Reviewerly/exHarmony huggingface dataset:
Visual Experience-Based Question Answering with Complex Multimodal Environments

This paper proposes a novel visual experience-based question answering problem (VEQA) and the corresponding dataset for embodied intelligence research that requires an agent to do actions, understand 3D scenes from successive partial input images, and answer natural language questions about its visual experiences in real time. Unlike the conventional visual question answering (VQA), the VEQA problem assumes both partial observability and dynamics of a complex multimodal environment. To address this VEQA problem, we propose a hybrid visual question answering system, VQAS, integrating a deep neural network-based scene graph generation model and a rule-based knowledge reasoning system. The proposed system can generate more accurate scene graphs for dynamic environments with some uncertainty. Moreover, it can answer complex questions through knowledge reasoning with rich background knowledge. Results of experiments using a photo-realistic 3D simulated environment, AI2-THOR, and the VEQA benchmark dataset prove the high performance of the proposed system.
THE ESSENCE OF COMPETENCE APPROACH IN PROFESSIONAL TRAINING OF THE FUTURE TEACHER

The article discusses the main provisions of the competency-based approach as one of the narratives of ongoing modernization in the field of education; reveals the essence of the professional competency-based model of teacher training. The authors present an analysis of the conceptual-categorical apparatus associated with the competency-based approach from the perspective of various researchers; consider fundamental factors related to the implementation of this approach to teacher training. The article reveals the content and the necessary requirements for the teacher, the level of his professional training in the context of the main provisions of the competency-based approach; a fragment of the study on the identification of personal qualities in the structure of professional competencies of the future teacher is presented.
IMPLEMENTATION OF TASIKMALAYA MAYOR REGULATION NO. 18 YEAR 2011 ABOUT AREAS WITHOUT CIGARETTES IN THE SCHOOL OF THE TASIKMALAYA CITY SCHOOL

This research is motivated by the absence of written policies and monitoring staff about No-Smoking Areas in schools, the Regional Government has not fully provided guidance in the form of socialization, weak Internal Supervision (Principal) and External Supervision conducted by Regional Apparatus Organizations and the absence of sanctions or reprimands strict to everyone and / or body that violates. The purpose of this study is to uphold discipline and provide understanding to students and those in the school environment about the dangers of smoking to health. The research method used in this research is descriptive qualitative method. Tasikmalaya Mayor Regulation No. 18 of 2011 concerning the No Smoking Area in the Tasikmalaya City School Environment has not been implemented well or was not carried out well. The obstacles faced include the absence of Standard Operating Procedures and strict sanctions, lack of socialization and provision of facilities and infrastructure to support policies in schools, there are still policy implementers who are still smoking and efforts that have been carried out namely the school makes and issues written policies or SOPs and accompanied by strict sanctions, deliver full socialization on a regular basis, increase leadership supervision in the school environment on an ongoing basis.
THE IMPACT OF USING GENERATED DATA IN LEARNING OBJECT DETECTION

The development of autonomous cars is in full progress. For the autonomous cars to be able to detect where other cars are, many machine learning models are utilised. One problem in the field of o ...
Omnichannel path to purchase : Viability of Bayesian Network as Market Attribution Models

Market attribution is the problem of interpreting the influence of advertisements onthe user’s decision process. Market attribution is a hard problem, and it happens to be asignificant reason for G ...
Direct Policy Search Reinforcement Learning Based on Variational Bayesian Inference

Direct policy search is a promising reinforcement learning framework particularly for controlling continuous, high-dimensional systems. Peters et al. proposed reward-weighted regression (RWR) as a direct policy search. The RWR algorithm estimates the policy parameter based on the expectation-maximization (EM) algorithm and is therefore prone to overfitting. In this study, we focus on variational Bayesian inference to avoid overfitting and propose direct policy search reinforcement learning based on variational Bayesian inference (VBRL). The performance of the proposed VBRL is assessed in several experiments involving a mountain car and a ball batting task. These experiments demonstrate that VBRL yields a higher average return and outperforms the RWR.
基于组块分析的汉语块依存语法(Chinese Chunk-Based Dependency Grammar)

基于词单元的经典依存语法在面向中文的句子分析中遇到诸多汉语特性引起的困难。为此,本文提出汉语的块依存语法,以谓词为核心,以组块为研究对象,在句内和句间寻找谓词所支配的组块,构建句群级别的句法分析框架。这一操作不仅仅是提升叶子节点的语言单位,而且还针对汉语语义特点进行了分析方式和分析规则上的创新,能够较好地解决微观层次的逻辑结构知识,并为中观论元知识和宏观篇章知识打好铺垫。本文主要介绍了块依存语法理念、表示、分析方法及特点,并简要介绍了块依存树库的构建情况。截至目前为止,树库规模为187万字符(超过4万复句、10万小句),其中包含67%新闻文本和32%百科文本。
LibGuides: Videos for HBU Students- Online or On Campus: How to use the Library Databases.

This guide was created to hold the new video to assist HBU Students as they navigate Moody Library Databases and other resources Online. By Diane Casebier
DevOps with Azure SQL

DevOps is a discipline that brings together people, processes, and products to enable continuous delivery of value to end users. It does so by helping to bridge the gap between development, operations, and management. From a developer standpoint, probably the most important aspect of the DevOps discipline is the focus on having a healthy CI/CD pipeline. Continuous Integration (CI) and Continuous Deployment (CD) are two processes that help to support development agility while assuring product quality and stability.
The Use Of Comic Strips To Improve The Students’ Reading Comprehension Of Narrative Text (An Action Research At The Seventh Grade Students Of MTsN 3 Medan In Academic Year 2019/2020)

This study was conducted to find out the improvement of students’ reading comprehension of narrative text through comic strips. The sample of study was 32 students taken from one class. It was accomplished in two cycles. Each cycles consisted of four steps. They are planning, action, observation and reflection. The first cycle consisted of two meetings including pre-test, giving material and post-test I. The second cycle consisted of one meeting. They were given material and post test II. The test were given to the students were pre-test, post test I in the first cycle and post test II in the second cycle.Based on the result of the research, it could be concluded that teaching reading narrative text by using comic strips could improve the students’ achievement in comprehending the narrative text. It could be seen that the higher score of writing test was 95. Before using comic strips, the students’ achievement in reading narrative text was low. It could be seen that 11 students who passed the test and 21 students failed in the test.
Hierarchical Sentiment Estimation Model for Potential Topics of Individual Tweets

Twitter has gradually become a valuable source of people’s opinions and sentiments. Although tremendous progress has been made in sentiment analysis, mainstream methods hardly leverage user information. Besides, most methods strongly rely on sentiment lexicons in tweets, thus ignoring other non-sentiment words that imply rich topic information. This paper aims to predict individuals’ sentiment towards potential topics on a two-point scale: positive or negative. The analysis is conducted based on their past tweets for the precise topic recommendation. We propose a hierarchical model of individuals’ tweets (HMIT) to explore the relationship between individual sentiments and different topics. HMIT extracts token representations from fine-tuned Bidirectional Encoder Representations from Transformer (BERT). Then it incorporates topic information in context-aware token representations through a topic-level attention mechanism. The Convolutional Neural Network (CNN) serves as a final binary classifier. Unlike conventional sentiment classification in the Twitter task, HMIT extracts topic phrases through Single-Pass and feeds tweets without sentiment words into the whole model. We build six user models from one benchmark and our collected datasets. Experimental results demonstrate the superior performance of the proposed method against multiple baselines on both classification and quantification tasks.
Guides: Subject Guide: Earth Sciences: Archives and Special Collections

A guide to getting the most out of the Library and Collections resources for Earth Sciences
Measuring Performance of Generative Adversarial Networks on Devanagari Script

The working of neural networks following the adversarial philosophy to create a generative model is a fascinating field. Multiple papers have already explored the architectural aspect and proposed systems with potentially good results however, very few papers are available which implement it on a real-world example. Traditionally, people use the famous MNIST dataset as a Hello, World! example for implementing Generative Adversarial Networks (GAN). Instead of going the standard route of using handwritten digits, this paper uses the Devanagari script which has a more complex structure. As there is no conventional way of judging how well the generative models perform, three additional classifiers were built to judge the output of the GAN model. The following paper is an explanation of what this implementation has achieved.
Learning to Correspond Dynamical Systems

Many dynamical systems exhibit similar structure, as often captured by hand-designed simplified models that can be used for analysis and control. We develop a method for learning to correspond pairs of dynamical systems via a learned latent dynamical system. Given trajectory data from two dynamical systems, we learn a shared latent state space and a shared latent dynamics model, along with an encoder-decoder pair for each of the original systems. With the learned correspondences in place, we can use a simulation of one system to produce an imagined motion of its counterpart. We can also simulate in the learned latent dynamics and synthesize the motions of both corresponding systems, as a form of bisimulation. We demonstrate the approach using pairs of controlled bipedal walkers, as well as by pairing a walker with a controlled pendulum.
OPDPR Framework for Classification of Big Data using Meta-Heuristics

In this modern era big data is a buzzword and no one can define big data precisely and consensually. The management over servers of this fast generating colossal data with assorted variety becomes a challenge for researchers. Conventional methods are not able to cope with big data but meta-heuristics proved as a good solution to handle the big data by reducing the data population or features of the data. In this research paper, a new framework OPDPR is proposed for dimensionality reduction using meta-heuristics and classification of big data. Initially preprocessing of data is done with the goal that only important data is fed into the next step then meta-heuristics are used to reduce the population for better results.
Bifurcation Curves of Two-Dimensional Quantum Walks

The quantum walk differs fundamentally from the classical random walk in a number of ways, including its linear spreading and initial condition dependent asymmetries.Using stationary phase approximations, precise asymptotics have been derived for one-dimensional two-state quantum walks, one-dimensional three-state Grover walks, and two-dimensional four-state Grover walks.Other papers have investigated asymptotic behavior of a much larger set of two-dimensional quantum walks and it has been shown that in special cases the regions of polynomial decay can be parameterized.In this paper, we show that these regions of polynomial decay are bounded by algebraic curves which can be explicitly computed.We give examples of these bifurcation curves for a number of two-dimensional quantum walks.
Research Guides: Library Resources for Distance Learning - Videos: Citations

Library resources can be accessed remotely via the Leonard Lief Library website - here are videos showing you how.
Adaptive Mutation based on Population Distribution in DE with an individual-dependent mechanism

Differential evolution (DE) is a method of evolutionary computation(EC) with a superior search capability for real-value optimization in nonlinear problems, nondifferential problems and multimodal problems, among others. While various DE methods have been proposed, DE with an individual-dependent mechanism (IDE) features a pair of control strategies and its superior search performance has been confirmed in various benchmark problems. This study aims to further improve the individual-dependent mutation (IDM) strategy in IDE. The IDM strategy divides individuals into superior and inferior populations over the search process and applies mutation in each population. Further, the ps criterion is used as a reference in dividing the population and applying mutation. This criterion changes by the number of generations, but it is not based on the individuals 'information. From our preliminary experiments, premature convergence occurs in some problems. This study focuses on the ps criteria and proposes a method of adaptively controlling the value based on the variance of individuals. Performance evaluation was performed using a 30, 50 and 100-dimensional benchmark problem generally used in EC. In the performance evaluation, we showed that the proposed method significantly outperformed conventional DE and IDE, and improved the search performance by preventing convergence to the local minima in problems prone to such an issue by controlling the variance of the individuals.
ON THE CHALLENGE OF SERVICE RECOMMENDATION TO MOBILE USERS IN SMART CITIES: CONTEXT AND ARCHITECTURE

Abstract. The industrial and academic interest of the research on mobile service recommendation systems based on a wide range of potential applications has significantly increased, owing to the rapid progress of mobile technologies. These systems aim to recommend the right product, service or information to the right mobile users at anytime and anywhere. In smart cities, recommending such services becomes more interesting but also more challenging due to the wide range of information that can be obtained on the user and his surrounding. This quantity and variety of information create problems in terms of processing as well as the problem of choosing the right information to use to offer services. We consider that to provide personalized mobile services in a smart city and know which information is relevant for the recommendation process, identifying and understanding the context of the mobile user is the key.This paper aims to address the issue of recommending personalized mobile services in smart cities by considering two steps: defining the context of the mobile user and designing an architecture of a system that can collect and process context data. Firstly, we propose an UML-based context model to show the contextual parameters to consider in recommending mobile services in a smart city. The model is based on three main classes from which others are divided: the user, his device and the environment. Secondly, we describe a general architecture based on the proposed context model for the collection and processing of context data.
SharedWealth: Disincentivizing Mining Pools Through Burning and Minting

Bitcoin has provided a framework for a decentralized currency system. However, the irregular payouts of its winner-takes-all reward strategy have proven to be unacceptable for the miners who are responsible for verifying transactions. To address this problem, miners have formed mining pools to ensure more regular rewards, but these mining pools undermine the decentralization that is one of Bitcoin’s key benefits. Previous work has sought to make it impossible for mining pools to form, but it is not clear that the situation would improve without addressing the economic incentives that lead to the formation of mining pools in the first place. This work introduces SharedWealth, a protocol designed to reward miners who find “near misses” to the required proof-of-work target to make a Bitcoin block. We show how this approach provides miners with more regular pay, decreasing their incentives to join a mining pool. We use a burn-and-mint strategy, where new rewards are paid out based on the expected number of near misses rather than dividing up the rewards according to the actual number of near misses, thus eliminating any incentive for a block producer to discard the near misses of other miners.
Monitoring and Debugging

Azure SQL and its underlying SQL Server query engine are well known in the industry for the low administrative barrier generally required for applications to get good performance and reliability on most conditions with different data sizes and shapes. That said, it is important for application developers to understand the foundations and internals of how query processing works in Azure SQL and what tools and capabilities are available to monitor and troubleshoot performance on both development and production phases.
Object Oriented Design Metrics for Software Defect Prediction: An Empirical Study

Reliability of software in respect to defect prediction is a trending topic of study. It is observed that many effective categorical models rely on requirement and design phase metrics and few consistent models include metrics from design phase. It is also noted that most studies show qualitative advantages by using Early Software Defect Prediction models. It is necessary to validate software metrics in error prevision for object-driven methods by means of statistical methods and machine learning. The quality of the software product in a software organization is assured by the validation process. Object-oriented metrics play a key role in fault estimation. This paper discusses the use of Chidamber and Kemerer (CK) metrics, QMOOD, Size, Complexity and Martin's metrics for the assessment of software defects. In this study bug/defect is treated as a dependent variable and the considered metric fit as independent variables. For defect detection two data analysis techniques, logistic regression and Decision Tree, are applied, validated and their statistical efficacy and performance measures are discussed.
Analyzing Indonesian EFL Students’ Communication Skill in Debate Classroom

EFL students always think that the ability to communicate with others in English is one of the most uneasy skills that must be gained. The previous studies found that debate is implemented as one of techniques used by lecturers and teachers to improve students’ speaking skill. Yet, the fact showed that EFL students of Universitas Muslim Nusantara Al Washliyah remained have negative self-concept that led to the lack of speaking skill eventhough they had been continously given debate subject for two semesters. This research aimed to reveal EFL students’ communication skill through debate classroom. Qualitative research method with case study was applied in which 9 students with three different speaking skills; 3 students with higher communication skill, 3 students with moderate communication skill, 3 students with lower communication skill were chosen as the subject of the research. The comprehensively structured observation was carried out to collect the data, and all the data found that the students with higher communication skill were able to meet three factors of effective communication, namely; listening actively, asking question, and delivering trust in which all of them can be clearly seen from the way they express the idea, opinion, and argument during debate activity. In short, EFL students who are enthusiastically in participating debate classroom activity smoothly boost communication skill by using English
Robust Disentanglement of a Few Factors at a Time

Disentanglement is at the forefront of unsupervised learning, as disentangled representations of data improve generalization, interpretability, and performance in downstream tasks. Current unsupervised approaches remain inapplicable for real-world datasets since they are highly variable in their performance and fail to reach levels of disentanglement of (semi-)supervised approaches. We introduce population-based training (PBT) for improving consistency in training variational autoencoders (VAEs) and demonstrate the validity of this approach in a supervised setting (PBT-VAE). We then use Unsupervised Disentanglement Ranking (UDR) as an unsupervised heuristic to score models in our PBT-VAE training and show how models trained this way tend to consistently disentangle only a subset of the generative factors. Building on top of this observation we introduce the recursive rPU-VAE approach. We train the model until convergence, remove the learned factors from the dataset and reiterate. In doing so, we can label subsets of the dataset with the learned factors and consecutively use these labels to train one model that fully disentangles the whole dataset. With this approach, we show striking improvement in state-of-the-art unsupervised disentanglement performance and robustness across multiple datasets and metrics.
LibGuides: Increasing Virtual Presence in Physical Spaces: Example Plan

This guide discusses potential improvements to be made to public library spaces to incorporate and cultivate productive virtual experiences. A toolkit and plan for accommodating post-COVID computer use.
Induction of Non-monotonic Logic Programs To Explain Statistical Learning Models

We present a fast and scalable algorithm to induce non-monotonic logic programs from statistical learning models. We reduce the problem of search for best clauses to instances of the High-Utility Itemset Mining (HUIM) problem. In the HUIM problem, feature values and their importance are treated as transactions and utilities respectively. We make use of TreeExplainer, a fast and scalable implementation of the Explainable AI tool SHAP, to extract locally important features and their weights from ensemble tree models. Our experiments with UCI standard benchmarks suggest a significant improvement in terms of classification evaluation metrics and running time of the training algorithm compared to ALEPH, a state-of-the-art Inductive Logic Programming (ILP) system.
PATH PLANNING FOR DRONES

Path Planning for autonomous robots is finding the shortest optimum path from a given source to a destination while also taking obstacles into consideration. In our case the autonomous robots are drones.. The algorithm needs to work dynamically and continuously  as the drone is traversing as all the information about the environment would not be given initially but will be perceived as traversing. Thus the purpose of this project is to study and compare 3 methods for path planning on drones with low-end hardware, thus the algorithm needs to have time and space complexity less than or equal to what is required. The algorithms that are studied are path planning using fuzzy logic, path planning using reinforcement learning, and modified A-star algorithm.
Enrichment Of Student Learning And Homework Management With Use Of GitHub In An Introductory Cross-Disciplinary Engineering Course Series On Software Engineering And Data Science

GitHub is increasingly finding a role in academia to both encourage reproducible computational research and to foster a collaborative environment. We show that employing GitHub as an implementation of version control in engineering-related fields reinforces the 'design-build-evaluate' way of thinking and prepares students with skills they can use well beyond the classroom. Additionally, course features such as homework submission and feedback via GitHub enhanced student learning. Finally, we demonstrate that GitHub can be used to actively measure homework progress and teamwork.