Reviewerly/exHarmony huggingface dataset:
Improved dependency management for issue trackers in large collaborative projects.

Issue trackers, such as Jira, have become the prevalent collaborative tools in software engineering for managing issues, such as requirements, development tasks, and software bugs. However, issue trackers inherently focus on the life-cycle of single issues although issues have and express dependencies on other issues that constitute an issue dependency network in a large complex collaborative projects. The objective of this study is to develop supportive solutions for the improved management of dependent issues in an issue tracker. This study follows Design Science methodology, consisting of elicitation of drawbacks, and construction and evaluation of a solution and system. The study was carried out in the context of The Qt Company's Jira, which exemplifies an actively used, almost two decade old issue tracker with over 100,000 issues. The drawbacks capture how users operate with issue trackers to handle issue information in large, collaborative and long-lived projects. The basis of the solution is to keep issues and dependencies as separate objects and automatically construct an issue graph. Dependency detection complements the issue graph by proposing missing dependencies, and consistency check and diagnosis identify incompatible issue priorities and release assignments. Jira's plugin and service-based system architecture realizes the functional and quality concerns of the system implementation. We show how to adopt the supporting intelligent techniques of an issue tracker in a complex use context and a large data-set. The solution takes into account integrated and holistic system-view, practical applicability and utility, and the practical characteristics, such as inherent incompleteness, of issue data.
Analisis Kesesuaian Materi Dan Bahasa Buku Tematik Kelas 1 Terbitan Kementerian Pendidikan Dan Kebudayaan Serta Terbitan Erlangga Dengan Perkembangan Kognitif Anak

This study aims to determine and describe the suitability of the presentation of material and language in the thematic books of class 1 theme 6 sub-theme 1 published by the Ministry of Education and Culture and published by Erlangga with children's cognitive development. The method used in this research is a qualitative research method with the type of content analysis research. The results of this study are: 1) Presentation of thematic book material for grade 1 students with theme 6 sub-theme 1 published by the Ministry of Education and Culture has been in accordance with the cognitive development of students but there are some inappropriate material presentations. 2) For the presentation of thematic book material for grade 1 students with theme 6 sub-theme 1 published by Erlangga, it is in accordance with cognitive development but there is an inadequacy in the presentation of learning one SBdP material description. 3) The results of the analysis of the language suitability aspects of the thematic books for grade 1 students with theme 6 sub-theme 1 published by the Ministry of Education and Culture show conformity to children's cognitive development, however, some descriptions of language use are not in accordance with the indicators because in the use of language there are ambiguous and illogical sentences and sentence descriptions too long. 4) The presentation of the thematic book language for grade 1 students with theme 6 sub-theme 1 published by Erlangga is in accordance with the cognitive development of the child.The material is presented using simple sentences with general vocabulary and in each material description shows a logical relationship between the material description one and other material descriptions in accordance with the concept of thematic learning.
Which Mutual-Information Representation Learning Objectives are Sufficient for Control?

Mutual information maximization provides an appealing formalism for learning representations of data. In the context of reinforcement learning (RL), such representations can accelerate learning by discarding irrelevant and redundant information, while retaining the information necessary for control. Much of the prior work on these methods has addressed the practical difficulties of estimating mutual information from samples of high-dimensional observations, while comparatively less is understood about which mutual information objectives yield representations that are sufficient for RL from a theoretical perspective. In this paper, we formalize the sufficiency of a state representation for learning and representing the optimal policy, and study several popular mutual-information based objectives through this lens. Surprisingly, we find that two of these objectives can yield insufficient representations given mild and common assumptions on the structure of the MDP. We corroborate our theoretical results with empirical experiments on a simulated game environment with visual observations.
Contextual Skipgram: Training Word Representation Using Context Information

The skip-gram (SG) model learns word representation by predicting the words surrounding a center word from unstructured text data. However, not all words in the context window contribute to the meaning of the center word. For example, less relevant words could be in the context window, hindering the SG model from learning a better quality representation. In this paper, we propose an enhanced version of the SG that leverages context information to produce word representation. The proposed model, Contextual Skip-gram, is designed to predict contextual words with both the center words and the context information. This simple idea helps to reduce the impact of irrelevant words on the training process, thus enhancing the final performance
Bi-APC: Bidirectional Autoregressive Predictive Coding for Unsupervised Pre-training and Its Application to Children's ASR

We present a bidirectional unsupervised model pre-training (UPT) method and apply it to children's automatic speech recognition (ASR). An obstacle to improving child ASR is the scarcity of child speech databases. A common approach to alleviate this problem is model pre-training using data from adult speech. Pre-training can be done using supervised (SPT) or unsupervised methods, depending on the availability of annotations. Typically, SPT performs better. In this paper, we focus on UPT to address the situations when pre-training data are unlabeled. Autoregressive predictive coding (APC), a UPT method, predicts frames from only one direction, limiting its use to uni-directional pre-training. Conventional bidirectional UPT methods, however, predict only a small portion of frames. To extend the benefits of APC to bi-directional pre-training, Bi-APC is proposed. We then use adaptation techniques to transfer knowledge learned from adult speech (using the Librispeech corpus) to child speech (OGI Kids corpus). LSTM-based hybrid systems are investigated. For the uni-LSTM structure, APC obtains similar WER improvements to SPT over the baseline. When applied to BLSTM, however, APC is not as competitive as SPT, but our proposed Bi-APC has comparable improvements to SPT.
Towards Credit-Fraud Detection via Sparsely Varying Gaussian Approximations

In today's age, financial institutions, and corporations, annually incur billions of dollars to safeguard against fraudulent activities. In this context, bank credit card frauds account for the majority of frauds. We address this problem by utilizing the Bayesian learning technique with an uncertainty score to ascertain any unwarranted positive, with a fair model accuracy by tackling unbalanced data that is common in this domain. In this direction, a sparse Gaussian classification method with the concept of pseudo inducing points has been proposed to handle the large dataset. The same has been performed using different kernels and a varying number of inducing points. The best results are obtained with the selection of RBF kernel and while considering a maximal number of inducing points. The model has been developed in a robust fashion so as to work over large financial data with an accuracy of 98.05 percent over the test dataset. Our result with low variance over the prediction indicates the robustness of the model. These results would have high applications in the context of credit fraud detection in high-class imbalanced data obtaining a high accuracy and confidence score.
Optimal Path Selection Model of Public Transportation Network Based on Improved Genetic Algorithm

At present, the optimal path selection model of public transport network is not comprehensive considering the public transport factors, which leads to the long time of the optimal path selected by the model, and the selected path is not the optimal path. Therefore, algorithm, the optimal path selection model of public transport network is designed. The model constraints such as start and end point walking arc, transfer arc, driving arc and cost are calculated. Based on the optimal iterative selection characteristics of improved genetic algorithm, the optimal path selection model of public transport network is established; By determining the optimal path selection process of the public transportation network, the optimal public transportation network path is selected. Experimental results: The optimal path selection model proposed this time, when selecting the optimal path of the public transportation network, the optimal path selected by the public transportation network is shorter than the optimal path selected by other models, and it is in the public transportation network. In the network, the path selection time is also the shortest.
Path Planning Method of Self-Propelled Bridge Inspection Vehicle based on Support Vector Machine

Present, there is a high rate of missing inspection in the path planning method of bridge inspection vehicle, and the planning path is repeated, which affects the detection efficiency. In order to overcome the above technical deficiencies, a self-propelled bridge inspection vehicle path planning method based on support vector machine is designed. Through the sample training based on support vector machine, the maximum interval method is used to partition the hyperplane to reduce the collision of obstacles. The grid method is used to construct the environment model of the bridge to determine the specifications of the grid. Each grid is given different values and meanings to represent different environmental information. Finally, the path planning of the operation vehicle is realized. In the experiment, two methods are used to plan the path of a bridge inspection project, and the two paths obtained from the plan are evaluated. The experimental results show that the path of the inspection vehicle obtained by the designed path planning method can achieve all-round coverage of the bridge inspection. And there is no path duplication, which verifies the effectiveness of the design method.
Library Guides: Academic Librarian - Margo Stewart: Welcome

This guide explains academic librarians' role within the University and support they offer for students, staff and community.
Impact of Ethical Leadership on Team Performance: the Mediating Role of Resolution Efficacy and Individual Performance

The purpose of this study is to investigate the impact of ethical leadership on team performance with mediating role of resolution efficacy and individual performance. Design/Methodology/Approach: This study is conducted in telecom sector of Qatar; self-administrative questionnaire is used for data collection. The collected data for the study was tested with the help of two statistical software SPSS and AMOS. Different tests were run in order to test the research hypothesis and confirm it objectives, i.e., descriptive, correlation KMO and factor loading, CFA and SEM. Findings: The findings of the study indicated that ethical leadership has positive and significant impact on team performance in Qatar telecom sector. Results also prove that resolution efficacy and individual performance significantly mediates between ethical leadership and team performance. Implications: This study will be significant both theoretically and practically. In theory, evidence in the literature on the study topic will increase. In the practical field, this study will help in implementing the teamwork in telecom sector of Qatar to improve it outcomes. Policymakers can also benefit from the study as it will help those updating policies on ethical leadership for positive outcomes. Originality/Value: The study presents a research model that is investigated for the first time in the telecom sector of Qatar.
Recognizing Transliterated English Words in Persian Texts

One of the most important problems of text processing systems is the word mismatch problem.This results in limited access to the required information in information retrieval.This problem occurs in analyzing textual data such as news, or low accuracy in text classification and clustering.In this case, if the text-processing engine does not use similar/related words in the same sense, it may not be able to guide you to the appropriate result.Various statistical techniques have been proposed to bridge the vocabulary gap problem; e.g., if two words are used in similar contexts frequently, they have similar/related meanings.Synonym and similar words, however, are only one of the categories of related words that are expected to be captured by statistical approaches.Another category of related words is the pair of an original word in one language and its transliteration from another language.This kind of related words is common in non-English languages.In non-English texts, instead of using the original word from the target language, the writer may borrow the English word and only transliterate it to the target language.Since this kind of writing style is used in limited texts, the frequency of transliterated words is not as high as original words.As a result, available corpus-based techniques are not able to capture their concept.In this article, we propose two different approaches to overcome this problem: (1) using neural network-based transliteration, (2) using available tools that are used for machine translation/transliteration, such as Google Translate and Behnevis.Our experiments on a dataset, which is provided for this purpose, shows that the combination of the two approaches can detect English words with 89.39% accuracy.
Provenance Research and Circulation: Examples from the Maximilian von Goldschmidt-Rothschild Collection

This is the final of three pieces on provenance research that we are publishing in conjunction with the 6th German/American Provenance Exchange Program ...
LibGuides: ACRL Academic Library Services for Graduate Students Interest Group: Submit Notable Works

Programming Information for ACRL's Academic Library Services for Graduate Students Interest Group.
There Is Still No Silver Bullet

Throughout its existence, Computer has been at the center of discussion over how software should be engineered and how it should be developed. No article has been so central to the discussion as “No Silver Bullet” by Frederick P. Brooks. Yet, almost 35 years after he wrote this contribution to knowledge, Brooks’s observation remains true. Software engineering is a complex and demanding field that poses a host of problems to the practitioner, and there is no single solution, that is, no silver bullet, that will provide a simple way to reduce the work required to create a software product.
Body Field: Structured Mean Field with Human Body Skeleton Model and Shifted Gaussian Edge Potentials

An efficient method for simultaneous human body part segmentation and pose estimation is introduced. A conditional random field with a fully-connected graphical model is used. Possible node (image pixel) labels comprise of the human body parts and the background. In the human body skeleton model, the spatial dependencies among body parts are encoded in the definition of pairwise energy functions according to the conditional random fields. Proper pairwise edge potentials between image pixels are defined according to the presence or absence of human body parts that are near to each other. Various Gaussian kernels in position, color, and histogram of oriented gradients spaces are used for defining the pairwise energy terms. Shifted Gaussian kernels are defined between each two body parts that are connected to each other according to the human body skeleton model. As shifted Gaussian kernels impose a high computational cost to the inference, an efficient inference process is proposed by a mean field approximation method that uses high dimensional shifted Gaussian filtering. The experimental results evaluated on the challenging KTH Football, Leeds Sports Pose, HumanEva, and Penn-Fudan datasets show that the proposed method increases the per-pixel accuracy measure for human body part segmentation and also improves the probability of correct parts metric of human body joint locations.
Leveraging Acoustic and Linguistic Embeddings from Pretrained speech and language Models for Intent Classification

Intent classification is a task in spoken language understanding. An intent classification system is usually implemented as a pipeline process, with a speech recognition module followed by text processing that classifies the intents. There are also studies of end-to-end system that takes acoustic features as input and classifies the intents directly. Such systems don't take advantage of relevant linguistic information, and suffer from limited training data. In this work, we propose a novel intent classification framework that employs acoustic features extracted from a pretrained speech recognition system and linguistic features learned from a pretrained language model. We use knowledge distillation technique to map the acoustic embeddings towards linguistic embeddings. We perform fusion of both acoustic and linguistic embeddings through cross-attention approach to classify intents. With the proposed method, we achieve 90.86% and 99.07% accuracy on ATIS and Fluent speech corpus, respectively.
Spatial-temporal switching estimators for imaging locally concentrated dynamics

The evolution of images with physics-based dynamics is often spatially localized and nonlinear. A switching linear dynamic system (SLDS) is a natural model under which to pose such problems when the system's evolution randomly switches over the observation interval. Because of the high parameter space dimensionality, efficient and accurate recovery of the underlying state is challenging. The work presented in this paper focuses on the common cases where the dynamic evolution may be adequately modeled as a collection of decoupled, locally concentrated dynamic operators. Patch-based hybrid estimators are proposed for real-time reconstruction of images from noisy measurements given perfect or partial information about the underlying system dynamics. Numerical results demonstrate the effectiveness of the proposed approach for denoising in a realistic data-driven simulation of remotely sensed cloud dynamics.
CAC: A Clustering Based Framework for Classification

In data containing heterogeneous subpopulations, classification performance benefits from incorporating the knowledge of cluster structure in the classifier. Previous methods for such combined clustering and classification either are classifier-specific and not generic or independently perform clustering and classifier training, which may not form clusters that can potentially benefit classifier performance. The question of how to perform clustering to improve the performance of classifiers trained on the clusters has received scant attention in previous literature despite its importance in several real-world applications. In this paper, we theoretically analyze when and how clustering may help in obtaining accurate classifiers. We design a simple, efficient, and generic framework called Classification Aware Clustering (CAC), to find clusters that are well suited for being used as training datasets by classifiers for each underlying subpopulation. Our experiments on synthetic and real benchmark datasets demonstrate the efficacy of CAC over previous methods for combined clustering and classification.
Implementation of P-N Learning Based Compression in Video Processing

Now-a-days, visual information is playing a more and more important role in our daily life and effects our way of communication and living in many aspects. Digital images and video applications usually involve storage or transmission of vast amount of data. For secure transmission of data and to reduce the size of the video, video compression techniques are used. Some of the techniques that are used in present days reduce the quality, data size and the original data cannot be retrieved. To overcome these problems, P-N learning based compression technique can be used.In this work, multi-scale filter banks are used for performing necessary operations to obtain the compressed video. The image extraction from video is obtained by performing sequential frame selection method, which is converted to grayscale and then encrypted by using sparse matrix generation technique. MATLAB is a multiparadigm numerical computing environment and proprietary programming language. It allows matrix manipulations, plotting of functions and data, Implementation of algorithm, creation of user Interface. This work helps in providing security for images containing information it also helps in the reduction of bandwidth utilization since only the information contained image is transmitted instead of whole video.
Attribution Mask: Filtering Out Irrelevant Features By Recursively Focusing Attention on Inputs of DNNs

Attribution methods calculate attributions that visually explain the predictions of deep neural networks (DNNs) by highlighting important parts of the input features. In particular, gradient-based attribution (GBA) methods are widely used because they can be easily implemented through automatic differentiation. In this study, we use the attributions that filter out irrelevant parts of the input features and then verify the effectiveness of this approach by measuring the classification accuracy of a pre-trained DNN. This is achieved by calculating and applying an \textit{attribution mask} to the input features and subsequently introducing the masked features to the DNN, for which the mask is designed to recursively focus attention on the parts of the input related to the target label. The accuracy is enhanced under a certain condition, i.e., \textit{no implicit bias}, which can be derived based on our theoretical insight into compressing the DNN into a single-layer neural network. We also provide Gradient\,*\,Sign-of-Input (GxSI) to obtain the attribution mask that further improves the accuracy. As an example, on CIFAR-10 that is modified using the attribution mask obtained from GxSI, we achieve the accuracy ranging from 99.8\% to 99.9\% without additional training.
Personalization of mobile application interfaces based on the ontological model for people with special needs

The process of developingadaptive interfaces is a non-trivial task in the field of rapidly developing information technologies. An intelligent systembased onan ontological model can provide assistance in the development of applications for people with special needs.
Design Iterations for Passive Aerial Manipulator

Grabbing a manoeuvring target using drones is a challenging problem. This paper presents the design, development, and prototyping of a novel aerial manipulator for target interception. It is a single Degree of Freedom (DoF) manipulator with passive basket-type end-effector. The proposed design is energy efficient, light weight and suitable for aerial grabbing applications. The detailed design of the proposed manipulation mechanism and a novel in-flight extending propeller guard, is reported in this paper.