Reviewerly/exHarmony huggingface dataset:
Development of the Cadets’ Regulative Skills at Practical English Classes by a Means of Formative Assessment Technology

The new paradigm of education requires teaching stuff to search for efficient ways of forming of the key competences integral to personal and professional development of future officers of the Russian Army. Due to this reason, the search for the brand new assessment methods and technologies used in measuring cadets’ personal achievements in a military college is a challenging problem of foreign language learning theory and practice. The implementation of the modern assessment techniques involves the development of students’ regulative skills at foreign language classes and meets the requirements of the new federal educational standard to the teaching process in a higher educational institution, provides the sustained personal growth conditions and encourages cadets to conscious learning activities. The present article is devoted to the analysis of the formative assessment didactic potential relating to the field of foreign language teaching. The author proves the effectiveness of its use at the English classes as a means of improving cadets’ monitoring, evaluation and reflexive skills. Formative assessment is considered as a learning technology necessary for cadets’ personal development and improving the quality of foreign language learning through providing the feedback from all the parties involved in the educational process organization. The advantages of formative assessment in comparison with traditional methods of assessment are given. The cadets’ regulative skills developed by means of this technology at the English classes are also listed. The author provides the examples of the main formative assessment techniques implementation (criteria-based assessment, self-report, survey, etc.) at the English practical classes. The article contains the figures and tables which demonstrate the implementation of formative assessment.
A class of power functions with four-valued Walsh transform and related cyclic codes

Let GF(p2m) be the finite field with p2m elements, where p is a prime number and m is a positive integer. Let F(x)=x2pm−1 be a power function over GF(p2m). When pm≢2(mod3), the Walsh transform of F was first studied in [15], and the value distribution of its four-valued Walsh transform was determined in [6]. In this paper, we prove that the Walsh transform of F is also four-valued when pm≡2(mod3). Moreover, the value distribution of the Walsh transform of F is determined. This result is a complement of the works shown in [15] and [6]. Furthermore, a class of four-weight p-ary cyclic codes are obtained.
A Comparative Study of Misclassification Cost Assignment Strategies for Cost-sensitive AdaBoost in Imbalance Data Classification

Classification of data where the number of examples in each class differ significantly is not uncommon. Imbalance data can be found in various application domains including biomedical data analysis. The traditional supervised learning following the Empirical Risk Minimisation principle, which minimises the misclassification regardless of the types of error, often yields a classification model that generalises well only on the majority class but tends to perform poorly on the minority class. Interestingly, correct classification of the minority class is usually of greater importance. Cost-sensitive learning is one of the promising approaches to introducing inductive bias into the model for imbalance data classification. Nonetheless, there is currently a limited body of research on how to properly set the misclassification costs in imbalance settings. To elucidate that, in this paper, we studied three strategies for determining misclassification costs for an imbalance dataset and incorporated such costs into a cost-sensitive AdaBoost algorithm. The experimental results based on five imbalance biomedical testbeds suggested that the proposed distribution correction method is the most effective strategy in terms of imbalance-aware performance measures.
The cross-correlation values of a class of sequences with different lengths

In this paper, let Fq be a finite field with q elements and m1,m2 two positive integers with d=gcd⁡(m1,m2). Let s(1)=(T1(α1ei))i=0n−1 and s(2)=(T2(α2i))i=0n−1 be two q-ary sequences of period qm1−1 and qm2−1, respectively, where Fqmi⁎=〈αi〉, Ti denote the trace function from Fqmi to Fq, i=1,2, n=(qm1−1)(qm2−1)/(qd−1), and gcd⁡(e,qm1−1)=1. If e≡1(modqd−1) and m2=m1+d, then two sequences have p values cross correlations and are asymptotically optimal with respect to the cross correlation bound. If e≡1(modqd−1) and m2=m1+2d, then the cross correlations have at most qd−1 values by closed-form expressions of Kloosterman sums over finite fields. If e≡−1(modqd−1), then the cross correlations also have at most qd−1 values. Furthermore, we support some examples by Magma program.
Retail Management on Mobile Application using Product Classification

A retail store management system is a retail store that uses stock management and orders through the basket by adding a function to classify products from images to facilitate customers and create competitive efficiency for traditional retailers. Product classification is used to improve the performance of traditional retailers by classifying products within a modern retail store by using deep learning on product images to create a model for mobile applications. The best model is selected by comparing the results from learning and evaluating models based on MobileNetV2, VGG16, CNN, InceptionV3 and ResNet. Then, mobile applications have developed by using the best model to support product management for modern retailers. In a mobile application for modern retail management, there are users consisting of retailers and customers, whose functions include stock arrangement, shopping cart arrangement, and product classification. A mobile application is developed by implementing Flutter with the Dart language. It is a cross-platform mobile application development tool that uses Firestore as a database on the Firebase cloud.The results of the five models, it was found that the MobileNet, VGG16, CNN, InceptionV3 and ResNet models validity were 92.80, 80.79, 55.67, 49.68, and 04.59 respectively. The model with the highest accuracy was developed as a system for modern retail stores. The system has been tested for 6 months. The retailers 1, 2 and 3 have the highest number of products sold in the 1st, 6th, and 4th months, respectively.
Development of a System and Interface for Speech Synthesis in Ukrainian for Websites

The paper describes the system of content synthesis and sounding on websites in Ukrainian, designed to simplify the consumption of content for the visually impaired, which features easy integration into the most popular content management system on sites, namely CMC Wordpress. Currently, people with visual impairments are severely limited in their use of Internet products because most web resources are not tailored to their needs. Modern information technology allows such people to receive information along with healthy ones thanks to solutions developed by scientists and engineers from different countries. Text information can be delivered to the visually impaired with a magnifying glass or by enlarging the font by software, blind - by sounding the text using computer programs or displaying texts on the Braille screen of the monitor. This is an effective solution, but the choice of methods for reproducing such information must be fully provided by people with disabilities themselves, which is a significant problem due to the significant time spent on information consumption. To facilitate the perception of visually impaired people when using websites, an international standard for webmasters has been developed - a guide to web content accessibility called the W3 Web Content Accessibility Guidelines (WCAG) Consortium 2.0. The standard describes in detail the requirements of visually impaired people that are recommended to be met in order for them to view the website without any problems. The basic provisions of WCAG 2.0 will describe the parameters and algorithms for scaling, clustering and separation of information by programs for the visually impaired and provide recommendations for writing website code. However, to implement such recommendations, webmasters need to learn new programming principles and algorithms and use additional development tools. This is often difficult, requiring additional training, which entails non-compliance by webmasters with such requirements.
An Automatic Error Correction Method for English Composition Grammar Based on Multilayer Perceptron

In order to improve the timeliness of English grammar error correction and the recall rate of English grammar error correction, this paper proposes an automatic error correction method for English composition grammar based on a multilayer perceptron. On the basis of preprocessing the English composition corpus data, this paper extracts the grammatical features in the English composition corpus and constructs a grammatical feature set. We take the feature set as the input information of the multilayer perceptron and realize feature classification through network learning and training. The grammatical error items in the English composition are detected according to the similarity, and the error correction is completed by setting the penalty parameter and reducing the deviation parameter. The experimental results show that the syntax error detection time of this method is less than 6 minutes, the recall rate is higher than 90%, and the detection error rate is lower than 6%. The method improves the timeliness of grammatical error correction and improves the efficiency of error correction.
Machine Learning based Face Mask Classifier

The COVID19 pandemic has affected almost entire world. The virus is spreading at a rapid rate through droplets generated while sneezing or coughing. All the governments in the world have made it mandatory to wear face masks. But almost 30 to 40% of public is not following the rules. Some people wear masks but they do not wear them in a proper way i.e., they wear masks below their nose. This paper proposes a model based on InceptionV3 algorithm which classifies people into 3 categories namely: face fully covered, face not covered and face partially covered with mask. This paper will help the police to detect people without masks or people not wearing masks properly at a crowded place. The police can then keep record of such people and fine them.
Re-Encryption Model for Multi-Block Data Updates in Network Security

Nowadays, online cloud storage networks can be accessed by third parties. Businesses that host large data centers buy or rent storage space from individuals who need to store their data. According to customer needs, data hub operators visualise the data and expose the cloud storage for storing data. Tangibly, the resources may wander around numerous servers. Data resilience is a prior need for all storage methods. For routines in a distributed data center, distributed removable code is appropriate. A safe cloud cache solution, AES-UCODR, is proposed to decrease I/O overheads for multi-block updates in proxy re-encryption systems. Its competence is evaluated using the real-world finance sector.
Power Analysis Attack Based on Hamming Weight Model without Brute Force Cracking

Power analysis attack is an attack method to obtain the key in cryptographic chip by analyzing the power information of the cryptographic chip. Machine learning has been widely used in power analysis attacks in recent years. Machine learning can effectively establish the model between the power traces and the SBOX output value or the HM (Hamming) weight of the SBOX output value so that the SBOX output value or the HM weight of the SBOX output value can be obtained through the power traces. HM weight model is widely used because it has less classification of nine and can achieve better machine learning accuracy. However, in the HM weight model, the key cannot be obtained directly by obtaining the median HM weight; instead, the key needs to be deduced by brute force cracking of the median HM weight. Usually, the brute force cracking of a byte key requires 51 enumeration times on average. The HM weight distribution of the SBOX output value is unbalanced, so the power analysis attack based on the HM weight model without brute force cracking is proposed in this paper. Based on the HM weight of the SBOX output value, the method selects the best plaintext for the next power analysis attack, and Euclidean distance is chosen as the optimal plaintext selection judgment algorithm. It makes the HM weight distribution of the SBOX output value more evenly, thus reducing the possible key space and confirming the key more easily. This scheme does not require brute force cracking. It only needs to input 3.332 plaintexts on average and up to 4 plaintexts to determine the unique key, which effectively improves the efficiency of the power analysis attack. In this paper, the authors test the DPA competition V4 data set and Kizhvatov’s data set with random defense. Experiments show that this scheme enjoys the high accuracy of the HM weight model in machine learning. Compared with the Whole Byte scheme, the accuracy based on this scheme can be increased by about 360%. Compared with the brute force cracking HM weight scheme, the guessing entropy can be decreased by about 1700%.
Experimental implementation of spike-based neuromorphic XOR operation based on polarization-mode competition in a single VCSOA

We experimentally and numerically propose an approach for implementing spike-based neuromorphic exclusive OR (XOR) operation using a single vertical-cavity semiconductor optical amplifier (VCSOA). XOR operation is realized based on the neuron-like inhibitory dynamics of the VCSOA subject to dual-polarized pulsed optical injections. The inhibitory dynamics based on the polarization-mode competition effect are analyzed, and the inhibitory response can be obtained in a suitable range of wavelength detuning. Here, all input and output bits are represented by spikes that are compatible with the photonic spiking neural network. The experimental and numerical results show that XOR operation can be realized in two polarization modes by adjusting the time offset in the inhibitory window and setting defined reference thresholds. In addition, the influences of delay time and input intensity ratio on XOR operation are studied experimentally. This scheme is energy efficient because VCSOA neuromorphic photonics computing and information processing.
µV: An Articulation, Rotation, Scaling, and Translation Invariant (ARST) Multi-stroke Gesture Recognizer

Finger-based gesture input becomes a major interaction modality for surface computing. Due to the low precision of the finger and the variation in gesture production, multistroke gestures are still challenging to recognize in various setups. In this paper, we present µV, a multistroke gesture recognizer that addresses the properties of articulation, rotation, scaling, and translation invariance by combining $P+'s cloud-matching for articulation invariance with !FTL's local shape distance for RST-invariance. We evaluate µV against five competitive recognizers on MMG, an existing gesture set, and on two new versions for smartphones and tablets, MMG+ and RMMG+, a randomly rotated version on both platforms. µV is significantly more accurate than its predecessors when rotation invariance is required and not significantly inferior when it is not. µV is also significantly faster than others with many samples and not significantly slower with few samples.
Field-based Description of the Coupling between a Transmon Qubit and a Transmission Line Geometry

Currently, circuit quantum electrodynamics architectures have emerged as one of the most popular approaches to implement practical quantum information processing hardware. Although significant progress has been made, many technical issues remain that limit the performance of fabricated devices. One approach to accelerate progress in the engineering design of these devices is to develop improved numerical modeling methods. Current modeling methods generally rely on approximate lumped element circuit models to describe the complex network of microwave transmission lines required to operate a circuit quantum electrodynamics device. This reduction in complexity in the theoretical model is valuable for gaining insight into the operation of a device, but does limit the opportunity for using these models to optimize the performance of practical devices. To develop rigorous numerical modeling methods, it is necessary to move toward theoretical descriptions of circuit quantum electrodynamics devices that retain the full details of the three-dimensional vector electromagnetic fields that exist in these systems. In this work, we present details on such a theoretical model for one of the most commonly used circuit quantum electrodynamics systems, a transmon qubit coupled to microwave transmission lines. We discuss the quantization of our new model and show that by adopting relevant approximations our model can be reduced to the same lumped element descriptions commonly used in the literature. We also discuss the derivation of quantum equations of motion for the coupled field-transmon system, which can be used as the starting point for developing full-wave numerical solvers for these circuit quantum electrodynamics systems in the future.
Application of Virtual Reality Technology in College Students' Social Anxiety Intervention Platform with C# Implementations

This paper studies the construction of college students' social anxiety intervention platform based on the virtual reality technology implemented by C#. The results of an empirical study on the intervention of virtual reality technology on anxiety disorders (including specific phobias, obsessive-compulsive disorder and social anxiety) in autism spectrum disorder groups show that: the intervention effect of virtual reality technology on adult patients with specific phobias in the autism spectrum disorder group It is better than school-age children. In order to further understand the reliability and ecological validity of the test subject's assessment under the intervention of the virtual environment, a controlled experiment was used to analyze the experimental data, and the Cronbach reliability coefficient was used to determine the relationship between the self-awareness scale and the The reliability of the Social Anxiety Scale was analyzed, and the correlation and significance level of each scale were analyzed by S PS S software.
HUBUNGAN PENGGUNAAN APLIKASI ZOOM DENGAN MUTU PENDIDIKAN DI MADRASAH TSANAWIYAH

The problem that often occurs when online learning is that the teaching and learning process is not conducive, due to environmental factors that make students not focus, as well as in online meetings between educators, education staff or school principals. This research was conducted to determine the relationship between the use of the zoom application and the quality of education in Madrasah Tsanawiyah throughout the city of Bandung. This research is descriptive, using a quantitative approach to the type of correlation. The data collection technique was using a questionnaire questionnaire. Based on the results of the study, it is known that the use of the zoom application has a significant relationship with the quality of education in Madrasah Tsanawiyah throughout the city of Bandung, with a significance value of 0.000. The correlation coefficient obtained is 0.540 and is at a moderate level of strength. The contribution of the variable is 29.1%
Vronicle

An increasing number of mobile devices are incorporating cameras, allowing users to record videos at any time, anywhere. This opens up a wide variety of applications, most notably security-critical ones, where videos are used as evidence or include sensitive content. Examples of such applications include (but are not limited to): (i) citizen journalists recording important events (e.g., protests), (ii) courts using videos as evidence, and (iii) electronic legal contract-signing platforms using videos to identify signing users [1].
Engineering the Transition of Interactive Collaborative Software from Cloud Computing to Edge Computing

The "Software as a Service" (SaaS) model of cloud computing popularized online multiuser collaborative software. Two famous examples of this class of software are Office 365 from Microsoft and Google Workspace. Cloud technology removes the need to install and update the software on end users' computers and provides the necessary underlying infrastructure for online collaboration. However, to provide a good end-user experience, cloud services require an infrastructure able to scale up to the task and allow low-latency interactions with a variety of users worldwide. This is a limiting factor for actors that do not possess such infrastructure. Unlike cloud computing which forgets the computational and interactional capabilities of end users' devices, the edge computing paradigm promises to exploit them as much as possible. To investigate the potential of edge computing over cloud computing, this paper presents a method for engineering interactive collaborative software supported by edge devices for the replacement of cloud computing resources. Our method is able to handle user interface aspects such as connection, execution, migration, and disconnection differently depending on the available technology. We exemplify our approach by developing a distributed Pictionary game deployed in two scenarios: a nonshared scenario where each participant interacts only with their own device and a shared scenario where participants also share a common device, including a TV. After a theoretical comparative study of edge vs. cloud computing, an experiment compares the two implementations to determine their effect on the end user's perceived experience and latency vs. real latency.
AI based Dynamic Model for Question Paper Generation and Performance Monitoring

By generating defined problems out of generic algorithms and assessing the students&#x0027; abilities at each point of the practice paper, a robust universal platform is created for learning math by personalizing each problem to help the students perform much better by guiding them through. Every problem has a degree of difficulty, which increases when the student solves it successfully. This will allow us to keep track of students&#x0027; capabilities, and will provide them with practice papers that are based on the difficulty of the previous attempt. The process will start with database generation of Algebra vertical and then building an algorithm to generate the question papers based on students&#x0027; performance.
SnuQS

Since the state-of-the-art quantum computers are still noisy and error-prone, classical simulation of quantum circuits is essential in verifying/calibrating quantum computers and prototyping/debugging complex quantum algorithms. Classical simulation of large quantum systems is challenging due to its exponential increase in space and computation requirements. In this paper, we propose a full-state simulation framework, SnuQS. It exploits storage devices, such as HDDs and NVMe SSDs, to enlarge the available main memory capacity at a small cost. To achieve maximum I/O bandwidth, we propose an overlay-based memory management technique and optimization techniques. We also propose an I/O subsystem architecture that guarantees the maximum bandwidth of each storage device. We evaluate SnuQS on a 64-core CPU and 4-GPU system with 80 2TB HDDs and 10 4TB NVMe SSDs using quantum supremacy and quantum Fourier transform circuits. The experimental result indicates that SnuQS and the proposed I/O subsystem together is an effective and practical solution to scale the full-state simulation of large quantum circuits at about 300X lower cost than the DDR4 DRAM main-memory-only system.