Reviewerly/exHarmony huggingface dataset:
Analysis of the external context of educational organizations that significantly affects educational outcomes in regional education management systems

Introduction. The relevance of the study is due to the contradiction between the presence of common strategic objectives for the education development to ensure the competitiveness of Russian schoolchildren in international comparative studies and the lack of a mechanism that allows one to objectively assess the differences between educational organizations due to the peculiarities of the student body and the characteristics of the territory where they are located. The research purpose is to form a list of characteristics that are consistent with international and Russian monitoring studies and forms of statistical reporting, making it possible to identify educational organizations operating in different socio-economic conditions. Materials and methods. The study was carried out on the databases of the results of monitoring studies and procedures for assessing the quality of general education in the Tomsk region. The Mann-Whitney U test was used to assess the statistical significance of differences in data. Research results. An information base has been formed, including aggregated socio-economic characteristics correlated with the educational achievements of the student body from 273 general education organizations of the Tomsk region (data as of 2019). The revealed statistically significant differences (p&lt;0.001) between small, rural and urban educational organizations will make it possible to objectively analyze the educational achievements of a particular educational organization, specifically identify deficiencies and effective management practices, as well as coordinate the socio-economic policy of the Tomsk region. Discussion and conclusion. The agreement of the list of socio-economic characteristics with international studies initiated by the Organization for Economic Cooperation and Development (OECD) will allow regional education authorities, regardless of participation in international comparative studies, to calculate a quantitative index reflecting educational advantages or disadvantages due to external factors relating to the educational organization. The index calculation in Russian practice is a new urgent task, which is difficult and resource-intensive without special software tools.
EFL college students writing anxiety of English language education at Universitas Internasional Batam

Writing is considered to be the most difficult skill to master for EFL students. This experience was faced by English Language Education (ELE) students at Universitas Internasional Batam (UIB). Based on the observation, the results of their writings were not satisfying. This study assumed that the students might feel anxious when they were writing in English. Therefore, this study aims to examine the types and causes of writing anxiety of ELE students at UIB. This study employed explanatory design of mixed method research. The research instruments were questionnaires adopted from SLWAI and CWAI developed by Cheng (2004). To extend the deeper analysis, this study conducted interviews to the high- and low-level writing anxiety students. This study found that the most dominant levels of writing anxiety is high-anxiety (71.43%). In terms of writing anxiety, this study found that cognitive anxiety is the most dominant type with the mean of 26.37. Next, when studying the cause of writing anxiety, the most dominant cause was linguistic difficulties. This result was in line with the interviews with high-and low- writing anxiety students. Students with both high and low writing anxiety still found that linguistic difficulties affect their writing anxiety. Therefore, the learning process at English Language Education needs to be equipped with sufficient linguistic competences in the first year, such as vocabulary, grammar, and punctuation.
The effectiveness of feature attribution methods and its correlation with automatic evaluation scores

Explaining the decisions of an Artificial Intelligence (AI) model is increasingly critical in many real-world, high-stake applications. Hundreds of papers have either proposed new feature attribution methods, discussed or harnessed these tools in their work. However, despite humans being the target end-users, most attribution methods were only evaluated on proxy automatic-evaluation metrics. In this paper, we conduct the first, large-scale user study on 320 lay and 11 expert users to shed light on the effectiveness of state-of-the-art attribution methods in assisting humans in ImageNet classification, Stanford Dogs fine-grained classification, and these two tasks but when the input image contains adversarial perturbations. We found that, in overall, feature attribution is surprisingly not more effective than showing humans nearest training-set examples. On a hard task of fine-grained dog categorization, presenting attribution maps to humans does not help, but instead hurts the performance of human-AI teams compared to AI alone. Importantly, we found automatic attribution-map evaluation measures to correlate poorly with the actual human-AI team performance. Our findings encourage the community to rigorously test their methods on the downstream human-in-the-loop applications and to rethink the existing evaluation metrics.
Improving Character-Aware Neural Language Model byWarming Up Character Encoder under Skip-gram Architecture

Character-aware neural language models can capture the relationship between words by exploiting character-level information and are particularly effective for languages with rich morphology.However, these models are usually biased towards information from surface forms.To alleviate this problem, we propose a simple and effective method to improve a character-aware neural language model by forcing a character encoder to produce wordbased embeddings under Skip-gram architecture in a warm-up step without extra training data.We empirically show that the resulting character-aware neural language model achieves obvious improvements of perplexity scores on typologically diverse languages, that contain many low-frequency or unseen words.
Negative Sample is Negative in Its Own Way: Tailoring Negative Sentences for Image-Text Retrieval

Matching model is essential for Image-Text Retrieval framework.Existing research usually train the model with a triplet loss and explore various strategy to retrieve hard negative sentences in the dataset.We argue that current retrieval-based negative sample construction approach is limited in the scale of the dataset thus fail to identify negative sample of high difficulty for every image.We propose our TAiloring neGative Sentences with Discrimination and Correction (TAGS-DC) to generate synthetic sentences automatically as negative samples.TAGS-DC is composed of masking and refilling to generate synthetic negative sentences with higher difficulty.To keep the difficulty during training, we mutually improve the retrieval and generation through parameter sharing.To further utilize fine-grained semantic of mismatch in the negative sentence, we propose two auxiliary tasks, namely word discrimination and word correction to improve the training.In experiments, we verify the effectiveness of our model on MS-COCO and Flickr30K compared with current state-of-theart models and demonstrates its robustness and faithfulness in the further analysis.
Fraudulent Detection Model Using Machine Learning Techniques for Unstructured Supplementary Service Data

The increase in mobile phones accessibility and technological advancement in almost every corner of the world has shaped how banks offer financial service. Such services were extended to low-end customers without a smartphone providing Alternative Banking Channels (ABCs) service, rendering regular financial service same as those on smartphones. One of the services of this ABC’s is Unstructured Supplementary Service Data (USSD), two-way communication between mobile phones and applications, which is used to render financial services all from the bank accounts linked for this USSD service. Fraudsters have taken advantage of innocent customers on this channel to carry out fraudulent activities with high impart of fraudulent there is still not an implemented fraud detection model to detect this fraud activities. This paper is an investigation into fraud detection model using machine learning techniques for Unstructured Supplementary Service Data based on short-term memory. Statistical features were derived by aggregating customers activities using a short window size to improve the model performance on selected machine learning classifiers, employing the best set of features to improve the model performance. Based on the results obtained, the proposed Fraudulent detection model demonstrated that with the appropriate machine learning techniques for USSD, best performance was achieved with Random forest having the best result of 100% across all its performance measure, KNeighbors was second in performance measure having an average of 99% across all its performance measure while Gradient boosting was third in its performance measure, its achieved accuracy is 91.94%, precession is 86%, recall is 100% and f1 score is 92.54%. Result obtained shows two of the selected machine learning random forest and decision tree are best fit for the fraud detection in this model. With the right features derived and an appropriate machine learning algorithm, the proposed model offers the best fraud detection accuracy.
Semi-Supervised Learning based on Auto-generated Lexicon using XAI in Sentiment Analysis

In this study, we proposed a novel Lexicon-based pseudo-labeling method utilizing explainable AI(XAI) approach.Existing approach have a fundamental limitation in their robustness because poor classifier leads to inaccurate soft-labeling, and it lead to poor classifier repetitively.Meanwhile, we generate the lexicon consists of sentiment word based on the explainability score.Then we calculate the confidence of unlabeled data with lexicon and add them into labeled dataset for the robust pseudo-labeling approach.Our proposed method has three contributions.First, the proposed methodology automatically generates a lexicon based on XAI and performs independent pseudolabeling, thereby guaranteeing higher performance and robustness compared to the existing one.Second, since lexiconbased pseudo-labeling is performed without re-learning in most of models, time efficiency is considerably increased, and third, the generated high-quality lexicon can be available for sentiment analysis of data from similar domains.The effectiveness and efficiency of our proposed method were verified through quantitative comparison with the existing pseudo-labeling method and qualitative review of the generated lexicon.
Cervical Cancer Image Classification Using CNN Transfer Learning

Cervical cancer is a major global public health problem, Indonesia is among top 3 countries in the world with the highest number of cervical cancer incidents.An early diagnosis for cervical cancer is one of the key approaches to prolong patient's life expectancy.The Papanicolaou (Pap smear) test is a cervical cancer screening test that has been widely utilized.Pap smear test is a tedious, labour-intensive, and time-consuming task, which leads to high inter operator's variability.A computer-based classification algorithm to assist the task has been proposed.In this paper we focus on the approaches using Convolutional Neural Network (CNN) to handle the classification task.Moreover, our proposal employs a parameter efficient model.Thus, the computational cost is greatly reduced.We use a transfer learning method for model adaptation.We trained the pre-trained SqueezeNet architecture with the three class of pap smear images dataset in caffe.The fine-tuning process was started with the initialization of the model's features to the object's broader spectrum.Then, the last layer output number was changed to fit the number of labels for cervical cancer class.
An Evaluative Study of Criminalistics: A Case of South African Counterfeiting and Knock-Off Menace

Counterfeit goods are a health hazard to human life, environment and business. More so as the goods destroy the viability of legitimate businesses across the world. Counterfeit goods are characterized as a threat to the financial viability of legitimate businesses and has a dire impact on the tax revenue of countries, necessitating its effective and efficient policing for the financial wellbeing of countries. If not well managed, counterfeit goods could lead to unemployment, disinvestment and capital flight in countries. The available evidence reveals that even though counterfeit goods has dire consequences for the economic wellbeing of countries, its penalties in most African countries are less severe in comparison to penalties for other crimes. The sight of counterfeit goods in many places, such as on busy street corners and transportation terminals in the Southern parts of Africa, attests to its prevalence in the continent. Especially in South Africa as the country has become a transit point for contrabands. Post 1994, South Africa opened its borders to the world of branded and protected goods and this enabled rogue people to trade in counterfeit goods. Effective policing is hindered by the use of sophisticated skills and expertise that leads to counterfeit goods resembling genuine goods. Moreover, in South Africa, the problem persists despite the promulgation of the Counterfeit Goods Act 37 of 1997, thus questioning the effectiveness of the policing strategies in use to quell the problem.
XR-TECAN Teaching Model for Chinese Traditional Art Education

Chinese traditional art education plays a unique role in transmitting traditional culture and in developing social civilization but is limited by the monotony of the teaching model structure, its inflexibility in learning time and space, constraints in the teaching scale, and lack of immersive experience. This article discusses the application of extended reality (XR) technology to the seven links of Chinese traditional art education, namely introduction, evaluation, analysis, practice, adjustment, appraisal and research, initiating a new human-machine interactive virtual reality teaching model, known as the XR-TECAN teaching model for Chinese traditional art education. This new teaching model covers basic technology, the equipment environment, content resources, application implementation and the digital cloud. Compared with the old apprenticeship model in traditional art education, the XR-TECAN teaching model is a breakthrough that allows learners to freely switch between virtual reality and the real world in a deeply immersive experience that improves education efficacy.
IOT Security with Blockchain

Internet of Things (IoT) plays a very vital role in almost every field of technology. Its key elements of security are very much crucial elements which are to be maintained as it is used universally. Traditional security and privacy methods be likely to be inapplicable for IoT, mainly due to its distributed network topology and the supply-constraints of most of its devices. For creating these concepts, a new technology called blockchain can be used. Blockchain that reinforce the crypto- currency Bitcoin have been just now used to provide security and privacy in peer-to-peer networks with similar topologies to IoT. In this paper, we present basic part of IoT enabled with blockchain, their distinctive features of both the technologies, their futuristic solutions for different real-world issues, different communicational standards etc. Both the technologies have several distinctive characteristics in every field, but certain limits too exist which gives futuristic path for research.
Retraction Note: Active components of mountain soil based on clustering algorithm and evaluation of Internet English teaching

According to the ICPP report, by the end of this century, global temperature will rise, leading to sea level rise and frequent extreme weather conditions, and ecosystem services and biodiversity decline, threatening global food supply and human safety and health. Soil organic carbon is the largest carbon pool in terrestrial ecosystems and plays an important role in global greenhouse gas balance. Enzymatic reaction is the rate-limiting step of SOC degradation. Therefore, it is necessary to understand the heating characteristics and the binding characteristics of SOC and soil enzymes. This relationship is very important for soil carbon sequestration and emission reduction. On this basis, combined with the adaptive weighted data fusion technology, this paper uses the clustering algorithm to optimize the dual sensor cluster to study the active components of mountain soil. With the development and popularization of the Internet, the Internet has become an important part of students’ study and life. A network learning platform can make smartphones become an important learning tool, so that students can learn new methods and teachers can provide new ideas and models of English teaching, and combine information technology to improve the efficiency of English teaching, thus greatly improving the students’ interest in learning. In this context, the traditional teaching evaluation method should be changed, and students should be regarded as the core part of classroom evaluation, so that teachers can have a more complete understanding of students, pay attention to students’ personal needs, and achieve the goal of obtaining high-quality education. Therefore, this paper analyzes how to effectively evaluate English teaching according to informatization, and puts forward several effective strategies.
Opinion Analysis of Traveler Based on Tourism Site Review Using Sentiment Analysis

Technology development nowadays makes it easier for people to access information. One of them is to find information regarding a place. Many prospective visitors would read reviews from people who have visited a place to find out how they rate a place. Opinion on other people’s reviews is very influential in influencing others’ decisions in assessing a place they want to visit. Opinion analysis can be done by conducting a sentiment analysis of hotel customer reviews. The data used are traveler reviews of hotels in East Java on the Tripadvisor site. Traveler reviews data was taken by crawling on tourist sites, and the unstructured reviews data would be a preprocessing and weighted term from reviews using the TF-IDF method. The classification process is done using the support vector machine method to find opinions from traveler reviews, which are positive or negative. Based on the classification results, hotels that have the most positive sentiments in Surabaya are Harris Hotel Gubeng and Pop! Hotel Gubeng with the same number of reviews, 252 reviews. In comparison, hotels with the most positive sentiment in Malang are Harris Hotel Malang with 311 reviews. The opinion analysis results are expected to help the hotel manager evaluate and develop to increase the number of tourist visits.
Learning to Navigate in a VUCA Environment: Hierarchical Multi-expert Approach

Despite decades of efforts, robot navigation in a real scenario with volatility, uncertainty, complexity, and ambiguity (VUCA for short), remains a challenging topic. Inspired by the central nervous system (CNS), we propose a hierarchical multi-expert learning framework for autonomous navigation in a VUCA environment. With a heuristic exploration mechanism considering target location, path cost, and safety level, the upper layer performs simultaneous map exploration and route-planning to avoid trapping in a blind alley, similar to the cerebrum in the CNS. Using a local adaptive model fusing multiple discrepant strategies, the lower layer pursuits a balance between collision-avoidance and go-straight strategies, acting as the cerebellum in the CNS. We conduct simulation and real-world experiments on multiple platforms, including legged and wheeled robots. Experimental results demonstrate our algorithm outperforms the existing methods in terms of task achievement, time efficiency, and security. A video of our results is available at https://youtu.be/lAnW4QIWDoU.
Implementation of Shor’s Quantum Factoring Algorithm Using ProjectQ Framework

Prime number factorization is a problem in computer science where the solution to that problem takes super-polynomial time classically. Shor’s quantum factoring algorithm is able to solve the problem in polynomial time by harnessing the power of quantum computing. The implementation of the quantum algorithm itself is not detailed by Shor in his paper. In this paper, an approach and experiment to implement Shor’s quantum factoring algorithm are proposed. The implementation is done using Python and a quantum computer simulator from ProjectQ. The testing and evaluation are completed in two computers with different hardware specifications. User time of the implementation is measured in comparison with other quantum computer simulators: ProjectQ and Quantum Computing Playground. This comparison was done to show the performance of Shor’s algorithm when simulated using different hardware. There is a 33% improvement in the execution time (user time) between the two computers with the accuracy of prime factorization in this implementation is inversely proportional to the number of qubits used. Further improvements upon the program that has been developed for this paper is its accuracy in terms of finding the factors of a number and the number of qubits used, as previously mentioned.
Anchored versus Anchorless Detector for Car Detection in Aerial Imagery

With the increase in the traffic on roadways, traffic monitoring is the major need we have at this moment. Using UAVs for traffic monitoring has numerous advantages such as broader field of view, higher mobility, no effect on detected traffic, etc., however, variation in camera orientation, UAV height, cluttered background imposes challenges to this aerial object detection. To provide a UAV-based traffic monitoring solution, we have proposed a car detection system for UAV images using deep learning approaches. We compared the performance of the anchorless Fully Convolutional One Stage (FCOS) object detection algorithm with the popular YOLOv3 algorithm. The performance analysis of these models based on mean Average Precision (mAP) indicates that FCOS yields better results over YOLOv3, whereas in terms of computation speed YOLOv3 performed better.
NEURON NETWORKS DESIGN IN MATLAB AND VIVADO

Abstract This article is devoted to design of a measurement system based on specialized FPGA. A balance of ACD and DAC channels through output from one side and computation power of FPGA from another side is considered. Possibilities for obtaining one more tool for screening the signal processing is proposed. Keywords: FPGA, analog signal, digital signal processing, measurement system.
Transfer learning for Czech Historical Named Entity Recognition

Nowadays, named entity recognition (NER) achieved excellent results on the standard corpora.However, big issues are emerging with a need for an application in a specific domain, because it requires a suitable annotated corpus with adapted NE tag-set.This is particularly evident in the historical document processing field.The main goal of this paper consists of proposing and evaluation of several transfer learning methods to increase the score of the Czech historical NER.We study several information sources, and we use two neural nets for NE modeling and recognition.We employ two corpora for evaluation of our transfer learning methods, namely Czech named entity corpus and Czech historical named entity corpus.We show that BERT representation with fine-tuning and only the simple classifier trained on the union of corpora achieves excellent results.