Reviewerly/exHarmony huggingface dataset:
A Separation Method of Superimposed Gratings in Double-Projector Fringe Projection Profilometry Using a Color Camera

Fringe projection profilometry has been intensively studied for several decades. However, due to the limitation of the field range of a single projector, when measuring objects with complex surfaces, there are always shadow areas in the captured images, resulting in missing measurement data in the dark areas. To solve this problem, systems with double projectors and single camera were employed. Not only were the shadow areas reduced, but system recalibration and multiple measurements were not needed, improving measuring efficiency. Nevertheless, separating the corresponding projection pattern from the superimposed fringe presented a difficult problem. A color camera has RGB three color channels. When the color camera is applied to fringe projection profilometry, the information obtained is three times as much as that of the monochrome camera. Due to the small overlap between the red- and blue-light spectra response of color cameras, the channel color crosstalk can be ignored. This paper proposes a method to project red and blue fringe patterns from two projectors and utilize the characteristics of the red and blue channels of the color camera to separate the superposition grating pattern. The original patterns can be recovered integrally and easily. To explain the effectiveness of superimposed fringe separation, a simulation and experiments were carried out. Both of them showed that the superimposed fringe can be separated correctly, proving that our method is feasible.
An improved object tracking algorithm based on adaptive weighted strategy and occlusion detection mechanism

Due to complex background and volatile object shape-appearance in image, the stability and accuracy of tracking algorithm is often disturbed and reduced. So how to accurately and robustly track object in object tracking application is a challenge topic at home and abroad. Built upon the methodologies of compressive tracking and spatio-temporal context, a simple yet robust object tracking method is proposed for solving the drift and occlusion problems in paper. It combines two existing classical ideas into a single framework: adaptive weighted idea and occlusion detection mechanism. In order to weaken interference problems of object background, object area is firstly partitioned into equal-sized sub-patches and the different weight related with location information is assigned for each patch; Then, for improving its robustness, Bhattacharyya distance is adopted to find out these samples with maximum discrimination; In addition, our proposed occlusion detection mechanism is for recapturing the tracked object when occlusion occurs. Many simulation experiments show that our proposed algorithm achieves more favorable performance than these existing state-of-the-art algorithms in handing various challenging infrared videos, especially occlusion and shape deformation.
Quality of Online Learning Participation in a Context of Crisis

The COVID-19 pandemic has forced educational institutions to adopt online tools in order to conduct emergency remote teaching and make efficient use of virtual learning contexts. However, although these contexts may serve to improve teaching processes, a number of issues must be taken into consideration in order to ensure quality student learning. We analyze a computer programming module taught during the first year of a Computer Science degree course at a Chilean university. The module is taught online using a Learning Management System (LMS). We discuss the type of participation required in order to achieve the construction of more complex knowledge in virtual contexts, including the need for students to formulate and post longer messages. We also emphasize the need for these messages to be simple and precise. Furthermore, we observe a relationship between the final grades obtained by students and the levels of information and quality of the messages that they post. Our results reveal that students with a positive perception of their own learning are not necessarily those who contribute to the highest level. Finally, we discuss the need for particular attention to be paid to the guidelines that define activities, as these have a considerable influence on the generation of dialogue that is conducive to the construction of new knowledge.
Rebuilding Trust in Cryptocurrency Exchanges after Cyber-attacks

Cryptocurrency exchanges are subjected to cyberattacks and cryptocurrencies worth millions of US dollars are lost every year.The value of cryptocurrencies is volatile and the cyber-attacks on the exchanges make them even more volatile.Whenever these cyber-attacks happen, the customers might lose their trust not only on a given exchange but also in cryptocurrencies in general.Hence, the exchanges need to rebuild trust among their current and potential customers after a cyber-attack.In this paper, we present findings from a study on cyber-attacks on seven different exchanges, focusing on how they responded after the cyber-attacks to rebuild customers' trust.Analyzing the responses of current and potential customers to the trust rebuilding techniques used by the exchanges, we also assessed the efficiency of these techniques.
Best practices for embedded librarian service: Connecting with students online

Embedded librarianship has been around for a long time. It was considered a buzz word and began appearing in journals and conferences early in the 21st century, according to Kathy Drewes and Nadine Hoffman. Embedded librarianship became a way for librarians to provide research help and assistance to distance education students, as more library resources came online. Embedded librarianship has been an important service no more so than now in the new reality of the pandemic world.
Toward high accuracy and visualization: An interpretable feature extraction method based on genetic programming and non-overlap degree

Genetic programming (GP) has shown promising results in interpretable feature extraction, but few works considered both classification accuracy and data visualization as objectives. Evaluating the extracted features based on the combination of accuracy measures and visualization measures can help to achieve the two objectives simultaneously. However, the exploitation of improper visualization measures and combination methods will decrease the classification accuracy. In this paper, a novel feature extraction method based on GP and non-overlap degree is proposed to extract interpretable features for high accuracy and visualization. And a novel function that maximizes the product of the accuracy of a linear classifier and the non-overlap degree is proposed to evaluate the extracted features. The proposed method, named GP-ANO, is compared with other methods on five medical datasets by six common machine learning methods. The experimental results demonstrate that the GP-ANO method outperforms other compared methods in terms of both classification accuracy and data visualization.
Path Planning and Trajectory Tracking Strategy of Autonomous Vehicles

With the development of global urbanization and the construction of regional urbanization, residents around urban cities are increasingly making demands on urban public transportation system. A new kind of modern public transportation vehicle named Multi-Articulated Guided Vehicle based on Virtual Track (MAAV-VT) with the advantages of beautiful, smart energy conservation and environmental protection is proposed in this paper, which aims at optimizing the public transportation system between and within urban areas. Therefore, concentrating on the general design and control strategy, the main contents of this paper are as follows. At first, the design concepts and key technologies of MAAV-VT are introduced. It is the fusion of urban rail transit operation mode and advanced automotive technologies, which have the characteristics of 100% low-floor, medium to high velocity, medium to big capacity, and low construction cost. Then, as the core subsystem, to guarantee the properties of self-guiding and trajectory tracking of the new vehicle, this paper is focused on the control system based on the dynamics and kinematics model of the whole multi-articulated vehicle. The multi-trace-points cooperative trajectory tracking control strategy on the basis of the circulation of feasible path generation method is proposed and the lateral controller is designed for trajectory tracking. The process of feasible path generation is conducted once the tracking error exceeded. A simulation platform is built considering the mechanical properties of each vehicle element and the characteristic of articulated mechanism. Finally, the function of control system is validated. The tracking error of each vehicle elements would be reduced to make sure the whole multi-articulated vehicle moves along the preset virtual track.
Metal enrichment in lithologically complex black shales: a case study from the Tremadocian of NE Estonia

Significantly elevated U, Mo, Zn and Pb contents characterize the Early Ordovician black shales in the Sillamäe area, NE Estonia.The presence of silty interlayers with sulphidic mineralization and authigenic carbonates suggests unique physicochemical conditions for metal enrichment in this location.We investigated metallogenesis of these shallowwater black shales based on high resolution mapping of element distribution in the Sõtke drill core and nearby Päite outcrop using Xray fluorescence spectroscopy, inductively coupled plasma mass spectrometry and organic elemental analysis, complemented by optical microscopy and scanning electron microscopy of selected samples.Enriched metals in the black shales of the study area show dissimilar distribution trends with sharp vertical concentration gradients.The recorded variance ranges were 88-275 ppm for U, 70-2467 ppm for Mo, 85-1600 ppm for V, 21-17 283 ppm for Zn and 95-26 549 ppm for Pb, while total organic carbon varied from 0.5 to 13 wt%.In most cases, the metals showed no clear covariance with organic matter or other major compounds such as S or P. The development of a sulphate reduction zone near the sediment-water interface with a sharp decrease in Eh, production of H 2 S, elevated alkalinity and pH, and (re)distribution of phosphorus probably controlled the syngenetic capture of Mo and U. Enhanced transfer of fluids and solutes in coarsegrained permeable beds facilitated the accumulation of metals, while the deposit probably acted as a semiopen geochemical system throughout its geological evolution.Local Zn and Pb enrichment developed due to the intrusion of late diagenetic metal bearing fluids and the entrapment of metals in beds that contained authigenic carbonates.
No-reference Image Blur Assessment Based on Multi-scale Spatial Local Features

Blur is an important type of image distortion.How to evaluate the quality of blurred image accurately and efficiently is a research hotspot in the field of image processing in recent years.Inspired by the multi-scale perceptual characteristics of the human visual system (HVS), this paper presents a no-reference image blur/sharpness assessment method based on multi-scale local features in the spatial domain.First, considering various content has different sensitivity to blur distortion, the image is divided into smooth, edge, and texture regions in blocks.Then, the Gaussian scale space of the image is constructed, and the categorized contrast features between the original image and the Gaussian scale space images are calculated to express the blur degree of different image contents.To simulate the impact of viewing distance on blur distortion, the distribution characteristics of local maximum gradient of multi-resolution images were also calculated in the spatial domain.Finally, the image blur assessment model is obtained by fusing all features and learning the mapping from features to quality scores by support vector regression (SVR).Performance of the proposed method is evaluated on four synthetically blurred databases and one real blurred database.The experimental results demonstrate that our method can produce quality scores more consistent with subjective evaluations than other methods, especially for real burred images.
Unsupervised Anomaly Detection with Distillated Teacher-Student Network Ensemble

We address the problem of unsupervised anomaly detection for multivariate data. Traditional machine learning based anomaly detection algorithms rely on specific assumptions of normal patterns and fail to model complex feature interactions and relations. Recently, existing deep learning based methods are promising for extracting representations from complex features. These methods train an auxiliary task, e.g., reconstruction and prediction, on normal samples. They further assume that anomalies fail to perform well on the auxiliary task since they are never trained during the model optimization. However, the assumption does not always hold in practice. Deep models may also perform the auxiliary task well on anomalous samples, leading to the failure detection of anomalies. To effectively detect anomalies for multivariate data, this paper introduces a teacher-student distillation based framework Distillated Teacher-Student Network Ensemble (DTSNE). The paradigm of the teacher-student distillation is able to deal with high-dimensional complex features. In addition, an ensemble of student networks provides a better capability to avoid generalizing the auxiliary task performance on anomalous samples. To validate the effectiveness of our model, we conduct extensive experiments on real-world datasets. Experimental results show superior performance of DTSNE over competing methods. Analysis and discussion towards the behavior of our model are also provided in the experiment section.
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes

Few-shot classification (FSC), the task of adapting a classifier to unseen classes given a small labeled dataset, is an important step on the path toward human-like machine learning. Bayesian methods are well-suited to tackling the fundamental issue of overfitting in the few-shot scenario because they allow practitioners to specify prior beliefs and update those beliefs in light of observed data. Contemporary approaches to Bayesian few-shot classification maintain a posterior distribution over model parameters, which is slow and requires storage that scales with model size. Instead, we propose a Gaussian process classifier based on a novel combination of Polya-gamma augmentation and the one-vs-each softmax approximation that allows us to efficiently marginalize over functions rather than model parameters. We demonstrate improved accuracy and uncertainty quantification on both standard few-shot classification benchmarks and few-shot domain transfer tasks.
TEAM EFFECTIVENESS IN SOFTWARE DEVELOPMENT: THE ROLE OF PERSONALITY AND WORK FACTORS

Globalization and dynamic economic conditions have exponentially increased the use of virtual work in organizations. Effectiveness in software development teams, therefore, is now a relevant issue in business, psychology or other fields. Despite the uniqueness of software development teams and their work conditions, their effectiveness has not been sufficiently researched to clearly determine how it relates to personality traits, work challenges and the virtualization level. To expand the understanding of the mechanisms that influence team effectiveness in software development, an ecological framework is selected. The purpose of this study is to explore the relationship between personality traits, work factors and team effectiveness within the context of software development teams. Methodology: The exploratory study was conducted in international software development companies operating in Lithuania. Forty-five software development teams (N = 142) filled in the Team task performance scale, Hexaco-PI-R questionnaire and rated perceived work challenges and team virtualization level. The statistical analysis was conducted with SPSS. This survey is an initial stage of a larger project. Findings: Personality traits Conscientiousness and Extraversion relate to team effectiveness. In software development teams the most prevalent work challenges were time zone differences, lack of information when working on a project, and lack of a clear purpose. Lack of information and poor foreign language skills negatively affect team effectiveness. A higher virtualization level is associated with more frequent work challenges (culture and foreign language challenges, time zone differences) and team performance.
Data Sovereignty in Data Donation Cycles - Requirements and Enabling Technologies for the Data-driven Development of Health Applications

Personalized healthcare is expected to increase the efficiency and the effectiveness of health services using different kinds of algorithms on existing data.This approach is currently confronted with the lack of digital data and the desire for self-determined personal data handling.However, the issue of health data donation is on the political agenda of some governments.Within this work, a knowledge base will be created by reviewing existing approaches and technologies regarding this topic with the focus on chronic diseases.A list of requirements will be derived from which we conceptualize a data donation cycle to demonstrate the challenges and opportunities of health data sovereignty and its future possibilities concerning data-driven health application development.By linking the requirements to technological approaches, the baseline for future open ecosystems will be presented.
An Intelligent Mobile Application for Assisting Visually Impaired in Daily Consumption Based on Machine Learning with Assistive Technology

Visually impaired people are facing many difficulties in daily life because of the disability in visual sensation. In daily consumption, they are hard to perform the transactions solely. They need to be assisted by other people in daily life, especially in purchasing, since they cannot recognize the different products with similar shapes and sizes. Also, in processing payment, visually impaired people are hard to differentiate the denominations (values) of banknotes. And many derived problems, such as cheating and providing the improper products, are being occurred. In this paper, an intelligent recognition system has been developed for visually impaired people in Hong Kong. The application system can help the users recognize canned foods and differentiate the nominal values of the banknotes so as to complete the monetary transactions efficiently and effectively. The application can be installed in smartphones with voice and vibration. By applying neural networks with assistive technology, the accuracies of recognizing banknotes and canned food are over 95% and 75%, respectively.
ChipNet: Budget-Aware Pruning with Heaviside Continuous Approximations

Structured pruning methods are among the effective strategies for extracting small resource-efficient convolutional neural networks from their dense counterparts with minimal loss in accuracy. However, most existing methods still suffer from one or more limitations, that include 1) the need for training the dense model from scratch with pruning-related parameters embedded in the architecture, 2) requiring model-specific hyperparameter settings, 3) inability to include budget-related constraint in the training process, and 4) instability under scenarios of extreme pruning. In this paper, we present ChipNet, a deterministic pruning strategy that employs continuous Heaviside function and a novel crispness loss to identify a highly sparse network out of an existing dense network. Our choice of continuous Heaviside function is inspired by the field of design optimization, where the material distribution task is posed as a continuous optimization problem, but only discrete values (0 or 1) are practically feasible and expected as final outcomes. Our approach's flexible design facilitates its use with different choices of budget constraints while maintaining stability for very low target budgets. Experimental results show that ChipNet outperforms state-of-the-art structured pruning methods by remarkable margins of up to 16.1% in terms of accuracy. Further, we show that the masks obtained with ChipNet are transferable across datasets. For certain cases, it was observed that masks transferred from a model trained on feature-rich teacher dataset provide better performance on the student dataset than those obtained by directly pruning on the student data itself.
Prediction of Indonesia Strategic Commodity Prices during the COVID-19 Pandemic based on a Simultaneous Comparison of Kernel and Fourier Series Estimator

This study describes a new idea about comparing two new estimations in nonparametric regression for multiresponse cases or simultaneously model based on Fourier series and kernel estimators enabling the prediction of Indonesian strategic commodity prices during the COVID-19 pandemic. Based on the National Strategic Food Price Information Center in Indonesia, there are 10 strategic commodities in the agriculture, livestock, fishery, and horticultural sectors, which have had the biggest endowment to secure food supplies and the formation of inflation figures in Indonesia. These commodities include rice, chicken meat, beef, chicken, egg, onion, garlic, chili, cayenne, cooking oil, and sugar. Using the goodness estimator of criteria in nonparametric regression, such as the smaller Generalized Cross-Validation, the smaller Mean Square Error, and the larger determination coefficient (R2), the result of this study is Fourier series estimator to predict the prices of 10 food commodities, simultaneously. When compared with the kernel estimator, the Fourier series estimator meets the criteria of goodness with a smaller Mean Square Error value of 0.052 and a larger determination coefficient of 99.0472%. The selected estimator has very good performance to predict the prices of 10 food commodities, because the prediction result has very small Mean Absolute Percentage Error equaling 0.0443%. This prediction result can be used for the government to monitor and evaluate price fluctuations for 10 commodities so that the stability of national strategic commodities becomes daily consumption to be maintained, especially during the COVID-19 pandemic.
Developing a Product Knowledge Graph of Consumer Electronics to Manage Sustainable Product Information

Transformational computing paradigms, such as artificial intelligence, home automation, and the Internet of Things, are being rapidly applied to consumer electronics products, thus aiding in the development of integrated and innovative features. Hence, ubiquitous computing and electronic devices are increasingly becoming essential to everyday life. In this context, a wide gulf often exists between the capabilities and technical features of consumer electronic devices and the consumers’ understanding of such devices and ability to operate them correctly and effectively. This study proposes a machine-readable knowledge model representing technical terms in product specifications along with a product knowledge graph to discover semantic relationships among various products. Formal concept analysis is applied to conceptually analyze the specification terms of heterogeneous electronic products and design a hierarchical knowledge structure of extracted concepts, to elaborate the proposed knowledge model.