Reviewerly/exHarmony huggingface dataset:
Abstraction Logic: The Marriage of Contextual Refinement and Separation Logic

Contextual refinement and separation logics are successful verification techniques that are very different in nature. First, the former guarantees behavioral refinement between a concrete program and an abstract program while the latter guarantees safety of a concrete program under certain conditions (expressed in terms of pre and post conditions). Second, the former does not allow any assumption about the context when locally reasoning about a module while the latter allows rich assumptions. In this paper, we present a new verification technique, called abstraction logic (AL), that inherently combines contextual refinement and separation logics such as Iris and VST, thereby taking the advantages of both. Specifically, AL allows us to locally verify a concrete module against an abstract module under separation-logic-style pre and post conditions about external modules. AL are fully formalized in Coq and provides a proof mode that supports a combination of simulation-style reasoning using our own tactics and SL-style reasoning using IPM (Iris Proof Mode). Using the proof mode, we verified various examples to demonstrate reasoning about ownership (based on partial commutative monoids) and purity ($i.e.$, termination with no system call), cyclic and higher-order reasoning about mutual recursion and function pointers, and reusable and gradual verification via intermediate abstractions. Also, the verification results are combined with CompCert, so that we formally establish behavioral refinement from top-level abstract programs, all the way down to their assembly code.
Шляхи формування студентського колективу на основі індивідуально-типологічного підходу

International scientific and practical conference «Pedagogy, psychology and teaching methods: international experience» : conference proceedings, July 16–17, 2021. Riga, Latvia : «Baltija Publishing», 2021. 295 pages.
Immortal AR Augmented Reality Game

Augmented reality (AR) is a technology which is picking up recently and aims in combining both real world and virtual objects. AR is also described as the Walkman of the 21st century, it has gained immense popularity in the field of games recently. AR not only finds its application in the field of games but even finds its application in various other fields such as the medical industry and film industry.  As compared to all the games it is pretty evident by statistics that people prefer to play shooting games than probably games of any other genre. In order to carry this project, we are using a few AR associates like unity editor, android platform. This project revolves around the creation of a mobile AR shooting game covering all the physical movements and providing users a great gaming experience but ensuring players safety. The aim of the game is to create an illusion in the minds of players as though they are really in a battlefield with weapons to defend themselves and conquer the game. The objective is to eliminate all the enemy objects in the game and protect themselves from getting eliminated. This game brings the virtual objects into the real world through augmented reality to make gaming experience more immersive and enjoyable.Keywords:Android platform, Augmented Reality, Gaming experience, Unity editor, Virtual Reality.
Wearable-based Classification of Running Styles with Deep Learning

Automatic classification of running styles can enable runners to obtain feedback with the aim of optimizing performance in terms of minimizing energy expenditure, fatigue, and risk of injury. To develop a system capable of classifying running styles using wearables, we collect a dataset from 10 healthy runners performing 8 different pre-defined running styles. Five wearable devices are used to record accelerometer data from different parts of the lower body, namely left and right foot, left and right medial tibia, and lower back. Using the collected dataset, we develop a deep learning solution which consists of a Convolutional Neural Network and Long Short-Term Memory network to first automatically extract effective features, followed by learning temporal relationships. Score-level fusion is used to aggregate the classification results from the different sensors. Experiments show that the proposed model is capable of automatically classifying different running styles in a subject-dependant manner, outperforming several classical machine learning methods (following manual feature extraction) and a convolutional neural network baseline. Moreover, our study finds that subject-independent classification of running styles is considerably more challenging than a subject-dependant scheme, indicating a high level of personalization in such running styles. Finally, we demonstrate that by fine-tuning the model with as few as 5% subject-specific samples, considerable performance boost is obtained.
Efektivitas Komunikasi Pembelajaran Daring terhadap Pemahaman Materi Kuliah Mahasiswa UIN Sunan Ampel Surabaya

This study discusses the effectiveness of online learning communication on the understanding of lecture material for UIN Sunan Ampel Surabaya students. This research method uses a quantitative approach with the type of correlational research. The data was collected using a questionnaire technique through the google form application, the sampling technique was carried out using a random sample technique, and analyzed using the Chi Square statistical formula and the Correlation Coefficient. The results showed that the calculation of the data using the Chi Square formula with the suitability of the table 0.05% resulted in x2 arithmetic &gt; x2 table (60.69 &gt; 3.84), so Ha was accepted while Ho was rejected. It can be concluded that there is an effectiveness of online learning communication on the understanding of lecture material for UIN Sunan Ampel Surabaya students. Then, to measure the level of effectiveness, the researcher used the correlation coefficient formula and obtained a value of 0.134 with a very low category.
Improving Data Security using Compression Integrated Pixel Interchange Model with Efficient Encryption Technique

We exist in a digital era in which communication is largely based on the transfer of digital knowledge over data networks.Disclosures are sometimes regarded as a transmitter that transmits a digital file.A system of encoding, efficient and easy yet secure model must be developed for quick and prompt transmission.The file is sent from source to destination where it is difficult to maintain the privacy of knowledge.The encryption of images is vital for securing sensitive information or images from unauthorized readers.By using a technique of selective encryption, the original image pixel values are completely obscured in this way and an intruder cannot retrieve statistical data from its original image.This paper introduces a new methodology for the use of a protective facility for the transmission of digital data over the public network.A highly established field with image compression is allowed for speedy transmission and efficient information storage.During the initial process, the original image is divided into blocks of the same size and sub segmentation is performed for accurate extraction of images within the boundaries.A random matrix is used to swap the pixels of the neighboring sub blocks.Afterwards, each pixel is randomly exchanged for the neighboring blocks with a random matrix, then each block is encrypted with the proposed function and then the encrypted data can be stored in cloud.The proposed method uses Image Segmentation based Compression Model with Pixel Interchange Encryption (ISbCPIE) Model for providing high security to the Image transmitted in the network.Compressor is needed to achieve rapid transmission and efficient storage.The proposed model is compared with the traditional models and the results show that the proposed model security levels are better than the existing models.
THE USE OF GRAMMAR-TRANSLATION METHOD TO TEACH READING TO THE TENTH CLASS STUDENTS AT SMK YPM 12 TUBAN ACADEMIC YEAR 2020/2021

English is the first foreign language in Indonesia which is considered important in transferring and developing science and technology, arts and building the relationship among countries. Based on the 1994 basic course outline, the purpose of English teaching at Technical Senior High Schools is to develop the four language skills: listening, speaking, reading and writing. The teaching of those four language skills can be done in integrative way in which the emphasis is on developing the students’ reading skill. The less attention to a certain language skill may cause the students’ unbalanced mastery of English. However, teaching reading using certain method like grammar-translation method is also important because by it students will be able to understand the English text, grammar and structure and translation well through which they will be a means of improving other skills such as speaking. From the reasons, the researcher wants to study about The Use of Grammar-Translation Method to Teach reading to the Tenth Class Students at SMK YPM 12 Tuban Academic Year 2020/2021. This title is chosen because he is sure that reading is also important besides speaking, writing and listening.&#x0D; &#x0D; This study is in form of descriptive analysis which means that he describes what he finds in the real life and he reveals them to prove the rightness of his assumption. He tries to find the data and process them and just describes his finding. He analyzes and describes about the use of grammar-translation method in teaching reading and the problems the teacher and students face in the process of teaching and learning. Besides them, it is also prominent to tell the process or activities in the teaching of reading using grammar-translation method. It is hoped that some alternatives to solve these problems can be found. Therefore it is expected that this study will be useful for English teacher as the alternative method to teach reading, for the readers to add their knowledge about different ways in teaching reading and for the students in general to be the reference to add their knowledge and to do further research.
A Theoretical Framework of Secure Environment Of Virtual Reality Application in Tertiary TVET Education using Blockchain Technology

The Pandemic of Covid-19 has changed the landscape of many sectors including the education sector. Technical and Vocational Education Training (TVET) which is one of the critical drivers in the Eleventh Malaysian Plan is one of the affected educations sectors. Issues arise when the learning process as well as the assessment and management need to be online. Online security has been a main concern among the universities, lecturers, and the students. This paper aims to propose a secure theoretical framework of virtual reality application in tertiary TVET education by using Blockchain technology. Usage of VR can be seen as psychomotor acquisition devices that can offers efficient online distance learning experience. Measuring learning outcome will be done from the virtual reality simulator by using HMDs. By having online distance learning and everything will be put into the cloud and can be accessed by anyone. Blockchain technology can be seen as potential technology to increase security of the data that will be available in the cloud. Thus, the university’s management of TVET education, students and lecturers will have a better learning experience without worrying about the security of online learning through virtual reality.
Calibration and Asset Management Software

Abstract: In an industry, to ensure smooth and reliable working of machines, calibration is an important phase to rectify the changes of devices. This solution is a new milestone in calibration and asset management industry. A web-based application provides portability to the application hence; share-holders/proprietors/industrialist can manage their assets any time, anywhere. To make any application reliable and Secure, testing is an important phase. The intent of a security testing is to identify the vulnerabilities, so that the developers can pull out these vulnerabilities from the application and make the web application and data safe from any unauthorized action. Tools such as Zenmap, OWSAP, and OpenSSL used to ensure unbreakable working of application. Keywords: Calibration, Asset Management, Security Testing, HTTP’s Conversion
CHARACTERIZING AND PREDICTING EARLY REVIEWERS FOR EFFECTIVE PRODUCT MARKETING ON ECOMMERCE WEBSITES

Online reviews have become an important source of instruction for usersbefore manufacture an informed procure decision. Early reviews of a product tend to have ahigh effect on the ensuing product sales. In this paper, we take the initiative to study thebehavior characteristics of early reviewers through their posted reviews on two real-worldlarge ecommerce platforms, i.e., Amazon and Yelp. In specific, we divide product lifetimeinto three uninterrupted phase, namely early, majority and straggler. A user who has posted areview in the early stage is contemplating as an untimely observer. We quantitativelycharacterize early reviewers based on their rating behaviors, the helpfulness scores receivedfrom others and the correlation of their reviews with product popularity. We have found that(1) an early observer tends to assign a higher average rating score; and (2) an early observertends to post more helpful reviews. Our analysis of product reviews also indicates that earlyreviewers' ratings and their received helpfulness scores are likely to influence productpopularity. By viewing review posting process as a multiplayer competition game, we presenta novel margin-based embedding model for early reviewer divination. Extensive experimentson two different ecommerce datasets have shown that our proposed approach outperforms anumber of aggressive baselines.
Aleatoric Description Logic for Probailistic Reasoning (Long Version)

Description logics are a powerful tool for describing ontological knowledge bases. That is, they give a factual account of the world in terms of individuals, concepts and relations. In the presence of uncertainty, such factual accounts are not feasible, and a subjective or epistemic approach is required. Aleatoric description logic models uncertainty in the world as aleatoric events, by the roll of the dice, where an agent has subjective beliefs about the bias of these dice. This provides a subjective Bayesian description logic, where propositions and relations are assigned probabilities according to what a rational agent would bet, given a configuration of possible individuals and dice. Aleatoric description logic is shown to generalise the description logic ALC, and can be seen to describe a probability space of interpretations of a restriction of ALC where all roles are functions. Several computational problems are considered and model-checking and consistency checking algorithms are presented. Finally, aleatoric description logic is shown to be able to model learning, where agents are able to condition their beliefs on the bias of dice according to observations.
Understanding the Logit Distributions of Adversarially-Trained Deep Neural Networks

Adversarial defenses train deep neural networks to be invariant to the input perturbations from adversarial attacks. Almost all defense strategies achieve this invariance through adversarial training i.e. training on inputs with adversarial perturbations. Although adversarial training is successful at mitigating adversarial attacks, the behavioral differences between adversarially-trained (AT) models and standard models are still poorly understood. Motivated by a recent study on learning robustness without input perturbations by distilling an AT model, we explore what is learned during adversarial training by analyzing the distribution of logits in AT models. We identify three logit characteristics essential to learning adversarial robustness. First, we provide a theoretical justification for the finding that adversarial training shrinks two important characteristics of the logit distribution: the max logit values and the "logit gaps" (difference between the logit max and next largest values) are on average lower for AT models. Second, we show that AT and standard models differ significantly on which samples are high or low confidence, then illustrate clear qualitative differences by visualizing samples with the largest confidence difference. Finally, we find learning information about incorrect classes to be essential to learning robustness by manipulating the non-max logit information during distillation and measuring the impact on the student's robustness. Our results indicate that learning some adversarial robustness without input perturbations requires a model to learn specific sample-wise confidences and incorrect class orderings that follow complex distributions.
Finding the Surface Area and Volume of the Hyperspheres Using Simple Calculus

String theory gives us a different vision for the world around us. Many scientists are working on finding how things behave beyond our three-dimensional world or the existence of things beyond three dimensions. After the emergence of mathematics of string theory, it seems possible to work in this area for the existence of things in more than three dimensions all around us. We can find many ways for visualizing the objects in more than three dimensions (hyper shapes) and for calculating their volume, surface area, etc. In this chapter, we intend to find the volume and surface area of shapes belonging to the family of hyperspheres by using simple calculus, which was not proposed earlier, and also, to derive a formula.
Compression Network with Transformer for Approximate Nearest Neighbor Search

We propose a generic feature compression method for Approximate Nearest Neighbor Search (ANNS) problems, which speeds up existing ANNS methods in a plug-and-play manner. Specifically, we propose a new network structure called Compression Network with Transformer (CNT) to compress the feature into a low dimensional space, and an inhomogeneous neighborhood relationship preserving (INRP) loss that aims to maintain high search accuracy. In CNT, we use multiple compression projections to cast the feature into many low dimensional spaces, and then use transformer to globally optimize these projections such that the features are well compressed following the guidance from our loss function. The loss function is designed to assign high weights on point pairs that are close in original feature space, and keep their distances in projected space. Keeping these distances helps maintain the eventual top-k retrieval accuracy, and down weighting others creates room for feature compression. In experiments, we run our compression method on public datasets, and use the compressed features in graph based, product quantization and scalar quantization based ANNS solutions. Experimental results show that our compression method can significantly improve the efficiency of these methods while preserves or even improves search accuracy, suggesting its broad potential impact on real world applications.
Three-Round Secure Multiparty Computation from Black-Box Two-Round Oblivious Transfer.

We give constructions of three-round secure multiparty computation (MPC) protocols for general functions that make black-box use of a two-round oblivious transfer (OT). For the case of semi-honest adversaries, we make use of a two-round, semi-honest secure OT in the plain model. This resolves the round-complexity of black-box (semi-honest) MPC protocols from minimal assumptions and answers an open question of Applebaum et al. (ITCS 2020). For the case of malicious adversaries, we make use of a two-round maliciously-secure OT in the common random/reference string model that satisfies a (mild) variant of adaptive security for the receiver.
What Have Been Learned & What Should Be Learned? An Empirical Study of How to Selectively Augment Text for Classification.

Text augmentation techniques are widely used in text classification problems to improve the performance of classifiers, especially in low-resource scenarios. Whilst lots of creative text augmentation methods have been designed, they augment the text in a non-selective manner, which means the less important or noisy words have the same chances to be augmented as the informative words, and thereby limits the performance of augmentation. In this work, we systematically summarize three kinds of role keywords, which have different functions for text classification, and design effective methods to extract them from the text. Based on these extracted role keywords, we propose STA (Selective Text Augmentation) to selectively augment the text, where the informative, class-indicating words are emphasized but the irrelevant or noisy words are diminished. Extensive experiments on four English and Chinese text classification benchmark datasets demonstrate that STA can substantially outperform the non-selective text augmentation methods.
Research on Subpixel Algorithm of Fixed-Point Tool Path Measurement

Tool safety is an important part of machining and machine tool safety, and machine tool path image detection can effectively obtain the in-machine condition of a tool. To obtain an accurate image edge and improve image processing accuracy, a novel subpixel edge detection method is proposed in this study. The precontour is segmented by binarization, the second derivative in the neighborhood of the demand point is calculated, and the obtained value is sampled according to the specified rules for curve fitting. The point whose curve ordinate is 0 is the subpixel position. The experiment proves that an improved subpixel edge can be obtained. Results show that the proposed method can extract a satisfactory subpixel contour, which is more accurate and reliable than the edge results obtained by several current pixel-level operators, such as the Canny operator, and can be used in edge detection with high-accuracy requirements, such as the contour detection of online tools.
The Effect of Words Walls Strategy to students’ Vocabulary Mastery At SMP-IT Suara Da’I Muda Langkat

The objective of the study was to find out the significant effect of usingword wall strategy on student’s vocabulary mastery. The Subject of the study werethe seventh grade students of SMP-IT Suara Da’I Muda Langkat in 2020/2021academic year. This study apply by quantitative research. In taking the sample ofthis research, the researcher took the same students in each classes as the sampleused and divided into 2 classes, experimental and control class. There were 25students in the experimental and 25 students in control class. The students taughtby using word walls strategy in experimental class and taught by using lecturingmethod in control class. The researcher used two test, pre-test was given beforetreatment and post-test was given after treatment n learning process. Theexperimental class increased 16 points, from 50.00 becomes 66.00, when thecontrol class increases 28.8 points from 39.20 to 68.00 points. Then, the researcheranalyzed the data y using SPSS V 22. After analyzed the data, the researcher foundthat the tvalue of the observed was higher than ttable. It can be seen from t observedof tvalue = 5.825 with the Sig. (2-tailed) = 0.003. And ttable of 0.05 (5%) as thesignificance level is 2.01 with 44 the degree of freedom (df). It can be found thattvalue = 5,825 > ttable = 2.01 and the Sign. (2-tailed) is 0.004 < 0.05. It means thatthe alternative hypothesis (Ha) was accepted and the null hypothesis (Ho) is rejected.It can be conclude that words walls strategy has significant effect on students’vocabulary mastery at seventh grade at SMP-IT Suara Da’I Muda Langkat.