Reviewerly/exHarmony huggingface dataset:
Challenges of accountability and development in Nigeria

Purpose The purpose of this paper is to examine the challenges of accountability and development in Nigeria. In the literature, corruption is seen as an indicator of a lack of political accountability in most countries of the world, especially in less developed countries such as Nigeria. The Nigerian Government has taken several actions to address the problems of bad governance and corruption that have impeded economic development, but unfortunately these measures have not yielded the desired results. Design/methodology/approach Thus, this study examined accountability and developmental issues in Nigeria using secondary data and then made use of the auto-regressive distributed lag econometric technique to analyze the data. Findings The results from the study found that a rise in total government expenditure poses a danger of reducing Nigeria’s economic development in the long run and that control of corruption and political (the institutional variables) has a direct and significant effect on Nigeria’s economic development. Originality/value Therefore, upon these findings, this paper recommended that for Nigeria to experience development, corruption should be eliminated, and the Nigerian Government should spend on viable projects and economic activities that will be beneficial to the populace and the society at large and hence bring about economic development. Accountability is the hallmark of a prudent government that ensures efficient management of resources and transparency in the utilization of funds by the government. The absence of accountability mechanism allows corruption to thrive, which hinders the developmental process.
ViHand: Gesture Recognition with Ambient Light

Hand gesture recognition has become increasingly important in human-computer interaction (HCI) and can support a broad range of emerging applications, such as smart home, virtual reality, and mobile gaming. During the last few years, more and more researchers are exploring ubiquitous modalities, such as radio frequency signals and acoustic signals, to enable gesture recognition. Compared with existing methods, the light-based approach leverages ambient light (daylight, lighting, etc.) to detect and recognize human gestures, which is totally non-intrusive and very convenient for daily use. In this paper, we develop a prototype system, named ViHand, to facilitate automatic detection and recognition of gestures by using ambient light. The key idea of light-based gesture recognition is quite straight forward: when moving with different gestures, the hand will shade the sensor from the light with different orders, which will generate a unique shadow pattern. ViHand uses photodiode sensor arrays to capture this unique shadow by proposing a two-step recognition approach. Specifically, we use the order in which sensors are blocked to recognize the sliding gestures. Furthermore, for the recognition of complex gestures such as digital gestures, we first establish a priori template library based on the signal characteristics caused by the motion of different gestures. Then, an improved dynamic time warping(DTW) algorithm is used to match the template, and the kNN algorithm is used for classification. We conduct a set of experiments to verify the effectiveness of our system, and the experimental results indicate that the classification accuracy of sliding gestures reaches 100%, and the accuracy of digital gestures reaches 82.3%.
Using web technologies in the process of development of students' critical thinking

The article deals with the issues of development of critical thinking of students by means of web technologies. It presents the analysis of different scientific approaches to the organization of critical thinking training of students. The authors state that among the means of the above mentioned critical thinking development scientists distinguish information and communication technologies, particularly web technologies, as having a significant didactic and critical thinking potential. The experience of using web resources to verify information and data in the process of students' critical thinking development is explained. The article gives examples of the using remote special course on the basis of web resources implementation into real learning process. The application of web resources provides an opportunity to intensify critical thinking motivation of students, involves them in solving the discussed problems, and develops critical thinking skills, the ability to independently create and develop new knowledge based on the information received. Comparison of the results of ascertaining and forming sections allows to ascertain the effectiveness of the proposed pedagogical condition for the development of students' critical thinking by means of web technologies and the effectiveness of the developed remote special course for developing students' critical thinking by means of modern web technologies.
Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers

In this paper, an experimental study was carried out to determine the influence of imbalanced datasets preprocessing in the performance of associative classifiers, in order to find the better computational solutions to the problem of credit scoring. To do this, six undersampling algorithms, six oversampling algorithms and four hybrid algorithms were evaluated in 13 imbalanced datasets referring to credit scoring. Then, the performance of four associative classifiers was analyzed. The experiments carried out allowed us to determine which sampling algorithms had the best results, as well as their impact on the associative classifiers evaluated. Accordingly, we determine that the Hybrid Associative Classifier with Translation, the Extended Gamma Associative Classifier and the Naïve Associative Classifier do not improve their performance by using sampling algorithms for credit data balancing. On the other hand, the Smallest Normalized Difference Associative Memory classifier was beneficiated by using oversampling and hybrid algorithms.
Curriculum Pre-training for End-to-End Speech Translation

End-to-end speech translation poses a heavy burden on the encoder, because it has to transcribe, understand, and learn cross-lingual semantics simultaneously. To obtain a powerful encoder, traditional methods pre-train it on ASR data to capture speech features. However, we argue that pre-training the encoder only through simple speech recognition is not enough and high-level linguistic knowledge should be considered. Inspired by this, we propose a curriculum pre-training method that includes an elementary course for transcription learning and two advanced courses for understanding the utterance and mapping words in two languages. The difficulty of these courses is gradually increasing. Experiments show that our curriculum pre-training method leads to significant improvements on En-De and En-Fr speech translation benchmarks.
Recurrent neural network for abstractive summarization of documents

Automatic Text Summarization is the process which utilizes machine power to process large paragraphs in order to create brief and refined summary. Text summarization can be classified into two classes viz. extractive and abstractive. The pith of the extractive is a choice issue, which consequently picks significant sentences from the input content as indicated by different assessment measures; while abstractive requires a profound semantic and talk comprehension of the content to create a base rephrased summary. Deep learning gives an achievable system to create an abstractive summarizer. Intermittent neural system Recurrent Neural Network (RNN) based arrangement to-grouping number of layers for learning has made as rounding progress in different natural language processing errands. The proposed model principally comprises of two sections - an encoder and a decoder - each one of which is a Recurrent Neural Network (RNN). ROUGE assessment metric is utilized to assess the likeness between real feature and anticipated feature.
Pre-Service Primary English Teachers’ AI Chatbots

This study evaluates 17 AI English-language chatbots that were developed by nine groups of pre-service primary school teachers (N = 26).According to the achievement standards for the two grade bands in the curriculum (Grades 3-4 and 5-6: Ministry of Education, 2015), each group developed two chatbots, using Dialogflow API.The first and second chatbots were designed to talk the way a new friend would and in a specific situation, respectively.The chatbots have been found to provide opportunities for primary school students to engage in playful and interactive practice during which the students use a variety of communicative functions, and the chatbots are therefore expected to make solid contributions to the attainment of speaking and listening achievement standards.It was also noted that compositional techniques in chatbot development would generate facilitative factors for foreign language learning, such as topic consistency, flow variability, and grounding.
Sentiment Analysis for Customer Review: Case Study of GO-JEK Expansion

Background: Market prediction is an important thing that needs to be analyzed deeply. Business intelligence becomes an important analysis procedure for analyzing the market demand and satisfaction. Since business intelligence needs a deep analysis, sentiment analysis becomes a powerful algorithm for analyzing customer review regarding to the business intelligence analysis.Objective: In this study, we perform a sentiment analysis for identifying the business intelligence analysis in GO-JEK.Methods: We use Twitter posts collected from the Twint library which consists of 3111 tweets. Since the dataset did not provide a ground truth, we perform Microsoft Text Analytic for determining positive, neutral, and negative sentiment. Before applying Microsoft Text Analytic, we conduct a pre-processing step to remove the unwanted data such as duplicate tweets, image, website address, etc.Results: According to the Microsoft Text Analytic, the results are 666 positive sentiment numbers, 2055 neutral sentiment numbers, and 127 negative sentiment numbers.Conclusion: According to these results, we conclude that most GO-JEK customers are satisfied with the GO-JEK services. In this research, we also develop classification model to predict the sentiment analysis of new data. We use some classifier algorithms such as Decision Tree, Naïve Bayes, Support Vector Machine and Neural Network. In the result, the system shows that the decision tree provides the best performance.
Detection of SQL Injection Vulnerability in Embedded SQL

Embedded SQL inserts SQL statements into the host programming language and executes them at program run time. SQL injection is a known attack technique; however, detection techniques are not introduced in embedded SQL. This paper introduces a technique based on candidate code generation that can detect SQL injection vulnerability in the C/C++ host programming language.
Characterization of lip sets

We denote the local “little” Lipschitz constant of a function f:R→R by lip f. In this paper we settle the following question: For which sets E⊂R is it possible to find a continuous function f such that lipf=1E? In an earlier paper we introduced the concept of strongly one-sided dense sets. Our main result characterizes lip1 sets as countable unions of closed sets which are strongly one-sided dense. We also show that a stronger statement is not true i.e. there are strongly one-sided dense Fσ sets which are not lip1.
Local differential privacy for human-centered computing

Abstract Human-centered computing in cloud, edge, and fog is one of the most concerning issues. Edge and fog nodes generate huge amounts of data continuously, and the analysis of these data provides valuable information. But they also increase privacy risks. The personal sensitive data may be disclosed by untrusted third-party service providers, and the current solutions to privacy protection are inefficient, costly. It is difficult to obtain available statistics. To solve these problems, we propose a local differential privacy sensitive data collection protocol in human-centered computing. Firstly, to maintain high data utility, the selection of the optimal number of hash functions and the mapping length is based on the size of the collected data. Secondly, we hash the sensitive data, add the appropriate Laplace noise to the client side, and send the reports to the server side. Thirdly, we construct the count sketch matrix to obtain privacy statistics on the server side. Finally, the utility of the proposed protocol is verified by synthetic datasets and a real dataset. The experimental results demonstrate that the protocol can achieve a balance between data utility and privacy protection.
FAIR + FIT: Guiding Principles and Functional Metrics for Linked Open Data (LOD) KOS Products

Abstract Purpose To develop a set of metrics and identify criteria for assessing the functionality of LOD KOS products while providing common guiding principles that can be used by LOD KOS producers and users to maximize the functions and usages of LOD KOS products. Design/methodology/approach Data collection and analysis were conducted at three time periods in 2015–16, 2017 and 2019. The sample data used in the comprehensive data analysis comprises all datasets tagged as types of KOS in the Datahub and extracted through their respective SPARQL endpoints. A comparative study of the LOD KOS collected from terminology services Linked Open Vocabularies (LOV) and BioPortal was also performed. Findings The study proposes a set of Functional, Impactful and Transformable (FIT) metrics for LOD KOS as value vocabularies. The FAIR principles, with additional recommendations, are presented for LOD KOS as open data. Research limitations The metrics need to be further tested and aligned with the best practices and international standards of both open data and various types of KOS. Practical implications Assessment performed with FAIR and FIT metrics support the creation and delivery of user-friendly, discoverable and interoperable LOD KOS datasets which can be used for innovative applications, act as a knowledge base, become a foundation of semantic analysis and entity extractions and enhance research in science and the humanities. Originality/value Our research provides best practice guidelines for LOD KOS as value vocabularies.
Compressive learning with privacy guarantees

Abstract This work addresses the problem of learning from large collections of data with privacy guarantees. The compressive learning framework proposes to deal with the large scale of datasets by compressing them into a single vector of generalized random moments, called a sketch vector, from which the learning task is then performed. We provide sharp bounds on the so-called sensitivity of this sketching mechanism. This allows us to leverage standard techniques to ensure differential privacy—a well-established formalism for defining and quantifying the privacy of a random mechanism—by adding Laplace of Gaussian noise to the sketch. We combine these standard mechanisms with a new feature subsampling mechanism, which reduces the computational cost without damaging privacy. The overall framework is applied to the tasks of Gaussian modeling, k-means clustering and principal component analysis, for which sharp privacy bounds are derived. Empirically, the quality (for subsequent learning) of the compressed representation produced by our mechanism is strongly related with the induced noise level, for which we give analytical expressions.
Research Challenges and Technology Progress of Data Mining with Bigdata

'Big Data' has spread quickly in the framework of Data Mining as well as Business Intelligence. This brand-new circumstance can be de?ned by means of those troubles that can not be efficiently or ef?ciently resolved making use of the common computing resources that we currently have. We have to highlight that Big Data does not simply imply huge volumes of data but likewise the requirement for scalability, i.e., to make sure a response in an acceptable elapsed time. This paper discusses about the research challenges and technology progress of data mining with big data.
Pose-Forecasting Aided Human Video Prediction With Graph Convolutional Networks

Human video prediction is still a challenging problem due to the uncertainty of future actions and complexity of frame details.Recent methods tackle this problem in two steps: firstly to forecast future human poses from the initial ones, and then to generate realistic frames conditioned on predicted poses.Following this framework, we propose a novel Graph Convolutional Network (GCN) based pose predictor to comprehensively model human body joints and forcast their positions holistically, and also a stacked generative model with a temporal discriminator to iteratively refine the quality of the generated videos.The GCN based pose predictor fully considers the relationships among body joints and produces more plausible pose predictions.With the guidance of predicted poses, a temporal discriminator encodes temporal information into future frame generation to achieve high-quality results.Furthermore, stacked residual refinement generators make the results more realistic.Extensive experiments on benchmark datasets demonstrate that the proposed method produces better predictions than state-of-the-arts and achieves up to 15% improvement in PSNR.
Transcription of Historical Ciphers and Keys

Historical ciphertexts and keys contain a wide range of symbols from digits and letters from known alphabets to various types of graphic signs.To be able to study ciphertexts and keys empirically in large(r) scale, consistent representation of the symbol systems used in ciphers is inevitable.In this paper, we present guidelines for transcription of ciphertexts, keys and cipher-related cleartext documents.We hope that the guidelines contribute not only to the systematic and consistent text representation across ciphertexts and keys, but also help in more accurate and reliable transcriptions.
BBB Secure Nonce Based MAC Using Public Permutations

In the recent trend of CAESAR competition and NIST light-weight competition, cryptographic community have witnessed the submissions of several cryptographic schemes that are build on public random permutations. Recently, in CRYPTO 2019, Chen et al. have initiated an interesting research direction in designing beyond birthday bound PRFs from public random permutations and they proposed two instances of such PRFs. In this work, we extend this research direction by proposing a nonce-based MAC build from public random permutations. We show that our proposed MAC achieves 2n/3 bit security (with respect to the state size of the permutation) and the bound is essentially tight. Moreover, the security of the MAC degrades gracefully with the repetition of the nonce.
Hash-Based Signatures Revisited: A Dynamic FORS with Adaptive Chosen Message Security

FORS is the underlying hash-based few-time signing scheme in SPHINCS $$^{+}$$ , one of the nine signature schemes which advanced to round 2 of the NIST Post-Quantum Cryptography standardization competition. In this paper, we analyze the security of FORS with respect to adaptive chosen message attacks. We show that in such a setting, the security of FORS decreases significantly with each signed message when compared to its security against non-adaptive chosen message attacks. We propose a chaining mechanism that with slightly more computation, dynamically binds the Obtain Random Subset (ORS) generation with signing, hence, eliminating the offline advantage of adaptive chosen message adversaries. We apply our chaining mechanism to FORS and present DFORS whose security against adaptive chosen message attacks is equal to the non-adaptive security of FORS. In a nutshell, using SPHINCS $$^{+}$$ -128s parameters, FORS provides 75-bit security and DFORS achieves 150-bit security with respect to adaptive chosen message attacks after signing one message. We note that our analysis does not affect the claimed security of SPHINCS $$^{+}$$ . Nevertheless, this work provides a better understanding of FORS and other HORS variants, and furnishes a solution if new adaptive cryptanalytic techniques on SPHINCS $$^{+}$$ emerge.
IDENTIFY METHODS OF TEACHING AND LEARNING TO CREATE INTEREST, SELF-STUDY, AND CREATIVITY OF STUDENTS

Purpose: This research indicates the need to identify the right teaching and learning methods, ways to design teaching and learning methods for lecturers, and students in Vietnam's higher education system. From there, point out the limitations of current teaching methods and the advantages when teachers and learners design their new teaching and learning methods.&#x0D; Methodology: This research uses qualitative and quantitative research methods through interviews about teaching methods of lecturers and student learning methods; evaluation of lecturers and students about the advantages and disadvantages of the current teaching and learning method. Respondents included 1.000 students and 130 lecturers at several universities in the Ho Chi Minh City of Vietnam.&#x0D; Main Finding: Based on these data and analysis, this study shown a new paradigm in developing active teaching and learning methods for lecturers and students, optimal methods for achieving the expectations that teaching strategies and active learning by the instructors and students.&#x0D; Implications/Applications: This study contributes to the development of positive teaching and learning strategies, creating interest, and promoting students' self-study ability. The results will help lecturers, and students build proper teaching and learning strategies, creating funny, development capacity, and creativity of students.&#x0D; Novelty/Originality: The research, analyze and design the essential contents of the strategy development process for both teachers (lecturers) and learners (students). The main contributions of the research are the initial conditions necessary for teachers and students to develop effective teaching and learning strategies for themselves.
Verified Runtime Assertion Checking for Memory Properties

Runtime Assertion Checking (RAC) for expressive specification languages is a non-trivial verification task, that becomes even more complex for memory-related properties of imperative languages with dynamic memory allocation. It is important to ensure the soundness of RAC verdicts, in particular when RAC reports the absence of failures for execution traces. This paper presents a formalization of a program transformation technique for RAC of memory properties for a representative language with memory operations. It includes an observation memory model that is essential to record and monitor memory-related properties. We prove the soundness of RAC verdicts with regard to the semantics of this language.
Sequence-to-Sequence Models for Automated Text Simplification

A key writing skill is the capability to clearly convey desired meaning using available linguistic knowledge. Consequently, writers must select from a large array of idioms, vocabulary terms that are semantically equivalent, and discourse features that simultaneously reflect content and allow readers to grasp meaning. In many cases, a simplified version of a text is needed to ensure comprehension on the part of a targeted audience (e.g., second language learners). To address this need, we propose an automated method to simplify texts based on paraphrasing. Specifically, we explore the potential for a deep learning model, previously used for machine translation, to learn a simplified version of the English language within the context of short phrases. The best model, based on an Universal Transformer architecture, achieved a BLEU score of 66.01. We also evaluated this model’s capability to perform similar transformation to texts that were simplified by human experts at different levels.