Reviewerly/exHarmony huggingface dataset:
An Analysis on the Use of Knowledge Organization Systems in the Process of Requirements Engineering

Some of the fundamental activities of the software development process are related to the discipline of Requirements Engineering. Their objectives are to discover, analyze, document, and verify the system’s requirements. The requirements are the conditions or capabilities that software needs to have or fulfill to meet its users’ needs, and problems in its identification can mean the failure of a software project. This study is part of the research that is being developed to propose a model based on Know­ledge Organization Systems to be used in the Requirements engineering process. This article aims to present the results of an analysis on a set of Know­ledge Organization Systems to identify whether they are likely to be applied in the Requirements engineering process and identify at which stage of this process each one of them can be implemented. The Know­ledge Organization Systems analyzed were the authority files, gazetteers, glossaries, subject headings, classification systems, thesauri, semantic networks, and ontologies. Based on the results obtained, it was possible to conclude that the Know­ledge Organization Systems analyzed can be used in the Requirements engineering process and, consequently, contribute to increasing the software requirements’ quality.
Vowel Harmony Peculiarities in Ekwulobia Igbo

Patterns of vowel harmony in the Igbo language vary from one variant of Igbo to the other. Employing a descriptive framework, this study examines the operations of vowels and their harmonic peculiarities in the Ekwulobia dialect of Igbo. It established that Ekwulobia Igbo has eight phonemic vowels dividing neatly into two sets, and one phonetic vowel [ɛ] that occurs in a mutually exclusive environment. Unlike the Standard and the Onitsha Igbo, the perfective marker suffix in Ekwulobia Igbo adheres strictly to vowel harmony rule within the perfective verb form. Observed also is the fact that the cv (where c is the alveolar nasal /n/) and vcv (where c is the voiced glottal fricative /h/) demonstrative markers also conform to the vowel harmony rule; a pattern which is not obtainable in Standard and the Onitsha Igbo. Finally, in marking negation in the Ekwulobia Igbo, there is a strict correspondence of the vowel of the suffix with the vowel of the root. It might suffice to say, therefore; that in the Ekwulobia Igbo, there seems to be a stronger harmony pattern as demonstrated in the perfective marker suffix, demonstrative markers, and negation marker suffix than seems to operate in the Standard and Onitsha Igbo dialects, a feature which is quite peculiar to it.
How Agile Organizations Use Metrics: A Systematic Literature Mapping

Agile Software Development Methods (ASDMs) have addressed the complexity of managing software development processes through small increments, continuous and frequent delivery, openness to change, and work visibility. These practices have been complemented by the use metrics adapted to ASDMs context, generating a large number and types of metrics. Thus, this secondary study presents a Systematic Literature Mapping to understand how agile organizations use metrics. Seven research questions were defined, and 33 primary studies related to the use of agile metrics were selected. The results indicate that most organizations that use agile metrics use Scrum, develop large size general-purpose software products, desktop, or web applications, in small teams. The metrics are selected by primary studies based on literature search, interviews with the team members or expert groups. The majority of the organizations use Product Quality, Resource and Cost, and Process performance metrics, focusing in the agile team, management and customer. The primary studies results indicate that using agile metrics can positively impact the quality of processes, products, and services.
Characterizing Commits in Open-Source Software

Mining software repositories has been the basis of many studies on software engineering. Many of these works rely on commits’ data extracted since commit is the basic unit of information about activities performed on the projects. However, not knowing the characteristics of commits may introduce biases and threats in studies that consider commits’ data. This work presents an empirical study to characterize commits in terms of four aspects: the size of commits in the total number of files; the size of commits in the number of source-code files, the size of commits by category; and the time interval of commits performed by contributors. We analyzed 1M commits from the 24 most popular and active Java-based projects hosted on GitHub. The main findings of this work show that: the size of commits follows a heavy-tailed distribution; most commits involve one to 10 files; most commits affect one to four source-code files; the commits involving hundreds of files not only refer to merge or management activities; the distribution of the time intervals is approximately a Normal distribution, i.e., the distribution tends to be symmetric, and the mean is representative; in the average, a developer proceed a commit every eight hours. The results of this study should be considered by researchers in empirical works to avoid biases when analyzing commits’ data. Besides, the results provide information that practitioners may apply to improve the management and the planning of software activities.
Optimizing Aggregate Computation of Graph Neural Networks with on-GPU Interpreter-Style Programming

Graph Neural Networks (GNNs) generalize deep learning to graph-structured data and show great success in many tasks. However, their irregular aggregation kernels make them inefficient on GPUs. The unpredictable control flow and memory references of irregular kernels prohibit most optimizations designed for regular ones. For example, even if the nodes have overlapped neighbors, reusing them via shared memory is non-trivial, as the neighborhoods used are runtime information. This paper presents regGNN, an aggregation implementation that can benefit from the optimizations designed for regular kernels. It proposes a concept named "semi-regular" to describe the aggregate computation: the irregularity only comes from the neighborhood traversal; aggregating the high-dimensional vectors, which dominates the computation, is data-independent and thus incurs no irregularity. regGNN encodes the aggregate computation steps of each thread block into an aggregate script, which replaces the graph as an input of the GPU kernel. The GPU kernel is like an interpreter, and the aggregate script can be regarded as written in a simple GPU scripting language. The optimizations designed for regular kernels can then be applied to the aggregate script, as it is static and regular. regGNN demonstrates three optimizations: (1) intelligently scheduling nodes and customizing shared memory replacement to maximize data reuse, (2) reassigning nodes among warps for load balancing, and (3) aligning the aggregate script to improve memory latency hiding. Compared with the state-of-the-art GNN frameworks, regGNN achieves 2.81× throughput on average for moderate-scale GNNs. The speedup increases to 5.21× for GNNs with small hidden sizes and 100s × for deep GNNs.
Zero-shot Hand-held Objects Recognition Based on Global Feature Relationships

In recent years, there have been frequent terrorist attacks both at home and abroad. Accurate detection of a wide variety of handheld objects is a problem that needs an urgent solution. To better exploit the effects of handheld movements on handheld object recognition, this paper proposes a zero-shot hand-held object recognition based on global feature relations that comprise two modules: an image processing module and a semantic relations module. The image processing module learns the visual classifier of the input image to obtain the classification weights. The semantic relations module propagates structural knowledge through semantic learning through graph convolution operations on the knowledge graph and obtains the classification weights for all categories. Results indicate that this method significantly improves the recognition rate compared to traditional handheld object recognition methods.
CATS#: A Testing Technique to Support the Specification of Test Cases for Context-Aware Software Systems

Context-Aware Software Systems (CASS) capture the context and have their behavior affected by it. Therefore, these systems must be prepared to deal with the context and its variation. If they don’t, a failure can cause profound damage since they deal closely with the real world. Nevertheless, there is a lack of software technologies to support their testing. Thus, this work presents CATS#, a testing technique to help the specification of test cases for Context-Aware Software Systems (CASS). CATS# was applied in a project by undergraduate students. The results indicate its initial feasibility to support the specification of CASS test cases for situations not covered by conventional testing techniques.
Report on the 5th International Workshop on Narrative Extraction from Texts (Text2Story 2022) at ECIR 2022

The Fifth International Workshop on Narrative Extraction from Texts (Text2Story'22) was held on the April 10 th , 2022, in conjunction with the 44 th European Conference on Information Retrieval (ECIR 2022) in Stavanger, Norway. Due to the COVID-19 restrictions that are still active in some countries, the workshop was held as an hybrid event, combining a "live" in-person and a "virtual" online participation. The online participation was allowed using the Zoom platform. During the course of the day, more than 50 attendees - about 40 in person and 10 in zoom - had the opportunity to follow-up and discuss the recent advances in topics related to representation, extraction, and generation of narratives. The workshop program included two invited keynotes and twelve paper presentations. The proceedings of the workshop are available online at http://ceur-ws.org/Vol-3117/ Date: 10 April, 2022. Website: https://text2story22.inesctec.pt/.
Report on the 1st Workshop on Audio Collection Human Interaction (AudioCHI 2022) at CHIIR 2022

This is a report from the AudioCHI 2022 workshop which was organised in conjunction with the CHIIR conference in March 2022. The workshop was organised to bring together researchers in spoken content retrieval with expertise on human computer interaction in information access to examine opportunities and challenges for advancing technologies for search and interaction with spoken content. The workshop was originally planned to be a full day event, but due to pandemic-imposed travelling constraints, it was instead held as a virtual half day event to accommodate time zone differences for participants. Date: 14 March, 2022. Website: https://speechretrievalworkshop.github.io/.
Fuzzy Classifier Using the Particle Swarm Optimization Algorithm for the Diagnosis of Arterial Hypertension

The main objective of this article is the creation of a new fuzzy classifier, using the optimization algorithm by means of particles, to optimize the structures of the type-1 and type-2 fuzzy systems, (such as parameters and type of membership functions, type of system, and number of rules).Tests were carried out with 40 patients and the blood pressure readings of the patients were taken at a time interval for 24 h, and these were taken through an ambulatory blood pressure monitor (ABPM). In this work, good results for Classification and Diagnosis of Arterial Hypertension with the proposed model are shown.
Integration of Fractal Geometry and Correspondence Analysis Reconnaissance for Geochemically High-Potential Promising Areas NE Iran

Most geochemical and geostatistical analysis in mining exploration requires removing regional trends in order to obtain local anomalies. In this paper, stream sediment samples, which collected from Khusf area (NE Iran), was studied based on Concentration-Area (C-A) fractal model as well as correspondence analysis methods to find high-potential areas elements. Correspondence analysis with 170 samples through 20 elements concentration values in each sample was performed. According to correspondence analysis, among one or several elements in the study area, local anomalies were separated which the highest concentration relates to the variables Pb, As, and Cd elements. After the correspondence analysis, the best variogram for the Khusf area was studied. Elemental concentration maps was then produced through estimating the values using kriging method. Therefore, using the fractal method, between three statistical sets of elements, it was concluded that the third set showed the anomaly for Pb, As, and Cd elements with local anomaly values respectively determined as 55, 7.2, and 0.88 ppm. Although Cd element is not genetically related to same source of Pb and As, utilizing integrated approach, Cd anomalies has also been detected in the area as a promising element zone. Finally, it is suggested that this region has the possibility of Basic Metals occurrence and suggests further geophysical operations on a local identification scale.
TA-WHI

The healthcare data available on social media has exploded in recent years. The cures and treatments suggested by non-medical experts can lead to more damage than expected. Assuring the credibility of the information conveyed is an enormous challenge. This study aims to categorize the credibility of online health information into multiple classes. This paper proposes a model named Text Analysis of Web-based Health Information (TA-WHI), based on an algorithm designed for this. It categorizes health-related social media feeds into five categories: sufficient, fabricated, meaningful, advertisement, and misleading. The authors have created their own labeled dataset for this model. For data cleaning, they have designed a dictionary having nouns, adverbs, adjectives, negative words, positive words, and medical terms named MeDF. Using polarity and conditional procedure, the data is ranked and classified into multiple classes. The authors evaluate the performance of the model using deep-learning classifiers such as CNN, LSTM, and CatBoost. The suggested model has attained an accuracy of 98% with CatBoost.
Multivariate pattern recognition by machine learning methods

In pattern recognition, the goal is to identify patterns and regularities from data with minimal intervention. Finding the patterns and regularities is done using machine learning algorithms. In other words, pattern recognition is the engineering application, while machine learning origin back in computer science. Machine learning algorithms map the input data to the output which these algorithms have two main steps: the training step and the inference step. In the training step, the training data is utilized to learn a model, and in the inference step, the learned model is used to map unseen data to the output space. Machine learning algorithms are divided into four categories from the view of the available training data: (1) supervised learning, (2) semi-supervised learning, (3) weakly supervised learning, and (4) unsupervised learning. In supervised learning, all the available training data are fully annotated (labeled) compared to unsupervised learning, where none of the training data is labeled. In semi-supervised learning, some portion of training data is labeled, and a large portion is unlabeled. If the whole training data is not fully labeled (i.e., partially labeled), then it is weakly supervised learning. In this chapter, we focus on supervised learning.
Acquisition, representation, and rendering of omnidirectional videos

An omnidirectional image depicts the light field converging to one central point. To build such a spherical visual data, complex capture systems may be considered. A first contribution of this book chapter consists in reviewing the existing acquisition techniques (multiple perspective views, catadioptric, fish-eye) and to give their corresponding projection model. Second, we study the different ways of representing the omnidirectional data lying on the sphere. In particular, we review the different sphere mapping (equirectangular, cube-map, etc.) and analyze their advantages and drawbacks. We also introduce the different pseudo-uniform sampling of the sphere, and their corresponding processing tools. The third part of this chapter is dedicated to a particular capture system based on multiple synchronized omnidirectional cameras, whose purpose is to reach 6 degrees of freedom navigation.
Emotion-based classification through fuzzy entropy-enhanced FCM clustering

In the era of the social web, microblogging is a popular social media that enables users to share their feelings, ideas, and comments about everyday-life events, trends, and advertisements. Great emphasis has been placed on the investigation of human behavior in social networks by analyzing user-generated content to capture feelings and emotions from natural language. Delving into human reactions to events from the social message streams allows understanding collective moods and preferences of web users and supporting market strategies as well as political consensus analysis. This paper proposes a novel approach to the emotion-based classification of microblogging messages such as Twitter. The classification method is unsupervised and exploits the well-known fuzzy c-means (FCM) clustering algorithm, proposing an enhanced version called entropy weighted FCM (shortly, EwFCM) that overcomes the main drawback of the FCM, viz., the sensitivity to the random cluster initialization by leveraging a fuzzy measure to evaluate the entropy in the data distribution. The fuzzy entropy measure allows minimizing the fuzziness of clustering, speeding up the convergence of the clustering algorithm. The proposed approach aims at analyzing microblogging-based trends to detect the main emotions expressed in the textual content, according to a well-known computational analysis of emotions whose main models are attributable to Ekman and Plutchik. The experiments reveal that the presented approach offers high accuracy in the emotion-based classification of textual resources; comparisons with the FCM algorithm applied for emotion classification show that the proposed method converges faster and provides promising classification performance, as evaluated by common metrics such as accuracy, precision, and F1-score.
Acquisition of light field images &amp; videos

Even though there are many different ways acquiring rays from a light field (e.g., [5]), we will in this chapter restrict ourselves to three major architectural implementations: Plenoptic cameras: Due to the long history of photography and the availability of devices, the first implementations of plenoptic imagery have been by adding so-called MLAs (microlens arrays) to standard 2D-cameras. Such hand-held plenoptic photography [3] reused 2D sensors to capture a set of SAIs (sub-aperture images), so that the resolution of the sensor is split between the spatial domain (aka the resolution of each individual SAI) and the angular domain (aka the number of such SAIs). Light field gantries: Based on the insight that light fields for sufficient quality do require a large number of rays (actually the spatial resolution should be comparable to 2D-photography so that the overall data-rate is multiplied by the number of SAIs), light field gantries have been proposed. They use high-resolution 2D-cameras and very fine granular positioning systems (down to the 100 μm-domain) to capture a very large number of SAIs with sufficient resolution each. Light field arrays: To overcome the limitation of gantries to still image photography (the scene and illumination need to remain constant during the whole capturing process), most of the recent light field captures have been taken by assemblies of cameras in camera-arrays. Different geometries (from planar to spherical arrays) are used. Since arrays offer the most intuitive capture of light fields (the resolution of each SAI is given by the individual camera, and the angular resolution is given by number and spacing of such individual cameras), in this book, we adapt our nomenclature to light field arrays.
A technique of data collection

The preparation stage of data is the most fundamental block to dive into the world of artificial intelligence. The magic lies in the efficient usage of clean data from a heap of raw information. In this section the author will walk through a concealed layer of data preprocessing. Data collection makes the initial contribution in the life cycle of data. There are various effective ways to collect data from worldwide. This section represents a familiar technique to obtain data from heap which is often known as Web Scraping. It is an automated system that extricates data from the internet. The benefit added to this system is it involves open-source tools in its functionality. The author will discuss how Python language with its vast library support implements web scraping to collect data from internet.
Light field processing for media applications

Light fields describe the amount and direction of light rays flowing through every point in space. They allow reproducing different perspectives of a captured scene or a digitized object. This is particularly beneficial for photo-realistic virtual reality scenarios. Unfortunately, practical applications are still rare, as capturing and processing of information divided up in rich angular and spatial details contained in a light field is difficult. To overcome this situation, this chapter will describe algorithms and software solutions for both sparse and dense light fields. At first, a complete pipeline from capturing and processing to rendering of light fields is described. Light field capture is performed using a custom-built camera array. A node-based processing solution in a professional post-production environment permits to steer the light field reconstruction process for best achievable quality. The result can then be integrated into a VR environment with the help of a game engine. In the second part of this chapter, possibilities of neural networks to increase achievable rendering quality or reduce processing and capturing efforts are explored in detail.
Attribute selection to improve spam classification

Spammers are increasingly targeting online social network (OSN) sites, placing hazardous content on them as they grow in popularity. Filtering spam posts and spam accounts from OSNs is therefore crucial. Several previous efforts on spam categorization on OSNs have used a variety of criteria to separate spam from real entities. The goal of this research is to improve spam categorization by creating a process of selection of attributes that allows for a smaller collection of attributes to be found, resulting in improved classification. To create the attribute selection algorithm, we specifically use rough set theory principles. The proposed methodology's performance is compared to that of numerous baseline feature selection approaches on five distinct spam classification datasets spanning a variety of OSNs. We discovered that, for the majority of datasets, the suggested approach selects a smaller attribute subset than the baseline methodologies, but generates better performance in classification than the other methods.