Reviewerly/exHarmony huggingface dataset:
A Multi-Task Learning Model for Multidimensional Relevance Assessment

In recent years, deep learning models have been successfully applied to Information Retrieval (IR), mainly for assessing the topical relevance of documents with respect to queries. However, relevance is a multidimensional concept, which can be assessed based on several criteria, depending on the document type, the domain considered, the search task performed, etc. Given that recent advancements in deep neural networks enable several learning tasks to be solved simultaneously, in this paper we examine the possibility of modeling multidimensional relevance by jointly solving a retrieval task, to learn topical relevance, and a classification task, to learn additional relevance dimensions. To instantiate and evaluate the proposed model, we consider three query-independent relevance dimensions beyond topicality, i.e., readability, trustworthiness, and credibility. The reported findings show that the proposed joint modeling can improve the performance of the retrieval task.
Route Design of a String for a Tying Tool to Bind Wire Harnesses for Airplanes

Wire harnesses for airplanes are manufactured by bundling multiple wires together with strings by human workers. We designed a guide part to develop a tool to improve the efficiency of the bundling process. We proposed a method to calculate the optimal path in the whole process while minimizing the number of predetermined design parameters, and made a prototype of a guide part with the path determined by this met hod for verification.
Organizational Challenges of Development and Implementation of Virtual Reality Solution for Industrial Operation

This research investigated the organizational challenges related to the development and implementation of virtual reality (VR) technology for operation in a conservative heavy machinery industry. The incorporation of a VR solution for heavy machinery equipment enhanced the safety and convenience of operation under dangerous work conditions. However, the development and implementation processes faced challenges. Furthermore, the adoption of the solution by users was perceived to be slower than anticipated. We aimed to explore the main challenges that the developer organization faced and how it also influenced user organizations. Due to the exploratory nature of the research, qualitative analysis was chosen, interviews were conducted, and thematic analysis was applied. The themes and subthemes were identified and discussed. The results showed the existence of challenges related to technology maturity, managerial challenges regarding communication and support coordination, workload, and multiple stakeholder management. The findings emphasize the importance of attending to the existing and potential organizational challenges before and throughout technological innovation. Theoretical and managerial implications are discussed, and a future research agenda is suggested.
Optical quality evaluation for active afocal systems

SimVis Gekko is a novel see-through binocular visual simulator that is based on liquid-membrane tunable lenses (TLs) projected onto the pupil of the eye using a twisted miniaturized 4-f system. Following a temporal multiplexing approach that introduces periodic defocus variations in optical power at 50Hz, the TL generates multifocal images on the retina of the observer, that look static. In this study, the image quality of different tentative designs of SimVis Gekko was evaluated for different optical powers. The full optical system of SimVis Gekko was computer simulated to get the spot size, prismatic shift, angular magnification, and field curvature up to 20&deg; of field of view. An image quality bench was developed to capture and process images through the SimVis Gekko simulator. The system comprises a grayscale camera and a 19- mm focal-length lens with an adjustable diaphragm. A high-resolution screen was placed at one meter with two different targets: (1) a checkerboard, imaged through a 1-mm diaphragm, to measure optical quality, prismatic shift, magnification, and optical distortion; (2) a binary noise, imaged through a 5-mm diaphragm, used to measure the local field curvature and image quality. Images were obtained from 1 to 3D of the TL and automatically analyzed. Theoretical simulations and experimental measurements showed good agreement. Magnification and curvature were the major differences across designs. The last version measured was free of optical distortions with a central curvature-free area with high optical quality. The developed system could guide the assembly and fine adjustment of active afocal optical systems.
Attention-Based Multi-view Feature Fusion for Cross-Domain Recommendation

Cross-domain recommendation can effectively alleviate the data sparsity problem in recommender systems. Existing methods for cross-domain recommendation can be roughly divided into two categories: specific-feature-based methods and sharing-feature-based methods. Specific-feature-based methods focus on learning users’ and items’ domain-specific features in each domain and transferring the same or similar features across domains for recommendation. Sharing-feature-based methods concentrate on obtaining latent transferable features cross domains and utilizing them for recommendation. However, both methods have defects: 1) specific-feature-based methods fail to capture latent transferable features cross domains, which greatly reduces the availability of source domain information; 2) sharing-feature-based methods ignore users’ and items’ domain-specific features so that the recommended items of different domains have high similarity. Since user preferences are distinct in different domains, the recommended items may be unsuitable for users. To overcome the above problems, we propose an Attention-based Multi-View Feature fusion model (AMVF). To improve the availability of source domain information and recommend suitable items, AMVF learns latent transferable features and domain-specific features simultaneously. Since various features make distinct contributions to recommendation, seamlessly fusing different features is a challenge. Therefore, an attention-based feature fusion algorithm is designed to learn different importance of various features. To demonstrate the effectiveness of AMVF, extensive experiments are conducted on two pairs cross-domain datasets. The results empirically verify the superior performance of AMVF.
Tolerance-Based Short Text Sentiment Classifier

Sentiment classification identifies the polarity of text such as positive, negative or neutral based on textual features. A tolerance near set-based text classifier (TSC) is introduced in this paper to classify sentiment polarities of text with vectors from a pre-trained SBERT algorithm. One of the datasets (Covid-Sentiment) was hand-crafted with tweets from Twitter of opinions related to COVID. Experiments demonstrate that TSC outperforms five classical ML algorithms with one dataset, and is comparable with all other datasets using a weighted F1-score.
Pixel-based Continuous State Prediction with Perceptual Loss

Predicting the future is an essential but challenging process because of inherently uncertain model dynamics in our nature. In this paper, a perceptual loss is utilized to improve uncertainty and blurriness of video prediction in a stochastic approach. High dimensionality is reduced to a latent variable using a variation of the beta variational autoencoder. The latent variable contains temporal information from the consecutive state images and utilizes to predict the future states from the past. Perceptual loss is used rather than per-pixel loss between the reconstructed image and the original image to extract high-level image features. The perceptual loss using pre-trained network and KL divergence loss are combined during the training process. The proposed algorithm could show improved reconstruction ability and result in clear future state predictions.
Reliability Assessment of Tiny Machine Learning Algorithms in the Presence of Control Flow Errors

With the advances in hardware technologies, embedded and Edge devices are now able to offer sufficient memory and computational power to accommodate light-weight machine-learning (ML) classifiers. However, due to the intensive code optimization and summarization in the design phase, the reliability of light-weight ML applications is at risk. In this paper, we study the reliability of three prototypical light-weight ML applications against the well-known control flow (CF) errors. We have injected a total of 66,156 CF errors into Bonsai, ProtoNN, and TensorFlow Lite ML applications running on the Arduino board. Based on the results obtained from the error-injections, we found that CF errors could affect either the functionality or the classification accuracy of the ML-based inference as the embedded application. We conclude that making a single decision only after a long sequence of computations/branches may be more error-prone. This issue can be addressed by combining the inferences of intermediate nodes in the chain to obtain the final classification decision.
Towards a Tesseract of Sugeno Integrals

Structures of opposition, such as the hexagon and different cubes, derived from the square of opposition of ancient logic, have for more than a decade shown their interest in the analysis of various frameworks for the representation and processing of information (possibly pervaded with uncertainty). The use of a renewed and less constrained vision of the structures of opposition leads in this article to consider a general cube and a hypercube of opposition applicable to binary or gradual settings, which is here exemplified on Sugeno integrals and related integrals
MiniRos: an Autonomous UGV Robot for Education and Research

MiniRos is a small autonomous UGV-Unmanned ground vehicle developed for education and research purposes. In this paper, we develop MiniRos with a kinematic skid-steering, high torque and compact structure drive system that can take re-search to the outdoor environment. Its small payload capacity and power system are suitable for various types of loads, customized to meet research needs. MiniRos is run on a Robot Operating System (ROS), which has been developed to be compatible with standard robotics programming environments. The robot senses the environment with various kinds of sensors like depth cameras, 2D lidar scanners and performs all processing on embedded computers. The robot can build a 2D map with various SLAM algorithms such as Karto SLAM, Gmapping, Hector SLAM, and navigation in the built map. Moreover, the study evaluated both the computational and the memory requirements based on each SLAM algorithm within different sizes of maps. Finally, skid-steering type robot MiniRos simulation on Gazebo environment is available as open-source, this is a useful tool for researchers and educators.
A Novel Multi-objective Differential Evolution Algorithm for Clustering Data Streams

The world is full of data and day by day this is growing in an exponential manner. To deal with this data and extract useful information from it, a very effective field has been evolved called big data mining. The actual work of this process is to fetch various useful and effective patterns from this enormous amount of data. However, this work has been confined to the field of data stream clustering. In the field of data stream clustering, identification of clusters and its shape from the vast amount of complex real-world data is still a major challenge. In addition, the enormous dimensionality of a dataset brings more complexity to the extraction of information. This paper proposes a novel multi-objective Differential Evolution (DE) algorithm based on elitism that tactfully addresses the local minimum problem. In addition, for the automatic estimation of initial number of clusters in the dataset, three targets are considered. Our experimental results show that the proposed algorithm with three objectives perform better in the prediction of the total number of clusters in the dataset as compared to some single objective as well as some existing multi-objective algorithms. Moreover, our approach can be used to identify overlapping clusters in the dataset.
An Analysis and Validation Toolkit to Support the Undergraduate Course of Computer Organization and Architecture

As an important part of the computer organization and architecture (COA) course, the experiment teaching is generally about the computer system design. Students use the hardware description languages (HDLs) tools to implement the computer system on the Field Programmable Gate Array (FPGA) based platform. However, the HDLs tools are made for expert hardware engineers and the computer system is a very complex hardware project. It is hard for students to implement their computer system design in the limited lab hours. How to help students get the design validation and find the failure root is important in COA experiment teaching. To this end, an analysis and validation toolkit which is special for COA experiment teaching is designed. For two main steps of FPGA-based hardware design, waveform simulation and on-board testing, two packages were implemented for them respectively. The comparison results of using and not using our toolkit show it improves the effectiveness of experiment teaching greatly.
Word weapons? Letters to editors

This short commentary argues that academic letters to editors on politically-contested topics must be treated with particular ethical care. The interface between science and Māori/Indigenous knowledge is one such topic, vulnerable to inadequate but commonly-held ideas about both science and Māori/Indigenous knowledge. Letters to editors by scientists are personal opinion but carry the imprimatur of science expertise. When such letters contain lay views masquerading as expert opinion, they have negative effects on both science and Māori knowledge, hence qualifying to be described as ‘word weapons’.
MANet: Multi-Scale Attention Network for Correspondence Learning

Establishing reliable correspondences from a putative correspondence set is a challenging task. Most of state-of-the-art methods utilize the local context and global context to address the task. However, the local and global context often contains large number of outliers, which have a negative impact on capturing scene geometry. In this paper, we propose a Multi-scale Attention Network (called MANet), which introduces the attention mechanism for feature matching, to improve the ability of capturing scene geometry. Specifically, we first fuse the features of low and high levels by an multi-scale strategy network to enhance the representative ability of features. Then, we propose an attentive PointCN block and an attentive pooling layer, to discriminatively capture global context and local context information, respectively. We demonstrate through extensive experiments on both indoor and outdoor datasets that MANet provides a significant improvement in the performance of the two-view geometry and correspondences accuracy compared to the state-of-the-art methods.
A Universal Grammar for Specifying Visualization Types

Abstract A ‘universal grammar’ for the full spectrum of visualization types is discussed. The grammar enables the analysis of any type of visualization regarding its syntactic constituents, such as the types of visual encodings and visual components that are used. Such an analysis of a type of visualization, describing its compositional syntax, can be represented as a specification tree. Colour coded tree branches between constituent types enforce the combination rules visually. We discuss how these specification trees differ from linguistic parse trees, and how visual statements differ from verbal statements. The grammar offers a basis for generating visualization options, and the potential for formalization and for machine-readable specifications. This may serve as a basis for a system providing computer-generated visualization advice.
Semantic Improvement of Question-Answering System with Multi-Layer LSTM

Question answering (QA) is a popular method to extract information from a large pool of data. However, effective feature extraction and semantic understanding, and interaction between question and answer pairs are the main challenges for the QA model. Mitigating these issues, here, we propose a Long-Short-Term-Memory (LSTM) model using the recurrent neural network. This model aims to extract the effective interaction between questions-answers pairs using the Softmax and Max-pooling function. The experiment performs on a publicly available Wiki-QA dataset to identify the effectiveness of the proposed model. The evaluation performs by comparing the result with other existing models. The comparison shows that the proposed approach is significant and competitive for QA and achieves 83% training and 81% testing accuracy on the Wiki-QA dataset.
Comparative Analysis of Classification Algorithms in the Analysis of Medical Images From Speckle Tracking Echocardiography Video Data

Background. Machine learning allows applying various intelligent algorithms to produce diagnostic and/or prognostic models. Such models can be used to determine the functional state of the heart, which is diagnosed by speckle-tracking echocardiography. To determine the patient's heart condition in detail, a classification approach is used in machine learning. Each of the classification algorithms has a different performance when applied to certain situations. Therefore, the actual task is to determine the most efficient algorithm for solving a specific task of classifying the patient's heart condition when applying the same speckle-tracking echocardiography data set. Objective. We are aimed to evaluate the effectiveness of the application of prognostic models of logistic regression, the group method of data handling (GMDH), random forest, and adaptive boosting (AdaBoost) in the construction of algorithms to support medical decision-making on the diagnosis of coronary heart disease. Methods. Video data from speckle-tracking echocardiography of 40 patients with coronary heart disease and 16 patients without cardiac pathology were used for the study. Echocardiography was recorded in B-mode in three positions: long axis, 4-chamber, and 2-chamber. Echocardiography frames that reflect the systole and diastole of the heart (308 samples in total) were taken as objects for classification. To obtain informative features of the selected objects, the genetic GMDH approach was applied to identify the best structure of harmonic textural features. We compared the efficiency of the following classification algorithms: logistic regression method, GMDH classifier, random forest method, and AdaBoost method. Results. Four classification models were constructed for each of the three B-mode echocardiography positions. For this purpose, the data samples were divided into 3: training sample (60%), validation sample (20%), and test sample (20%). Objective evaluation of the models on the test sample showed that the best classification method was random forest (90.3% accuracy on the 4-chamber echocardiography position, 74.2% on the 2-chamber, and 77.4% on the long axis). This was also confirmed by ROC analysis, wherein in all cases, the random forest was the most effective in classifying cardiac conditions. Conclusions. The best classification algorithm for cardiac diagnostics by speckle-tracking echocardiography was determined. It turned out to be a random forest, which can be explained by the ensemble approach of begging, which is inherent in this classification method. It will be the mainstay of further research, which is planned to be performed to develop a full-fledged decision support system for cardiac diagnostics.
Toxic Comment Classification Service in Social Network

The article discusses the development of an online tool for moderating the content of social network groups. The use of classification using machine learning methods is proposed as the main element of the system. The creation of the feature set of messages is assumed by extracting the content features of the text, as well as the use of word embeddings vectors. The authors conducted a series of experiments to find the best combination of vector representation, content features and classification method. Tests on a dataset of 11 thousand messages in Russian showed the result of 87% accuracy. The architecture of the group moderator’s web application with the ability to automatically apply classification results to control users and display posts is proposed.
Take a seat - the influence of sitting down in a virtual environment on workload, user experience and presence

The integration of real, physical objects into virtual scenarios enables direct haptic feedback for the user. In addition to the interaction with small, hand-held, hybrid objects, i.e., objects that exist both in the virtual and real environments, it is also possible to interact with more massive objects, such as tables and chairs. A virtual assembly scenario for both a seated and a standing assembly workstation to be used in conjunction with a standing-height drill station was developed and matching physical and virtual representations of the furniture, a movable work chair, was added. The virtual world was dynamically aligned to the real object by means of tracking hardware, so that virtuality and reality shared the same spatial position in relation to the user. Within a study, the participants had to carry out a manual assembly task on both workstations, thus physically and dynamically interacting with and using (sitting down on, getting up from) the real chair integrated in the virtual scenario of the seated workbench. The chair was absent in the scenario of the standing assembly workbench. The collected data on subjective cognitive workload, user experience and presence are supposed to provide an initial indication of the influence of such physical objects in the virtual world. Furthermore, the question of how trust in physical objects can be increased in the virtual world is investigated.
The Experience “Mondrian from Inside”. An Immersive and Interactive Virtual Reality Experience in Art

Technology and art can come together to create exciting experiences that bring the general public closer to different types of artworks. Virtual reality experiences are transforming the way cultural heritage institutions exhibit their works of art and the contextual information in which they were conceived. In this manuscript we present a virtual reality experience that combines the world of the sensory and the world of divulgation. The work carried out involved an in-depth analysis of the painting “Composition A” by Piet Mondrian and the design from scratch of a virtual environment based on it that highlights and deepens the nuances of the original painting. The developed experience allows to bring the painting to the public in an original, surprising and interactive way. The user is introduced into a virtual space where the painting takes on a special role, acting as the main element that allows the user to advance in a storytelling made up of ten scenes. When the user interacts with the painting, the virtual space in which they are immersed is modified, allowing them to advance from one scene to the next. Each scene delves into a relevant aspect of the painter’s work and life, presenting the intended information in an attractive way using interactivity as the main tool. The experience is therefore immersive but also interactive, the latter characteristic being the main difference with respect to other virtual experiences related to the world of art that are purely immersive.