Reviewerly/exHarmony huggingface dataset:
Ensemble of fast learning stochastic gradient boosting

Boosting is one of the most popular and powerful learning algorithms. However, due to its sequential nature in model fitting, the computational time of boosting algorithm can be prohibitive for big data analysis. In this paper, we proposed a parallel framework for boosting algorithm, called Ensemble of Fast Learning Stochastic Gradient Boosting (EFLSGB). The proposed EFLSGB is well suited for parallel execution, and therefore, can substantially reduce the computational time. Analysis of simulated and real datasets demonstrates that EFLSGB achieves highly competitive prediction accuracy in comparison with gradient tree boosting.
Agent-Based Simulation of Hardware-Intensive Design Teams Using the Function–Behavior–Structure Framework

Agile processes have been used in software development, with many case studies indicating positive changes in productivity when these processes are used. Agile processes are beginning to be applied to work beyond software-centric systems. There does not yet exist a diverse set of studies on the effectiveness of Agile processes on hardware-intensive systems. The research in this article applies a modeling and simulation-based approach which uses the function–behavior–structure framework to evaluate the effectiveness of waterfall and Agile processes. The simulation was validated against case studies of software-centric design efforts. When applied to a space launch vehicle—a highly coupled, hardware-intensive system—the simulation shows that the benefits of Agile may not be as great as those seen with software-intensive systems.
Geological control of mineral deposits in Nepal

Systematic information and in-depth knowledge of geological controls, mode of occurrences, distribution patterns and mineral association are very essential for exploring the new deposits and development of mineral resources of the country in general. In present study, an attempt has been made to clarify the geological controls of economic and sub-economic mineral deposits of Nepal. Investigation has shown that mineral deposits in Nepal Himalaya are controlled by particular geological conditions and tectonics. Till the date, economic potential of 63 mineral commodities from Nepal have been established by Department of Mines and Geology. Several economic to sub economic deposits of the country are explored and some are under mining process. Analysis of geological controls of above mentioned economic and sub-economic mineral deposits shows that they are either syngenetically or epigenetically confined to particular stratigraphic and tectonic units. These deposits are related in time and space to certain events of crustal movement representing different metallogenic epochs.
New results on approximate Hilbert pairs of wavelet filters with common factors

In this paper, we consider the design of wavelet filters based on the Thiran's common-factor approach proposed in [13]. This approach aims at building finite impulse response filters of a Hilbert-pair of wavelets serving as real and imaginary part of a complex wavelet. Unfortunately it is not possible to construct wavelets which are both finitely supported and analytic. The wavelet filters constructed using the common-factor approach are then approximately analytic. Thus, it is of interest to control their analyticity. The purpose of this paper is to first provide precise and explicit expressions as well as easily exploitable bounds for quantifying the analytic approximation of this complex wavelet. Then, we prove the existence of such filters enjoying the classical perfect reconstruction conditions, with arbitrarily many vanishing moments.
NURTURING STUDENTS' WRITING NARRATIVE INTEREST THROUGH MIND MAPPING AND COOPERATIVE INTEGRATED READING AND WRITING

This study aims to investigate the motivation of senior high school students at 11st grade in reading and writing English texts, especially narrative text.� The study also explores the insight toward the use of mind mapping and cooperative integrated reading and writing in nurturing students� interest and motivation in writing narrative text. The study used qualitative case study to learn and pay attention to phenomena that the students� motivation in reading and writing narrative text are needed to be nurtured. There were 36 students of 11st grade in senior high school in Bandung were involved in this study. Questionnaire and interview were given to the students. The results show that the students who show positive view toward reading and writing narrative texts are 74% students. They interest and willingness in making mind mapping of the narrative, because they know that narratives texts are full of information and moral value. While only 26% students who seem force to read and write narratives text to compose mind mapping. Eventually, integrating reading and composition in the classroom can nurture their reading interest and increase their willingness to read narrative text. Since, many students can understand the moral value of the narrative text through discussing with their friends at the class and making a mind mapping through cooperative learning about the narrative text is very useful to develop their mind.
Success in Studying at the University of Montenegro: Is there Hyper-production of Diplomas?

The article presents the results of research of the study success at the University of Montenegro (UoM).The theoretical framework of the study points to the complexity and multidimensionality of the problem studied.The aim of the research was to identify the success of studying at UoM.Some of the variables were observed statistically, with the evidence based on empirical data.We have followed the relationship between the completed secondary school, regional characteristics and gender dimensions at the completion of bachelor studies in the foreseen time period, in the fields of study: natural and technical, social-humanistic and artistic (total of 19 faculties, N = 4 517).The results of research reject the hypothesis about the outstanding success of the study, i.e., the overproduction of diplomas, because 27,5 % of students graduated in natural and technical sciences, and 32 % in the field of social and humanistic sciences up-to-date.Higher success is noted only in the art disciplines (74,6 %).The results and their interpretation point to the need for introducing a qualification exam during enrolment, reducing the number of the enrolled at individual faculties, but also continually improving and innovating teaching and learning at the university.The results suggest the necessity of innovations in order to improve study success.Starting with students' enrolment university need to develop innovative approaches in the area of teaching and learning, but also monitoring and evaluation.
Grounded Word Sense Translation

Recent work on visually grounded language learning has focused on broader applications of grounded representations, such as visual question answering and multimodal machine translation. In this paper we consider grounded word sense translation, i.e. the task of correctly translating an ambiguous source word given the corresponding textual and visual context. Our main objective is to investigate the extent to which images help improve word-level (lexical) translation quality. We do so by first studying the dataset for this task to understand the scope and challenges of the task. We then explore different data settings, image features, and ways of grounding to investigate the gain from using images in each of the combinations. We find that grounding on the image is specially beneficial in weaker unidirectional recurrent translation models. We observe that adding structured image information leads to stronger gains in lexical translation accuracy.
The impact of job competencies to achieve team work collaboration: Evidence from Jordanian firms

This study aims to investigate the impact of job competencies in achieving team work collaboration in Jordanian industrial companies.The study population includes all 6 Jordanian industrial companies at Aljezah distract, Jordan.A random sample of the employees including managers, deputies, assistants and sector managers is selected from the Jordanian industrial companies with the population of 130 individuals.To achieve the study objectives, the researcher uses quantitative, descriptive and analytical methods through several statistical tools to test the study hypotheses.The results show that competencies, knowledge, skills and attitudes, among the employees of Jordanian industrial companies played essential role in achieving the team work collaboration.The study also finds significant differences on the effects on team work collaboration dimensions; including culture differences, time differences and language on job performance.The researcher recommends the organizations to enhance job competencies in the Jordanian industrial companies to improve the performance of the businesses by acquiring appropriate knowledge and skills in achieving the team work collaboration.
Mixed Gaussian Models for Modeling Fluctuation Process Characteristics of Photovoltaic Outputs

In order to model fluctuation process characteristics of photovoltaic (PV) outputs, this paper proposes a novel mixed Gaussian model with the expectation maximization (EM) algorithm. Firstly, random components of PV outputs are obtained through computing the difference between the measured data of PV output and its theoretical outputs. Secondly, the EM algorithm is used to determine the weight of different Gaussian distribution functions. Finally, the mixed Gaussian model is obtained by linearly superimposing these Gaussian functions with the weight. Based on the simulation results on the measured data in Xichang City, China, the effectiveness of the proposed model is verified. Furthermore, this model has proven to be significantly better than other traditional models including t location-scale (TLS) distribution model.
A Probabilistic Logic for Resource-Bounded Multi-Agent Systems

Resource-bounded alternating-time temporal logic (RB-ATL), an extension of Coalition Logic (CL) and Alternating-time Temporal Logic (ATL), allows reasoning about resource requirements of coalitions in concurrent systems. However, many real-world systems are inherently probabilistic as well as resource-bounded, and there is no straightforward way of reasoning about their unpredictable behaviours. In this paper, we propose a logic for reasoning about coalitional power under resource constraints in the probabilistic setting. We extend RB-ATL with probabilistic reasoning and provide a standard algorithm for the model-checking problem of the resulting logic Probabilistic Resource-Bounded ATL (pRB-ATL).
Type-preserving matrices and security of block ciphers

We introduce a new property for mixing layers which guarantees protection against algebraic attacks based on the imprimitivity of the group generated by the round functions. Mixing layers satisfying this property are called non-type-preserving. Our main result is to characterize such mixing layers by providing a list of necessary and sufficient conditions on the structure of their underlying binary matrices. Then we show how several families of linear maps are non-type-preserving, including the mixing layers of AES, GOST and PRESENT. Finally we prove that the group generated by the round functions of an SPN cipher with addition modulo $ 2^n $ as key mixing function is primitive if its mixing layer satisfies this property.
Strategy of the Negative Sampling for Training Retrieval-Based Dialogue Systems

The article describes the new approach for quality improvement of automated dialogue systems for customer support service. The analysis produced in the paper demonstrates the dependency of the quality of the retrieval-based dialogue system on the choice of negative responses required for training the system. The proposed approach implies choosing the negative samples according to the distribution of responses in the training set. In this implementation, the negative samples are randomly chosen from the original response distribution and from the “artificial” distribution of negative responses, such as uniform distribution or the distribution obtained by transformation of the original one. The results obtained for the implemented systems and reported in this paper confirm the significant improvement of automated dialogue systems quality in case of using the negative responses from transformed distribution.
Complete Weight Distributions and MacWilliams Identities for Asymmetric Quantum Codes

In 1997, Shor and Laflamme defined the weight enumerators for quantum error-correcting codes and derived a MacWilliams identity. We extend their work by introducing our double weight enumerators and complete weight enumerators for qubit codes and then investigate the MacWilliams identities for these enumerators. Based on the generalized MacWilliams identities, we solve an open problem, namely, the Singleton-type bound for asymmetric quantum codes (AQCs). Besides, the Hamming-type and the first linear-programming-type bounds for the AQCs are deduced similarly.
Extractive Persian Summarizer for News Websites

Automatic extractive text summarization is the process of condensing textual information while preserving the important concepts. The proposed method after performing pre-processing on input Persian news articles generates a feature vector of salient sentences from a combination of statistical, semantic and heuristic methods and that are scored and concatenated accordingly. The scoring of the salient features is based on the article's title, proper nouns, pronouns, sentence length, keywords, topic words, sentence position, English words, and quotations. Experimental results on measurements including recall, F-measure, ROUGE-N are presented and compared to other Persian summarizers and shown to provide higher performance.
Higher Education Support for Adjunct Faculty on Institutional Websites

This exploratory study explores the types and level of explicit support available for adjunct faculty on institution's public-facing websites by analyzing a random sample of 50 non-profit four-year institutions of higher education in the United States. The sample, drawn from the Carnegie Classification, indicates the information strategies for a range of colleges and universities. Public-facing institutional websites are an important source of information that is used by adjunct faculty to acquire information regarding work requirements, academic policies, financial payment information, etc. This study considers the variations in website content and concludes by recommending content and features that support adjunct faculty.
Fighting Against XSS Attacks. A Usability Evaluation of OWASP ESAPI Output Encoding

Cross Site Scripting (XSS) is one of the most critical vulnerabilities exist in web applications.XSS can be prevented by encoding untrusted data that are loaded into browser content of web applications.Security Application Programming Interfaces (APIs) such as OWASP ESAPI provide output encoding functionalities for programmers to use to protect their applications from XSS attacks.However, XSS still being ranked as one of the most critical vulnerabilities in web applications suggests that programmers are not effectively using those APIs to encode untrusted data.Therefore, we conducted an experimental study with 10 programmers where they attempted to fix XSS vulnerabilities of a web application using the output encoding functionality of OWASP ESAPI.Results revealed 3 types of mistakes that programmers made which resulted in them failing to fix the application by removing XSS vulnerabilities.We also identified 16 usability issues of OWASP ESAPI.We identified that some of these usability issues as the reason for mistakes that programmers made.Based on these results, we provided suggestions on how the usability of output encoding APIs should be improved to give a better experience to programmers.
Opinion-Aware Knowledge Embedding for Stance Detection

As an emerging text classification task, stance detection is much helpful in reviewing subjective text and mining expressed attitudes of a person or organization towards an object. Due to the similarity with other text classification tasks, stance detection is always tackled by conventional classification methods. However, there is a big difference between stance detection and others, since stance detection depends much on human background knowledge while others do not. Therefore, to address such a unique problem, we propose a novel method, which leverages knowledge graph and incorporates text-mentioned knowledge with a deep classifier, by a key component named Opinion-aware Knowledge Embedding (OKE). The proposed OKE can integrate the objective knowledge facts and subjective text opinion well by a customized and effective attention mechanism. Our experiments also show that the proposed method comprehensively outperforms all the baselines on real datasets.
Penerapan Algoritma C4.5 Untuk Memprediksi Keuntungan Pada PT SMOE Indonesia

In the construction project activities, planning is used as a reference for job implementers and becomes the standard of project implementation, including: documents, technical specifications, schedule and budget. Inappropriate planning, inaccurate project realization investigations, inadequate project management skills and lack of professional service providers, are closely related to the outcome of a construction project process. PT SMOE Indonesia which is a company engaged in construction consulting services. At the present time PT SMOE Indonesia has done many construction planning projects both from government and private, this research will discuss how data mining with algorithm C4.5 process data from budget plan consultant planner cost to predict company profit. Data mining is a technique for extracting new information from piles or data warehouses, as we know information is seen as something that is very important and valuable because by mastering information it is easy to achieve a desired goal, this makes everyone race to while C4.5 algorithm is one of induction algorithm of decision tree that is ID3 (Iterative Dichotomiser 3). ID3 was developed by J. Ross Quinlan. In the ID3 algorithm procedure, the inputs are training samples, training labels and attributes. which will illustrate the profit prediction, the results of this study will result in the rules of profit and loss decisions company.
Integrating Five Feature Types Extracted From Ultrasonograms to Improve the Prediction of Thyroid Papillary Carcinoma

Ultrasonogram is one of the main techniques for the non-invasive observation and the diagnosis of the thyroid gland. And, the thyroid papillary carcinoma (TPC) was usually diagnosed during the regular examination of the thyroid gland. The current diagnosis guideline heavily replies on the experienced clinical endoscopists. This paper comprehensively evaluated four classification algorithms and five image feature extraction algorithms for the TPC diagnosis problem. Our data demonstrated that the Hessian features extracted from the transverse ultrasonograms performed better than those from the longitudinal view. The best model (Acc = 0.9949) was achieved by the seven-layer shallow neural network with the LBP and Hessian features extracted from both the longitudinal and transverse views of the ultrasonograms.
Towards the Data-driven System for Rhetorical Parsing of

Results of the first experimental evaluation of machine learning models trained on Ru-RSTreebank – first Russian corpus annotated within RST framework – are presented. Various lexical, quantitative, morphological, and semantic features were used. In rhetorical relation classification, ensemble of CatBoost model with selected features and a linear SVM model provides the best score (macro F1 = 54.67 ± 0.38). We discover that most of the important features for rhetorical relation classification are related to discourse connectives derived from the connectives lexicon for Russian and from other sources.
Temporal Anomaly Detection: Calibrating the Surprise

We propose a hybrid approach to temporal anomaly detection in access data of users to databases — or more generally, any kind of subject-object co-occurrence data. We consider a high-dimensional setting that also requires fast computation at test time. Our methodology identifies anomalies based on a single stationary model, instead of requiring a full temporal one, which would be prohibitive in this setting. We learn a low-rank stationary model from the training data, and then fit a regression model for predicting the expected likelihood score of normal access patterns in the future. The disparity between the predicted likelihood score and the observed one is used to assess the “surprise” at test time. This approach enables calibration of the anomaly score, so that time-varying normal behavior patterns are not considered anomalous. We provide a detailed description of the algorithm, including a convergence analysis, and report encouraging empirical results. One of the data sets that we tested is new for the public domain. It consists of two months’ worth of database access records from a live system. This data set and our code are publicly available at https://github.com/eyalgut/TLR anomaly detection.git.
An Ecolinguistics Perspectives For English Syllabus Development

This current study was intended to develop the ecolinguistic perspective-based syllabus. The descriptive qualitative method was applied and natural phenomena were used to develop syllabus. The phenomena focused on to what extent English was mastered by learners and what learning and teaching strategies were applied. The data were obtained through test, questionnaire and direct observation for the need analysis. The State Document related to the curriculum policy was referred to in the situation analysis. The syllabus was developed based on the results of the need analysis and situation analysis and the ecolinguistic perpective-related theories. The contents of the syllabus were developed based on the following elements; they are text, context, linguistic elements, learning situation, teaching and learning activities (Kegiatan Belajar Mengajar, abbreviated to KBM) and evaluation. The content of the text was adjusted to the transactional and interpersonal functions. The context was created based on the inter-, intra-, and trans-cultural contexts of the ecological, social and ideological dimensions. KBM was prepared in accordance with the cognitive linguistic process for the language-related activities and based on the situation chosen by learners for the language production. The outcome of language performance was measured using the performance assessment system, causing the contents of the syllabus to reflect that one element was connected with another in such a way that they formed one integral language meaning.