Reviewerly/exHarmony huggingface dataset:
Group ID-Based Encryption with Equality Test

In era of cloud computing, how to search on encrypted data has been studied extensively. ID-based encryption with equality test (IBEET) as a type of searchable encryption allows a tester (insider) to check whether two ciphertexts encrypted under different identities contain the same message. Due to its equality test functionality, IBEET has many interesting applications, such as personal health record systems. In this paper, we first introduce group mechanism into IBEET and propose a new primitive, namely group ID-based encryption with equality test (G-IBEET). By the group mechanism, G-IBEET supports group granularity authorization. That is, a group administrator, who is trusted by group users, would issue the insider a group trapdoor to specify that it can only compare on ciphertexts of the group users but cannot compare with ciphertexts of any users other than them. Moreover, the workload of generation and management of trapdoors can be greatly reduced due to the group granularity authorization. For the insider attack which exists in most IBEET schemes with the goal of recovering the message from a ciphertext by mounting an offline message recovery attack, G-IBEET provides a nice solution for IBEET to resist it by the group mechanism. We propose a G-IBEET scheme in bilinear pairings, prove its security in the random oracle model and show that the proposed scheme has a more efficient test algorithm.
ABARUAH at SemEval-2019 Task 5 : Bi-directional LSTM for Hate Speech Detection

In this paper, we present the results obtained using bi-directional long short-term memory (BiLSTM) with and without attention and Logistic Regression (LR) models for SemEval-2019 Task 5 titled "HatEval: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter".This paper presents the results obtained for Subtask A for English language.The results of the BiLSTM and LR models are compared for two different types of preprocessing.One with no stemming performed and no stopwords removed.The other with stemming performed and stopwords removed.The BiLSTM model without attention performed the best for the first test, while the LR model with character n-grams performed the best for the second test.The BiLSTM model obtained an F1 score of 0.51 on the test set and obtained an official ranking of 8/71.
Detecting and Simulating Artifacts in GAN Fake Images

To detect GAN generated images, conventional supervised machine learning algorithms require collection of a number of real and fake images from the targeted GAN model. However, the specific model used by the attacker is often unavailable. To address this, we propose a GAN simulator, AutoGAN, which can simulate the artifacts produced by the common pipeline shared by several popular GAN models. Additionally, we identify a unique artifact caused by the up-sampling component included in the common GAN pipeline. We show theoretically such artifacts are manifested as replications of spectra in the frequency domain and thus propose a classifier model based on the spectrum input, rather than the pixel input. By using the simulated images to train a spectrum based classifier, even without seeing the fake images produced by the targeted GAN model during training, our approach achieves state-of-the-art performances on detecting fake images generated by popular GAN models such as CycleGAN.
Motivation to Higher Education and its Structure

Features of motivation for obtaining higher education, leading motives, motivational structure are investigated. The relevance of the research is determined by the influence of the motivation peculiarities on the success in studies educational activities and subsequent work. The students of the first courses of full-time education of 12 universities of different regions of Russia took part in the research. N = 559. The analysis of such indicators of central tendency as median, mode and arithmetic mean was used to assess the relative importance of motives. Correlation analysis and cluster analysis were used to identify the structure of motivation for obtaining higher education. Motivation for obtaining higher education was defined as a distant, external to the training activity, an anticipatory motivation. The motives for ensuring material well-being in the future, professionalization, self-realization are the most significant. The structure of motivation includes two clusters of motives that are grouped around the motives of material well-being and self-realization, and a separate motive of professionalization. The motives of the first cluster can be marked as motives for socialization. Motives of the second cluster can be marked as motivation for individuation.
Media Use Among Students From Different Health Curricula: Survey Study

Background Mobile devices such as smartphones, tablets, and laptop computers enable users to search for information and communicate with others at any place and any time. Such devices are increasingly being used at universities for teaching and learning. The use of mobile devices by students depends, among others, on the individual media literacy level and the curricular framework. Objective The objective of this study was to explore whether there were differences in media use in students from various curricula at the Faculty of Health, Witten/Herdecke University. Methods During the 2015-16 winter term, a survey was conducted at the Faculty of Health, Witten/Herdecke University, in which a total of 705 students (out of 1091 students; response rate: 705/1091, 64.61%) from 4 schools participated voluntarily: medicine (346/598), dentistry (171/204), psychology (142/243), and nursing science (46/46). The questionnaire developed for the study included 132 questions on 4 topics: (1) electronic and mobile devices (19 questions), (2) communication and organization of learning (45 questions), (3) apps/programs/websites/media (34 questions), and (4) media literacy (34 questions). The questionnaire was distributed and anonymously completed during in-class courses. Results Students from all 4 schools had at least two electronic devices, with smartphones (97.4%, 687/705) and laptops (94.8%, 669/705) being the most common ones. Students agreed that electronic devices enabled them to effectively structure the learning process (mean 3.16, SD 0.62) and shared the opinion that university teaching should include imparting media literacy (mean 2.84, SD 0.84). Electronic device ownership was the highest among medical students (mean 2.68, SD 0.86) and medical students were the only ones to use a tutorial (36.1%, 125/346). Dental students most widely used text messages (mean 3.41, SD 0.49) and social media (mean 2.57, SD 1.10) to organize learning. Psychology students considered mobile devices to be most ineffective (mean 2.81, SD 0.83). Nursing science students used emails (mean 3.47, SD 0.73) and desktop computers (39%, 18/46) most widely. Conclusions The results show that almost all students use electronic learning (e-learning) tools. At the same time, different profiles for different degree programs become apparent, which are to be attributed to not only the varying curricula and courses but also to the life circumstances of different age groups. Universities should, therefore, pay attention to the diverse user patterns and media literacy levels of students when planning courses to enable successful use of e-learning methods.
A Review on Cyber Security and the Fifth Generation Cyberattacks

Cyber attacks have become quite common in this internet era. The cybercrimes are getting increased every year and the intensity of damage is also increasing. providing security against cyber-attacks becomes the most significant in this digital world. However, ensuring cyber security is an extremely intricate task as requires domain knowledge about the attacks and capability of analysing the possibility of threats. The main challenge of cybersecurity is the evolving nature of the attacks. This paper presents the significance of cyber security along with the various risks that are in the current digital era. The analysis made for cyber-attacks and their statistics shows the intensity of the attacks. Various cybersecurity threats are presented along with the machine learning algorithms that can be applied to cyber attacks detection. The need for the fifth generation cybersecurity architecture is discussed.
Effects on academic performance in secondary students according to the use of ICT

The main interest of this study was to evaluate the degree of association that the use of ICT could have to school performance in mathematics, within a limited geographical area. The data was obtained thanks to the International Student Assessment Program (PISA). 1376 students from the Canary Islands participated. To obtain the best possible approximation to the phenomenon, a mixed research method was used, in which a linear hierarchical model (HLM) analysis was used to assess the characteristics of students and schools. The results reflected positive changes in school performance influenced by the effective use of ICT. It was shown that learning can be enhanced thanks to ICT, unless its use is not appropriate.
Exploiting Sentence Embedding for Medical Question Answering

Despite the great success of word embedding, sentence embedding remains a not-well-solved problem. In this paper, we present a supervised learning framework to exploit sentence embedding for the medical question answering task. The learning framework consists of two main parts: 1) a sentence embedding producing module, and 2) a scoring module. The former is developed with contextual self-attention and multi-scale techniques to encode a sentence into an embedding tensor. This module is shortly called Contextual self-Attention Multi-scale Sentence Embedding (CAMSE). The latter employs two scoring strategies: Semantic Matching Scoring (SMS) and Semantic Association Scoring (SAS). SMS measures similarity while SAS captures association between sentence pairs: a medical question concatenated with a candidate choice, and a piece of corresponding supportive evidence. The proposed framework is examined by two Medical Question Answering(MedicalQA) datasets which are collected from real-world applications: medical exam and clinical diagnosis based on electronic medical records (EMR). The comparison results show that our proposed framework achieved significant improvements compared to competitive baseline approaches. Additionally, a series of controlled experiments are also conducted to illustrate that the multi-scale strategy and the contextual self-attention layer play important roles for producing effective sentence embedding, and the two kinds of scoring strategies are highly complementary to each other for question answering problems.
A descriptive study of variable discretization and cost-sensitive logistic regression on imbalanced credit data

Training classification models on imbalanced data tends to result in bias towards the majority class. In this paper, we demonstrate how variable discretization and cost-sensitive logistic regression help mitigate this bias on an imbalanced credit scoring dataset, and further show the application of the variable discretization technique on the data from other domains, demonstrating its potential as a generic technique for classifying imbalanced data beyond credit socring. The performance measurements include ROC curves, Area under ROC Curve (AUC), Type I Error, Type II Error, accuracy, and F1 score. The results show that proper variable discretization and cost-sensitive logistic regression with the best class weights can reduce the model bias and/or variance. From the perspective of the algorithm, cost-sensitive logistic regression is beneficial for increasing the value of predictors even if they are not in their optimized forms while maintaining monotonicity. From the perspective of predictors, the variable discretization performs better than cost-sensitive logistic regression, provides more reasonable coefficient estimates for predictors which have nonlinear relationships against their empirical logit, and is robust to penalty weights on misclassifications of events and non-events determined by their apriori proportions.
A Deep Learning Framework for Joint Image Restoration and Recognition

Image restoration and recognition are important computer vision tasks representing an inherent part of autonomous systems. These two tasks are often implemented in a sequential manner, in which the restoration process is followed by a recognition. In contrast, this paper proposes a joint framework that simultaneously performs both tasks within a shared deep neural network architecture. This joint framework integrates the restoration and recognition tasks by incorporating: (i) common layers, (ii) restoration layers and (iii) classification layers. The total loss function combines the restoration and classification losses. The proposed joint framework, based on capsules, provides an efficient solution that can cope with challenges due to noise, image rotations and occlusions. The developed framework has been validated and evaluated on a public vehicle logo dataset under various degradation conditions, including Gaussian noise, rotation and occlusion. The results show that the joint framework improves the accuracy compared with the single task networks.
MACE: Mobile Artificial Conversational Entity for Adapting Domain Knowledge and Generating Personalized Advice

Mobile learning offers, with the help of handheld devices, a continuous access to the learning process. With the advent of mobile learning, educational systems are changing, offering the possibility of distance education without the restrictions of place and time. As such, new technological advancements are employed by mobile learning. This paper presents the design, development and evaluation of a novel artificial conversational entity, incorporated in a mobile learning system for personalized English language instruction. More specifically, it offers amelioration of the domain knowledge model by adapting it to the students’ needs and to the pace that they prefer to receive learning. Moreover, it creates personalized tutoring advice in order to support students in the educational process. Finally, it can assist the procedure of assessments since it automatically generates questions to assess the knowledge level of students. The evaluation of the mobile tutoring system presents promising results regarding the incorporation of this new technology in digital education with the aim of creating a student-centric learning experience.
Minimizing State Preparations in Variational Quantum Eigensolver by Partitioning into Commuting Families

Variational quantum eigensolver (VQE) is a promising algorithm suitable for near-term quantum machines. VQE aims to approximate the lowest eigenvalue of an exponentially sized matrix in polynomial time. It minimizes quantum resource requirements both by co-processing with a classical processor and by structuring computation into many subproblems. Each quantum subproblem involves a separate state preparation terminated by the measurement of one Pauli string. However, the number of such Pauli strings scales as $N^4$ for typical problems of interest--a daunting growth rate that poses a serious limitation for emerging applications such as quantum computational chemistry. We introduce a systematic technique for minimizing requisite state preparations by exploiting the simultaneous measurability of partitions of commuting Pauli strings. Our work encompasses algorithms for efficiently approximating a MIN-COMMUTING-PARTITION, as well as a synthesis tool for compiling simultaneous measurement circuits. For representative problems, we achieve 8-30x reductions in state preparations, with minimal overhead in measurement circuit cost. We demonstrate experimental validation of our techniques by estimating the ground state energy of deuteron on an IBM Q 20-qubit machine. We also investigate the underlying statistics of simultaneous measurement and devise an adaptive strategy for mitigating harmful covariance terms.
An Evaluation of Content Creation for Personalised Learning Using Digital ICT Literacy Module among Aboriginal Students (mLICT-OA)

ICT in the Malaysian education system serves as a tool to accomplish the National Education Blueprint 2015-2025. Under this blueprint, privileged groups such as the aborigines or Malaysian aboriginals will be given equal opportunity in education. However, a specific classroom teaching method is required to maintain the aboriginal students’ focus and attention on their learning experiences due to their unique paradigm of learning. This study used the Partial Least Square (PLS) and Structural Equation Modelling (SEM) tool to examine the factors influencing personalised learning and digital self-learning ICT literacy module. It also measured the impact of personalised learning and digital ICT literacy module among secondary aboriginal students. This study involved 92 aboriginal students who participated in a transferable skills and ICT programme held in a public university in Malaysia. Results show that personalised learning positively supported the use of a digital ICT literacy module as a teaching and learning approach. Therefore, this study suggests that the digital ICT literacy module is an adaptive educational resource for personalised learning to meet the learning needs of aboriginal students.
Scientific work of the Russian State Library: Its subjects and presentation of results

The article is devoted to the modern period of research activity of the Russian State Library ‒ one of the Russian Federation’s national libraries, which, in accordance with the current legislation, are research institutions in library science, bibliography and book studies, and methodological, scientific-information and cultural centers of federal importance, participating in development and implementation of the federal policy in the field of librarianship. The article presents the main directions and subjects of the RSL’s research work in 2015–2018. In addition to traditional areas (the history, theory and practice of librarianship, the disclosure of collections, and the study of monuments of book culture), there have also been developed some research projects aimed at solving current issues of library development in the digital environment (the formation and organization of access to electronic information resources, the creation of digital services, etc.). The article demonstrates major publications of the RSL employees, representing the research results (monographs and articles, catalogues, bibliographic indexes, manuals and reference books). The article analyzes the innovations in organizing scientific activities and the achieved results (including scientometric characteristics), which confirm the status of the Russian State Library as a leading research center in the field of library science, bibliography and book studies.
Eye‐controlled, power wheelchair performs well for ALS patients

Abstract Background Our pilot study tested the feasibility and performance of an eye‐controlled power wheelchair for amyotrophic lateral sclerosis (ALS) patients. Methods In this prospective pilot study, participants drove the wheelchair three times around an indoor course. We assessed the time to complete the course; starting and stopping on command; turning 90, 135, and 180 degrees; time to backup; and obstacle negotiation. Following their use of the wheelchair, subjects were given a questionnaire to assess user experience. Results Twelve patients participated, and all were able to complete three trials without difficulty. Eight participants completed all of the individual tasks (eg, turning, stopping, etc.) without any errors. Overall performance ratings were high across all participants (4.6/5‐excellent). Conclusions Our eye‐controlled power wheelchair prototype is feasible and has a very favorable user experience. This system has the potential to improve the mobility and independence of ALS patients, and other groups with motor impairments.
Securing V2X Communications for the Future

Over recent years, emphasis in secure V2X communications research has converged on the use of Vehicular Public Key Infrastructures (VPKIs) for credential management and privacy-friendly authentication services. However, despite the security and privacy guarantees offered by such solutions, there are still a number of challenges to be conquered. By reflecting on state-of-the-art PKI-based architectures, in this paper, we identify their limitations focusing on scalability, interoperability, pseudonym reusage policies and revocation mechanisms. We argue that in their current form such mechanisms cannot capture the strict security, privacy, and trust requirements of all involved stakeholders. Motivated by these weaknesses, we then proceed on proposing the use of trusted computing technologies as an enabler for more decentralized approaches where trust is shifted from the back-end infrastructure to the edge. We debate on the advantages offered and underline the specifis of such a novel approach based on the use of advanced cryptographic primitives, using Direct Anonymous Attestation (DAA) as a concrete example. Our goal is to enhance run-time security, privacy and trustworthiness of edge devices with a scalable and decentralized solution eliminating the need for federated infrastructure trust. Based on our findings, we posit open issues and challenges, and discuss possible ways to address them.
Visualizing a Moving Target: A Design Study on Task Parallel Programs in the Presence of Evolving Data and Concerns

Common pitfalls in visualization projects include lack of data availability and the domain users' needs and focus changing too rapidly for the design process to complete. While it is often prudent to avoid such projects, we argue it can be beneficial to engage them in some cases as the visualization process can help refine data collection, solving a "chicken and egg" problem of having the data and tools to analyze it. We found this to be the case in the domain of task parallel computing where such data and tooling is an open area of research. Despite these hurdles, we conducted a design study. Through a tightly-coupled iterative design process, we built Atria, a multi-view execution graph visualization to support performance analysis. Atria simplifies the initial representation of the execution graph by aggregating nodes as related to their line of code. We deployed Atria on multiple platforms, some requiring design alteration. We describe how we adapted the design study methodology to the "moving target" of both the data and the domain experts' concerns and how this movement kept both the visualization and programming project healthy. We reflect on our process and discuss what factors allow the project to be successful in the presence of changing data and user needs.