Reviewerly/exHarmony huggingface dataset:
Automatic Extraction of Facets for User Queries [AEFUQ]

A user query facet is a collection of items that summarizes the content covered by a query. In general, the most significant information of a user query is present in the top retrieved document that are in the form of lists. In this work, we propose a framework Automatic Extraction of Facets for User Queries [AEFUQ] that extract the user query facets automatically by grouping the list based on three categories namely HTML tags, free text patterns and repeat regions. Grouping of the list is based on domain sites present in the list. We observe that some of the lists are not relevant for extracting the facets. In order to prune these lists, the importance of each item in the lists that are present in the group G is evaluated and Cosine Similarity (CS) between two items is calculated. Further, based on CS score obtained, High Quality Clustering (HQC) algorithm is proposed to cluster the items that has the most number of point in each iteration to obtain more number of facets. Finally, the top most items from each cluster are provided as the best facets for the user query. Experiments are conducted on User Q and Random Q dataset. It is observed that the proposed method AEFUQ outperforms by providing a large number of useful query facets compared to QDMiner method [1].
A new method for sequential learning of states and parameters for state-space models: the particle swarm learning optimization

Accuracy of parameter estimation and efficiency of state simulation are common concerns in the implementation of state-space models. Even widely used methods such as Kalman filters with MCMC and Particle Filter, still present concerns with efficiency and accuracy, despite their successful results in their respective applications.This article presents a new method combining the structure of particle learning and bare bones particle swarm optimization (BBPSO) to the process of smoothing and filtering the states in the state-space models, thus overcoming the efficiency and accuracy problems. Sampling importance re-sampling is used to estimate the states of the model, then the parameters can be estimated via BBPSO, as an alternative to the kernel approximation of Liu and West. Our method is applied to stochastic volatility and AR(1) state-space models. Empirical results with Ibovespa and SP500 index show better performance when compared to particle filters, thus improving efficiency and accuracy.
Comparing tagging suggestion models on discrete corpora

Purpose This paper aims to investigate the methods for the prediction of tags on a textual corpus that describes diverse data sets based on short messages; as an example, the authors demonstrate the usage of methods based on hotel staff inputs in a ticketing system as well as the publicly available StackOverflow corpus. The aim is to improve the tagging process and find the most suitable method for suggesting tags for a new text entry. Design/methodology/approach The paper consists of two parts: exploration of existing sample data, which includes statistical analysis and visualisation of the data to provide an overview, and evaluation of tag prediction approaches. The authors have included different approaches from different research fields to cover a broad spectrum of possible solutions. As a result, the authors have tested a machine learning model for multi-label classification (using gradient boosting), a statistical approach (using frequency heuristics) and three similarity-based classification approaches (nearest centroid, k-nearest neighbours (k-NN) and naive Bayes). The experiment that compares the approaches uses recall to measure the quality of results. Finally, the authors provide a recommendation of the modelling approach that produces the best accuracy in terms of tag prediction on the sample data. Findings The authors have calculated the performance of each method against the test data set by measuring recall. The authors show recall for each method with different features (except for frequency heuristics, which does not provide the option to add additional features) for the dmbook pro and StackOverflow data sets. k-NN clearly provides the best recall. As k-NN turned out to provide the best results, the authors have performed further experiments with values of k from 1–10. This helped us to observe the impact of the number of neighbours used on the performance and to identify the best value for k . Originality/value The value and originality of the paper are given by extensive experiments with several methods from different domains. The authors have used probabilistic methods, such as naive Bayes, statistical methods, such as frequency heuristics, and similarity approaches, such as k-NN. Furthermore, the authors have produced results on an industrial-scale data set that has been provided by a company and used directly in their project, as well as a community-based data set with a large amount of data and dimensionality. The study results can be used to select a model based on diverse corpora for a specific use case, taking into account advantages and disadvantages when applying the model to your data.
Personality Parameters as Predictors of the Burnout Syndrome Development in School Teachers

The article features the professional burnout syndrome in teachers. Teaching is one of the most stressful social activities. The authors pointed out personal traits that may determine the development of the professional burnout syndrome in school teachers. They identified, justified, and experimentally verified the relationship between burnout and personality parameters. Together with age, work experience, marital and parental status, these personal traits can serve as burnout predictors. The article gives a general description of the emotional burnout phenomenon in teachers, which includes negative psychological experiences caused by the regular high emotional content or cognitive complexity. The paper also introduces factors of development of emotional burnout. The authors identified the burnout predictors based on the results of an empirical study. They determined strong empathy and internality as provoking factors that triggered emotional stress. As a rule, the stress was associated with teacher's attitude to his or her role in interpersonal relationships. The authors proposed some methods for the prevention of burnout syndrome in teachers.
Exploring Habits and Habitus of Biomedical Students with Modelling Tasks

This chapter aims to explore the potentialities of mathematical modelling tasks that underscore first year biomedical technology students’ habits and habitus. When students are engaged in modelling tasks for the first time, ingrained habits – imposed by traditional teaching and learning approaches – are challenged and, in turn, affect students’ future expectations and aspirations, a notion that Bourdieu calls habitus. Three questionnaires were employed to analyse students’ perceptions of four modelling tasks, spread over a semester. The novel modelling environment was an impetus for change; however, modification of habits and a refinement of habitus were marginally demonstrated.
Elastic-Move: Passive Force Feedback Devices for Virtual Reality Locomotion

With advances in virtual reality, users can experience in the immersive virtual environment by a head-mounted display. One of the most widely used interaction techniques in VR application is locomotion. Most VR applications currently use teleportation as a locomotion method. Because teleportation does not involve any visible translational motion, it can avoid VR sickness. However, teleportation would reduce user’s immersion and make them lose the sense of direction due to the lack of moving process. In this paper, we propose two haptic devices with force feedback: ”Elastic-Rope” and ”Elastic-Box” to reduce VR sickness and allow users to maintain immersion and sense of direction. Elastic-Rope is consisting of elastic ropes and controllers, and Elastic-Box is consisting of eight springs and a steel frame. We conducted an experiment that compared the degree of VR sickness by simulator sickness questionnaire(SSQ) survey while moving in the virtual environment among Dash, Teleport, Elastic-Rope, and Elastic-Box. As a result, Elastic-Rope and Elastic-Box reduce the VR sickness of users. Besides, Elastic-Box can be transposed in numerous applications because it is a low-cost and versatile device. This work suggests that passive force feedback can effectively reduce VR sickness.
Virtual Reality in Libraries

Librarians are beginning to offer virtual reality (VR) services in libraries. This chapter reviews how libraries are currently using virtual reality for both consumption and creation purposes. Virtual reality tools will be compared and contrasted, and recommendations will be given for purchasing and circulating headsets and VR equipment. Google Tour Creator and a smartphone or 360-degree camera can be used to create a virtual tour of the library and other virtual reality content. These new library services will be discussed along with practical advice and best practices for incorporating virtual reality into the library for instructional and entertainment purposes.
Micro-blog Short Text Clustering Algorithm Based on Bootstrapping

In micro-blog short text clustering, the amount of text information contained in micro-blog short text is small, with timeliness, sparseness and singularity, so the artificial selection of attribute words has some limitations. This paper puts forward the feature extraction of text information in micro-blog using Bootstrapping algorithm, it can choose the higher theme information reflect the characteristics of words, and then use the improved TFIDF algorithm to calculate the weight of micro-blog based text clustering. Finally, K-means clustering algorithm is used to cluster micro-blog short text. The experimental results show that the clustering algorithm of micro-blog short text proposed in this paper is better than other algorithms, which improves the clustering effect of micro-blog short text.
A Qualitative Evaluation of Student Experience with a Virtual Heritage Application

Virtual heritage involves the use of digital media to reconstruct historic artifacts, places, or events. In this paper we explore the use of virtual reality (VR) technology for presenting virtual heritage applications to college students. Specifically, we describe our preliminary evaluation of a virtual reality application that introduces students to the Stockton State Hospital, a mental institution that was active from 1851 to 1995 in Stockton, California. The objective of the project is to allow students to learn about the hospital in an immersive virtual environment that may elicit more empathy for complex socio-historical experiences than traditional learning methods such as books, oral histories, or documentary films. To assess whether this objective was satisfied, students experienced the VR application as part of a college course and then completed a questionnaire that asked about their experience. Analysis of questionnaire responses suggests that students did develop some degree of empathetic understanding of social issues related to the diagnosis and treatment of mental illness during the late 1800s. Students also appreciated the visual and immersive aspect of the application, and some mentioned that they would prefer this type of learning experience to watching videos or reading articles.
Protection Via Business Impact Analysis in a Cyber World: A 3-Part Series

The purpose of this text is to understand the business impact analysis (BIA) needed to guard against cyber-attacks and survive attacks if and when they occur. Business impact analysis, an accepted risk management term for the process of defining the proportional significance or criticality of elements, determines the prioritization, planning, preparation, and other activities of business management strategy. First, the researcher presented BIA as key planning, preparatory and related activities proposed to confirm that companies’ significant business functions will either continue to operate notwithstanding grave disasters or incidents of cyber terrorism, or will be recovered to an operational state within a reasonably short period. This research showed how BIA and procedure provides a multiplicity of benefits. Conclusions were the development of a BIA structure to guide analysis, as well as strategy determination and documentation. This text offers to learners, practitioners, and academicians information for long and short term, BIA risk and BIP management strategies.
Participatory Communication Strategies Used in the Implementation of Public Water Projects in Murang'a County, Case Study of Northern Collector Tunnel, Kenya

Development communication plays a major role in informing and influencing the behavior of the receiver of information. Effective development communication motivates people to participate in planned activities, changing the behavior and attitudes of people and empower them to focus on interactions between different stakeholders to address a common problem. Community involvement is a very important aspect in implementation of any project. Without community buy-in, a project may never get off the ground or will not be accepted once it is completed. Community involvement is regarded as a nuisance and is ineffectively addressed, or there is a lack of knowledge as to how to engage the community.  Either way, the results can be disastrous and would lead to delays in construction, loss of money, filing of law suits, disgruntled citizens providing negative comments to the media or non-acceptance of the revitalization project, the very thing that was supposed to be remedied.  The study sought to establish Participatory Communication Strategies used in implementation of public water projects in Murang'a County, study of Northern Collector Tunnel. The Study was anchored on the theory of Kinkaid Convergence Model of Communication. The design was descriptive survey. Target population was 192,589 households. Krejcie and Morgan sampling formula was used to arrive at 384 respondents. Data was collected using a structured questionnaire and analyzed using descriptive and inferential statistics. The study established that participatory communication strategies play a critical role in the implementation of public water projects. The study recommends the involvement of community grouping, meeting with opinion leaders and the use of various media platforms including radios, local newspapers, and television and billboard illustration will go a long way in facilitating the successful implementation of Northern Collector Tunnel project.
Distributed pool mining and digital inequalities, From cryptocurrency to scientific research

Purpose This paper aims to look at shifts in internet-related content and services economies, from audience labour economies to Web 2.0 user-generated content, and the emerging model of user computing power utilisation, powered by blockchain technologies. The authors look at and test three models of user computing power utilisation based on distributed computing (Coinhive, Cryptotab and Gridcoin) two of which use cryptocurrency mining through distributed pool mining techniques, while the third is based on distributed computing of calculations for scientific research. The three models promise benefits to their users, which the authors discuss throughout the paper, studying how they interplay with the three levels of the digital divide. Design/methodology/approach The goal of this article is twofold as follows: first to discuss how using the mining hype may reduce digital inequalities, and secondly to demonstrate how these services offer a new business model based on value rewarding in exchange for computational power, which would allow more online opportunities for people, and thus reduce digital inequalities. Finally, this contribution discusses and proposes a method for a fair revenue model for content and online service providers that uses user device computing resources or computational power, rather than their data and attention. The method is represented by a model that allows for consensual use of user computing resources in exchange for accessing content and using software tools and services, acting essentially as an alternative online business model. Findings Allowing users to convert their devices’ computational power into value, whether through access to services or content or receiving cryptocurrency and payments in return for providing services or content or direct computational powers, contributes to bridging digital divides, even at fairly small levels. Secondly, the advent of blockchain technologies is shifting power relations between end-users and content developers and service providers and is a necessity for the decentralisation of internet and internet services. Originality/value The article studies the effect of services that rely on distributed computing and mining on digital inequalities, by looking at three different case studies – Coinhive, Gridcoin and Cryptotab – that promise to provide value in return for using computing resources. The article discusses how these services may reduce digital inequalities by affecting the three levels of the digital divide, namely, access to information and communication technologies (ICTs) (first level), skills and motivations in using ICTs (second level) and capacities in using ICTs to get concrete benefits (third level).
Agricultural information resource scheduling algorithm based on firefly algorithm in cloud computing

For the problem of slow scheduling speed and poor load balancing of agricultural information resource scheduling in the current cloud computing, an agricultural information resource scheduling algorithm based on firefly algorithm is proposed in this paper. In cloud computing, according to the idea of earliest completing resource scheduling and using the least cost, the agricultural information resource scheduling model with time constraint is constructed. The minimum completion time and scheduling cost of resource scheduling are determined and the objective function of resource scheduling is built. The chaos algorithm is used to optimize the firefly algorithm to solve the objective function and improve the convergence speed of resource scheduling. Agricultural information resource scheduling is achieved according to the convergence results and by using Lagrangian relaxation function. Experimental results show that the proposed algorithm is faster in scheduling agricultural information resources and higher in load balancing.
Utilizing AR Glasses as Mobility Aid for People with Low Vision

Augmented reality technology has the potential to improve what people with low vision can see by supplementing the real-world with visually accessible augmentations, thus providing useful information for mobility. This project aims to explore how AR can be used as a mobility aid for those people.
Short Text Similarity Hybrid Algorithm for a Chinese Medical Intelligent Question Answering System

The medical-oriented intelligent question answering (QA) system, which provides users with fast and accurate answers, has gradually caught the attention of the medical and health research community. In a QA system, one of the critical processes is determining how to calculate the textual similarity and match it, for example, the similarity between the question asked by a user and the question as it exists in the system template. However, the question texts are shorter, individual noisy words pose new challenges for semantic parsing of the entire text. In this paper, we propose a new shared layer-based convolutional neural network (SH-CNN) model to calculate the semantic similarity of Chinese short text. The SH-CNN uses a shared layer to extract the prominent features from a pair of questions and then yields textual similarity based on their features. Concurrently, our approach employs the term frequency-inverse document frequency (TF-IDF) algorithm in the feature extraction process, reducing the interference of noisy words during short text similarity calculations. The experimental results show that the textual similarity hybrid algorithm combining SH-CNN and TF-IDF achieves a successful performance in our intelligent QA system and demonstrates a meaningful application value.
Comparative Performance Analysis of Enhancement Methods Applied to Arabic Manuscripts

The ancient Arabic manuscripts are considered to be more complex regarding enhancement compared to others written in other languages. Complexity comes from character degradation, stains, low-quality images, curves of the text, character overlapping, etc. To facilitate the restoration, a set of well-known binarization techniques designed for historical document images is presented in this paper. Existing binarization techniques focus on either finding an appropriate global threshold for the whole image or adapting a local threshold for each area to achieve better enhancement quality. This improvement aims to remove noises, strains, uneven illumination, etc. The goal of our work is to assess these methods when applied to Arabic manuscripts in terms of readability, elimination of original spots and production of unwanted noise. Results show that no techniques work well for all types of manuscripts, but some techniques work better than others for particular types. Experimental results also indicate that Nick and Wolf techniques performed the best in terms of readability in most of the processed manuscripts.
An efficient Deep Learning of scarce and diverse faces for Face recognition

Face recognition (FR) is a challenging task when the samples per person are scarce and that too with each sample belonging to different facial expressions. A good classifier requires sufficient large numbers of training samples to train and produce accurate output when subjected to test samples. Nowadays, Deep learning (DL) is been extensively used where numerous handling layers tend to acquire records with numerous stages of feature at many levels. The use of Deep learning has radically upgraded the state of art presentation and solved many existent domain presentations. The main contribution of this work is the way 18 features corresponding to four frequencies, four orientations and two with spatial context for each individual pixel are extracted using Gabor filtering and finally reducing the 18 dimensional feature spaces to 1 dimensional feature vector using principal component analysis (PCA). This work uses a small YALE dataset of fifteen subjects with eleven facial expressions. Deep learning with Convolutional neural network (CNN) is used to train 154 samples in random and the remaining 10 samples were subjected for testing the CNN. The classification result obtained with randomly selected test sample was 100% for single test sample and 95% recognition rate when 10 samples are subjected to test.