Reviewerly/exHarmony huggingface dataset:
Logarithm Design on Encrypted Data with Bitwise Operation

Privacy-preserving big data analysis on cloud systems is becoming increasingly indispensable as the amount of information of the individuals is accumulated on our database system. As a way of maintaining security on cloud system, Homomorphic Encryption (HE) is considered to be theoretically eminent protecting against privacy leakage. However, insufficient number of operations on HE are developed, hindering many research developers to apply their knowledgeable techniques on this field. Therefore, we propose a novel approach in constructing logarithm function based on mathematical theorem of Taylor expansion with fundamental arithmetic operations and basic gate operations in usage. Moreover, we present a more accurate way of deriving answers for logarithm using square and shift method.
Psychological and pedagogical features of the organization of independent work of students-psychologists in the educational environment «Moodle»

Psychological and pedagogical features of the organization of independent work of students-psychologists in the educational environment the description of the electronic educational environment Moodle for the organization of independent work of students-psychologists. The features of the independent work, requirements for the design of electronic environments.Description the results of the evaluation of students of different types of tasks for independent work from the standpoint of interest and efficiency for the final certification. The conclusions about the feasibility of using specific types of tasks for independent work in the electronic environment.
Blockchain-Based Decentralized Key Management System with Quantum Resistance

The blockchain technique was first proposed called Bitcoin in 2008 and is a distributed database technology. Public Key Infrastructure (PKI) system, which is one of the key management systems, is a centralized system. There is a possibility of single point failure in currently used centralized PKI system. Classical digital signature algorithm; ECDSA has used the well-known cryptocurrencies such as Bitcoin and Ethereum. Using the Shor’s algorithm, it is vulnerable to an attack by the quantum adversary. In this paper, we propose a blockchain-based key management system using quantum-resistant cryptography. Since it uses a GLP digital signature scheme, which is a secure lattice-based digital signature scheme. Therefore, our construction is based on quantum-resistant cryptography, it is secure against the attack of a quantum adversary and ensures long-term safety. In addition, we design a decentralized blockchain structure with extended X.509 certificate, and it is secure for the single point of failure.
Conceptualized phrase clustering with distributed k-means

A vast majority of text mining and machine learning algorithms such as topic models, classification, clustering are based on statistical methods thus the semantics or meaning of the words or phrases are not considered. Interpretation of outputs generated by such algorithms are difficult for humans because of the absence of sufficient contextual information. Distributional semantics is a relatively new but active research area in natural language processing that quantifies semantic similarities between linguistic elements considering the context in which they occur. Conceptualization algorithms on the other hand enriches short text such as words and phrases. This paper proposes an approach that uses a map-reduce framework for combining these two techniques to generate conceptualized semantic clusters of phrases using distributional representation. Rigorous and systematic experiments on unstructured text datasets show that this approach can generate semantically rich and human interpretable concept clusters from large datasets. Further, the approach is scalable when dealing with high dimensional data since this method uses a map-reduce based framework for clustering.
A Neural Network Based Ranking Framework to Improve ASR with NLU Related Knowledge Deployed

This work proposes a new neural network framework to simultaneously rank multiple hypotheses generated by one or more automatic speech recognition (ASR) engines for a speech utterance. Features fed in the framework not only include those calculated from the ASR information, but also involve natural language understanding (NLU) related features, such as trigger features capturing long-distance constraints between word/slot pairs and BLSTM features representing intent-sensitive sentence embedding. The framework predicts the ranking result of the input hypotheses, outputting the top-ranked hypothesis as the new ASR result together with its slot filling and intention detection results. We conduct the experiments on an in-car infotainment corpus and the ATIS (Airline Travel Information Systems) corpus, for which hypotheses are generated by different types of engines and a single engine, respectively. The experimental results achieved are encouraging on both data corpora (e.g., 21.9% relative reduction in word error rate over state-of-the-art Google cloud ASR performance on the ATIS testing data), proving the effectiveness of the proposed ranking framework.
Semi-supervised Nuisance-attribute Networks for Domain Adaptation

How to overcome the training and test data mismatch in speaker verification systems has been a focus of research recently. In this paper, we propose a semi-supervised nuisance attribute network (SNAN) to reduce the domain mismatch in i-vectors and x-vectors. SNANs are based on the idea of nuisance attribute removal in inter-dataset variability compensation (IDVC). But instead of measuring the domain variability through the dataset means, SNANs use the maximum mean discrepancy (MMD) as part of their loss function, which enables the network to find nuisance directions in which domain variability is measured up to infinite moment. The architecture of SNANs also allows us to incorporate the out-of-domain speaker labels into the semi-supervised training process through the center loss and triplet loss. Using SNANs as a preprocessing step for PLDA training, we achieve a relative improvement of 11.8% in EER on NIST 2016 SRE compared to PLDA without adaptation. We also found that the semi-supervised approach can further improve SNANs' performance.
On the development of a web extension for text authentication on Google Chrome

Web extension is a software that can be installed on a web browser. A web-extension link is displayed as an icon on the toolbar of the browser. Based on browsing activity, the extension works automatically or by clicking the extension icon depending on the functionalities made in the extension software. In this work, we developed a web extension on Google Chrome browser to verify online texts by simply clicking on an extension button. Upon clicking the button, the underlying algorithm in the extension software retrieves the texts from the current web-page being displayed. Verification and authentication of texts are performed by comparing the retrieved texts with text database. According to the comparison, texts are highlighted in colors. We consider authentication of Arabic Hadith texts for a case study. The authentic Hadith texts are highlighted by green color; authentic texts with partial diacritics, by yellow; and unauthentic texts, by red. This technique can also be used to authenticate laws, constitutions and Government documents in any language.
Error Estimation for the Particle Filter

The particle filter is a popular algorithm for solving the state-space problem for its easy implement. Many previous studies have been conducted to study the asymptotical behavior of particle filters. In our previous works, we divided the error of particle filter into two parts. By using Lindeberg's central limit theorem, we showed that one of them is asymptotically normal. However, it's hard to estimate the covariance matrix of it's converged distribution. This paper aims at giving a computable estimator for the covariance matrix.
Data Re-Identification—A Case of Retrieving Masked Data from Electronic Toll Collection

With the growth of big data and open data in recent years, the importance of data anonymization is increasing. Original data need to be anonymized to prevent personal identification from being revealed before being released to the public. There is a growing variety of de-identification methods which have been proposed to reduce the privacy issues, however, there is still much to be improved. The purpose of this study is to demonstrate the possibilities of re-identification from masked data, and to compare the pros and cons of different de-identification methods. A set of electronic toll collection data from Taiwan was used and we successfully re-identified vehicles with specific patterns. Four de-identification methods were performed and finally we compared the strengths and weaknesses of these methods and evaluated their appropriateness.
Enhancing Learning Engagement on Minangkabau Traditional Food through Gamified Mobile Quiz

Some ethnic groups are reported not only dealing with the deskilling problems related to domestic coo-king proficiency but starting to lose the traditional food culture due to the impairment of old generation of bequeathing the knowledge of traditional food to the young generations. Nevertheless, teaching traditional knowledge is challenging due to the characteristics of the youth nowadays as digital natives that always adhere to modern technology instead of traditional one. Hence, there is a need to find an approach to teaching implementation which engages the youth in learning Minangkabau traditional food. This study aims to propose gamification as an approach for enhancing learning among young generation in Minangkabau. It focuses on the learning of Minangkabau traditional food using gamification elements in a mobile quiz. This study used methodology that was a part of quantitative, particularly a survey technique using a questionnaire to collect data to measure level of engagement. Finally, we examined the level of user engagement using modified Technology Engagement Model (TEM). Structural Equation Modelling was used to validate the variables which influenced the engagement such as Individual Expectation (IE), Facility Environment (FE), Social Environment (SE), Technology Engagement (TE), and Trust. A total of 160 participants between 17-25 years old were randomly assigned to contribute to this study. The results indicate that gamification which implemented in the mobile quiz application provides positive effects that enhance user engagement. Usefulness of fragmented learning, peer support, and trust were found to be the most significant factors to enhance the gamified quiz engagement.
PGR-Net:A Parallel Network Based on Group and Regression for Age Estimation

Age is an important biometric feature of human face. Estimating the specific age of facial images is challenging, because of face aging's highly nonlinearity and randomness. Commonly age predictors are based on classification or regression method, which may be affected greatly by the category number or data distribution of the labelled samples. In this paper, we design a parallel deep neural network, called PGR-Net. It is a unified learning model which combines the merits of traditional classification methods and regression methods. The model consists of a classification network and several age regressors. The classification network is designed to divide facial images into several age groups, and a regressor is trained for each group separately. We train the classification network and the re-gressors in parallel, and perform age estimation with the regressor of the group predicted by the classification network. Experiments show that the proposed approach is fairly competitive compared with the state-of-the-art methods on two public datasets.
Complex Network Analysis of a Genetic Programming Phenotype Network

The genotype-to-phenotype mapping plays an essential role in the design of an evolutionary algorithm. Since variation occurs at the genotypic level but fitness is evaluated at the phenotypic level, this mapping determines how variations are effectively translated into quality improvements. We numerically study the redundant genotype-to-phenotype mapping of a simple Boolean linear genetic programming system. In particular, we investigate the resulting phenotypic network using tools of complex network analysis. The analysis yields a number of interesting statistics of this network, considered both as a directed as well as an undirected graph. We show by numerical simulation that less redundant phenotypes are more difficult to find as targets of a search than others that have much more genotypic abundance. We connect this observation with the fact that hard to find phenotypes tend to belong to small and almost isolated clusters in the phenotypic network.
The decentralized control method for a multicopters group using unstable modes

In this paper we consider the problem of group control in the non-determined environment with obstacles. The control object is represented by the group of multicopters. The mathematical model of the multicopter is based on equations of kinematics and dynamics of 6DOF.The flight control algorithm for a multicopter in the group is described. By using the unstable modes, we propose an obstacle avoiding control algorithm for a group. This algorithm allows to take into account velocities of moving objects and obstacles besides positions. The use of unstable modes allows to reduce the requirements for the vision system. The developed group control algorithm allows each multicopter in a group to return to the initial trajectory after avoiding obstacles. The simulation results confirm the efficiency of the proposed method.
Obtaining phase-optimized states from superpositions of coherent states in phase-sensitive attenuating/amplifying reservoirs

In this paper we present new nonclassical states of a single mode of the electromagnetic field constructed by superpositions of coherent states distributed over a semi-straight line starting from the origin of the phase space. These states have highly concentrated phase distributions and can be useful as approximations of phase states. We propose feasible schemes to prepare such states in microwave high-Q cavities and as traveling modes. We investigate nonclassical properties and determine the Wigner functions of such pure states. Then we consider the system in contact with a reservoir, taking into account dissipation and gain processes in the Markovian approximation, and study the time-evolution of these states by solving the master equation in the Wigner representation. From explicit expressions of the time-dependent Wigner functions, we study the time-dependence of nonclassical properties and use the negativity of the Wigner function to analyze the disappearance of sub-Planck structures as time evolves.
Lightweight Cryptography Techniques for MHealth Cybersecurity

A new integration of wireless communication technologies into the mobile heath system also known as mHealth has instigated a momentous research interest in the field of mobile security. mHealth systems are unique in terms of characteristics, challenges, architecture, and applications. Consequently, security requirements related to mHealth system are more complex as compared to mobile networks and conventional wireless networks. This article presents a survey about developments in mHealth system from the perspective of lightweight cryptographic protocols and privacy preserving algorithms. Unique characteristics of mHealth systems are presented which make the embedded security applications computationally hard as well as memory constrained. The current study also deals with the fundamental security requirements, essential for mHealth communication. Furthermore, awareness of security threats and their cryptographic solutions in terms of future health industry are discussed. In addition, lightweight cryptographic solutions are summarized. These strategies can be enhanced or incorporated all in all to meet the security perquisites of future patients' data security.
Multi-level Utility Mining: Retrieval of High Utility Itemsets in a Transaction Database

Utility mining is a key field in data mining, meant to reveal high utility itemsets (HUIs). It retrieves HUIs from a multi-level dataset. An algorithm MUMA (multilevel utility mining algorithm) was proposed to retrieve HUIs in a multi-level dataset. MUM algorithm implements a tree structure, called MUMT (multilevel utility mining tree), to store utility information of the itemsets. Several enhanced tree-based algorithms namely multi-level utility mining using enumeration tree (MU_ET), multi-level utility mining using utility pattern tree (MU_UP), multi-level utility mining using lexicographic tree (MU_LG) are also analysed. MUMA was compared with MU_ET, MU_UP and MU_LG. The experiments are performed using different datasets like transaction datasets, weblogdatasets,and synthetic datasets.
Domain Adaptation Approach for Sentiment Analysis

Customer satisfaction surveys, marketing research, and associated analysis are necessary but very costly procedures for companies to understand how their services are, determine how to develop their product better, and choose how to organize their marketing promotion strategies. Being able to mark sentiment for each review as positive or negative by machine learning is highly desirable for companies if sentiment analysis can be conducted on the information on social media such as Facebook, Yelp and Twitter, where people post their opinions on how they feel and what they think of certain products on a large scale. However, there is a wide range of domain variations across different fields, while it is not always to collect enough data for each individual domain. To account for such domain variations, this paper applies domain adaptation approach for sentiment analysis, where a system is trained from one source domain but deployed on another target domain. The method shows satisfactory effectiveness and efficiency when comparing to other methods. This work provides a framework for future data mining on understanding the sentiment across multiple domains using domain adaptation approach.
Network Adaptation Strategies for Learning New Classes without Forgetting the Original Ones

We address the problem of adding new classes to an existing classifier without hurting the original classes, when no access is allowed to any sample from the original classes. This problem arises frequently since models are often shared without their training data, due to privacy and data ownership concerns. We propose an easy-to-use approach that modifies the original classifier by retraining a suitable subset of layers using a linearly-tuned, knowledge-distillation regularization. The set of layers that is tuned depends on the number of new added classes and the number of original classes. We evaluate the proposed method on two standard datasets, first in a language-identification task, then in an image classification setup. In both cases, the method achieves classification accuracy that is almost as good as that obtained by a system trained using unrestricted samples from both the original and new classes.
A Kaczmarz Algorithm for Solving Tree Based Distributed Systems of Equations

The Kaczmarz algorithm is an iterative method for solving systems of linear equations. We introduce a modified Kaczmarz algorithm for solving systems of linear equations in a distributed environment, i.e., the equations within the system are distributed over multiple nodes within a network. The modification we introduce is designed for a network with a tree structure that allows for passage of solution estimates between the nodes in the network. We prove that the modified algorithm converges under no additional assumptions on the equations. We demonstrate that the algorithm converges to the solution, or the solution of minimal norm, when the system is consistent. We also demonstrate that in the case of an inconsistent system of equations, the modified relaxed Kaczmarz algorithm converges to a weighted least-squares solution as the relaxation parameter approaches 0.
Multisource Surveillance Video Coding by Exploiting 3D and 2D Knolwedge

The rapidly increasing surveillance video data has challenged the existing video coding standards. Even though knowledge based video coding scheme proposed for moving objects so far has achieved high efficiency, it does not take full advantages of local information and highly relies on the accuracy of pose parameter of the objects, thus leading to large prediction residuals. In this paper, a novel surveillance video coding utilizing 3D and 2D knowledge is proposed. On the one hand, we generate a knowledge based reference frame from 3D models of the objects and incorporate it into the block based coding framework to remove global redundancy while improve the robustness to pose errors. On the other hand, 2D knowledge in the form of visual appearances of the objects in the previously encoded frames is employed to rectify the knowledge based reference frame for local redundancy removal. Experimental results demonstrate the effectiveness of our proposed method against HEVC and the knowledge based coding method.
Calibrated adversarial algorithms for generative modelling

Generative adversarial networks are useful for unsupervised learning but may be difficult to train. We study a class of adversarial algorithms based on f ‐divergence minimization and provide an extension by allowing two objective functions instead of one to be chosen (hence calibrated) for updating the discriminator and the generator, respectively. The extension is derived and justified from theoretical analysis, which identifies specific objective functions for achieving stable gradients in the corresponding updates. Our experiments on synthetic data and MNIST and CIFAR‐10 datasets demonstrate that the proposed method consistently achieves competitive or superior results when compared with various existing methods.
Methodological Challenges for Detecting Interethnic Hostility on Social Media

Detection of ethnic hate speech and other types of ethnicity representation is user texts is an important goal both for social and computer science, as well as for public policy making. To date, quite a few algorithms have been trained to detect hate speech, however, what policy makers and social scientists need are complete pipelines, from definition of ethnicity to a user-friendly monitoring system able to aggregate results of large-scale social media analysis. In this essay, the author summarizes the experience of development of such a system in a series of projects under the author’s leadership. All steps of the offered methodology are described and critically reviewed, and a special attention is paid to the strengths and the limitations of different approaches that were and can be applied along the developed pipeline. All conclusions are based on prior experiments with several large datasets from Russian language social media, including 15 000 marked up texts extracted from a representative one-year collection of 2.7 million user messages containing ethnonyms.
The General Data Protection Regulation's Practical Impact on Software Architecture

The General Data Protection Regulation (GDPR) took effect on 25 May 2018, at which time many companies had not yet become compliant. This article provides advice for GDPR compliance now and in the future.
On the Tracing Traitors Math

We give an overview of the most important mathematical results related to different types of tracing traitors schemes, or schemes with identifiable parent property, especially for the case when the scheme's "length" goes to infinity.