Reviewerly/exHarmony huggingface dataset:
The Problem of Double Costs in Blockchain Systems

The paper investigates the probability of one of the most famous attacks - double spending attacks on the blockchain system, which are based on consensus mechanisms based on the proof of the work done using hash functions. Unlike previously known works describing the analytical expression for the probability of success of a double-spend attack, this paper proposes a model of independent players that is different from existing models. In our opinion, this model more adequately describes the processes that occur during the formation of a chain of blocks, but leads to other quantitative results. An expression is given that characterizes the probability of an attacker conducting a successful double-spend attack on a blockchain system depending on the number of confirmations used, as well as the hashrate by an honest network and the attacker. Quantitative results are presented, as well as a comparison of the results obtained with the most famous works in this area. Based on the results obtained, recommendations are given for determining the “safe” number of confirmations for successfully resisting a double spending attack on the blockchain system.
Towards language-parametric refactorings

A software refactoring is a program transformation that improves the structure of the code, while preserving its behavior. Most modern IDEs offer a number of automated refactorings as editor services. However, correctly implementing refactorings is notoriously complex, and these state-of-the-art implementations are known to be faulty and too restrictive.
On the Readiness of Teachers to Form Functional Mathematical Literacy of Schoolchildren

The article is devoted to the problem of the formation of mathematical literacy of primary school students, which, in accordance with the definition proposed in the international program for assessing the educational achievements of students PISA, is considered in the context of functional literacy. The article reflects the concept of a teacher's readiness to form mathematical literacy of schoolchildren, the structure of this concept has been developed, which includes five components: cognitive, motivational, emotional, operational and evaluative.&#x0D; The article describes the research carried out by the authors aimed at identifying the readiness of mathematics teachers of general education organizations to form students' mathematical literacy, presents a questionnaire developed to solve the research problems. The results of the analysis of ideas, attitudes and beliefs of teachers of mathematics regarding the formation of functional mathematical literacy of students are presented. The analysis of the cognitive, operational and evaluative components of readiness has been carried out in accordance with the following tasks: first, to reveal the level of the teacher's understanding of the pedagogical task and the assessment of its significance for society in modern conditions; secondly, to reveal the ideas of teachers about the methodology for the formation of mathematical literacy and mathematical content aimed at the formation of this ability; thirdly, to reveal the attitude towards the possibilities of forming mathematical literacy within the framework of a general education school, the problems and difficulties arising in solving this pedagogical problem.&#x0D; The analysis made it possible to reveal a contradiction between the high level of understanding by teachers of the significance of a new pedagogical task for them and the lack of didactic tools and teaching methods necessary for its solution. The directions of further research necessary to increase the level of mathematics teachers' readiness for the formation of functional mathematical literacy of students in Russian schools are proposed.
Discover Underlying Topics in Thai News Articles: A Comparative Study of Probabilistic and Matrix Factorization Approaches

Topic modeling is an unsupervised learning approach, which can automatically discover the hidden thematic structure in text documents. For text mining, topic modeling is a language-independent technique that disregards grammar and word order. Apart from semantic and structural issues, Thai language is typically considered more complex than others. Due to the lack of word delimiter and a surfeit of composite words. Errors from word tokenization can create significant problems for any post processes of text, such as document retrieval, sentiment analysis, machine translation, etc., adversely decreasing the performance of text applications. Despite a strong correlation between word ordering and semantic meaning, topic modeling has been widely reported that it can extract latent information, aka. latent topic or latent semantic, encoded in documents. Although there were few previous research works on studying topic modeling in Thai language, they mostly focused on upstream processes of Natural Language Processing (NLP) in, for example, applying a refined stop-word list to, or adding N-gram on a single specific topic modeling method. To our knowledge, this paper is the first to explore different topic modeling approaches, i.e., Latent Dirichlet Allocation (LDA) and Nonnegative Metrix Factorization (NMF), in Thai Language to compare their coherence. We also employ and compare a set of state-of-the-art evaluation metrics based on Topic Coherence.
PASAPTO: Policy-aware Security and Performance Trade-off Analysis--Computation on Encrypted Data with Restricted Leakage

This work considers the trade-off between security and performance when revealing partial information about encrypted data computed on. The focus of our work is on information revealed through control flow side-channels when executing programs on encrypted data. We use quantitative information flow to measure security, running time to measure performance and program transformation techniques to alter the trade-off between the two. Combined with information flow policies, we perform a policy-aware security and performance trade-off (PASAPTO) analysis. We formalize the problem of PASAPTO analysis as an optimization problem, prove the NP-hardness of the corresponding decision problem and present two algorithms solving it heuristically. We implemented our algorithms and combined them with the Dataflow Authentication (DFAuth) approach for outsourcing sensitive computations. Our DFAuth Trade-off Analyzer (DFATA) takes Java Bytecode operating on plaintext data and an associated information flow policy as input. It outputs semantically equivalent program variants operating on encrypted data which are policy-compliant and approximately Pareto-optimal with respect to leakage and performance. We evaluated DFATA in a commercial cloud environment using Java programs, e.g., a decision tree program performing machine learning on medical data. The decision tree variant with the worst performance is 357% slower than the fastest variant. Leakage varies between 0% and 17% of the input.
How Many Trials Do We Need for Reliable NISQ Computing?

Gate-based quantum computing is an attractive candidate in the post-Moore era. Noisy intermediate-scale quantum (NISQ) computers are expected to be available in the next few years. It is required to repeatedly execute the target quantum application for reliable NISQ computing, e.g., users can set 1,024 as a repetition parameter in the IBM-Q machine, because NISQ computers output follows the probability distribution of execution trials. Since the distribution depends strongly on the effects of noise, it is difficult to determine a sufficient number of repetitions. This paper proposes a novel statistical approach for efficient NISQ computing. The key idea is to introduce a Bayesian credible interval model to obtain convergence of the probability distributions. We demonstrate that our execution method can detect all significant output values, that occur more often than the random situation (probability is 1/2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ), using a NISQ simulator.
Towards requirements engineering with immersive augmented reality

Often, requirements engineering (RE) activities demand project stakeholders to communicate and collaborate with each other towards building a common software product vision. We conjecture that augmented reality (AR) can be a good fit to support such communication and collaboration. In this vision paper, we report on state-of-the-art research at the intersection of AR and RE. We found that requirements elicitation and analysis have been supported by the ability of AR to provision on-the-fly information such as augmented prototypes. We discuss and map the existing challenges in RE to the aspects of AR that can boost the productivity and user experience of existing RE techniques. Finally, we elaborate on various envisioned usage scenarios in which we highlight concrete benefits and challenges of adopting immersive AR to assist project stakeholders in RE activities.
Scalar Product Lattice Computation for Efficient Privacy-Preserving Systems

Privacy-preserving (PP) applications allow users to perform online daily actions without leaking sensitive information. The PP scalar product (PPSP) is one of the critical algorithms in many private applications. The state-of-the-art PPSP schemes use either computationally intensive homomorphic (public-key) encryption techniques, such as the Paillier encryption to achieve strong security (i.e., 128 b) or random masking technique to achieve high efficiency for low security. In this article, lattice structures have been exploited to develop an efficient PP system. The proposed scheme is not only efficient in computation as compared to the state-of-the-art but also provides a high degree of security against quantum attacks. Rigorous security and privacy analyses of the proposed scheme have been provided along with a concrete set of parameters to achieve 128-b and 256-b security. Performance analysis shows that the scheme is at least five orders faster than the Paillier schemes and at least twice as faster than the existing randomization technique at 128-b security. Also the proposed scheme requires six-time fewer data compared to the Paillier and randomization-based schemes for communications.
A permission‐combination scalable access control model for Internet of things

Abstract Development of the Internet of things (IoT) is considered as one of the major events in modern manufacturing industry, and IoT devices are expected to create and exchange vast amounts of data, thereby bringing forth unprecedented challenges in terms of robust and vendor‐neutral data sharing. While access control is widely used to protect the security of data sharing, it still has some security flaws and limitations, such as privacy leakage, fixed access permissions, security vulnerabilities, and so on. Aiming at these problems, this article proposes a permission‐combination scalable access control (PCS‐AC) scheme, in which the methods of access permissions assignment, data encryption, and data access are given. In contrast to prior works, PCS‐AC differs in several significant ways: (1) it supports anonymous access and traceability. Anonymous access can prevent leakage of users' privacy. Traceability can track the illegally accessed entity when the resource is illegally accessed; (2) it supports scalable access to data resources. Users use a combination of attribute permissions to access data at different security levels; (3) it achieves high efficiency because the entity can quickly search ciphertext resources by plaintext keywords. After searching and downloading the ciphertext, decrypt it by group key to obtain the corresponding plaintext information. PCS‐AC is proven secure under the hardness assumption of Discrete Logarithm problem and Inverse Computational Diffe‐Hellman problem. The performance analysis shows that PCS‐AC has higher efficiency than the referred works.
Eathentication: A Chewing-based Authentication Method

Nowadays, authentication systems are a milestone for the security of modern societies. In particular, researchers proposed several effective authentication mechanisms for mobile devices. Unfortunately, most of these still require the user to interact lately with the smartphone screen, which is often undesirable in many setting where the user can not take the phone (e.g., at an airport's gate, in the crowded subway, while driving). In several of these scenarios, users are anyway wearing earphones. In this paper, we propose Eathentication: a novel user-friendly authentication method based on an assessment of the ear channel movement during chewing. Eathentication exploits proximity led sensors mounted on earphones to measures the movement of ear channel. We conducted our experiments collecting data from 23 participants, during three chewing sessions. During the experiment, the subject performed the test, moving the jaw and chewing different types of food. We trained different Machine Learning models on single participant performing intra-subject and inter-subject prediction. Results show that Eathentication can effectively authenticate people based on their chewing behaviour on the investigated stimuli. For the better classifier, our method achieved a False Acceptance Rate of 0.041 ± 0.016 and a False Rejection Rate of 0.128 ± 0.043.
Craig Interpolation with Clausal First-Order Tableaux

We develop foundations for computing Craig-Lyndon interpolants of two given formulas with first-order theorem provers that construct clausal tableaux. Provers that can be understood in this way include efficient machine-oriented systems based on calculi of two families: goal-oriented such as model elimination and the connection method, and bottom-up such as the hypertableau calculus. We present the first interpolation method for first-order proofs represented by closed tableaux that proceeds in two stages, similar to known interpolation methods for resolution proofs. The first stage is an induction on the tableau structure, which is sufficient to compute propositional interpolants. We show that this can linearly simulate different prominent propositional interpolation methods that operate by an induction on a resolution deduction tree. In the second stage, interpolant lifting, quantified variables that replace certain terms (constants and compound terms) by variables are introduced. We justify the correctness of interpolant lifting (for the case without built-in equality) abstractly on the basis of Herbrand’s theorem and for a different characterization of the formulas to be lifted than in the literature. In addition, we discuss various subtle aspects that are relevant for the investigation and practical realization of first-order interpolation based on clausal tableaux.
Improving Data Literacy and Student Engagement Through Authentic Assessment and Performance Tasks: The Data Workshop Series

As libraries evolve, they accommodate and refine their services to support the varied institutional, student and faculty, and course needs, including developing workshops tailored to course requirements or that provide students and faculty with additional skills that promote their academic pursuits. Some services provided by academic libraries reflect strategic choices that promote the increased alignment of the users’ needs with the institution’s needs. Some needs anticipated and observed by many research-intensive institutions are data literacy, research, and software skills. This chapter describes the case of the academic library at the University of Nevada, Las Vegas (UNLV), an R1 research institution, which offered the Data Workshop Series (DWS) to help prepare students, faculty, and staff to clean, manipulate, analyze, and visualize research data. This applied, student-centered technical workshop series was guided by authentic assessment, specifically performance tasks, which were employed to ensure the participants’ engagement and comprehension of the applied techniques presented. The performance tasks also helped participants gain confidence in their data skills. From them, the participants learned that they can use the software and solve questions on their own. When reflecting on what they learned in the DWS, participants stated that they could develop their skills on their own with additional practice and that they plan on integrating the software into their academic work. By offering the DWS, UNLV Libraries has taken a step toward being part of the life of the user, a partner in more than information findings, but in knowledge creation.
Detecting Anomalous Computation with RNNs on GPU-Accelerated HPC Machines

This paper presents a workload classification framework that accurately discriminates illicit computation from authorized workloads on GPU-accelerated HPC systems at runtime. As such systems become increasingly powerful and widely-adopted, attackers have begun to run illicit and for-profit programs that typically require extremely high computing capability to be successful, depriving mission-critical and authorized workloads of execution cycles and increasing risks of data leaking and empowered attacks. Traditional measures on CPU hosts are oblivious to such attacks. Our classification framework leverages the distinctive signatures between illicit and authorized GPU workloads, and explores machine learning methods and workload profiling to classify them. We face multiple challenges in designing the framework: achieving high detection accuracy, maintaining low profiling and inference overhead, and overcoming the limitation of lacking data types and volumes typically required by deep learning models. To address these challenges, we use lightweight, non-intrusive, high-level workload profiling, collect multiple sequences of easily obtainable multimodal input data, and build recurrent neural networks (RNNs) to learn from history for online anomalous workload detection. Evaluation results on three generations of GPU machines demonstrate that the workload classification framework can tell apart the illicit workloads with a high accuracy of over 95%. The collected dataset, detection framework, and neural network models are released on github1.
Web-Based Information Systems: Developing a Design Theory

Abstract The purpose of this research is to determine a proposed taxonomy that classified information systems theory concerning how four main objectives are handled namely analysis, explanation, prediction, and prescription. This research used a descriptive qualitative and experimental research methods. This information technology system design stage is the stage after the analysis of the information technology system development cycle. The results show the definition of system requirements for information technology, preparation for design, or implementation describes how an information technology system can be formed. It can be seen in the form of planning, drawing, making a sketch, or arrangement of several separate elements into a unified whole and functioning. In conclusion, the most important thing in the development of information systems is the user and the system should be integrated.
Design of Information System Human Resource Management

Abstract The purpose of this research is to design an information system for managing human resources in order to make it easier for managers to plan or make decisions. This study used qualitative research methods. The result was to design a web-based human resource management information system that made the management of human resources easier and faster. The human resource management information system allows employees who work outside the office to be able to do attendance remotely and managers can manage data on the attendance of existing employees.
Opportunities for Application of Blockchain in the Scenario of Intelligence and Investigation Units

In the context of combating crime, government institutions in several countries have instituted units or sectors specialized in investigation and intelligence activities to act in different areas and expertise’s. Due to the considerable concern with security and confidentiality, as well as the characteristics inherent to the context of these units, an opportunity arises to adopt alternative solutions aimed at the management, storage and sharing of digital assets. Due to the characteristics of decentralization, enhanced security, tamper-proof, traceability and transparency, in recent years, blockchain technology has become very trendy and penetrated different domains. However, blockchain adoption and use in the context of in investigation and intelligence units is rather unexplored in academic literature. Exploring the main characteristics of blockchain technology, this paper presents an overview of different application trends of blockchain technology, and propose the use of blockchain as a support mechanism in the management, storage and sharing of generated digital assets in the context of these specialized units.
Visual Analytics to Build a Machine Learning Model

One of the most interesting and promising areas of development of machine learning is the active involvement of a human in the process of building a model. However, there are problems with the effective integration of humans into a workflow. It is necessary to develop techniques and information technologies that would allow the effective use of human intellectual capabilities, thereby expanding the machine learning tools. This work considers the use of visual analytics with the goal of building a machine learning model by a human and the technique of transferring this model to the machine level. This made it possible to expand the capabilities of machine learning through the active and productive use of human intellectual abilities.
Position-aware Hybrid Attention Network for Aspect-Level Sentiment Analysis

Aspect-level sentiment analysis aims to predict the sentiment polarity of a given target in a review sentence. Most of the previous methods focus on capturing the context information of words across the sentence related to the target, ignoring the importance of the independent relationship between the opinion words and the target. To address this limitation, we propose a position-aware hybrid attention network model for aspect-level sentiment analysis, which incorporates not only the context information of words related to the target, but also the independent relationship between the opinion words related to the target. We conduct several comparable experiments on public laptop and restaurant datasets. The experimental results show that our proposed model achieves a more effective performance than the baseline models.
FIDE: Fast and Interpretable 2D Embedding with correlation, distance, and rank considerations

FIDE is a method for embedding high-dimensional datasets on the Euclidean plane that seeks not only to preserve pairwise distances and correlations but also to provide an easily interpretable embedding on two dimensions. To enhance the interpretability of the resulting embedding we assume (but not require) the presence of rank or importance for each object. This information can be used to guide the embedding in such a way that more important objects are placed "higher" in one of the embedding dimensions. To solve this multi-objective problem, we formalize the preservation of distances/correlations and ranks through a linear system of equations. We present a randomized iterative method for solving the linear system in the least-squares sense. The method is distributed, in that only information pertaining to neighboring data points is used at each iteration. We compare our method with other prevalent embedding and visualization techniques (t-SNE, LLE, ISOMAP, MDS, UMAP) on real-life datasets from a wide range of application domains and showcase the merits of our approach in terms of accurate relationship preservation, particularly with respect to the high interpretability of the resulting embedding.