Reviewerly/exHarmony huggingface dataset:
Trust in Prediction Models: a Mixed-Methods Pilot Study on the Impact of Domain Expertise

People's trust in prediction models can be affected by many factors, including domain expertise like knowledge about the application domain and experience with predictive modelling.However, to what extent and why domain expertise impacts people's trust is not entirely clear.In addition, accurately measuring people's trust remains challenging.We share our results and experiences of an exploratory pilot study in which four people experienced with predictive modelling systematically explore a visual analytics system with an unknown prediction model.Through a mixed-methods approach involving Likert-type questions and a semi-structured interview, we investigate how people's trust evolves during their exploration, and we distil six themes that affect their trust in the prediction model.Our results underline the multi-faceted nature of trust, and suggest that domain expertise alone cannot fully predict people's trust perceptions.
Bilingual Question Answering System Using Bidirectional Encoder Representations from Transformers and Best Matching Method

Question answering (QA) system is built to answer asked queries based on an unstructured collection of documents in natural language. The implementation of the QA system makes QA more efficient because the system can answer similar questions automatically. However, similarity queries based on questions or answers alone fail to retrieve documents relevant to the query in some cases because the word choice used in the query is different from the word choice in the QA database even though the context is the same. The same context can be seen from the list of references used by a QA. Therefore, it is necessary to measure the similarity of the query that does not only take into account the question and answer but also the reference. In this paper, we propose to build a bilingual QA system that answers Indonesian questions based on the combination of query similarities among question, answer, and external reference in Arabic using Bidirectional Encoder Representation from Transformers (BERT) and Best Matching (BM25) method. The similarity between query and reference are able to help to recognize a QA that uses reference with similar context. Based on the experimental result, the combination parameter of query-Question followed by query-Answer achieves the highest evaluation score with the Mean Average Precision (MAP) score of 0.988 and the Mean Reciprocal Rank (MRR) score of 1.000.
Identifying and correcting invalid citations due to DOI errors in Crossref data

This work aims to identify classes of DOI mistakes by analysing the open bibliographic metadata available in Crossref, highlighting which publishers were responsible for such mistakes and how many of these incorrect DOIs could be corrected through automatic processes. By using a list of invalid cited DOIs gathered by OpenCitations while processing the OpenCitations Index of Crossref open DOI-to-DOI citations (COCI) in the past two years, we retrieved the citations in the January 2021 Crossref dump to such invalid DOIs. We processed these citations by keeping track of their validity and the publishers responsible for uploading the related citation data in Crossref. Finally, we identified patterns of factual errors in the invalid DOIs and the regular expressions needed to catch and correct them. The outcomes of this research show that only a few publishers were responsible for and/or affected by the majority of invalid citations. We extended the taxonomy of DOI name errors proposed in past studies and defined more elaborated regular expressions that can clean a higher number of mistakes in invalid DOIs than prior approaches. The data gathered in our study can enable investigating possible reasons for DOI mistakes from a qualitative point of view, helping publishers identify the problems underlying their production of invalid citation data. Also, the DOI cleaning mechanism we present could be integrated into the existing process (e.g. in COCI) to add citations by automatically correcting a wrong DOI. This study was run strictly following Open Science principles, and, as such, our research outcomes are fully reproducible.
Prediction of Rolling Force for Hot Strip Rolling Based on RFR

In the hot strip rolling, rolling force is one of the important factors affecting the shape of steel strip. There are some errors in the mechanism model used to predict the secondary rolling force of hot strip rolling in a steel company. In this paper, in order to improve the setting accuracy of the rolling force in production line, the rolling force prediction based on historical production data is studied in this paper. First, in order to reduce the computational complexity of the model and improve the prediction accuracy, correlation analysis and local outlier factor algorithm are used to preprocess the data. Secondly, a rolling force prediction model based on random forest regression algorithm is proposed in this paper, Experimental results based on actual data show that the proposed method has a better predictive effect.
Measurement method of high temperature full-field thermal deformation of materials based on digital image correlation

In order to measure the full-field thermal deformation of materials in high temperature environment, a full-field thermal deformation measurement method based on high temperature digital image correlation is proposed in this paper.In this method, monochromatic polarized light is used as the lighting source, and the polarizing camera combined with narrowband filter is used as the image acquisition system to collect images under high temperature environment. Through optical imaging and optical filtering system, visible radiation can be initially filtered to minimize the thermal radiation on the specimen surface. And the image gray average method is also used in this paper to eliminate the thermal disturbance error and vibration error in the measurement process to improve the measurement accuracy.Finally, a set of high temperature digital image correlation measurement system was built, and the thermal deformation test was carried out on the ceramic materials. The experimental results show that the system can realize the clear image with high-contrast acquisition under high temperature conditions, and can effectively eliminate the interference of vibration and thermal disturbance noise in the measurement under high temperature .
Influence of the Application of Sentiment Analysis as a Process Improvement in Different Sectors

Data processing with sentiment analysis is significantly improving the analysis of various sectors. However, there are currently several sectors where the potential use of sentiment analysis is not being exploited, either because of a lack of knowledge of techniques to use in a sector or because there is not much information to collect. The correct use of sentiment analysis in new sectors can increase the data analysis of that sector. The objective of this paper is to report on the extent to which sentiment analysis is being used in various sectors and to propose an architectural model for the education sector, a sector that is little frequented. The methodology used was a literature review of sentiment analysis implementations in different sectors. As a result, 145 potential articles were found where 21 were selected. From the results, we see that there is a tendency to use sentiment analysis in some sectors but at the same time an almost null use in some sectors, which is a call for further research in them.
Time and Memory Comparison of Parallel K-Nearest Neighbor Algorithms on GPUs

The K Nearest Neighbor (KNN) method is one of the most used machine-learning algorithms due to its simplicity. However, as the dataset grows, the running speed of the KNN algorithm decreases considerably. In this study, the performance of the two parallel KNN algorithms (i.e. GPU-based TUKNN and GPU-SME-KNN) were compared with the sequential KNN algorithm using five different datasets which have 520, 5110, 78,096, 500,000, and 2,916,697 samples. According to the experiments performed on these datasets, it is observed that the TUKNN and GPU-SME-KNN algorithms are 10.4 times and 347.3 times faster than the sequential KNN algorithm on average, respectively. TUKNN's average CPU consumption is 7.93%, and average RAM consumption is 1.49%. The average CPU consumption of the GPU-SME-KNN algorithm provides an efficiency of 8.59%, while the average RAM consumption increases by 6.29%.
Anechoic Multi-Channel Recordings of individual String Quartet musicians

Many and great efforts have been put into creating anechoic recordings of symphony orchestras, opera arias, solo instruments, speech and etc. However, chamber music, a typical music formation is poorly represented in the available anechoic material library. This is especially relevant in the context of small concert halls, where typically the most common form of concert would be of chamber music. With this need for material in mind, short excerpts of each movement of Haydn’s string quartet op. 76 n.1 have been recorded in an anechoic chamber. The recordings have been conducted with individual instruments with an array of 12 studio microphones positioned evenly around the musician. This paper reports on the methods, technical aspects and difficulties faced in producing such recording, as well as the post-processing required to achieve a realistic and usable performance of a string quartet. The finalized audio files will be made freely available for use in academic research.
Touch Point Prediction for Interactive Public Displays Based on Camera Images

Feedback latency during the use of interactive displays is an issue currently being considered in the HCI field. Several studies have focused on reducing latency using various approaches. This paper proposes a framework that uses a convolutional neural network to predict user touch points for interactive public displays. The framework predicts user touch events before the finger reaches the display surface to reduce the latency in feedback. As a training dataset, 1,651 tapping actions were collected from 18 participants in front of a display. The training of the convolutional neural network architecture was performed using the collected tapping actions. Validation test results showed that reasonable accuracy could be achieved at 390 ms before touching the display.
Continuous-Touch Text Entry for AR Glasses

The emergence of Augmented Reality (AR) has brought new challenges to the design of text entry interfaces. When wearing a pair of head-mounted AR glasses, a user’s visual focus could be anywhere 360 \(^\circ \)C around her. For example, a technician is looking up at an airplane engine, meanwhile sharing her view with remote technicians through the sensors on the AR glasses. In such a scenario, the technician has to keep her gaze at the parts and look away from input devices such as a wireless keyboard or a touchscreen. Thus, she will have limited ability to input text for tasks like taking notes about a certain engine part. In this work, we designed and developed two innovative text entry interfaces: Continuous-touch T9 (CTT9) and Continuous-touch Dual Ring (CTDR). Our methods employ a smartphone touchscreen and a projected text entry layout in AR space to help the users input texts without looking at the smartphone. Our user studies suggest the effectiveness of CTT9 and CTDR and provide clues on how to optimize them. Based on the user study results, we provide insights about applying the proposed Continuous-touch (CT) paradigms to text entry for AR glasses.
Antlers Are Shaped as Elements of Concave Surfaces that Reflect and Concentrate Incoming Sounds

In 1917 D'Arcy Thompson observed that the pair of antlers of a red deer, a sambur or a wapiti Make up between them a single surface that is more or less spherical or occasionally an ellipsoidal surface.The palmated antlers of the moose are explored to provide an example of how antlers can be instrumental in lowering the threshold sensitivity of hearing.
An Improved Harmony Search Based on Teaching-Learning Strategy for Unconstrained Binary Quadratic Programming

Unconstrained binary quadratic programming (UBQP) problem plays an important role in operational research due to its application potential and its computational challenge. This paper presents a new hybrid algorithm based on Harmony Search (HS) and Teaching-Learning-Based Optimization. The main features of the proposed algorithm called harmony search with teaching-learning (HSTL) are the integration of teaching-learning strategy in the basic harmony search. This hybridization has led to an efficient hybrid framework which achieves better balance between the exploration of HS and the exploitation capabilities of the Teaching-Learning-Based Optimization. Experiments on numerous benchmark problems having 50 to 2500 variables show the effectiveness of the proposed framework and its ability to achieve good quality solutions.
Graph-Based Keyword Extraction for Twitter Data

Keyword extraction is a text analysis technique tasked with the automatic identification of a cohesive group of keywords that best describe the subject of a record. It helps to analyze the content of a text and produce words that are of utmost importance in context to the text. A bulk of the data on the Internet is unstructured (roughly around 80%) which is hard to analyze and process, and therefore, keyword extraction is an essential task. The latest trends have used unsupervised or graph methods for keyword extraction when dealing with social media data. This paper showcases keyword extraction from social media using a graph-based method called k-core truss decomposition on data retrieved using the Twitter API based on a search query. The primary goal of this paper is to depict the manner of connection between words that exist in the tweets with respect to the topic. The proposed method is used to obtain insights from extensive sets of data in a short period of time. Extracted words are clustered into vertices in a complete graph. The extracted keywords are in the form of a social network to understand the relationship between the respective words where each node represents a keyword and the edges connecting them represent how they are related. Scores are calculated for these words and are cohesively grouped based on the score, better the score—more relevant the keyword is for the topic.
Performance Analysis on the Effect of Noise in Inverse Surface Adaptive Thresholding (ISAT)

Abstract Thresholding is one of the powerful methods in segmentation phase. Numerous methods were proposed to segment the foreground from the background but there is limited number of studies that analyse the effect of noise since the present of noise will affect the performance of the thresholding method. In this paper, the main idea is to analyse the effect of noise in Inverse Surface Adaptive Thresholding (ISAT) method. ISAT method is known as an excellent method to segment the image with the present of noise. The result of this analysis can be a guideline to researcher when implementing ISAT method especially in medical image diagnosis. Initially, several images with different noise variations were prepared and underwent ISAT method. In ISAT method, several image processing methods were incorporated namely edge detection, Otsu thresholding and inverse surface construction. The resulting images were evaluated using Misclassification Error (ME) to evaluate the performance of the segmentation result. Based on the obtained results, ISAT performance is consistent although the noise percentage increases from 5% to 25%.
Differentially-Private Deep Learning With Directional Noise

With the popularity of deep learning applications, the privacy of training data has become a major concern as the data sources may be sensitive. Recent studies have found that deep learning models are vulnerable to privacy attacks, which are able to infer private training data from model parameters. To mitigate such attacks, differential privacy has been proposed to preserve data privacy by adding randomized noise to these models. However, since deep learning models usually consist of a large number of parameters and complicated layered structures, an overwhelming amount of noise is often inserted, which significantly degrades model accuracy. We seek a better tradeoff between model utility and data privacy, by choosing directions of noise w.r.t. the utility subspace. We propose an optimized mechanism for differentially-private stochastic gradient descent, and derive a closed-form solution. The form of the solution makes the mechanism ready to be deployed in real-world deep learning systems. Experimental results on a variety of models, datasets, and privacy settings show that our proposed mechanism achieves higher accuracies at the same privacy guarantee compared to the state-of-the-art methods. Further, we extend the privacy guarantee to a mutual information bound, and propose a general form to the utility-privacy problem.
Specular Reflection Removal in Cervigrams

Removal of specular reflection alsoAgarwal, Ayush called specularity or highlights from an imageAli, Faraaz is very important before the image is processed or analyzed byKopparthi, Aditya computer for image processing, computerRanjan, Priya graphics and computer vision because itShrivastav, Kumar, Dron is considered as part of the image by the computer. Removal of specular reflection is important because when there are specular reflections in the image it causes errors in the results of many algorithms of computerJanardhanan, Rajiv vision and image processing. Due to specular reflections in images, shapes in images get distorted, and therefore, all shape detecting algorithms and algorithms based on shape detection, boundary detection results in erroneous outputs. Removal of specular reflection includes detection of the reflection or reflections in the given image and then changing those pixels with some other color pixel based on the other portions of the image which are not faulty. There exist many different techniques for the detection and removal of specular reflection. In this paper, some of such techniques and approaches to tackle the problem are discussed. We have also proposed a simple yet efficient algorithm for specular reflection removal.
Can Remote Virtual Simulation Improve Practice-Based Training? Presence and Performance in Incident Commander Education

Abstract An incident commander (IC) is expected to take command in any incident to mitigate consequences for humans, property, and the environment. To prepare for this, practice-based training in realistic simulated situations is necessary. Usually this is conducted in live simulation (LS) at dedicated (physical) training grounds or in virtual simulation (VS) situations at training centers, where all participants are present at the same geographical space. COVID-19-induced restrictions on gathering of people motivated the development and use of remote virtual simulation (RVS) solutions. This article aims to provide an increased understanding of the implementation of RVS in the education of Fire Service ICs in Sweden. Data from observations, questionnaires, and interviews were collected during an RVS examination of two IC classes (43 participants) following an initial pilot study (8 participants). Experienced training values, presence, and performance were investigated. The results indicated that students experienced higher presence in RVS, compared with previous VS studies. This is likely due to the concentration of visual attention to the virtual environment and well-acted verbal counterplay. Although all three training methods (LS, VS, and RVS) are valuable, future research is needed to reveal their respective significant compromises, compared with real-life incidents.
Chaotic Time Series Prediction using Three-Dimensional FOA and Echo State Network

Chaotic time series prediction is critical in the fields of artificial intelligence. Recently, the Echo State Network (ESN) with nonlinear computing capability has been widely used to perform time series prediction tasks. However, the parameters of ESN are needed to vary to application scenarios and are usually set by human experience, which affects the performance of ESN. In this work, the Three-Dimensional Fruit Fly Optimization Algorithm is proposed to optimize the parameters of ESN (TDFOA-ESN) by adding swarm collaboration and random perturbation. The effectiveness of the proposed algorithm is verified by using benchmark datasets (NARMA and Mackey-Glass) and real-world time series (Beijing PM2.5 Data). In addition, the root mean squared error, normal root mean squared error and determination coefficient are used to evaluate the performance of TDFOA-ESN model. The experiment shows that the normal root mean squared error is reduced by 80.85% compared with other approaches. Therefore, the proposed TDFOA-ESN model provides a beneficial alternative for the time series prediction tasks.
Progress of graph model for conflict resolution in conflict analysis: A systematic review and bibliometrics research

Conflict is an inevitable social phenomenon and the analysis of it can effectively resolve disputes, improve the position of decision makers (DMs), forecast compromise solutions as well. At present, the Graph Model for Conflict Resolution (GMCR), a completely non-quantitative decision support system (DSS) based on DM’s ordinal preference information, is constructed to combat conflict analysis complicated by multiple participant or multiple criteria or both. The purpose of the study is to introduce an overview of GMCR in conflict analyzing through bibliometrics. In order to achieve this goal, a systematic review of articles in leading journals of Web of Science Core Collection (WoSCC) during 1987–2019 is posed, referring to the distribution of countries, institutions, authors, subjects and journals, research topics and hotspots exploration as well as frontiers prediction, by utilizing VOSviewer and CiteSpace. The contributions of this study are not only providing a handy method to grasp generalized scientific research situation, but also demonstrating status quo and emerging trends of GMCR for researchers and everyone who interested in.
Research on Key Technologies of VR based on MEC

Abstract Multi access edge computing (MEC) is an extension of 5G network on the edge side. Its network characteristics of high bandwidth and low delay provide necessary guarantee for the extreme user experience of VR (virtual reality). At the same time, the distributed architecture of 5g MEC network and the fast data exchange capability between nodes provide the hardware foundation for VR application marginalization deployment and equipment mobility access. Considering the cost and experience of VR, this paper first analyzes the advantages and disadvantages of VR based on traditional hardware architecture, and then describes the benefits of MEC on VR cost and experience. Finally, taking VR live video scene as an example, the key technologies of VR based on MEC are described.