Reviewerly/exHarmony huggingface dataset:
CLASSIFICATION OF SHORT TECHNICAL TEXTS USING SUGENO FUZZY INFERENCE SYSTEM

The paper presents the results of classification of the short technical texts on the purpose of instruments using fuzzy sets theory and fuzzy logic. An important stage in designing special-purpose technical systems is the choice of equipment with specific operational characteristics. The need to categorize short technical texts, which present a brief description of equipment, annotations, fragments of databases, appears due to the fact that information about the equipment found in thematic abstract collections, technical and design documentation or in contextual advertising is often not structured and scattered. The other problems are a large number of typos, incorrect word usage and definitions in the texts. Much attention is paid to the characteristics of the objects of research and to recording their specific features – a large number of technical terms, abbreviations, symbols. The classifying technique is described, the expediency of application of fuzzy inference of Sugeno system associated with fuzziness of the natural language, the simplicity of mathematical calculations in the course of the experiment. A Sugeno model combines the description of the objects of research in the form of linguistic rules and functional dependencies. This approach greatly facilitates the interpretation of classification results
Fintech in Financial Reporting and Audit for Fraud Prevention and Safeguarding Equity Investments

Purpose – The aim of this paper is to explore the audit-related causes of financial scandals and advise how emerging technologies can provide solutions thereto. Specifically, this study seeks to look at the facilitators of financial statement fraud and explain specific fintech advancements that contribute to financial information reliability for equity investments.Design/methodology/approach – The study uses the case studies of Enron and Arthur Andersen to document the evidence of audit-related issues in historical financial scandals. Then, a comprehensive and interdisciplinary literature review at the intersection of business, accounting, and engineering, provides a foundation to propose technology advancements that can solve identified problems in accounting and auditing. Findings – The findings show that blockchain, internet of things, smart contracts and artificial intelligence solutions have different functionality and can effectively solve various financial reporting and audit-related problems. Jointly, they have a strong potential to enhance the reliability of the information in financial statements and generally change how companies operate.Practical implications – The proposed and explained technology advancements should be of interest to all publicly listed companies and investors, as they can help safeguard equity investments, thus build investors’ trust towards the company. Social implications – Aside from implications for capital markets participants, the study findings can materially benefit various stakeholder groups, the broader company environment, and the economy. Originality/value – This is the first paper that seeks solutions to financial fraud and audit-related financial scandals in technology, and not in implementing yet another regulation. Given the recent technology advancements, the study findings provide insights into how the role of an external auditor might evolve in the future.
Bayesian Context Aggregation for Neural Processes

Formulating scalable probabilistic regression models with reliable uncertainty estimates has been a long-standing challenge in machine learning research. Recently, casting probabilistic regression as a multi-task learning problem in terms of conditional latent variable (CLV) models such as the Neural Process (NP) has shown promising results. In this paper, we focus on context aggregation, a central component of such architectures, which fuses information from multiple context data points. So far, this aggregation operation has been treated separately from the inference of a latent representation of the target function in CLV models. Our key contribution is to combine these steps into one holistic mechanism by phrasing context aggregation as a Bayesian inference problem. The resulting Bayesian Aggregation (BA) mechanism allows principled handling of task ambiguity, which is key for efficiently processing context information. We demonstrate on a range of challenging experiments, including dynamics modeling and image completion, that BA consistently improves upon the performance of traditional aggregation methods while remaining computationally efficient and fully compatible with existing NP-based models.
Retrieving TOR Browser Digital Artifacts for Forensic Evidence

The TOR browser is the most popular browser for surfing the Internet while being anonymous. This paper studies the digital artifacts left behind by TOR browser over the network and within the host. These artifacts give the most crucial forensic evidence for digital investigators to prove any unauthorized or unlawful activities. The paper also presents methods for retrieving more useful artifacts when compared to previous works and also investigates on Firefox, Chrome Incognito, and Internet Explorer. The results show that even the much-acclaimed TOR browser also leaves evidence and traces behind.
Introduction to Artificial Intelligence and Deep Learning with a Case Study in Analyzing Electronic Health Records for Drug Development

Believe or not, we are in the era of the fourth industrial revolution. It is critical for statisticians to understand or even master the centerpiece of this revolution, artificial intelligence (AI). Google, Amazon, Facebook, IBM, and many other companies have broken new ground in AI including self-driving cars, cashier-free convenience stores, smart hospital care, personal assistants, and precision medicine. This chapter will introduce the concept of AI and give an overview of breakthroughs of AI in drug development including drug discovery, patient compliance and prediction of patient outcomes and prediction of clinical trial results, and a tutorial on deep learning methods, which are a set of novel tools for generating AI. They were developed based on a class of almost forgotten old algorithms, neural networks that were revived to become mainstream in big data analytics thanks to the advancement of big data and computer processing power. The utility of deep learning in analyzing electronic health records (EHR) will be illustrated in terms of predicting patient and clinical trial outcomes for clinical trial optimization. Last, an overview will be given on Python, a commonly used software for deep learning, and the very necessary computing environment, cloud.
Audio Steganography Algorithm Based on Genetic Algorithm for MDCT Coefficient Adjustment for AAC

An AAC steganography algorithm based on genetic algorithm and MDCT coefficient adjustment is proposed. Our algorithm selects the small value region of MDCT coefficient as the embedding bit and the coefficients in codebook 1/2 are designed to change. In order to be against steganalysis better, genetic algorithm is used to optimize the change of the coefficient. The experiment results show that the algorithm has good embedding capacity, high steganography and good imperceptibility.
HTML 5-Based Learning for 3D Computer Graphics and GIS

The online learning becomes more and more popular where visualization of 3D objects can be employed to lots of fields, e.g., 3D building information modeling for architecture, 3D city model for urban planning, 3D animation for film, 3D skeleton visualization of medical treatment, etc.. In tradition, the most popular tool for 3D modeling and visualization is OpenGL which is based on Client/Server. In another word, C++ code for OpenGL must be installed and configured firstly which is not convenient for new beginners. By contrast, this paper focus on visualization of 3D objects in Computer Graphics and GIS (Geographic Information System) purely depending on JavaScript technology where no additional software or plugins, applet, ActiveX control is needed. Typical JavaScript libraries covers ThreeJS for Computer Graphics, OpenLayers for 2D GIS and CesiumJS for 3D GIS. Based on the above libraries, e-learners could view and manipulate 3D objects in web browser (e.g., Google Chrome). Such intuitive and convenient 3D visualization and manipulation results in much more interesting for learning.
Deep Attributed Network Embedding with Community Information

Attributed Network Embedding (ANE) aims to learn low-dimensional representation for each node while preserving topological information and node attributes. ANE has attracted increasing attention due to its great value in network analysis such as node classification, link prediction, and node clustering. However, most existing ANE methods only focus on preserving attribute information and local structure, while ignoring the community information. Community information reveals an implicit relationship between vertices from a global view, which can be a supplement to local information and help improve the quality of embedding. So, those methods just produce sub-optimal results for failing to preserve community information. To address this issue, we propose a novel method named DNEC to exploit local structural information, node attributes, and community information simultaneously. A novel deep neural network is designed to preserve both local structure and node attributes. At the same time, we propose a community random walk method and incorporate triplet-loss to preserve the community information. We conduct extensive experiments on multiple real-world networks. The experimental results show the effectiveness of our proposed method.
Difference computation using change identification techniques for structured web documents

Abstract In this era of the competitive world, one needs to stay updated with all the information that is required for their professional and personal growth. But due to vast information, it is difficult to cope up with the ever-changing data. WWW is the main source of data. It consists of thousands and millions of web pages, out of which some are static pages while the other is dynamic pages. The contents of the dynamic web pages such as news websites, stock prices, weather broadcast change frequently. The changes include the insertion of some new data, deletion of data, or modification of existing data. To detect the changes that are variedly occurring in these web pages, various change detection tools and algorithms have been developed. This paper will discuss the categories of changes occurring on the structured document and their corresponding algorithms and tools which can be applied to retrieve the relevant changes.
Learning Coflow Admissions

Data-parallel applications are developed using different data programming models, e.g., MapReduce, partition/aggregate. These models represent diverse resource requirements of application in a datacenter network, which can be represented by the coflow abstraction. The conventional method of creating hand-crafted coflow heuristics for admission or scheduling for different workloads is practically infeasible. In this paper, we propose a deep reinforcement learning (DRL)-based coflow admission scheme - LCS - that can learn an admission policy for a higher-level performance objective, i.e., maximize successful coflow admissions, without manual feature engineering. LCS is trained on a production trace, which has online coflow arrivals. The evaluation results show that LCS is able to learn a reasonable admission policy that admits more coflows than state-of-the-art Varys heuristic while meeting their deadlines.
Ontology-Based Approach to Determine the Coverage of Examination Papers

Examinations play an important role in the learning process and the whole education system. It is a way of assessing what the students have learned concerning particular subjects. The strengths and weaknesses of the students can be assessed through the examination. Therefore, examination papers should have a standard and quality attributes. The question preparation process is a very challenging step for academics. Sometimes, academics fail to prepare the question papers to address all the points in the syllabus. It is a critical problem in education when it fails to cover all the learning outcomes. Currently, there is no automated system to check the depth of the examination papers. Checking the depth manually is a very difficult and time-consuming task. In this research, the ontology-based approach is proposed to determine the depth of the examination papers. First, the ontology for a particular subject was created using Protégé software. Then, the soft copies of the examination papers were collected relevant to the subject. Then the stop words of the examination papers were removed. The created ontology was read by Java and Jena library. After that, a list of the subclasses of the ontology had been retrieved. Next, the path of the word was included in the ontology was discovered. In parallel, the number of all concepts in the ontology was counted. Using the paths of the words, the depth of the examination papers was evaluated. The average of 84.81% is shown by the ontology based approach and 79.99% was provided by the experts as the coverage for 5 subjects we analyzed. The empirical study of our prototyping system has proved the effectiveness of our proposed ontology-based method.
Research on the optimal layout of pressure monitoring points in water-cooling pipelines of nuclear fusion device based on fusion distance

In view of the safety and stability requirements of the cooling water system (CWS) of the nuclear fusion device, it is necessary to optimize the pressure monitoring points of the water-cooling pipelines. To optimize the layout of pipeline pressure monitoring points, a method based on clustering by fast search and find of density peaks (CFSFDP) algorithm is proposed. By combining the fusion distance of the pipeline spatial attributes and the efficient CFSFDP clustering algorithm, the layout of the pressure monitoring points in the pipeline network can be effectively optimized. Further simulations and analysis show that this method can quickly realize the optimal layout of pressure monitoring points, and the rationality and effectiveness of the fusion distance is verified. Compared with other typical clustering algorithms, the CFSFDP algorithm for cluster analysis reflects the high efficiency advantages.
A method of Radiation Source Identification based on DST-IFS

Aiming at the current complex electromagnetic environment, radiation informations obtained by different sensorsare different, resulting in different identification confidence levels. Based on the abundant analyses on evidence weight measurement on information confidence, this paper applies the original evidence of decision-making to assign values to intuitionistic fuzzy membership and nonmembership under the DS framework, and then makes decision based on intuitionistic fuzzy decision -making methods, and proposes a radiation source identification method based on a combination of DS evidence theory and intuitionistic fuzzy set (Dempster-Shafer evidence theory-intuitionistic fuzzy set, DST-IFS). Experiments show that this algorithm can meet the actual requirements on confidence difference of information obtained by multiple sensors under complex electromagnetic environment, and improve the accuracy rate of radiation source identification results.
Development of DWT–SVD based Digital Image Watermarking for Multi-level Decomposition

In the age of digital data, the safety of digital content has gradually become paramount important. The demand for digital data in various forms such as text, images, video and audio has increased manifold. There are lots of vulnerabilities concerning digital data. It can easily be attacked, forged and manipulated to produce illegal copies. In the watermarking process, information, i.e., watermark image is inserted into a host data and extracted later in case of Copyright violation; owner of the content can demonstrate his ownership by regaining the embedded watermark. A watermark must be capable of being recovered even if a piece of content is modified or changed by several attacks. This paper uses frequency-domain technique, and the multi-level decomposition of the host image is done by using the combination of two-dimensional DWT (2D DWT) and SVD techniques. Here, the host/cover image is decomposed at six different levels separately, and the watermark image is inserted in the region of lowest frequency. After inserting a watermark, the host/cover image is reconstructed using an inverse DWT technique. By exposing the watermarked image to different image processing attacks such as pepper and salt, speckle and rotation, the robustness of various levels has been analyzed. The embedded watermark is extracted afterward and matched with the original one based on PSNR and MSE. The comparative experimental results show that the decomposition of the host image at the third level is found to be more suitable for watermarking among all other levels.
Library Smart Systems: New Opportunities of Access to Knowledge in Online Education

The purpose of the article is firstly to analyze the capabilities of library smart systems as a resource that ensures the availability of knowledge, and secondly to describe students’ practices in their use. Methods and methodology. The framework of the study adopted the conception of digitalization of social relations. Digital technologies create the conditions which is the combination of people and knowledge. The first stage: desk studies of development trends of library smart systems, their role in creating in the creation of conditions of availability of knowledge. At the second stage: a quantitative analysis of the practice of using smart library systems by students (1) in educational activities (2) in extracurricular activities (3) skills in using electronic library resources (4) advantages and limitations when working with electronic libraries. The results of the research. The research suggested that the dissemination of information and knowledge through digital technology does not directly correlate with the accessibility of knowledge. The widespread availability of affordable smart library systems does not directly lead to widespread and efficient use. At the same time, the spread of electronic libraries can become a growth point in the accessibility of knowledge, which requires focused work on the formation of competencies that allow the efficient use of this resource. Discussion and conclusion: The results can be used to determine the directions and forms of influence on the readiness of the reader-student to use smart library systems as a resource ensuring the availability of knowledge.
Directed Graph-based Researcher Recommendation by Random Walk with Restart and Cosine Similarity

In this study, we propose a researcher recommendation model using random walk with restart on a directed graph. The directed graph is constructed from the grants-in-aid for scientific research database, and three types of their edges are defined based on the history of research collaboration, the cosine similarity of research contents, and their combination. An evaluation experiment, which measures <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{nDCG}@ k$</tex> , recommendation accuracy, for each model applied the three edge functions, is performed. As the experimental result, it is confirmed that <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{nDCG}@ k$</tex> for the combination model of the history and cosine similarity edges achieves the best accuracy.
qATG: Automatic Test Generation for Quantum Circuits

Researchers now use randomized benchmarking or quantum volume to test quantum circuits (QC) in the laboratory. However, these tests are long and their fault coverage is unclear. In this paper, we propose behavior fault models based on the function of quantum gates. These fault models are scalable because the number of faults is polynomial, not exponential, to the size of QC. We propose a novel test generation that uses gradient descent to generate test configuration with short length. We revise the chi-square statistical method to decide the number of test repetitions under the specified test escape and overkill. Experimental results on IBM Q systems show that our generated test configurations are effective, and our test lengths are 1,000X shorter than traditional test methods.
Towards better understanding of agile teams through behavior change models

Agile software development welcomes changes throughout software development - but this implies that agile teams face several dilemmas. When to respond to a change; how to respond; how to manage the change. Our current understanding and support for agile teams during such change management is very limited. Psychological behavioral change models can be used to better understand the behavior of agile teams. Combining our understanding of agile teams and practices with a review of behavior change models, we propose several avenues for studying behavior and behavioral changes in agile teams. Our proposed interdisciplinary approach provides a much needed avenue to acknowledge and address the psychological and behavioral aspects of the humans central to the software engineering process, ultimately assisting with their well-being and productivity.
Fast Discrete Matrix Factorization Hashing for Large-Scale Cross-Modal Retrieval

Hashing-based cross-modal retrieval methods have obtained considerable attention due to their efficient retrieval performance and low storage cost. Recently, supervised methods have demonstrated their excellent retrieval accuracy. However, many methods construct a massive similarity matrix by labels and disregard the discrete constraints imposed on the hash codes, which makes it unscalable and results in undesired performance. To overcome these shortcomings, we propose a novel supervised hashing method, named Fast Discrete Matrix Factorization Hashing (FDMFH), which focuses on correlations preservation and the hash codes learning with the discrete constraints. Specifically, FDMFH utilizes matrix factorization to learn a latent semantic space in which relevant data share the same semantic representation. Then, the discriminative hash codes generated by rotating quantization and linear regression preserve the original locality structure of training data. Moreover, an efficient discrete optimization method is used to learn the unified hash codes with a single step. Extensive experiments on two benchmark datasets, MIRFlickr and NUS-WIDE, verify that FDMFH outperforms several state-of-the-art methods.