Reviewerly/exHarmony huggingface dataset:
Private Predicate Encryption for Inner Product from Key-Homomorphic Pseudorandom Function

Predicate encryption (PE), formalized by Katz et al., is a new paradigm of public-key encryption that conceptually captures the public-key encryption that supports fine-grained access control policy. Because of the nature of PE, it is used for cloud storage so that users can retrieve encrypted data without revealing any information about the data to cloud servers and other users. Although lots of PE schemes have been studied, the predicate-hiding security is seldom considered; that is, the user’s secret key may leak sensitive information of the predicate. Additionally, the security of the current predicate-hiding PE schemes relies on the discrete logarithm assumption which cannot resist the quantum attacks in the future. In this paper, we propose a generic PE for inner product under symmetric-key setting, called private IPE, from specific key-homomorphic pseudorandom function (PRF). The rigorous proofs are provided to show that the construction is payload-hiding, attribute-hiding, and predicate-hiding secure. With the advantage of the generic construction, if the underlying PRF can resist quantum attacks, then, through our proposed generic construction, a quantum-resistant private IPE can be obtained.
Predicting the impact of dataset composition on model performance

Real-world machine learning systems are often are trained using a mix of data sources with varying cost and quality. Understanding how the size and composition of a training dataset affect model performance is critical for advancing our understanding of generalization, as well as designing more effective data collection policies. We show that there is a simple, accurate way to predict the loss incurred by a model based on data size and composition. Our work expands recent observations of log-linear generalization error and uses this to cast model performance prediction as a learning problem. Using the theory of optimal experimental design, we derive a simple rational function approximation to generalization error that can be fitted using a few model training runs. Our approach achieves nearly exact (r2>.93) predictions of model performance under substantial extrapolation in two different standard supervised learning tasks and is accurate (r2>.83) on more challenging machine translation and question answering tasks where baselines achieve worse-than-random performance.
AUTHORSHIP VERIFICATION USING MODIFIED PARTICLE SWARM OPTIMIZATION ALGORITHM

Digital forensics is the study of recovery and investigation of the materials found in digital devices, mainly in computers. Forensic authorship analysis is a branch of digital forensics. It includes tasks such as authorship attribution, authorship verification, and author profiling. In Authorship verification, with a given a set of sample documents D written by an author A and an unknown document d, the task is to find whether document d is written by A or not. Authorship verification has been previously done using genetic algorithms, SVM classifiers, etc. The existing system creates an ensemble model by combining the features based on the similarity scores, and the parameter optimization was done using a grid search. The accuracy of verification using the grid search method is 62.14%. The time complexity is high as the system tries all possible combinations of the features during the ensemble model's construction. In the proposed work, Modified Particle Swarm Optimization (MPSO) is used to construct the classification model in the training phase, instead of the ensemble model. In addition to the combination of linguistic and character features, Average Sentence Length is used to improve the verification task accuracy. The accuracy of verification has been improved to 63.38%.
Learning Graphs with Multiple Temporal Resolutions

This paper proposes a framework for learning timevarying graphs with multiple temporal resolutions from multivariate time series signals. Our method estimates multiresolution graphs by a top-down approach: Graphs are learned from a segment of the time-series data corresponding to the desired temporal resolution, and we impose a constraint so that the learned graphs at the target temporal resolution are close to that in the lower temporal resolution. The proposed approach overcomes the problem of existing time-varying graph learning methods that must infer graphs in a single temporal resolution. Experimental results with synthetic data demonstrate that our method outperforms a baseline graph learning method.
Adaptive N-step Bootstrapping with Off-policy Data

The definition of the update target is a crucial design choice in reinforcement learning. Due to the low computation cost and empirical high performance, n-step returns with off-policy data is a widely used update target to bootstrap from scratch. A critical issue of applying n-step returns is to identify the optimal value of n. In practice, n is often set to a fixed value, which is either determined by an empirical guess or by some hyper-parameter search. In this work, we point out that the optimal value of n actually differs on each data point, while the fixed value n is a rough average of them. The estimation error can be decomposed into two sources, off-policy bias and approximation error, and the fixed value of n is a trade-off between them. Based on that observation, we introduce a new metric, policy age, to quantify the off-policyness of each data point. We propose the Adaptive N-step Bootstrapping, which calculates the value of n for each data point by its policy age instead of the empirical guess. We conduct experiments on both MuJoCo and Atari games. The results show that adaptive n-step bootstrap-ping achieves state-of-the-art performance in terms of both final reward and data efficiency.
THE USE OF MOBILE -ASSISTED LANGUAGE LEARNING IN EFL TEACHING AND LEARNING: PRACTICES AND READINESS

ABSTRACT The aims of this study; a) to find out how EFL students and teachers use their smart devices and b) to find out the students' readiness of using Mobile Assisted Language Learning (MALL). This study to widespread use of mobile phones and portable devices it is inevitable to think of Mobile Assisted Language Learning as a means of independent learning in Higher Education. In Mobile Learning, devices like smartphones, iPod, tablet, laptop, iPad are implemented to scaffold language learning. This paper focuses on the classification of mobile apps based on primary, secondary and tertiary learners. This research uses descriptive method that aims to describe all responses given by students through given a list of questions. These studies also discussed the design, method, theory and pedagogical features that underpin the current mobile apps. Since these apps are developed to enhance the language skills of the learners, emphasis should be primarily made on the acquisition of language skills such as listening, speaking, reading and writing skills through mobile technology. Thus, the finding showed that the students have positive attitude toward MALL. All the students have smartphone to support the educational purposes inside and outside classroom. Keywords: Mobile assisted language learning (MALL), Primary, secondary and tertiary learners.
Learning to Calibrate Quantum Control Pulses by Iterative Deconvolution

In experimental control of quantum systems, the precision is often hindered by imperfect applied electronics that distort pulses delivered to target quantum devices. To mitigate such error, the deconvolution method is commonly used for compensating the distortion via a convolutional model. However, its effectiveness is limited by model inaccuracies (e.g., imprecise parameters or unmodeled distortion dynamics). In this article, we propose a learning-based scheme to eliminate the residual calibration error by repeatedly applying the deconvolution operations. The resulting iterative deconvolution method is shown by simulation examples to be able to correct both linear and nonlinear model errors to the highest precision allowed by available finite sampling rates, and the intersampling error caused by finite sampling rate can be suppressed by actively introducing nonlinear components in the control electronics. The proposed method is also experimentally applied on a superconducting platform, which demonstrates improved performance than the noniterative deconvolution methods.
Using Chess for Identifying and Correcting “Problem Areas” in the School Math Course

The conceptual position of the research is that the integration of mathematical and chess game activities ensures the effective development of cognitive and intellectual abilities of students, motivation to study mathematics, and productive development of its “problem areas”. A technology for identifying and correcting the “problem zone” based on a chess game has been developed. All stages of the technology are presented on the example of mastering complex educational construct-combinatorial schemes. The effective influence of the chess game on the development of cognitive and mathematical abilities of students is theoretically and experimentally proved.
Estimating PQoS of Video Conferencing on Wi-Fi Networks Using Machine Learning

Video conferencing services based on web real-time communication (WebRTC) protocol are growing in popularity among Internet users as multi-platform solutions enabling interactive communication from anywhere, especially during this pandemic era. Meanwhile, Internet service providers (ISPs) have deployed fiber links and customer premises equipment that operate according to recent 802.11ac/ax standards and promise users the ability to establish uninterrupted video conferencing calls with ultra-high-definition video and audio quality. However, the best-effort nature of 802.11 networks and the high variability of wireless medium conditions hinder users experiencing uninterrupted high-quality video conferencing. This paper presents a novel approach to estimate the perceived quality of service (PQoS) of video conferencing using only 802.11-specific network performance parameters collected from Wi-Fi access points (APs) on customer premises. This study produced datasets comprising 802.11-specific network performance parameters collected from off-the-shelf Wi-Fi APs operating at 802.11g/n/ac/ax standards on both 2.4 and 5 GHz frequency bands to train machine learning algorithms. In this way, we achieved classification accuracies of 92–98% in estimating the level of PQoS of video conferencing services on various Wi-Fi networks. To efficiently troubleshoot wireless issues, we further analyzed the machine learning model to correlate features in the model with the root cause of quality degradation. Thus, ISPs can utilize the approach presented in this study to provide predictable and measurable wireless quality by implementing a non-intrusive quality monitoring approach in the form of edge computing that preserves customers’ privacy while reducing the operational costs of monitoring and data analytics.
A Deep Learning Approach for Recognizing Bengali Character Sign Langauage

For many years, researchers are trying to recognize Bengali sign language for helping deaf-mute people which is very challenging task. Every research has its own margins and is still incapable to be used commercially. For that reason, device interpreter is obligate to accommodate that deaf and hard-of-hearing community to communicate with normal people. In this paper, the main aim to construct a model to recognize Bengali Character Sign Language using deep leaning approach. For that reason, we use Convolutional Neural Network (CNN) to train individual signs. For those individual signs, a data set is constructed named Bengali Ishara-Lipi to achieve our goal. This model is trained by 5760 preprocessed images and tested by 1440 images. The quantitative relation of the trained and tested images was 80% and 20% severely. Finally, our model gained 92.7% accuracy to recognize Bangla alphabetical sign language. This model will avail for commencing to make Bengali sign language device interpreter.
Bayesian Neural Network Priors Revisited

Isotropic Gaussian priors are the de facto standard for modern Bayesian neural network inference. However, such simplistic priors are unlikely to either accurately reflect our true beliefs about the weight distributions, or to give optimal performance. We study summary statistics of neural network weights in different networks trained using SGD. We find that fully connected networks (FCNNs) display heavy-tailed weight distributions, while convolutional neural network (CNN) weights display strong spatial correlations. Building these observations into the respective priors leads to improved performance on a variety of image classification datasets. Moreover, we find that these priors also mitigate the cold posterior effect in FCNNs, while in CNNs we see strong improvements at all temperatures, and hence no reduction in the cold posterior effect.
Self-supervised Deep Learning Approaches to Speaker Recognition: A Ph.D. Thesis Overview

Recent advances in Deep Learning (DL) for speaker recognition have improved the performance but are constrained to the need of labels for the background data, which is difficult in practice.In i-vector based speaker recognition, cosine (unsupervised) and PLDA (supervised) are the basic scoring techniques, with a big performance gap between the two.In this thesis we tried to fill this gap without using speaker labels in several ways.We applied Restricted Boltzmann Machine (RBM) vectors for the tasks of speaker clustering and tracking in TV broadcast shows.The experiments on AGORA database show that using this approach we gain a relative improvement of 12% and 11% for speaker clustering and tracking tasks, respectively.We also applied DL techniques in order to increase the discriminative power of i-vectors in speaker verification task, for which we have proposed the use of autoencoder in several ways, i.e., (1) as a pre-training for a Deep Neural Network (DNN), ( 2) as a nearest neighbor autoencoder for i-vectors, (3) as an average pooled nearest neighbor autoencoder.The experiments on VoxCeleb database show that we gain a relative improvement of 21%, 42% and 53%, using the three systems respectively.Finally we also proposed a self-supervised end-to-end speaker verification system.The architecture is based on a Convolutional Neural Network (CNN), trained as a siamese network with multiple branches.From the results we can see that our system shows comparable performance to a supervised baseline.
EMPIRICAL PERFORMANCE EVALUATION OF KNUTH MORRIS PRATT AND BOYER MOORE STRING MATCHING ALGORITHMS

Manyalgorithms have been proposed for string matching in order to find a specific pattern in a given text. These algorithms have been used in many applications such as software editors, genetics, Internet search engines, natural language processing, etc. Theaim of this paper is to evaluate the performance of two popularalgorithms:BoyerMoore (BM) and KnuthMorrisPratt (KMP) in terms of execution time. The algorithms have been programmed usingJava and Java Microbenchmark Harnessto evaluatetheirexecution timeusinganumber of experimentaltest scenarios.Resultsshowthat the BMalgorithm outperformed the KMPalgorithm in all test scenarios
History and prospects for the using of geoinformation systems in geography software training

The purpose of the article is to study the history of the implementation of geographic information technologies in the process of teaching geography at school, the readiness of Ukrainian schools in this process and comparing the achievements of Ukrainian and foreign education systems regarding the use of geographic information technologies in the process of teaching geography. Main material. The use of geographic information systems (GIS) during the teaching of geography at school is an issue that is essential for the further development of education. The challenges faced by society, such as the consequences of scientific and technological progress, globalization, climate change, require the training of specialists who will cope with the tasks that the modern world dictates. To train specialists of a new generation, a specialized education is needed, which will give students new content with the help of new teaching methods. Geography as an educational discipline that develops spatial thinking and understanding of the world as a complex system is at the forefront of changes in the educational process. Therefore, in order for students to effectively use material adapted to the conditions of the modern world at geography classes, new methods are needed to help this problem be solved, and above all, the use of geographic information systems. As the analysis of foreign experience and our study on the use of GIS in the educational process with geography showed, the world faces common problems: overloading students, inability (unwillingness) of teachers to learn new technologies, financial and communication problems. In order to introduce geoinformatics methods and skills in working with GIS into the educational process from geography, it is necessary to have, in our opinion, at least four components: 1) the legislative base; 2) use of the best world experience; 3) training of specialist geographers competent in the field of work with geographic information systems; 4) appropriate software and hardware for schools to work with GIS. Conclusions and further research. Polls and experiments of domestic and foreign researchers have shown the effectiveness of GIS as a method in teaching geography in terms of increasing the academic performance of students and increasing the level of assimilation of knowledge. The SWOT analysis of the use of geographic information systems as educational technologies showed problems common to Ukrainian and foreign schools, such as the high cost of software, the lack or lack of technical equipment, and insufficient teacher training. At the same time, the results of the survey indicate that abroad the biggest problem is the lack of motivation of teachers, while in Ukraine the main obstacle is the high cost of logistics. It is promising to conduct a survey of geography teachers at the all-Ukrainian level in order to obtain a complete picture regarding the willingness to introduce the GS in the educational process, in particular on geography.
Populism and Transparency: The Political Core of an Administrative Norm

Transparency has become a preeminent administrative norm with unimpeachable status as a pillar of democracy. But the rise of right-wing populism, reminiscent of older forms of militaristic authoritarianism, threatens transparency’s standing. Recently elected governments in Europe and Latin and North America have represented a counter-movement away from liberal-democratic institutions that promote the visibility and popular accountability that transparency promises. Contemporary populist movements have not, however, entirely rejected it as an ideal. The populist rebuke of power inequities and its advocacy for popular sovereignty implicitly and sometimes explicitly include a demand for a more visible, accessible state. Populists’ seemingly hypocritical embrace of transparency in the face of their resistance to open government mandates demonstrates transparency’s important historical connections and conceptual affiliations with populism, in the process illuminating its complicated politics and the difficulty of ensuring legal compliance in a period of renewed populism. Drawing on transparency and populism’s historical development and using Donald Trump’s one-term presidency as a case study, this article reveals the relationship between an administrative norm and a political movement and style, and what democracy’s current state portends for transparency’s future.
Application of gamification elements in the training of students – future specialists in the field of information security

Currently, the training of students-future specialists in information security is significantly modified. The necessary skills of such a specialist now include "soft skills", super-professional skills that are responsible for the effectiveness of participation in the work process. Information security itself is becoming an integral part of business modeling. This raises the question of choosing the most effective technologies for developing such skills, expressed in the relevant competencies of an information security specialist. The purpose of the study is to evaluate the use of gamification elements in the process of training students-future information security specialists. The research was conducted at the Siberian State University of Science and Technology, at the Department of Information technology security, as well as in the framework of the project «Development of a package of game cases for the discipline «Information security management»». In total, 182 students of the 3rd-5th year of specialty, 1-2 year of master's degree, as well as students of the 4th year of bachelor's degree in Information security took part in pedagogical and experimental work. The study shows that the time required to complete tasks to master the competence decreases (the time is reduced by up to 30% during the semester), and it is also noted that students who are able to master the competence received results at the beginning of the course. At the same time, the share of passively participating in the course elements in small groups is reduced to no more than 10%, and the number of non-participants in the development is practically reduced to zero. The results of the study selective response of participants in networked collaboration showed that the involvement and study of the material remains at a consistently high level as in previous years, and the time slice at the end of the experiment. For example, the assessment of the project organizers ' communication in the social network Vkontakte for 4 years of the project: 100% of participants are connected directly through at least one project participant; 93.75% are listed as friends of at least one project participant, the share of high-intensity information connections was 21.87%.
The Analysis of English Textbook for Vocational School in Parepare Indonesia

This article analyses the English textbook content entitled "Practice Your English Competence for SMK/MAK Grade XI" based on the English syllabus of the 2013 curriculum.It evaluates the English textbook using an evaluation checklist to decide the textbook is ESP (English for Specific Purpose) or EGP (English for General Purpose) and appropriate for vocational school students.This study applied qualitative content analysis and applied the descriptive, evaluative design to evaluate the whole parts of the textbook based and measure the result against some evaluation checklist as standardization of the learning objectives.The findings show that the textbook "Practice Your English Competence" for Grade XI Vocational High School students is appropriate with the syllabus.The last is the textbook evaluation.It can be inferred that the textbook is categorized as "good," it cannot be excellent because of some reasons, first is the textbook preferred provided the language as a grammatical system, not as a communication system, the textbook gives no opportunity for the students to increase their speaking skill.However, all over the book can fulfil the learning materials based on the English syllabus.Therefore, the textbook also cannot be included as an ESP textbook.