goals that are driven by the fulfillment of autonomous needs, competencies and relationships with others. The purpose of this study was to determine the picture of self-determination in adolescents of class XI SMK As-Saabiq Singaparna, differences based on gender, based on majors and design guidance and counseling services to improve self-determination of adolescent students. This study uses quantitative descriptive methods to uncover the picture of self-determination of adolescent students. The sample in this study was class XI As-Saabiq Singaparna Vocational School, amounting to 191 students, the sample in this study was taken using a saturated sampling technique that is by making the population as a sample sample. The results of this study in general the self-determination of class XI students of SMK As-Saabiq are in the category of high self-determination of 54% (103 students). The highest aspect is competence while the lowest aspect is the autonomy aspect. Based on differences in majors there are differences and based on gender shows Sig. 0.002 which means there is a significant difference between self-determination in male and female students of class XI Vocational School in As-Saabiq. The weaknesses of this study are the sample is still limited so that it only knows the self-determination of class XI students of SMK As-saabiq
Smart Surveillance System for Abnormal Activity Detection Using CNN

Crime is the factor increases day by day and also needs the solution for identifying these activities in an efficient and quick manner. Many surveillance systems use artificial intelligence and image processing are incorporated with them to implement an intelligent surveillance system. But most of the systems are provided the alarm or identifies the crime after it happens. To solve this problem, camera footage-based theft detection will be used with the help of machine learning to detect theft occurrence. System will detect the human activity with the help of open-pose algorithm and convolution neural networks. Then the footage will be examined based on the pretrained model and it will be classified into three categories namely safe, abnormal or crime. Convolution neural network is used to classify the motion and an alert message will be sent to the owner along with captured image and options such as neglect or call the police.
Adaptive Hybrid Sampling Algorithm Based on BIRCH Clustering

Category imbalance has always been a hot spot in data analysis and research, where large differences between data samples can limit the performance of classifiers and lead to low sample detection rates. Resampling is currently a common method to deal with imbalance problems, aiming to balance the data by increasing the minority class samples or reducing the majority class samples. A hybrid sampling algorithm based on BIRCH clustering (HSAB) is proposed in this paper. By using BIRCH clustering to improve the shortcomings of the SMOTE algorithm, adaptively adjust the weights in different sparse regions of minority classes to improve the imbalance within and between classes. Gaussian mixture model (GMM) is used to cluster majority classes to remove redundant samples and construct a balanced dataset. The experimental results show that the balanced data set obtained by the algorithm can be effectively used for classification learning, and it provides a good strategy for classification of unbalanced data.