Reviewerly/exHarmony huggingface dataset:
SMART-Rain: A Degradation Evaluation Dataset for Autonomous Driving in Rain

Autonomous driving in the rain remains a challenge. One main problem is performance degradation caused by rain. This work introduces a new dataset to study this problem. Our dataset is collected from a full-scale vehicle equipped with a 3D LiDAR sensor and multiple forward-facing cameras under various rainy conditions. In addition, rainfall intensity is recorded in real-time from a rain sensor. The combination of sensor and rainfall intensity measurement is designed for studying algorithm performance under different levels of rainfall. In this work, in addition to presenting dataset creation details, we also introduce three degradation evaluation tasks with baseline results, including rainfall intensity estimation, LiDAR degradation estimation, and 2D object detection evaluation. This dataset, development kit, and baseline codes will be made available at https://smart-rain-dataset.github.io/
Croatian POS Tagger as a Prerequisite for Knowledge Extraction in Intelligent Tutoring Systems

In this article we present an knowledge extraction approach that can be used in systems that implement teaching in a fully automated manner. These systems are called Intelligent Tutoring Systems (ITS) and are conceived around the idea of one-to-one teaching. Many such systems use natural language processing to improve the communication interface between student and the system. These techniques can be also used on the content creator side to semi-automate or fully automate the task of teaching content creation. In such systems the knowledge representation plays a crucial role to successfully implement teaching and encourage learning. The output of the knowledge extraction phase is a knowledge in the form of a hyper graph that can be used for adaption to the students current knowledge level. We present a deep neural network architecture for precise POS tagging of words written in languages that are morphologically rich. Using sparse representations for words in this task increases the vector space and makes learning more complex. This problem can be solved to some extent by using traditional vector representations but there is also the problem with representing words that are ambiguous. Proposed architecture uses a Bidirectional Encoder Representations from Transformers (BERT) model that is pre-trained on Croatian language to achieve state-of-the-art accuracy for POS tagging.
Second language learners’ (English majors) writing apprehension and their use of online applications for writing

This study reports on a comparative study on writing anxiety among English majors who are second language learners. There is little research reporting the level of writing anxiety of second language learners majoring in English. The study examines the relationship between Malaysian and Thailand undergraduates’ writing anxiety and their self-perceived language ability. It also explores the relationship between students’ writing apprehension and their usage frequency of online writing applications. A descriptive research design was adopted in this study. The research measures the students’ level of writing anxiety using adapted Daly-Miller writing apprehension test (1975). The data was collected using questionnaire surveys from a total of 307 English majors of two public universities in Malaysia and one in Thailand. The statistical tests indicated that although the self-perceived proficiency level of the Thai students was significantly lower than the Malaysian students, there was no significant difference among the English majors in their level of writing apprehension. It was also found that there was no statistically significant difference among the students in their usage frequency of most of the online applications. However, only the usage frequency of Instagram was found to be correlated with their apprehension. The study also found no correlation between writing apprehension and the lecturer as the audience of their writing activities. Findings indicate that the technology was widely used by the students for writing. The study indicates that the students were ready to adopt changes in the mode of content delivery from face-to face to online.
Heuristics for Programming Codes

A programming code includes statements written in a programming language and specifies a series of instructions that are processed in a computer to perform a specific task. The programming codes created over time are often reused and/or require maintenance. In both cases, the programmer’s work is a difficult task to carry out if the programming codes are poorly written and documented, without following principles such as simplicity and order. User eXperience (UX) considers the perceptions of the users related to the use of products, systems, or services. In a broader approach, Customer eXperience (CX) refers to a person’s interactions with various artifacts (products, systems, and services). A specific case of UX is the concept of the Programmer eXperience (PX). We can consider that the programmer is, to a certain degree, a “user” of programming codes, and the programmer could also be considered as a “customer” that is using several artifacts. We present the development of a set of heuristics that incorporate elements of usability and UX to evaluate the programming codes. The heuristics were developed under the methodology established by Quiñones et al. This methodology establishes 8 steps that allow obtaining a set of heuristics in a specific domain. Based on the methodology, we obtained a set of 8 heuristics aimed to evaluate the usability/UX for programming codes. We performed an initial validation, and the results are encouraging. However, we think that the set of heuristics requires further validation and can still be refined.
Agile Development Practices Applied to Software Startups : A Systematic Mapping Review

Context: Previous studies considered software startups are not applying agile development practices, especially those related to quality. This leads to the accumulation of technical debt. Objective: This study aims at identifying primary studies that describe how startups are applying agile development practices and how they could benefit from these practices, thereby contributing to product quality improvement and better resource allocation. Method: A Systematic Mapping Study was conducted which applied automatic searches so as to identify relevant primary studies. Results: 60 primary studies were identified that present information regarding practices, challenges, and limitations of adopting agile development practices in startups and also the benefits, tools, and ways of simplifying their adoption. Conclusions: 55 agile development practices used by startups were found, the most cited ones being associated with DevOps, Test Automation, and Scrum. The main challenges and limitations are related to the difficulty of adapting legacy systems architecture to support DevOps practices, to market volatility, to the need for quick product release, and to resources, including financial resources, being limited. Benefits related to having improved internal processes were the most cited and 128 tools were classified. Few studies have offered answers on how to simplify adopting agile development practices, although all of them led to tools for automating processes being used.
Collaborative Multi-Agent Reinforcement Learning in Dynamic Environments using Knowledge Transfer for Herding Problem

Collaborative Multi-Agent Reinforcement Learning in Dynamic Environments using Knowledge Transfer for Herding Problem
Evaluation of Supervised Classification Techniques on Twitter Data using R

The phenomenal development of the World Wide Web has resulted in enormous social networking sites producing tremendous data on web 2.0. Social networking sites have widened to a higher degree of use, in which any field of information can be sort by researchers. Data obtained from social media has strategized from many new machine learning algorithms and natural language processing. The data is unstructured; mining the data leads to finding important sentiments about various entities via appropriate classification techniques. In this paper, tweets’ opinions are analyzed through machine learning algorithms such as naive Bayes and support vector machines using R programming; results are computed and compared. The SVM model manifests the higher precision, and naïve Bayes provides higher accuracy for sentiment analysis on the Bengaluru traffic data.
Research on Edge Cloud Quality Model and Evaluation System

Aiming at the system and software quality of the edge cloud, this paper proposes an Edge Cloud Quality Model (ECQM) and evaluation system. The ECQM is composed of capability quality model and process quality model. The capability quality model includes technical capability, product capability, service capability, application capability, and security capability. The process quality model includes procurement, design, deployment, delivery, operation, and cloud service level agreement. Based on the ECQM, we construct an evaluation system and give the definition and description of edge cloud Quality Evaluation Level (QEL).
Enterprise Entity Recognition System Based on AttentionOCR and Pointer Network

With the development of society, the number of all kinds of enterprises continues to increase, but it must also contain some bad businesses, and these bad businesses will cause unnecessary impact on our daily life to a certain extent. To address these problems, an enterprise entity recognition system based on AttentionOCR and the pointer network was proposed, combined with an improved ResNet101-FPN text network and an improved pointer network based on joint extraction of shared parameters. Both of the two optimized models improved their recognition accuracy by 1–2%, and the number of model parameters for entity recognition decreased by 12%. Under the practical application of image access via mobile devices and the improved text recognition and semantic analysis network access to the name of the enterprise, and then returned from the database, relevant information to the user of the enterprise system as a whole for the single image recognition arithmetic average speed in 1 s or so, to sum up, the system under natural scene has stronger robustness, decision making to provide reliable data for the user.
Histopathological Image Analysis by using Machine Learning Methods

Histopathological pictures (HIs) are the best quality level for assessing a few kinds of tumors for malignant growth finding. The examination of such pictures is time and asset devouring and exceptionally testing in any event, for experienced pathologists, bringing about between spectator and intra-onlooker conflicts. One of the methods of speeding up such an examination is to utilize PC supported conclusion (CAD) frameworks. Bountiful aggregation of computerized histopathological pictures has prompted the expanded interest for their investigation, for example, PC helped determination utilizing AI strategies. Notwithstanding, computerized obsessive pictures and related errands have a few issues to be thought of. In this little survey, we present the utilization of advanced neurotic picture investigation utilizing AI calculations, address a few issues explicit to such examination, and propose potential arrangements. Watchwords: Histopathology, Deep learning, Machine learning.
Selective Labeling in Learning with Expert Advice

An online active learning mechanism using the expert advice framework is considered where the goal is to learn the correct labels of a sequence of revealed items. The learning scheme's efficiency is measured in terms of the regret bound and reduced data labeling queries based on experts' predictions. Two efficient randomized algorithms EPSL and EPAL are proposed in which the opinion ranges of experts are examined in order to decide whether to acquire a label from users for a given instance. It is shown that both algorithms obtain nearly optimal regret bounds and up to a constant factor depending on the characteristics of experts' predictions. While EPSL yields a better regret bound than EPAL, it requires extra prior knowledge of experts' predictions. Relaxing this assumption, EPAL provides a more practical scheme by implying an adaptive time-varying learning rate whose regret is at worst √2 times of that for EPSL. Experimental results justify the outperformance of the proposed algorithms compared to the existing ones in this setting.
Application of Fuzzy-Taguchi theory in the optimization of teaching method and process design

Abstract Based on the design of the case reference teaching method, this study designs the teaching materials case through the design concept, class summary, evaluation, and teaching analysis. Innovative teaching method and course design optimization combined analysis mode with Fuzzy-Taguchi theory. The first use of the fuzzy theory system is combined with Taguchi L9 (orthogonal-table) to obtain high-precision experimental results with the smallest number of experimental designs, thereby developing the best design combination. The first paragraph describes the research motivation and instructional design concepts. The second paragraph analyzes various forms of teaching methods and their characteristics through a literature review. Third paragraph using Fuzzy-Taguchi theory to research and develop the teaching method and the analysis mode of the optimal combination of course design. Fourth paragraph summarizes the teaching practice results, including the research, practice, analysis, innovation and contribution of the promotion and reproduction of the teaching design.
UWB/PDR integrated indoor positioning method based on robust adaptive square root cubature Kalman filter

Abstract Aiming at the problem that the ultra wide band (UWB) wireless signal is easy to be interfered, a robust adaptive square root cubature Kalman filter (RA-SRCKF) is proposed to be used in UWB/PDR integrated indoor positioning. RA-SRCKF is based on square root cubature Kalman filter (SRCKF) and its statistical characteristic of measurement noise is updated with the method of first estimating and then modifying. The experimental results show that the proposed RA-SRCKF can effectively improve the accuracy and stability of UWB/PDR integrated positioning.
Improved hybrid blind IQA using alternative NSS characterization in the spatial domain

The adoption of a Natural Scene Statistics (NSS) model has been an important research direction in the selection of perceptual features capable of giving satisfactory results in the problem of image quality assessment (IQA). In this work, trying to improve the performance of a blind IQA methodology, we simultaneously consider quality aware features from both the spatial and the transform domains. Moreover, for the first time, a statistical description of the spatial domain is investigated through the Student's t distribution, trying to predict the subjective evaluation of humans and to reduce the total number of features. In essence, a large number of features are used, which are optimized by the consequent characterization with the distribution's parameters. The proposed model is then fed to a tool to learn a simple regression model. In this way the extracted trained model is used to predict the graded image quality score, based on known publicly available datasets. The results are interesting and show high levels of agreement with the subjective human perception while maintaining a low total number of features.
Summarizing RDF graphs using Node Importance and Query History

RDF graphs have been widely used in various cognitive and intelligent services in industry. Recent tremendous growth in knowledge base data volumes has made searching and querying RDF graphs increasingly difficult. Summarization is an effective solution for managing large RDF graphs by extracting critical data into summary graphs. Improving the accuracy of the summary relative to the original graph and the relevance to actual user demands improves the efficiency and usefulness of the queries against the summary. In this paper, we present a hybrid summarization method that takes into account both the graph structure and user query history. Specifically, we define a hybrid metric of node importance that captures both the structural importance and user query preferences. We propose two algorithms to extract summaries of a given RDF graph based on this hybrid metric. We evaluate our approach in experiments using three public datasets (DBpedia, YAGO, and Freebase), and the results demonstrate the efficiency of our approach.
Data Mining Technology and Sales Productivity Analysis in Malaysia

This chapter focuses on studying data mining technologies and the predictive analysis of product sales for Amway (M). Several parts have been adopted to outline the rationale for the research, which consisted primarily of reading books, journals, and internet articles on data mining and conducting interviews and observations to collect Amway data (M). Following data gathering, a model was constructed to understand the value of results achieved by integrating automation, visual representation, and predictive analysis into business operations. Finally, the author gained a great deal of knowledge about this project once it was completed. The completion of this project was, therefore, a very satisfying and fruitful journey. Generally, to have a better impact, this entire project needs to be polished.
Smart Inventory Access Monitoring System (SIAMS) using Embedded System with Face Recognition

In this paper, we present a system called Smart Inventory Access Monitoring System (SIAMS) that integrates an embedded system with face recognition into an inventory system. It is developed to prevent theft in warehouses from authorized staff. The embedded system is attached with an RGB camera and deployed three software modules: image capturing, face detection, and face recognition. The face detection module sends detected face images to the face recognition module to identify a person as the person's name or unknown class using a deep learning approach. The system achieved competitive accuracy by performing standard evaluation metrics for face detection and recognition. The inventory system that was developed will receive data via TCP/IP socket communication to log access history. The retrieved information can be used to investigate an unusual situation. The system can be improved with object detection and person tracking system to detect theft in real-time.
Citation Network, Knowledge and Evaluation: The Role of Reporting Markers in Citation Content Analysis

Linguistic features play an important role in the transition from traditional citation analysis to citation content analysis (CCA). In this paper, a novel language feature, Reporting Marker (RM), is introduced into CCA and acts as a key to open the black box of information structure of citation content. Through discourse analysis performed on a small-scale corpus, we clarify the mapping relationship between information pattern and language structure, and use the Language Technology Platform (LTP) to identify that relationship. A framework is constructed to realize CCA in three dimensions: citation network, knowledge, and evaluation. It is likely to contribute to the improvement of information retrieval and knowledge mining from academic documents.
User Defined Walking-In-Place Gestures for Intuitive Locomotion in Virtual Reality

Locomotion is one of the fundamental interactions in virtual reality (VR). As a simple yet naturalistic way to enable VR locomotion, walking-in-place (WIP) techniques have been actively developed. Even though various WIP gestures have been proposed, they were adopted or designed from the perspective of developers, not the users. This limits the benefits of WIP as unnatural gestures may result in a higher cognitive load to learn and memorize, worse presence, and increased sensory conflict. Therefore, this study elicited natural WIP gestures for forward, sideways, and backward walking directions from users. Twenty participants experienced the movement while wearing the VR headset and elicited the WIP gesture for 8 walking directions. The grouping results showed that Turn body + Stepping-in-place (SIP) and Step one foot + SIP/Rock/Stay were four promising WIP gesture sets for VR locomotion. A comparison between elicited and existing gestures revealed that elicited gestures have the potential to outperform existing gestures due to easier to perform, less fatigue, and higher presence. The generated WIP gesture sets could be used in gesture-based VR applications to provide a better user experience and greater movement options in VR locomotion.
Asking Users. Questionnaires as Indirect Observation Tools in Human-Centred Design Approach. Application Cases

Human-Centred Design (HCD) investigation and assessment methods record the user’s needs and satisfaction level and are widely used to evaluate usability and orientate design decision making.Questionnaires are among the most useful and popular observation tools to study users’ opinions using quantifiable and comparable data. Their drafting needs requirements and evaluations in order to use them efficiently. Therefore, this paper intends to provide designers and researchers with useful tips for designing these tools. For this reason, it describes features of method and systematizes instructions from the literature. It also shows application cases to exemplify the relationship between method and type of data collected and to explore impacts of using different types of questionnaires in design processes.
Formation of Software Development Teams Using the Project Network Diagram and Considering the Approach Based on Acmeology and Competence

This article discusses the method of creating a team for the implementation of IT projects. The authors raise the problem of increasing the export of domestic software, and therefore issues the importance of creating an effective team. The article reveals the main aspects of the acmeological and competence approach for the distribution of roles among the team members, depending on their type of personalities. Among the available human resources, this developed method allows to form a cohesive team that is ready to implement the project activity in the field of information technology.
The Action Consistency of Casting in Virtual Environment

Interactive techniques in Virtual Reality (VR) is rapidly developing in the recent years. However, the consistency of the interaction activities in VR and real-world remains a subject to be further studied. In this work, we study the consistency of casting action between VR and real-world. In our experiments, we let participants cast a ball to a walking human virtual agent, which is driven by a motion capture system in the virtual environment. By comparing to the casting accuracy in real-world, we found that the participants' performance under various of the experimental configurations is consistently reduced in VR. We also found that the moving `speed' factor of the casting target barely affected the accuracy in VR, which was different in the real-world environment. This study will provide an important experimental reference to develop future VR systems based on casting actions.
Convolution Neural Network for Image Registration

Image registration plays a fundamental role in many computer vision applications, such as medical image processing, camera pose estimation, etc. It includes the estimation of geometric transformation and image warping. The goal of this study is to design, develop and evaluate an end to end trainable convolution neural network (CNN) which can learn homography and affine transformation's controlling parameters. The trained model can be used for registering unseen image pairs to provide a better-quality image registration. The training data is based on publicly available image dataset and truth tagging is accomplished by generating synthetic image pairs instead of depending on manual annotations.