Reviewerly/exHarmony huggingface dataset:
Computer Science at the Level of Secondary General Education: Basic Approaches to Implementation

The article considers the concepts of level and profile differentiation of content, issues of differentiation of the content of computer science training at the level of education in secondary school. The characteristic of the basic and profile level of computer science in high school is given; the potential of the school course in computer science in the development of thinking, the formation of a worldview and the improvement of the digital skills of a school graduate are revealed. The content of the school course in computer science is described in terms of its compliance with the main areas of the scientific field "Computer Science" and through digital technologies. Examples of the implementation of various options for teaching computer science in high school using the existing educational and methodological support are given.
MicroPython as a Development Platform for IoT Applications

The paper is focused on the possibilities of using high-level language - Python in the development of IoT applications. Described are the basic features of the MicroPython implementation and its use in the development of peripherals on the STM32 class microcontroller platform, as well as the possibilities of modification and extension of the standard implementation. Further are mentioned terms of code portability, modularity, flexibility, scalability and expandability significant for MicroPython applications.
A Framework to Enhance ICT Security Through Education, Training &amp; Awareness (ETA) Programmes in South African Small, Medium and Micro-sized Enterprises (SMMEs): A Scoping Review

ICT security has proven to be one of the fundamental needs of organizations across the world. There has been a lot of research that has been conducted on the subject of ICT security. There has also been a lot of initiatives that have been implemented to improve ICT security education, training and awareness in South Africa. With all these efforts in place, there is still an increase in ICT-related crime in the country. ICT-related crime occurs almost on a daily basis in small enterprises in South Africa. There is a need for the enhancement of ICT security in South African organizations. The South African Small, Medium and Micro-sized Enterprises (SMMEs) play a vital role in improving the economy. Literature has shown that SMMEs create a number of employment opportunities thereby contributing to the country’s economy. Therefore it is important that these institutions run effectively, are profitable and sustainable. Many small organizations (SMMEs) have been victims of ICT-related crime in South Africa. Most of these crimes happen in these institutions because they do not necessarily have the resources to protect themselves. In some cases, these crimes occur because, due to issues such as financial constraints and lack of skills, ICT security is not one of their priorities. This research aims to explore the literature review that has been conducted; in an effort to propose a framework that will use of Education, Training & Awareness (ETA) programmes to improve ICT security in South African small enterprises.
White blood cell detection and classification using Euler’s Jenks optimized multinomial logistic neural networks

Due to the wide acceptance of White Blood Cells (WBCs) in disease diagnosis, detection and classification of WBC are hot topic. Existing methodologies have some drawbacks such as significant degree of error, higher accuracy, time bound and higher misclassification rate. A WBCs detection and classification called, Jenks Optimized Logistic Convolutional Neural Network (JO-LCNN) method has proposed. Initally, Eulers Principal Axis is used as a convolution model to obtain a rotation invariant form of image by differentiating the background and RBCs, then eliminating them which leaves only the WBCs. By eliminating the wanton features, inherent features are detected contributing to minimum misclassification rate. According to above, Jenks Optimization function is used as a pooling model to obtain feature map for lower resolution. Therefore JO-LCNN is used for removing tiny objects in image and complete nuclei. Finally, Multinomial Logistic classifier is used to classify five types of classes by means of loss function and updating weight according to the loss function, therefore classifying with higher accuracy rate. Using LISC database for WBCs with different parameters as classification accuracy, false positive rate and time complexity are performed. Result shows that JO-LCNN, efficiently improves accuracy with less time, misclassification rate than the state-of-art methods.
From fact-checking to rhetoric-checking: extending methods for evaluating populist discourse

One of the existing methods for assessing populist discourse is by fact-checking. This practice, however, is limited in scope in that it only assesses the truth of an isolated statement of fact. In order to enlarge the existing repertoire for critically responding to populist discourse, Plug and Wagemans propose to develop a method for what they call ‘rhetoric-checking’, which is informed by insights from argumentation theory and rhetoric. In this chapter, they make the first step in this direction by developing a procedure for what they call ‘bridge-checking’. This procedure enables an assessment of the support relationship between the premise and the conclusion of an argument. After having explained its theoretical background, the authors describe the various steps of the procedure and illustrate its use by means of analyzing examples of arguments that are put forward by politicians that are labeled as populist and have been fact-checked by others.
Pedestrian Target Tracking Algorithm Based on Improved RANSAC and KCF

In order to track the specific pedestrian targets in public places, a universal pedestrian tracking algorithm based on improved RANSAC and KCF (Kernel Correlation Filter) was proposed. First, the improved RANSAC algorithm is used to match the SURF descriptor, the surveillance video of the target is automatically identified and the location of the target is marked. Second, the KCF classifier and the foreground-background model based on the color feature are trained respectively to obtain the target confidence map through feature fusion, which are used to predict the location of the next frame of the target. Third, the improved algorithm and the original KCF were tracked on several public data sets respectively, the results show that accuracy of the improved algorithm is higher than the original one. Finally, the target tracking experiments of different public places are carried out, and the versatility of the proposed algorithm are verified.
A Proposal of Children Learning System to Promote Self-directed Choosing of Learning Tasks and Analysis of Learning Data in a Programming Classroom

In this study, a learning data gathering system linked to developmental stages and learning activities was developed for the purpose of children’s self-directed choosing of learning tasks. The ability elements necessary for children to choose their own learning tasks were categorized into three categories: “computational thinking,” “ICT literacy,” and “social perspective,” and corresponded to the “programming classroom,” “IT university,” and “Town for Children” that the authors promote. We gathered learning data in programming classroom. We report the results of analysis.
Adaptive Procedures for Discriminating Between Arbitrary Tensor-Product Quantum States

Discriminating between quantum states is a fundamental task in quantum information theory. Given two quantum states, ρ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sub> and ρ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-</sub> , the Helstrom measurement distinguishes between them with minimal probability of error. However, finding and experimentally implementing the Helstrom measurement can be challenging for quantum states on many qubits. Due to this difficulty, there is a great interest in identifying local measurement schemes which are close to optimal. In the first part of this work, we generalize previous work by Acin et al. (Phys. Rev. A 71, 032338) and show that a locally greedy (LG) scheme using Bayesian updating can optimally distinguish between any two states that can be written as a tensor product of arbitrary pure states. We then show that the same algorithm cannot distinguish tensor products of mixed states with vanishing error probability (even in a large subsystem limit), and introduce a modified locally-greedy (MLG) scheme with strictly better performance. In the second part of this work, we compare these simple local schemes with a general dynamic programming (DP) approach. The DP approach finds the optimal series of local measurements and optimal order of subsystem measurement to distinguish between the two tensor-product states. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Semantic Predictive Coding with Arbitrated Generative Adversarial Networks

In spatio-temporal predictive coding problems, like next-frame prediction in video, determining the content of plausible future frames is primarily based on the image dynamics of previous frames. We establish an alternative approach based on their underlying semantic information when considering data that do not necessarily incorporate a temporal aspect, but instead they comply with some form of associative ordering. In this work, we introduce the notion of semantic predictive coding by proposing a novel generative adversarial modeling framework which incorporates the arbiter classifier as a new component. While the generator is primarily tasked with the anticipation of possible next frames, the arbiter’s principal role is the assessment of their credibility. Taking into account that the denotative meaning of each forthcoming element can be encapsulated in a generic label descriptive of its content, a classification loss is introduced along with the adversarial loss. As supported by our experimental findings in a next-digit and a next-letter scenario, the utilization of the arbiter not only results in an enhanced GAN performance, but it also broadens the network’s creative capabilities in terms of the diversity of the generated symbols.
Feature Extraction by Using Attention Mechanism in Text Classification

In recent years, machine learning technology has made great success in fields of computer vision, natural language processing, speech recognition and so on. And machine learning model has been widely used in face recognition, automatic driving, malware detection, intelligent medical analysis and other practical tasks. In this paper, attention mechanism is proposed to combine with LSTM model to extract features in text classification. The results show that, on the one hand, LSTM + Attention can improve classification performance; on the other hand, by sorting by word weights generated by the attention layer, we find some meaningful word features, however, its recognition performance is not good. Some possible reasons were analyzed and it was found that attention mechanism sometimes misjudges wrong word features, resulting from these wrong words often appearing at the same time with meaningful word features.
Intersection Traffic Flow Counting Based on Hybrid Regression Model

In this paper, we present a new method for vehicle counting using surveillance video used on the road. As the video passing through the road tends to be jammed and blocked, moving slowly and highly regularly, we propose a counting algorithm based on the regression model. We first process the surveillance video to extract features from the video. Then we use the SVM classifier to verify the vehicle density. Finally, we tested our approach using real pass-through video. Our experiments show that this method can count the flow with higher accuracy and lower computational cost.
Emotion Based Facial Expression Detection Using Machine Learning Approach

In this study emotion-based face expression recognition framework has been proposed using a machine vision (MV) approach.The face emotion dataset has been collected local survey in Bahawalpur city dataset divide into 3 classes happy, sad, and angry.A total of 600 images of size (256 x 256) were transformed into a gray level format and employed a median filter for noise removal.Three nonoverlapping regions of interest (ROIs) of size (50 x 50) have been taken and analyze 1800 (600 x 3) ROIs on the overall dataset.Total 45 Statistical features named as texture, histogram, and binary features were extracted.Select optimize features using the correlationbased feature section technique.The optimized dataset employed of MV classifiers namely random forest (RF), logistic (Lg), and J48 are obtained very promising accuracy 96.33%, 95.67%, and 95.33% respectively.
VEHICLE DETECTION IN HIGH RESOLUTION IMAGE BASED ON DEEP LEARNING

Abstract. Despite its high accuracy and fast speed in object detection, Single Shot Multi-Box Detector (SSD) tends to get undesirable results especially for small targets such as vehicles on high-resolution images. In this paper, we propose a new convolutional neural network based on SSD to detect vehicles on high-resolution images. In the proposed framework, the feature fusion module and detection module are incorporated. In the feature fusion module, feature maps of different scales are integrated into a fusion feature for object detection, which could improve the accuracy effectively. Besides, to prevent the network from overfitting and speed up the training, the batch normalization layer is embedded between the detection layers in the detection module. Some ablation experiments provide strong evidence for the effectiveness of these above structures. On the UCAS-High Resolution Aerial Object Detection Dataset, our network has the ability to achieve the 0.904 AP (average precision) with 0.094 AP higher than SSD512 but similar speed to it.
Multi-stage News-Stance Classification Based on Lexical and Neural Features

The amount of fake news present on the internet poses a great challenge for many online communities including manual fact-checkers who struggle to prevent the spread of misinformation and its negative impact. Detecting the stance of a news article involves classifying its perspective (e.g. agree, disagree, discuss, or unrelated) to a particular claim or headline which could support human fact-checkers to determine the veracity of the claims. Prior work on fake-news stance detection has proposed one-stage multi-class classification solutions which have limited success in detecting related pairs due to imbalanced class distributions in the data. This paper describes an improved approach to the stance detection of Fake News Challenge (FNC-1) based on multi-stage feature-assisted Deep Learning approaches. We break down the multi-class classification problem into two-stage and three-stage classifiers by combining the lexical-overlap features with Deep Learning techniques in an effort to mitigate the class imbalance problem. The experimental results demonstrate that the proposed models improve upon the state-of-the-art Accuracy and F1 score for stance detection. We also experimentally show that our models achieve solid results on minority classes i.e. agree and disagree without using fine-tuning approach or adding more training samples.
An Enhance Security of the Color Image Using Asymmetric RSA Algorithm

The development of computer networks generates several advantages in the cyber world. Because of this nice advancement, these days networking facilities are offered at terribly low cost. However, sadly this quick access becomes a threat to data security. In this paper, a completely unique color image secret writing technique is projected that is predicated on the asymmetric RSA algorithmic rule. The asymmetric RSA algorithm is one of the best algorithms which used the two different keys to complete the process. One is public key and another is private key which is generated using the two different prime numbers. In this technique, the sender has the public key used to encrypt the image and send it to the receiver and the receiver has the personal key that employed to decrypt the image and find the first that sent by sender. During this paper, primarily specialize in the protection of the color image that is finished using the proposed asymmetric RSA formula.
Prediction and Evaluation of Groundwater Environmental Impact Based on Numerical Simulation Model

Fang, Z.; Gao, H., and Ma, Z., 2020. Prediction and evaluation of groundwater environmental impact based on numerical simulation model. In: Bai, X. and Zhou, H. (eds.), Advances in Water Resources, Environmental Protection, and Sustainable Development. Journal of Coastal Research, Special Issue No. 115, pp. 286-288. Coconut Creek (Florida), ISSN 0749-0208.The prediction and evaluation of groundwater environment is very important for the utilization and protection of water resources and environment. Therefore, the prediction and evaluation of groundwater environmental impact is studied based on numerical simulation model. The design and implementation of the software part of the float system are explained, mainly around the function of the upper software monitoring system. This system realizes the real-time control of water environment information and the evaluation of water eutrophication in the later stage, as well as the function of water bloom warning and prediction. Then, the optimized hierarchical analysis algorithm is emphasized, which provides an effective decision-making platform for the prevention and control of water bloom. With the addition of the cloud-based network camera, the buoy can monitor the water surface 24 hours in real time, and support video playback, mobile phone watching the water surface image and other functions. The study provides a reference for the evaluation of groundwater environment and the wide application of mathematical models.
The IBM 4769 Cryptographic Coprocessor

System security is currently a main focus area for all IT infrastructure providers. New system features like pervasive encryption, the transition to cloud-based offerings, and the demand for quantum-safe platforms demand increased cryptographic performance as well as more cryptographic agility. The new IBM 4769 Cryptographic Coprocessor addresses these trends. It brings performance improvements that match the requirements of the new IBM z15. A combination of newly available features allows IBM z15 to scale to greater than 5,000 Virtual Hardware secure modules per system and makes it suitable to support virtualized client environments such as cloud-scale datacenters. To meet the dense packaging and energy requirements of those data centers, the form factor and power consumption of the card were reduced significantly. The card also offers an expanded set of algorithms to support state-of-the-art as well as future workloads. For the first time, the user interface provides access to a selected set of quantum-safe algorithms. Infrastructure extensions add hardware-embedded, attestation-friendly trusted boot services, which improve system resiliency by providing hardware enabled measurements of the secure and trusted boot process. These extensions simultaneously simplify the security certifications built on them. This article provides an overview of the IBM 4769 cryptographic coprocessor, highlighting security characteristics, internal hardware, form factor, and enhanced firmware.
Text Line Segmentation for Medieval Devnagari Manuscript

Optical character recognition (OCR) is a process to make a document image to an editable text. The whole process contains many phases to reach to the final character recognition. One of most important phases of OCR is Segmentation. Segmentation is a phase where document image is fragmented in individual lines, words, and character. Character recognition rate or accuracy largely depends on correctly applied segmentation. An OCR system requires various kinds of segmentation like page segmentation, line segmentation, word segmentation, and character segmentation. This paper proposes a script-independent projection-based approach for line segmentation in medieval handwritten Devnagari manuscripts. Input document is scanned horizontally pixel by pixel, a histogram for each line is created, and line is segmented according to revised local minima. This revised minima makes this technique suitable for Indian scripts which have modifiers (matras) on above and below the characters. Proposed technique is suited for Indian scripts with modifiers on above and below of the character. This paper addresses the segmentation for medieval Devnagari manuscripts which are ages old. These manuscripts are degraded due to age, insects, weather, etc. To clean this image and make it noise-free, it is a big challenge, and noisy image can produce incorrect segmentation.
A Comparison of Regions of Interest in Parenchymal Analysis for Breast Cancer Risk Assessment

Computerized parenchymal analysis has shown potential to be utilized as an imaging biomarker to estimate the risk of breast cancer. Parenchymal analysis of digital mammograms is based on the extraction of computerized measures to build machine learning-based models for the prediction of breast cancer risk. However, the choice of the region of interest (ROI) for feature extraction within the breast remains an open problem. In this work we perform a comparison between five different methods suggested in the literature for automated ROI selection, including the whole breast (WB), the maximum squared (MS), the retro-areolar region (RA), the lattice-based (LB), and the polar-based (PB) selection methods. For the experiments, we built a retrospective dataset of 896 screening mammograms from 224 women (112 cases and 112 healthy controls). The performance of each ROI selection method was measured in terms of the area under the curve (AUC) values. The AUC values varied between 0.55 and 0.79 depending on the method and experimental settings. The best performance on an independent test set was achieved by the MS method (AUC of 0.59, 95% CI: 0.55-0.64). This method is fully-automated and does not require adjusting hyper-parameters. Based on our results, we prompt the use of the MS method for ROI selection in the computerized parenchymal analysis for breast cancer risk assessment.
Protecting Private Inputs: Bounded Distortion Guarantees With Randomised Approximations

Abstract Computing a function of some private inputs while maintaining the confidentiality of those inputs is an important problem, to which Differential Privacy and Secure Multi-party Computation can offer solutions under specific assumptions. Research in randomised algorithms aims at improving the privacy of such inputs by randomising the output of a computation while ensuring that large distortions of outputs occur with low probability. But use cases such as e-voting or auctions will not tolerate large distortions at all. Thus, we develop a framework for randomising the output of a privacypreserving computation, while guaranteeing that output distortions stay within a specified bound. We analyse the privacy gains of our approach and characterise them more precisely for our notion of sparse functions. We build randomisation algorithms, running in linearithmic time in the number of possible input values, for this class of functions and we prove that the computed randomisations maximise the inputs’ privacy. Experimental work demonstrates significant privacy gains when compared with existing approaches that guarantee distortion bounds, also for non-sparse functions.