Reviewerly/exHarmony huggingface dataset:
Tool Support for Software Artefact Traceability in DevOps Practice

Software development in DevOps practice is a widely used approach to cope with the demand for frequent artefact changes. These changes require a well-defined method to manage artefact consistency to ease the continuous integration process. This chapter proposes a traceability management approach for the artefact types in the main phases of the software process including requirements, design, source code, testing, and configuration. This chapter addresses traceability management, including trace link creation, change detection, impact analysis, change propagation, validation, and visualisation. This chapter presents a tool named SAT-Analyser that is applicable for any software development method and designed for continuous integration, multi-user collaboration, and DevOps tool stack compatibility. The SAT-Analyser is assessed using case studies and shown an impact analysis accuracy of 0.93 of F-measure. Further, the feedback by DevOps practitioners has shown the suitability and innovativeness of the proposed approach.
ELFIE - The OGC Environmental Linked Features Interoperability Experiment

The OGC Environmental Linked Feature Interoperability Experiment (ELFIE) sought to assess a suite of pre-existing OGC and W3C standards with a view to identifying best practice for exposing cross-domain links between environmental features and observations. Environmental domain models concerning landscape interactions with the hydrologic cycle served as the basis for this study, whilst offering a meaningful constraint on its scope. JSON-LD was selected for serialization; this combines the power of linked data with intuitive encoding. Vocabularies were utilized for the provision of the JSON-LD contexts; these ranged from common vocabularies such as schema.org to semantic representations of OGC/ISO observational standards to domain-specific feature models synonymous with the hydrological and geological domains. Exemplary data for the selected use cases was provided by participants and shared in static form via a GitHub repository. User applications were created to assess the validity of the proposed approach as it pertained to real-world situations. This process resulted in the identification of issues whose resolution is a prerequisite for wide-scale deployment and best practice definition. Addressing these issues will be the focus of future OGC Interoperability Experiments.
An Attention Based Deep Neural Network for Automatic Lexical Stress Detection

Lexical stress detection is one of important tasks in self-directed language learning application. We address this task by leveraging two successful attention techniques in natural language processing, inner attention and self-attention. First, combined with LSTM to model time-series features, inner attention could extract most important information and then convert length-varying input into a fixed-length feature vector; Second, self-attention intrinsically supports words with different number of syllables as input to model contexture information. Besides, our model is straightforward to expand to include hand-crafted features to further improve performance, and also can be applied to similar tasks, such as pitch accent detector. Experiments on LibriSpeech, TedLium and a third self-recored datasets show the high performance of our proposed attention based neural network.
Comparison of simple and stratified random sampling on porn videos recognition using CNN

Video classification is challenging because the video consists of many frames. In the videos recognition system, the proper sampling method affects the classification process because it uses image recognition model on each frame to recognize the video. This study focuses on comparing the sampling methods used in pornographic video recognition systems. Porn videos have high heterogeneity so that a sophisticated approach is needed to analyze the provided data for learning the data patterns. The Convolutional Neural Network method is employed because it can automatically detect features or similarity from the given training data. Besides that, this method could recognize the image quickly because it just fit test data into the final weights and biases from training. In this research, the stratified random sampling method gives 80% of accuracy while the simple random sampling method is the fastest method, which recognizes the video in 94 seconds. Additionally, all porn videos provided can be identified entirely so that the recall value for all test video is 100% while specificity average is 55.2%.
Generating General Levels using Markov Chains

The use of machine learning techniques for content generation has recently emerged on the scene. Procedural Content Generation via Machine Learning is the generation of game content by models that have been trained on existing game content. The aim of this paper is to generate general video game levels using Markov chains. For this purpose, we created a random level generator that generates level dataset in order to train Markov models. The results show that Markov chains can generate playable levels for a large variety of games in the General Video Game Level Generation Framework. The generated levels are also evaluated using agent based testing.
Principal Component Analysis for High Dimension Stochastic Gaussian Process Model Fitting

Stochastic Gaussian Process models are widely used in stochastic simulation metamodeling to predict the response of noisy simulations. Often, many real-world engineering problems are high-dimensional (more than 10 dimensions), and the Gaussian Process models' prediction for such high-dimensional inputs are computationally expensive due to multiple inversions of the covariance matrix. Therefore, the “curse of dimensionality” on these models prevent them from being used in a high-dimensional setting. To overcome this problem, a methodology for high-dimensional stochastic Gaussian Process models' fitting is proposed in this paper. The input data is first projected onto a reduced set of dimensions found by Principal Component Analysis before being used to fit the model. Numerical experiments prove that the method helps significantly in reducing the computation time.
Fuzzytechie languaging and consilience: Dataphilosophy and transdisciplinary digital force for justice

Digitalization of our educational systems is moving faster and deeper into our institutions. I applaud it because it has the potential to save time and effort, allowing me to spend more time on the most important part of my work as a scholar. But I am regularly sick to my stomach about what I applaud, because there is no potential and any news or justice in it unless I conquer it. Moreover, unless I do- conquer that is, it works as much against (non)knowledges as the opposite, and this is speeding up. Hence, I speculate with the concepts of speed and force. I ask, how is it possible to add or speed up and strengthen digital diversity for justice to highlight the entangled contours of knowledges, inequality, equality and quality in education? My gut feeling tells me that my subjective professionalism is under siege and that I need to take back the concept of objectivity to survive. It is paradoxically slow but fast, paradoxically objective but subjective, strong but weak. It implies a liberating loss of control to importance. Education becoming eventicized, professionals and teachers becoming transdisciplinary dataphilosophers.
Minimizing Energy of Heterogeneous Computing Systems by Task Scheduling Approach,

As an important component of computer system, GPU has been used more widely in the system under the support of general computing. In addition to focusing on its performance, the issues of its energy consumption and environmental problem have gradually attracted the concerns of researchers, computer architects, and developers. Current researches only consider single-task scheduling for saving energy, lacking the focus on energy saving from scheduling the overall tasks. In view of the shortcomings of current researches, we propose a METS (Minimizing Execution Time Slot) approach to reduce energy by rationally allocating the tasks across GPUs. It first collects the number of tasks and the corresponding estimated performance information. Next, it decides whether to turn the problem into a 0–1 knapsack problem or to use FIFO method based on the number of tasks. Then, we conduct our experiment on typical platform to verify our proposed approach. The experimental results show that METS can save on average 8.43% of energy when compared with the existing approaches. This shows that the proposed METS method is effective, reasonable and feasible.
Emotion Predictor Using Social Media Text and Graphology

Social media is a growing source of data and Social Media activities such as WhatsApp group communications have opened up a myriad of data and information exchange - especially amongst the modern student community. On an average, they tend to express their feelings and opinions far more freely amongst their net cronies than anywhere else. In this paper, the target is to evaluate the emotional state of students within such a group, by extracting hidden nuances through usage of words revealed by text analytic measures. The study is further augmented with records of personal statistics. Some of these, such as age, educational background and social status, help to categorize the participants for ease of comparative analysis at a later stage. The rest, gleaned through collecting signatures and handwritten scripts of participants, support the discipline of graphology, for delving into the depths of the writer's character traits. Overall, the primary goal is to propose a Case Based Reasoner (CBR) to hold all the details of each student as individual cases with attached sentiment polarity scores reflecting the personal positivity or negativity. The Case Base needs to undergo incremental learning, until the model is judged to have gained sufficient experience to start predicting polarities on its own.
Efficient Pupil Detection with a Convolutional Neural Network

The paper proposes an efficient convolutional neural network for pupil detection capable of state-of-the art accuracy without pretraining. The system architecture consists of twelve convolution layers and two max pool layers with only 108450 prediction parameters. The validation conducted on ExCuSe datasets show that our solution achieves state-of-the-art performance in terms of detection rate within a pixel error bound.
Progressive Kalman Filter and Its Application in Magnetic Target Tracking

A nonlinear filter is proposed for magnetic dipole tracking. Firstly, the state-space model of magnetic dipole target is established. Secondly, the Bayesian discrete-time update is reformulated to a continuous progressive Bayesian problem, and the analytical solution is derived under linear Gaussian condition. Further the first-order Taylor expansion is applied to obtain the nonlinear approximate solution expression, thus deriving the progressive extended Kalman filter (PEKF). Simulation and real-world magnetic dipole tracking experiments are performed to demonstrate the effectiveness of PEKF. Both the simulation and measured target tracking test results suggest that the progressive extended Kalman filter has good precision and convergence, which can effectively suppress the performance degeneration and filter divergence caused by large initial error in magnetic target tracking, and the computational efficiency is equivalent to that of extended Kalman filter, which is suitable for practical magnetic dipole tracking application.
Analysis of Association Measures Used to Discover Antimicrobial Resistance Patterns

Association rule mining is a powerful tool in identifying patterns in data but the resulting associations depend on the criteria used to rank/filter the subsets of related data. The analysis conducted in this paper reveals some insights on the desirable properties of the measures used in the process of mining for patterns arising in the antimicrobial resistance data.
Breast Cancer Pathological Image Auto-Classification using Weighted GLCM-SVM

At present, when radiologists screen the breast cancer, they always miss about 10-30% tumors for pathological edge blurring and asthenopia after the diagnosis for a long time. For this reason, setting up the Computer Aided Diagnosis (CAD) system is of great clinical application value. This thesis proposes the method of weighted GLCM to extract the texture features of mammogram and classify abnormal regions according to the evaluation of the performance of each individual by using support vector machines (SVM). The experiment shows our system is effective for breast cancer pathological image.
Dense Semantic Matching Network for Multi-turn Conversation

Mining the semantic information in the text to model the multi-turn conversation has attracted great interests. Previous models ignore the capturing and utilizing of the matching patterns at different levels, which causes the loss of the valuable information for the calculation of the matching degree. To address the problem, we propose a dense semantic matching network (DMN). Given a context-response pair, DMN first constructs semantic representations for the response candidate and each utterance in the context. Then, DMN models the interaction between the response candidate and each utterance in the context to generate interactive matrices. By processing the interactive matrices with dense convolutional blocks, the hierarchical matching patterns are generated for each utterance-response pair. The matching patterns of all the utterance-response pairs are finally accumulated in chronological order with bidirectional long short-term memory network. The final matching score of the response candidate and the multi-turn context is finally calculated. We evaluate the performance of our network on the benchmark dataset. The results show that our network yields a significant performance gain compared with other methods.
The Blind Date: Improving the Accessibility of Mobile Dating Applications for Individuals with Visual Impairments

Online dating platforms such as Tinder, Bumble, and Coffee Meets Bagel have become not only culturally accepted, but incredibly common methods of meeting new people. However, many of these online dating platforms user profiles rely heavily on visual data. This reliance creates a barrier to access for individuals with visual impairments. Using voice-over systems with any of these applications leads to a very confusing interaction that withholds a lot of the available data from users with visual impairments. We propose a method of providing users with visual impairments access to the data encoded in the images of other users profiles. To learn what information people are most interested in knowing about the person in the dating profile, we surveyed both individuals with and without visual impairments to learn what visual features are important in an individual's dating profile. We then built a series of classifiers for the most popular features that were identified by the survey. Because the APIs for the dating applications mentioned above are not public, we created an application that works via screenshots. When the user would like to know more about a given profile, they just need to activate the native screenshot functionality on their phone, and our application will find the image in the screenshot folder on the phone, send it to our web service where the image is securely analyzed and output text is formed. This text is sent back to the phone and read out, providing the user with a coarse idea of what the image contained and helping them to make a more informed decision as to whether they are interested in the individual in the profile or not.
Artefact Consistency Management in DevOps Practice

DevOps practices preserve the continuous innovation in software development. The collaborative nature and stakeholder communication are keys in DevOps that lead to highly effective and quality software outcomes with customer satisfaction. The software artefacts involved in a DevOps practice must adapt to frequent changes due to continuous stakeholder feedback. Hence, it is challenging to artefact consistency throughout the software life cycle. Although artefact traceability preserves the consistency management with theoretical support, there are practical limitations in traceability visualisation, change impact analysis, and change propagation aspects. This chapter presents an analysis of existing studies focused on software artefact traceability for the suitability in DevOps. It also identifies leading limitations and possible future research directions to resolve for the benefit of researchers and software practitioners.
Unsupervised inference of implicit biomedical events using context triggers

Event extraction from the biomedical literature is one of the most actively researched areas in biomedical text mining and natural language processing. However, most approaches have focused on events within single sentence boundaries, and have thus paid much less attention to events spanning multiple sentences. The Bacteria-Biotope event (BB-event) subtask presented in BioNLP Shared Task 2016 is one such example; a significant amount of relations between bacteria and biotope span more than one sentence, but existing systems have treated them as false negatives because labeled data is not sufficiently large enough to model a complex reasoning process using supervised learning frameworks.We present an unsupervised method for inferring cross-sentence events by propagating intra-sentence information to adjacent sentences using context trigger expressions that strongly signal the implicit presence of entities of interest. Such expressions can be collected from a large amount of unlabeled plain text based on simple syntactic constraints, helping to overcome the limitation of relying only on a small number of training examples available. The experimental results demonstrate that our unsupervised system extracts cross-sentence events quite well and outperforms all the state-of-the-art supervised systems when combined with existing methods for intra-sentence event extraction. Moreover, our system is also found effective at detecting long-distance intra-sentence events, compared favorably with existing high-dimensional models such as deep neural networks, without any supervised learning techniques.Our linguistically motivated inference model is shown to be effective at detecting implicit events that have not been covered by previous work, without relying on training data or curated knowledge bases. Moreover, it also helps to boost the performance of existing systems by allowing them to detect additional cross-sentence events. We believe that the proposed model offers an effective way to infer implicit information beyond sentence boundaries, especially when human-annotated data is not sufficient enough to train a robust supervised system.
A Hypergraph Coloring Based Modelling of Service Oriented System

Service Oriented Architecture (SOA) provides a high level structure and related facets for developing service oriented system (SOS). In recent days, various SOA modelling approaches, such as set theoretic and graph, have been proposed. These approaches hinder the scalability in the design of large scale SOS. In such system, the computational complexity is increasing due to the multiple numbers of service consumers, providers, and associated groups. In this context, this paper proposes a hypergraph coloring approach to represent such a system in more effective way. Two algorithms, service discovery and publishing of services are proposed for such system. Further, a case study is illustrated for the practical implementation of the proposed approach. The novelty of the proposed research work is to map the idea of large scale SOA along with its different modelling elements into a graphical concept, so that it will be more useful for a large scale SOS.
Analysis of Fuzzy C-Means Algorithm on Indonesian Translation of Hadits Text

Hadith is the second source of Islamic religious law after the Al-Qur'an, in the hadith there are many chapters that discuss several cases and will be interesting to be combined with data mining techniques, especially text mining in order to group the hadith into several groups based on Matan (content hadith) automatically. Clustering is a technique of grouping data based on criteria, in clustering has several methods including K-Means and Fuzzy C-Means. This research will try to group the Indonesian translation of Hadith texts and compare K-Means and Fuzzy C-Means algorithms with some parameters and experiments that are determined. This comparison is used to determine the most accurate method in the Hadith clustering. The results of this research indicate that some of the parameters used to affect the results of cluster evaluation, especially in reducing data dimensions. In Silhouette Coefficient and F-Measure calculations, the Fuzzy C-Means method has an accuracy of 0.83079 and 0.97128 while the K-Means method has an accuracy of 0.67828 and 0.95078 with the results above show that the Fuzzy C-Means method is better in grouping the Indonesian hadith text.
Automated Diagnosis of Cervical Lesions using Deep Learning Models

Cervical cancer occurs mainly from a deterioration of pre-cancerous lesions and represents a global threat among females, ranking fourth for both incidence and mortality among cancers. Prevention is able to significantly diminish this concerning issue. In that sense, a valuable support for physicians could be a tool that is able to automatically classify images from colposcopy. In this paper, two methods based on the use of deep neural networks have been implemented and tested for the automatic classification of colposcopy images. One of the methods consisted in constructing a Convolutional Neural Network, specific for image classification, which obtained a training accuracy of 41% and a validation of 31%. Another method was implemented to improve the previous results, a Residual Neural Network composed of 50 hidden layers. This second implementation achieved a training accuracy of 93% and a validation of 60%, with validation costs similar to the first method. These preliminary results suggest that deep learning could be applied successfully to the automated classification of colposcopy images.
E-assessments and Feedback Mechanisms in Moocs

Assessment can be defined as the interpretation of a student's work performed. It can be the guiding as well as decision making point for various stakeholders. It checks the learner on three pillars as to how student represent knowledge, how to develop competence and how to draw influence from the topic. Assessment is the best way to identify what the student wants as in support to increase his/her knowledge base. It encourages the desire in the learner to excel in various fields and progress further. Assessment is sometimes mistaken as evaluation but they are different in many perspective such as: Assessment is to evaluate the effectiveness of the teaching done, the process of appraising something, to check the level of performance whereas evaluation is judging the learner based on the standards, to determine to which degree the goals are satisfied. From the reference of all the assessment, we can propose m-assessment in which the practice tests and enhancement of the topic related questions can be uploaded and be assessed frequently and immediately.