Reviewerly/exHarmony huggingface dataset:
Variational Deep Semantic Text Hashing with Pairwise Labels

With the rapid growth of the Web, the amount of textual data has increased explosively over the past few years. Fast similarity searches for text are becoming an essential requirement in many applications. Semantic hashing is one of the most powerful solutions for fast similarity searches. Semantic hashing has been widely deployed to approximate large-scale similarity searches. We can represent original text data using compact binary codes through hashing. Recent advances in neural network architecture have demonstrated the effectiveness and capability of this method to learn better hash functions. Most encode explicit features, such as categorical labels. Due to the special nature of textual data, previous semantic text hashing approaches do not utilize pairwise label information. However, pairwise label information reflects the similarity more intuitively than categorical label data. In this paper, we propose a supervised semantic text hashing method that utilizes pairwise label information. Experimental results on three public datasets show that our method can exploit pairwise label information well enough to outperform previous state-of-the-art hashing approaches.
Efficacy of Ultrasonography in Breast Cancer Screening

A good screening test should be widely available, inexpensive, and have high sensitivity and specificity.Mammography is the only screening modality proven to reduce the mortality of breast cancer.On the other hand, with the increasing awareness of mammography limitations, particularly in a dense breast, supplemental screening for breast cancer with other imaging modalities has been expanding.Breast ultrasonography has the advantage of detecting mammographically occult breast cancers in a dense breast.On the other hand, operator dependence, false positive rates, and rising costs due to increased biopsy rates are a problem.Thus far, the guidelines for the implementation of screening breast ultrasonography have not been established.Physicians should be able to understand the efficacy of breast ultrasonography as the screening modality for breast cancer and perform risk-based screening approaches for individual women.This paper reviews the efficacy, limitations and implementation of screening breast ultrasonography for breast cancer.
Theory and practice in artificial intelligence

Artificial Intelligence is an interdisciplinary field, and formed about 60 years ago as an interaction between mathematical methods, computer science, psychology, and linguistics. Artificial Intelligence is an experimental science and today features a number of internally designed theoretical methods: knowledge representation, modeling of reasoning and behavior, textual analysis, and data mining. Within the framework of Artificial Intelligence, novel scientific domains have arisen: non-monotonic logic, description logic, heuristic programming, expert systems, and knowledge-based software engineering. Increasing interest in Artificial Intelligence in recent years is related to the development of promising new technologies based on specific methods like knowledge discovery (or machine learning), natural language processing, autonomous unmanned intelligent systems, and hybrid human-machine intelligence.
Incremental Learning of Bayesian Networks from Concept-Drift Data

Bayesian network utilizes graphical model to describe dependencies among variables in probabilistic way, it is one of the most important model for uncertainty processing in Artificial Intelligence. Incremental learning of Bayesian networks has been received more attentions in recent years, in this paper a novel method is proposed to learn Bayesian network from incremental data. In this method, a novel incremental scoring function is designed to adaptively adjust the tendency of matching new and old data in the process of incremental learning. We propose an improved adaptive incremental structure learning algorithm for Bayesian network. Theoretical analysis and experimental results both demonstrate the proposed method outperforms other state-of-the-art methods.
Analytic Pattern and Tool for Analysis of a Gap of Changes in Enterprise Architectures

Change analysis of enterprise architectures is usually done by observing differences between two enterprise architectures, As-Is and To-Be. If the As-Is and To-Be have a lot of differences, it is problematic to manually create a correct view on changes. This paper proposes a revision of a definition of the Gap of Changes and defines it as a language independent analytic pattern suitable for using in tools. The paper describes a tool built on the basis of this definition. The change analysis without the tool and with the tool output has been tested in a workshop. The added value of the tool was acknowledged by the workshop participants.
Stylized Quotations as Parodic Practice in Teenage Dating Blogs: Stylizing Patterns, Quotative Marking and Language-Ideological Meanings

The linguistic practices in young people's computer-mediated communication (CMC) have attracted great interest both in linguistic scholarship and in public discourse, and are expected to exert the richest influences on language in the new millennium.Nevertheless, youth's own perspectives on language and technology are rarely explored in their own right, beyond sensationalist popular descriptions of a "whatever generation" (Baron 2002) oblivious to the rules of language when communicating online.The present paper draws attention to this gap, by focusing on one specific phenomenonthe parodic stylization of teenagers' language online by teenagers themselves, found to be a common practice in personal blogs written by American youth.The first part of the paper focuses on the pragmatic features of stylizations and their quotative marking, extending insights into quotation marking as one major ongoing change in all varieties of English (Tagliamonte 2016); the second part presents a discursive analysis of the stances and social ideologies indexed by the teenagers' stylizations.Overall, the findings highlight great metalinguistic awareness in an online context where it was little expected, and strongly challenge the view of youth CMC as linguistically "whateverist".
Effects of Varying Concentrations of Crude Oil on Some Physicochemical Properties of Agricultural Soil

This research investigated the effects of varying concentrations of crude oil on some physicochemical characteristics of crude oil polluted agricultural soils from Igodan- Lisa, Oba-Ile and Ido-Ani areas of Ondo State, Nigeria. The soil samples were exposed to 1-4% (w/w) crude oil and analyzed monthly for six periods using standard physical and chemical analytical techniques. Results indicated that the physicochemical properties were altered. The physicochemical parameters varied with increase in the amount of crude oil spilled and time. The pH and moisture contents (MC) progressively decreased with increase in concentration of crude oil applied to the samples. Polluted soils had lower pH values (4.91- 6.17) and MC (15.24% to 26.83%) relative to control samples. The organic matter content increased with increased amount of crude oil spilled in the range of 6.65-10.93%. The organic carbon contents progressively increased with concentration of crude oil and sampling days. At 4% crude oil pollution, the organic carbon content in the samples were 6.04-8.28%, 5.39-7.82% and 6.05-8.21% for Igodan-Lisa, Oba-Ile and Ido-Ani soils respectively at 0-180 days of experiment. The changes in soil physicochemical suggested that soil integrity and quality is altered by crude oil contamination. The increased acidity with time also suggested the release of acidic metabolites in bioremediation by intrinsic microorganisms.
Analytical structures of interval type-2 fuzzy controllers using product AND operations

Interval type-2 fuzzy controllers have received attention because of their potential in handling uncertainties better than type-1 (T1) fuzzy controllers. However, directly deriving the analytical structure of interval type-2 fuzzy controllers is a great challenge due to the iterative computation of Karnik–Mendel algorithm and similar ones. Analytical structure is necessary for the analysis and design of control systems (e.g. stability analysis). In this study, a novel technique is proposed for deriving the analytical structure of interval type-2 fuzzy controllers based on the configurations of the triangle interval type-2 input fuzzy sets, T1 output fuzzy singletons, product AND operators, Karnik–Mendel center-of-set type reducer, and centroid defuzzifier. Input space is partitioned into a group of input combinations (ICs), which are produced by superposing input ICs and case ICs, to obtain an analytical structure. Input ICs are evaluated by the upper/lower membership functions and case ICs using the equation of the terminating condition of Karnik–Mendel algorithm. An explicit analytical expression is obtained on any IC because each IC has the same membership functions and switch points. Finally, two examples are used to verify the validity of our method.
Mobile technologies in physics education in Latvian secondary schools

The availability of ICT has raised the issue of meaningful use of Mobile technologies (smartphones, tablets, sensors, data loggers, data collectors, as well as other devices and options) in the teaching/learning process of physics to deepen the students' knowledge and develop their research skills. Mobile technologies are constantly and rapidly evolving, and there is practically no social domain where they are not used, including education. Thus, the acquisition of mobile technology usage skills is an integral part of the learning process in a modern school. Education experts are now emphasizing not only the integration of Mobile technologies into the learning process but also the need to improve the efficiency of the learning process for both teachers and students. Success is based not only on what or how much one knows, but also on one's ability to think and act creatively. Mobile technology-based learning process is based on the Educational Technology Competency Standards for Teachers and accentuates the demand for improving teachers’ technology skills and with a focus on knowledge and capacity building. This paper summarizes the research on the use of Mobile technologies in teaching physics in Latvia for the last nine years, as well as analyses the issues and suggestions for improving the process. A pedagogical experiment is conducted based on pre-planned and prepared participation in the learning process.
Enhancing Knowledge Graphs with Data Representatives

Due to the digitalization of many processes in companies and the increasing networking of devices, there is an ever-increasing amount of data sources and corresponding data sets. To make these data sets accessible, searchable and understandable, recent approaches focus on the creation of semantic models by domain experts, which enable the annotation of the available data attributes with meaningful semantic concepts from knowledge graphs. For simplifying the annotation process, recommendation engines based on the data attribute labels can support this process. However, as soon as the labels are incomprehensible, cryptic or ambiguous, the domain expert will not receive any support. In this paper, we propose a semantic concept recommendation for data attributes based on the data values rather than on the label. Therefore, we extend knowledge graphs to learn different dedicated data representations by including data instances. Using different approaches, such as machine learning, rules or statistical methods, enables us to recommend semantic concepts based on the content of data points rather than on the labels. Our evaluation with public available data sets shows that the accuracy improves when using our flexible and dedicated classification approach. Further, we present shortcomings and extension points that we received from the analysis of our evaluation.
Leveraging Cognitive Context Knowledge for Argumentation-Based Object Classification in Multi-Sensor Networks

It is a great challenge to achieve interpretable collaborative object classification in multisensor networks.In this situation, argumentation-based object classification has been considered a promising paradigm, due to its natural means of justifying and explaining complicated decision making within multiple agents.However, disagreements between sensor agents are often encountered because of various object category levels.To address this category of granularity inconsistent problem in multi-sensor collaborative object classification tasks, we propose a cognitive context knowledge-enriched method for classification conflict resolution.The cognitive context is concerned, in this paper, to investigate how rich contextual knowledge-equipped cognitive agents can facilitate semantic consensus in argumentation-based object classification.The empirical evaluation demonstrates the effectiveness of our method with improvement over state-of-the-art, especially in the presence of noisy sensor data, while giving argumentative explanations.Therefore, it is suggested that people who can benefit from the proposed method in this paper are the human user of multi-sensor object classification systems, in which explaining decision support is one of the important factors concerned.
Fast and accurate pseudo multispectral technique for whole-brain MRI tissue classification

Numerous strategies have been proposed to classify brain tissues into gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF).However, many of them fail when classifying specific regions with low contrast between tissues.In this work, we propose an alternative pseudo multispectral classification (PMC) technique using CIE LAB spaces instead of gray scale T1-weighted MPRAGE images, combined with a new preprocessing technique for contrast enhancement and an optimized iterative K-means clustering.To improve the accuracy of the classification process, gray scale images were converted to multispectral CIE LAB data by applying several transformation matrices.Thus, the amount of information associated with each image voxel was increased.The image contrast was then enhanced by applying a real time function that separates brain tissue distributions and improve image contrast in certain brain regions.The data were then classified using an optimized iterative and convergent K-means classifier.The performance of the proposed approach was assessed using simulation and in vivo human studies through comparison with three common software packages used for brain MR image segmentation, namely FSL, SPM8 and K-means clustering.In the presence of high SNR, the results showed that the four algorithms achieve a good classification.Conversely, in the presence of low SNR, PMC was shown to outperform the other methods by accurately recovering all tissue volumes.The quantitative assessment of brain tissue classification for simulated studies showed that the PMC algorithm resulted in a mean Jaccard index (JI) of 0.74 compared to 0.75 for FSL, 0.7 for SPM and 0.8 for K-means.The in vivo human studies showed that the PMC algorithm resulted in a mean JI of 0.92, which reflects a good spatial overlap between segmented and actual volumes, compared to 0.84 for FSL, 0.78 for SPM and 0.66 for K-means.The proposed algorithm presents a high potential for improving the accuracy of automatic brain tissues classification and was found to be accurate even in the presence of high noise level.
Identification and Assessment of Software Design Pattern Violations

The validation of design pattern implementations to identify pattern violations has gained more relevance as part of re-engineering processes in order to preserve, extend, reuse software projects in rapid development environments.If design pattern implementations do not conform to their definitions, they are considered a violation.Software aging and the lack of experience of developers are the origins of design pattern violations.It is important to check the correctness of the design pattern implementations against some predefined characteristics to detect and to correct violations, thus, to reduce costs.Currently, several tools have been developed to detect design pattern instances, but there has been little work done in creating an automated tool to identify and validate design pattern violations.In this paper we propose a Design Pattern Violations Identification and Assessment (DPVIA) tool, which has the ability to identify software design pattern violations and report the conformance score of pattern instance implementations towards a set of predefined characteristics for any design pattern definition whether Gang of Four (GoF) design patterns by Gamma et al [1] or custom pattern by software developer.Moreover, we have verified the validity of the proposed DPVIA tool using two evaluation experiments and the results were manually checked.Finally, in order to assess the functionality of the proposed tool, DPVIA is evaluated with a dataset containing 5,679,964 Lines of Code (LoC) among 28,669 Java files in 15 open-source projects, with a large and small size of open-source projects that extensively and systematically employing design patterns, to determine design pattern violations and suggest refactoring solutions, thus keeping costs of software evolution.The results can be used by software architects to develop best practices while using design patterns.
Image needs on social Q&amp;A sites: a comparison of Zhihu and Baidu Zhidao

Purpose This study aims to examine why users search for images, how users describe their image needs and what the images are used for by analysing questions obtained from two Chinese social Q&amp;A sites, Zhihu and Baidu Zhidao. Design/methodology/approach A total of 1,402 image questions were collected from Zhihu and Baidu Zhidao. Both quantitative analysis and qualitative content analysis were performed to identify user image needs and the potential differences on the two social Q&amp;A sites. Findings Question-asker’s intention varies in different platforms. Zhihu users asked questions mainly aiming at a promotion of subsequent discussion, whereas users of Baidu Zhidao often did so to seek information. Syntactic attributes were not frequently used in both two sites. Zhihu users were more likely to express subjective evaluations on images (concept, emotion, theme and style) in their questions than users of Baidu Zhidao. In contrast, questions from Baidu Zhidao showed a tendency to more frequently include descriptive metadata (rights, format, size, quality and authenticity) and semantic attributes (generic activity, specific people, fashion and text) of the images than questions from Zhihu. Learning was an important use on social Q&amp;A sites, especially on Baidu Zhidao. In addition, the images were primarily used to trigger emotion or served a persuasive purpose in Zhihu. Practical implications This study contributes to a better understanding of user image search behaviour, and the findings could be used to develop better image services on social Q&amp;A sites. Meanwhile, the image attributes extracted from the questions are conducive to the improvement of image retrieval systems. Originality/value This study explored the features of image needs on social Q&amp;A sites, especially considering image use specified in the question. The difference of image needs between two Chinese social Q&amp;A sites (Zhihu and Baidu Zhidao) was identified.
ECDSA Private Keys Study of Security

Cryptocurrencies are a mean of executing online transactions.They use a variety of cryptographic techniques to secure and verify these transactions, which are functionally supported by the Blockchain platform.Blockchain is a continuously growing, distributed ledger of files that contains all transactions between users of cryptocurrencies in a verifiable and permanent manner.It consists of blocks that are connected and secured cryptographically.Cryptocurrencies use algorithms to produce pairs of public and private keys.These pairs, cryptographically merged with a message between the participants, are the building blocks of the relevant transactions.Bitcoin uses the ECDSA algorithm to produce the above-mentioned keys.The purpose of our work is to present some useful motifs for the domain parameters of base point (P) and the order (n) of the subgroup produced by it, while choosing the elliptic curve and the Galois field on which we formulate the algorithm, in order to obtain safer private keys.The results of the research are experimental due to the limited infrastructure, but explanatory for the purpose of our work.The resulting conclusions highlight the value of the proper selection of the structural parameters of these algorithms and possible alternatives to the curve, field and domain parameters that can be used.
Teachers’ Perceptions on Using Smartphones in English as a Foreign Language Context

This study explored the perceptions of English as a foreign language (EFL) teachers at a foundation year in a university in Saudi Arabia. 41 male (27) and female (14) teachers completed a survey using Google Forms. The results indicated that the majority of teachers (83%) supported the integration of smartphones in EFL context, as 71% believe that smartphones would enhance students’ EFL learning. 72% reported that they used smartphones for university-related work. Sending and receiving emails, accessing the internet, and using educational applications were the most reported beneficial features whereas scanning/creating QR codes, using calculator, playing games, and playing a podcast were the least reported. 79 % believed that smartphones would have instructional benefits for the learners such as access to technology, motivation, creativity English language learning opportunities, and variation in instruction whereas 58 % did not see any barriers in the use of smartphones in EFL context. That smartphones may distract students’ attention was the only reported barrier. Recommendations and impactions for further research and applications were suggested.
Peningkatan Kemampuan Anak dalam Mengenal Konsep Bilangan Melalui Penggunaan Media Pembelajaran Berbasis Alam

Enhancing Early Childhood Ability to Know the Concept of Numbers through Using of Learning Media Based on Nature. The purpose of this study is to improve the ability of children to understand the concept of numbers through using of learning media based on nature. The type of research conducted is Classroom Action Research with data collection techniques in the form of observations and performance and then analyzed using qualitative descriptive analysis techniques. This research was conducted in two cycles, namely cycle I and cycle II. In the first cycle the percentage of student performance achieved 30% of the percentage before use the cycle, in this cycle students who managed to achieve the Minimum Completeness Criteria drove 11 people who had not succeeded in achieving a large of minimum completeness criteria students could participate in learning activities well and encourage very active when using media. So the research continued the second cycle in cycle II, the percentage reached 45% and all students who succeeded in reaching 20 people managed to reach the minimum completeness criteria while the observations showed that students were very interested in learning to use the prepared media. From the results of the implementation of the first cycle and the second cycle the increase in the percentage of student learning completeness reached 75% of the percentage before cycle use. Thus, it can be denied that the learning media based on nature can improve the ability of children to understand the concept of numbers to support learning activities in early childhood can use media that are either natural media or media that have been supportive in order to produce learning that is conducive to children's abilities.