Reviewerly/exHarmony huggingface dataset:
Differentiating dislocations, topicalisation, and extraposition in Biblical Hebrew: Evidence from negation

Left dislocation, right dislocation, topicalisation, and extraposition involve a constituent in a non-canonical position at the edges of the sentence. In Biblical Hebrew, the differentiation of these four constructions is complicated by two additional constructions. At the left edge of the sentence is a construction that is like topicalisation in that it has no resumptive element within the sentence although it apparently occurs outside of the left boundary of the matrix sentence. At the right edge of the sentence, we identify for the first time a comparable construction of “heavy extraposition” which is like extraposition in that it has no resumptive element within the sentence although it apparently occurs outside of the right boundary of the matrix sentence. In this article, we examine the evidence that negation provides for differentiating the syntactic features of the six constructions. We present the data for negation involving each of these constructions with attention to the scope of negation as sentential negation or constituent negation (Snyman and Naude 2003, Naude and Rendsburg 2013). In particular, we examine the implications of negation for delimiting and defining the constructions, especially left dislocation and topicalisation, which are shown to involve important asymmetrical features. The facts about negated sentences provide important evidence that the presence (or absence) of resumption is critical to differentiating left dislocation and topicalisation (contra Westbury 2014, 2016). Negation thus provides important positive information for the syntactic structures of these six constructions and provides a crucial foundation for differentiating their functions with respect to information structure.
Pentingnya Stepping the 5 Stairs “A” Teknik Untuk Anak-Anak Pesisir yang Belajar Grammar

The English learning experience especially Grammar should be done in a fun way and make the language learners master the concept well. The importance of mastering Grammar well facilitates language learners to construct English sentences from various tenses. Coastal language learners have not been given good grammar understanding and do not have enough time to learn grammar in schools. To master Grammar, Stepping the 5 Stairs "A" technique offers fun grammar learning. There are already many techniques offered. However, the existing techniques do not accommodate the characteristics of the language learning methods of those from certain regions. The descriptive approach is a method of implementing community service. The team conducted a site survey and interviews with several residents’ representatives and coastal learners’ parents. The community service location is in Senggarang Village where foreign tourists pay a visit. The results of the community service are: (1) An increase in the knowledge and understanding of coastal learners about Grammar learning by using the Stepping the 5 Stairs "A" technique, (2) Coastal learners’the motivation to continue learning Grammar, and (3) There are requests from coastal learners, parents, RT, RW, and Karang Taruna Senggarang to the team to continue similar activities going forward
Bloom의 신교육목표분류에 기초한 2015 개정 교육과정 생명과학 I성취기준과 교과서 학습목표 분석

The purpose of this study was to analyze the 2015 revised national curriculum 『Life science I』 achievement standards and the learning objectives in accordance with Bloom’s revised taxonomy. The subjects of this study were 2015 revised national curriculum achievement standards and eight 『Life science I』 textbooks. The results of the achievement standards analysis presented that conceptual knowledge was the most utilized. The second was factual knowledge, and the next was procedural knowledge. Analysis of the cognitive process showed that understanding was the most utilized. The analysis of textbook learning objectives showed a tendency very similar to the results of achievement standards analysis. Conceptual knowledge was the largest, and factual knowledge was presented next. In the analysis of the cognitive process, comprehension was most often presented. Achievement standards and learning objectives are very important because they present the direction of teachinglearning in the field of school education. In particular, the use of various knowledge and cognitive processes in setting achievement standards and learning objectives can be seen as essential to fostering the competencies of the subject. Therefore, efforts should be made to utilize various types of knowledge and cognitive processes in the process of constructing 『Life science I』 achievement standards and learning objectives.
Implementation of persuasive design principles in mobile application development: a qualitative study

&lt;span&gt;Persuasive design principles (PDP) of persuasive system features framework have shown impressive results from the context of user engagement and acceptance as well as continuous usage towards the persuasive systems involved. Yet, available literatures do not thoroughly address the implementation of these design principles specifically in mobile applications and there is insufficient discussion on the impact of the principles in relation to the overall achievement of mobile applications. Hence, this research was conducted with the aim to qualitatively explore the way PDP were implemented across three different levels of mobile applications’ attainment. For this study, seven semi-structured interviews were conducted with the involvement of fifteen (15) Android mobile applications in the area of utilities category. These mobile applications were then categorized into three categories which is successful, partially successful and less successful based on their numbers of mobile applications downloaded for three consecutive years. The results from the content analysis revealed that each of the PDP were implemented in many ways yet the most common applied principles are reduction, tailoring and personalization. However, the analysis also shows low numbers of implementation from the system credibility and social support category. &lt;span&gt;In addition, most of the mobile applications of the successful category have implemented a lot more PDP as compared to the other two mobile application categories. The results from this study has provide significance towards developers, practitioners as well as the scholars from the contextual perspective of persuasive system framework also the practical values of the principles specifically the implementation in mobile application development.&lt;/span&gt;&lt;/span&gt;
Frequency of Discrepancy between Bone Marrow Morphology and Immunophenotype in Acute Leukaemia

Background: Acute leukaemia (AL) are a heterogenous group of haematological malignancy characterized by uncontrolled clonal proliferation of haematopoietic progenitor cells. Objectives: The study was conducted to have a detailed understanding of immunophenotyping profile, the frequency of discrepancy between bone marrow morphology and immunophenotyping and importance of immunophenotyping in diagnosis of acute leukaemia. Methods: This prospective type of observational study was carried out with an aim to correlate the immunophenotype with bone&#x0D; marrow morphology and to see the discrepancy between this two in acute leukaemia. A total of 38 untreated acute leukemia patients attending in the Department of Haematology, Bangabandhu Sheikh Mujib Medical University, Dhaka, during the period from October 2016 to September 2017were included in this study. At first the morphological diagnosis was done. Then the immunophenotypic profile was compared. Result: Around eighty-two cases of acute leukaemia did find similarity with immunophenotyping and remaining 18.4% shows discrepancy. Diagnosis in this 18.4% changes after immunophenotyping. Aberrant phenotypes were detected in 20 (52.6%) samples among them 13 (34.21%) cases were AML, 3 (7.8%) cases were B-ALL and 4 (10.52%) cases were T-ALL. Significant relation was not found between aberrant marker and FAB subtypes. Conclusion: In acute leukaemia morphological appearance of bone marrow does not always match with immunophenotyping. It is therefore imperative and absolutely essential to ascertain the lineage of leukaemia by immunophenotyping before starting treatment.
Relationship between Emotional Disturbance, Family Conflict, Social Pressure and Drug Craving Among Former Drug Addicts

Research on drug cravings among former drug addicts is an important issue to be addressed in order to provide input to the government in an effort to prevent relapse among former drug addicts. This article aims to identify the relationship between emotional disturbance, family conflict, social pressure and drug cravings among former drug addicts. A survey study was conducted by using a cross sectional design. A total of 380 former addicts, who completed the rehabilitation program at the Cure and Care Rehabilitation Center (CCRC) and were undergoing a period of supervision by the National Anti-Drug Agency (AADK), were selected to participate in the survey study. The data were analyzed using an inferential statistic, the Pearson Correlation test. The results showed a moderate and positive significant relationship between drug cravings and emotional disturbance (r = .703, p &lt;0.01), family conflict (r = .540, p &lt;0.01) and social pressure (r = .606, p &lt;0.01) among former addicts. These findings indicated that emotional disturbance, family conflict and social pressure that experienced by former addicts should be tackled by the stakeholders as it has a significant relationship with cravings, which may lead to relapse among former addicts.
Alternative Learning Media Pre Reading Ability

The ability to read early is an important ability given to children. Through reading skills, children will be able to gain other knowledge. Learning media is a tool for delivering material and facilitating learning activities. Learning to procure media to improve the ability to read the beginning that is appropriate for children's development is very necessary. Wrong-name learning media is one of the new media alternatives to improve initial reading skills. The purpose of this article is to add learning media to request the ability to read the beginning. And to add to the literature about the ability to read the beginning.
Reinforcement Learning for Long-term Reward Optimization in Recommender Systems

Recommender systems help users to orient in the vast space of goods, services, and events. A user interacts with the recommender engine in a sequence of exchanges of recommendations and user feedback. The idea that previous interaction influence the later ones and the importance of the sequence of interactions can be modeled using Markov decision processes and solved by reinforcement learning. Several recent articles applying reinforcement learning to recommender systems have proved the viability of this direction. But it is still difficult to compare different approaches. We propose an environment with a unified interface that will permit to compare different modelization of recommender process and different algorithms on the same underlying sequential data. We also performed the extensive parameter study for deep deterministic policy gradient methods on the well-known MovieLens dataset.
A Systematic Approach to Abstract Interpretation of Program Transformations

Abstract interpretation is a technique to define sound static analyses. While abstract interpretation is generally well-understood, the analysis of program transformations has not seen much attention. The main challenge in developing an abstract interpreter for program transformations is designing good abstractions that capture relevant information about the generated code. However, a complete abstract interpreter must handle many other aspects of the transformation language, such as backtracking and generic traversals, as well as analysis-specific concerns, such as interprocedurality and fixpoints. This deflects attention. We propose a systematic approach to design and implement abstract interpreters for program transformations that isolates the abstraction for generated code from other analysis aspects. Using our approach, analysis developers can focus on the design of abstractions for generated code, while the rest of the analysis definition can be reused. We show that our approach is feasible and useful by developing three novel inter-procedural analyses for the Stratego transformation language: a singleton analysis for constant propagation, a sort analysis for type checking, and a locally-illsorted sort analysis that can additionally validate type changing generic traversals.
CIQ@FIRE

Given the scale of CQA forums on the web, manual identification of Insincere Questions becomes challenging and costly. Hence automated approaches are needed, in addition to manual moderation to filter insincere questions in CQA sites. While this can be simplistically formulated as a binary classification task, insincere questions exhibit diverse characteristics such as Rhetorical, or Disparaging-Inflammatory, Hypothetical, or Objectionable. The proposed CIQ track aims to find a suitable model for Categorisation of Insincere Questions(CIQ) on CQA forums. We propose that segmenting insincere/non-information seeking questions will not only provide better identification, but also help to enable effective counter-measures based on the fine grained category it belongs to.
Benefits of Corporate Mentoring for Business Organization

In todays business world it require high-quality and low-cost arrangements. Beginning a mentoring program in the business enables to exploit the most prominent asset, representatives. Deliberately building up their ability adds to the organization's development, advancement, and main concern. It shows the executives' help, intrigue, and worry for a worker's potential with the organization. It shows to representatives that administration is happy to contribute the time and assets important to assist workers with prevailing in their vocations. Consequently, employees are bound to be progressively gainful and faithful to the organization.
Improving Continuous Software Development in Academic Scenarios using GitHubTracker

Continuous software development enables the early identification of potential problems in terms of software code, system integration, and team communication. In the field of software engineering education, usually, software development depends on the particular methodology for evaluating the technical performance of students in terms of software code and system integration of software development. Actually, GitHub is used for mining the information stored in GitHub's event logs, trying to understand how its students to performance on software development. However, there are few studies and experiences describing how GitHub can support continuous software development. In this paper, we present a case study in two software engineering courses at the University of Talca using GitHubTracker. GitHubTracker is a tool for improving continuous software development using the main GitHub's features. The results indicate that student teams were more effective in terms of performance and productivity than the other teams.
WebShapes: Network Visualization with 3D Shapes

Network visualization has played a critical role in graph analysis, as it not only presents a big picture of a network but also helps reveal the structural information of a network. The most popular visual representation of networks is the node-link diagram. However, visualizing a large network with the node-link diagram can be challenging due to the difficulty in obtaining an optimal graph layout. To address this challenge, a recent advancement in network representation: network shape, allows one to compactly represent a network and its subgraphs with the distribution of their embeddings. Inspired by this research, we have designed a web platform WebShapes that enables researchers and practitioners to visualize their network data as customized 3D shapes (http://b.link/webshapes). Furthermore, we provide a case study on real-world networks to explore the sensitivity of network shapes to different graph sampling, embedding, and fitting methods, and we show examples of understanding networks through their network shapes.
Efficiency Improvement of Differential Evolution Algorithm Using a Novel Mutation Method

The differential evolution algorithm is one of the fast, efficient, and strong population-based algorithms, which has extended applications in solving various problems. Although the velocity, power, and efficiency of this algorithm have been demonstrated in solving many optimization problems, this algorithm, like other metaheuristic algorithms, is not guaranteed to achieve the global optimal points of the optimization problems and may be ceased at optimal local points. One of the reasons for stopping the algorithm at the local optimum points is the imbalance between the exploration and exploitation abilities of the algorithm. One of the operators of the differential evolution algorithm, which plays an essential role in establishing the proper balance between the exploitation and exploitation of the algorithm, is the mutation operator. In this paper, a new mutation method is proposed to improve the efficiency of the differential evolution algorithm to make an appropriate balance between the exploitation and exploitation abilities of the algorithm. Comparing the results of the proposed mutation method with other mutation methods indicates that the proposed method has better speed and accuracy convergence rather than other methods, and it can be employed to solve large-scale optimization problems.
Bayesian nonparametric density regression for ordinal responses

Abstract We present a Bayesian nonparametric model for ordinal responses, which is based on mixture modelling for the joint distribution of covariates and latent continuous responses. The modelling framework enables flexible inference for both the regression relationships and other aspects of the conditional response distribution. In typical ordinal regression models, assumptions include linearity and parametric distributions. These assumptions may be restrictive for many applications, and computational challenges arise when fitting these models. The nonparametric ordinal regression model avoids these challenges. This modelling approach falls into the class of Bayesian nonparametric density regression methods, which we also review. The density regression modelling approach is illustrated with both continuous and ordinal responses through an application to estimate the relationships between ozone concentration and other environmental characteristics.
Sensitivity Analysis of MODENAR Method for Mining of Numeric Association Rules

There are many association rules mining studies that focus on datasets consisting of only discrete or binary valued attributes. However, the data in many real-world applications are generally composed of quantitative or numeric values and classical association rule mining methods do not automatically work without preprocessing that disrupt the real data. It becomes a very hard problem to automatically mine high-quality rules in terms of many metrics. Recently, some researchers have considered numeric association rule mining as a multi-objective problem that best meets different criteria at the same time. In this paper, the parameter analysis of differential evolution based MODENAR, which is one of the few methods using multi-objective evolutionary algorithms for numeric association rules and aims to maximize support, confidence, comprehensibility, and amplitude of the ranges of attributes. For this purpose, the effects of the parameters of MODENAR such as population size, crossover rate, threshold solutions, and weight for support and confidence to the number of rules obtained, average support, confidence, lift, conviction, certainty factor, netconf, yulesQ, and the coverage percentage in five real-world data whose attributes consist of numeric values are carried out for the first time in this study.
Pose Recognition for Dense Vehicles under Complex Street Scenario

Locating vehicles in the surrounding environment and distinguishing their poses are crucial in autonomous driving systems. An intelligent transportation system expects a vision-based object detector not only to detect and classify vehicles but also to recognize the poses of them in estimating their future movement direction. However, most of the current object detection algorithms only classify different classes of vehicles; and there are rare studies on classifying different poses within the same class of vehicles. In addition, the currently available datasets have no annotation data that are specific to different poses of intra-class vehicles. To achieve the vehicles pose recognition, we build a vehicle pose dataset based on the Cityscapes dataset. Furthermore, we also improve the network model of the YOLO v2 detector to support simultaneous vehicle detection and pose classification with an improvement of 10.71% mean average precision (mAP) comparing to the default network which runs in real-time with 29 FPS.
Re-implementing and Extending Relation Network for R-CBIR

Relational reasoning is an emerging theme in Machine Learning in general and in Computer Vision in particular. Deep Mind has recently proposed a module called Relation Network (RN) that has shown impressive results on visual question answering tasks. Unfortunately, the implementation of the proposed approach was not public. To reproduce their experiments and extend their approach in the context of Information Retrieval, we had to re-implement everything, testing many parameters and conducting many experiments. Our implementation is now public on GitHub and it is already used by a large community of researchers. Furthermore, we recently presented a variant of the relation network module that we called Aggregated Visual Features RN (AVF-RN). This network can produce and aggregate at inference time compact visual relationship-aware features for the Relational-CBIR (R-CBIR) task. R-CBIR consists in retrieving images with given relationships among objects. In this paper, we discuss the details of our Relation Network implementation and more experimental results than the original paper. Relational reasoning is a very promising topic for better understanding and retrieving inter-object relationships, especially in digital libraries.
Efficient Algorithms for Constrained Clustering with Side Information

Clustering as an unsupervised machine learning method has broad applications within the area of data science and natural language processing. In this paper, we use background knowledge or side information of the data as constraints to improve clustering accuracy. Following the representation method as in [15], we first format the side information as must-link set and cannot-link set. Then we propose a constrained k-means algorithm for clustering the data. The key idea of our algorithm for clustering must-link data sets is to treat each set as a data with large volume, which is, to assign a set of must-link data as a whole to the center closest to its mass center. In contrast, the key for clustering cannot-link data set is to transform the assignment of the involved data points to the computation of a minimum weight perfect matching. At last, we carried out numerical simulation to evaluate our algorithms for constrained k-means on UCI datasets. The experimental results demonstrate that our method outperforms the previous constrained k-means as well as the classical k-means in both clustering accuracy and runtime.