Reviewerly/exHarmony huggingface dataset:
Effect of work environment on employee engagement: Mediating role of ethical decision-making

Organizations are increasingly being responsible for providing good and healthy work environment that can help in supporting employees performing their duties. Supportive work environment can increase the degree of employee engagement, which in turn, makes them more attached to their roles. The purpose of the current study is two-folded: to investigate the effect of work environment on engagement, and to test ethical decision making as a mediator between environment and engagement. Data were collected from a sample of 237 employees from transportation corporations listed on the Amman Stock Exchange (2019) in Jordan. Structural equation modeling was utilized to test the model. Results proposed that engagement was significantly related to work environment and ethical decision-making. Work environment has a greater effect on employee engagement than on ethical decision-making. Moreover, the effect of ethical decision-making on employee engagement was greater than the effect of work engagement on ethical decision-making. Furthermore, the study yielded support for the claim that ethical decision-making played a significant role in the relationship between work environment and employee engagement. Therefore, employees having good working conditions and making ethical decisions tend to get higher levels of employee engagement.
TRFH: towards real-time face detection and head pose estimation

Abstract Nowadays, face detection and head pose estimation have a lot of application such as face recognition, aiding in gaze estimation and modeling attention. For these two tasks, it is usually to design two different models. However, the head pose estimation model often depends on the region of interest (ROI) detected in advance, which means that a serial face detector is needed. Even the lightest face detector will slow down the whole forward inference time and cannot achieve real-time performance when detecting the head pose of multiple people. We can see that both face detection and head pose estimation need face features, so a shared face feature map can be used between them. In this paper, a multi-task learning model is proposed that can solve both problems simultaneously. We directly detect the location of the center point of the bounding box of face; at this location, we calculate the size of the bounding box of face and the head attitude. We evaluate our model’s performance on the AFLW. The proposed model has great competitiveness with the multi-stage face attribute analysis model, and our model can achieve real-time performance.
SimuRec: Workshop on Synthetic Data and Simulation Methods for Recommender Systems Research

There is significant interest lately in using synthetic data and simulation infrastructures for various types of recommender systems research. However, there are not currently any clear best practices around how best to apply these methods. We proposed a workshop to bring together researchers and practitioners interested in simulating recommender systems and their data to discuss the state of the art of such research and the pressing open methodological questions. The workshop resulted in a report authored by the participants that documents currently-known best practices on which the group has consensus and lays out an agenda for further research over the next 3–5 years to fill in places where we currently lack the information needed to make methodological recommendations.
A Comparative Evaluation of Deep Learning Anomaly Detection Techniques on Semiconductor Multivariate Time Series Data

In industrial processes, keeping equipment units in good operating conditions while reducing maintenance costs is one of the most important objectives to improve productivity. One of the ways to do so is to early detect equipment dysfunction. This can be done by analyzing the massive amounts of data collected via numerous sensors during production activities. Thanks to the advantages of distributed architecture and computation efficiency improvements, deep learning methods have gained much interest and have been investigated by researchers for thorough industrial data analysis, notably for anomaly detection. A comparative evaluation of data-driven deep learning methods used to detect anomalies occurring during equipment processing is proposed. This evaluation is done on a total of six methods, their ability to detect anomalies on raw sensor data, collected on semiconductor machines, with variable correlations and temporal dependencies is discussed. The evaluation gives an insight on the industrial performances on the methods and shows how the supervised learning methods outperform the other models with less training time but need labelled data meanwhile some self-supervised learning methods have good detection performances with training done on normal data only.
Understanding of Data Preprocessing for Dimensionality Reduction Using Feature Selection Techniques in Text Classification

The volume of textual data in digital form is growing with each day. For arranging these textual data, text classification has been used. To achieve efficient text classification, data preprocessing is an important phase. It prepares information for machine learning models. Text classification, however, has the issue of the high dimensionality of space for features. Feature selection is a technique for data preprocessing widely used on high-dimensional data. By feature selection techniques, this high dimensionality of feature space is solved and increases text classification efficiency. Feature selection explores how a list of features used to create text classification models may be chosen. Its goals include reducing dimensionality, deleting uninformative features, reducing the amount of data available to classifiers for learning, and enhancing classifiers’ predictive performance. The different methods of feature selection are presented in this paper. This paper also presents the advantages and limitations of feature selection methods.
Stationary excitation waves and multimerization in arrays of quantum emitters

We explore the features of an equally-spaced array of two-level quantum emitters, that can be either natural atoms (or molecules) or artificial atoms, coupled to a field with a single continuous degree of freedom (such as an electromagnetic mode propagating in a waveguide). We investigate the existence and characteristics of bound states, in which a single excitation is shared among the emitters and the field. We focus on bound states in the continuum, occurring in correspondence of excitation energies in which a single excited emitter would decay. We characterize such bound states for an arbitrary number of emitters, and obtain two main results, both ascribable to the presence of evanescent fields. First, the excitation profile of the emitter states is a sinusoidal wave. Second, we discuss the emergence of multimers, consisting in subsets of emitters separated by two lattice spacings in which the electromagnetic field is approximately vanishing.
Boosting End-to-end Multi-Object Tracking and Person Search via Knowledge Distillation

Multi-Object Tracking (MOT) and Person Search both demand to localize and identify specific targets from raw image frames. Existing methods can be classified into two categories, namely two-step strategy and end-to-end strategy. Two-step approaches have high accuracy but suffer from costly computations, while end-to-end methods show greater efficiency with limited performance. In this paper, we dissect the gap between two-step and end-to-end strategy and propose a simple yet effective end-to-end framework with knowledge distillation. Our proposed framework is simple in concept and easy to benefit from external datasets. Experimental results demonstrate that our model performs competitively with other sophisticated two-step and end-to-end methods in multi-object tracking and person search.
Considerations when learning additive explanations for black-box models

Many methods to explain black-box models, whether local or global, are additive. In this paper, we study global additive explanations for non-additive models, focusing on four explanation methods: partial dependence, Shapley explanations adapted to a global setting, distilled additive explanations, and gradient-based explanations. We show that different explanation methods characterize non-additive components in a black-box model's prediction function in different ways. We use the concepts of main and total effects to anchor additive explanations, and quantitatively evaluate additive and non-additive explanations. Even though distilled explanations are generally the most accurate additive explanations, non-additive explanations such as tree explanations that explicitly model non-additive components tend to be even more accurate. Despite this, our user study showed that machine learning practitioners were better able to leverage additive explanations for various tasks. These considerations should be taken into account when considering which explanation to trust and use to explain black-box models.
An adaptive approach for internet phishing detection based on log data

The Internet has become one of the most important daily socials, financial and other activities.the number of customers who use the Internet to conduct their business and purchases is very large.This results in billions of dollars being transferred every day online.Such a large amount of money attracts the attention of cybercriminals to carry out their illegal activities."Fraud" is one of the most dangerous of these methods, especially phishing, where attackers try to steal user credentials using fraudulent emails, fake websites, or both.The proposed system for this paper includes efficient data extraction from the web file through data collection and preprocessing.and web usage mining procedure to extract features that demonstrate user behavior.and feature-extracting URL analysis to detect website phishing addresses.After that, the features from the above two parts are combined to make the number of features sixty-three.Finally, a classification algorithm (Random Forests) is applied to determine if website addresses are phishing or legitimate.Suggested algorithms performance is determined by using a confusion matrix and a number of metrics that shows the robustness of the proposed system.
Mixed reality based environment for learning sensing technology applications in construction

With the growing rate of adoption of sensing technologies in the construction industry, there is an increased need for technically skilled workforce to successfully deploy these technologies on construction projects. Inspired by opportunities offered by mixed reality, this paper presents the development and evaluation of a holographic learning environment that can afford learners an experiential opportunity to acquire competencies for implementing sensing systems on construction projects. To develop the content of the learning environment, construction industry practitioners and instructors were surveyed, and construction industry case studies on the applications of sensing technologies were explored. Findings of the surveys revealed sensing technologies domain-specific skill gap in the construction industry. Further, the findings informed the requirements of the learning environment. Based on these requirements, key characteristics of the learning environment are identified and employed in designing the environment. Still, a formative evaluation is important for developing an effective mixed reality learning environment for teaching domain-specific competencies. Thus, it is imperative to understand the quality, appropriateness, and representativeness of the content of the learning environment. This paper also presents a learnability assessment of the developed mixed reality learning environment. The assessment was conducted utilizing a focus group discussion with construction industry practitioners. Feedback was sought from the participants regarding the reflectiveness of the layout of the virtual environment of an actual construction site and the appropriateness of the represented construction applications. This study contributes to the definition of the type of domain-specific skills required of the future workforce for implementing sensing technologies in the construction industry and how such skills can be developed and enhanced within a mixed reality learning environment.
Going Above and Beyond eCommerce in the Future Highly Virtualized World and Increasingly Digital Ecosystem

In 2020, the digital world experienced ten years of eCommerce growth in 90 days. With Covid-19 modifying consumer habits, 100,000 stores are expecting to close within five years. Transiting toward online experiences, the way human beings consume made immediate headway with higher shopping and personalization expectations. Generation Z members are even enjoying a virtual life with 80% of their friends never met in the physical world. These drastic consumer behavior changes also bring momentum for a new range of virtual experiences. Considering 3% is the average Retail eCommerce sale conversion rate, understanding the remaining part of the traffic, the 97% of visitors not buying on eCommerce platforms, is mandatory to ensure a sustainable digital future. It is critical for brand manufacturers and retailers to understand if eCommerce reached its full potential and how consumers respond to Extented Reality (XR) in this context. The purpose of this explanatory study is to discuss prospects of consumers about the current state of eCommerce and to examine their attitudes toward XR Commerce. This is confirmed by this study which focused on the requirements of consumers when adopting digital ecosystems: consumers find eCommerce experiences sterile and demand more online individualization of their brand experiences. Customization of the brand experience should not be seen as a mass goal, but from individual perspective: one-to-one and virtually augmented. At the end of this exploratory study, appropriate measures are discussed to satisfy the right consumers with the right digital experiences. A total of 335 subjects participated in the study.
A Support Vector Machine and Decision Tree Based Breast Cancer Prediction

The first step in diagnosis of a breast cancer is the identification of the disease. Early detection of the breast cancer is significant to reduce the mortality rate due to breast cancer. Machine learning algorithms can be used in identification of the breast cancer. The supervised machine learning algorithms such as Support Vector Machine (SVM) and the Decision Tree are widely used in classification problems, such as the identification of breast cancer. In this study, a machine learning model is proposed by employing learning algorithms namely, the support vector machine and decision tree. The kaggle data repository consisting of 569 observations of malignant and benign observations is used to develop the proposed model. Finally, the model is evaluated using accuracy, confusion matrix precision and recall as metrics for evaluation of performance on the test set. The analysis result showed that, the support vector machine (SVM) has better accuracy and less number of misclassification rate and better precision than the decision tree algorithm. The average accuracy of the support vector machine (SVM) is 91.92 % and that of the decision tree classification model is 87.12 %.
Overview of UAV path planning algorithms

The paper first summarizes the path planning, then divides the path planning algorithm suitable for UAV into global path planning and local path planning in continuous domain. According to the related algorithms, the background, design ideas, advantages and disadvantages are summarized. Finally, the prospect of the development direction of future research.
A Robust Discontinuous Phase Unwrapping Based on Least-Squares Orientation Estimator

Weighted least-squares (WLS) phase unwrapping is widely used in optical engineering. However, this technique still has issues in coping with discontinuity as well as noise. In this paper, a new WLS phase unwrapping algorithm based on the least-squares orientation estimator (LSOE) is proposed to improve phase unwrapping robustness. Specifically, the proposed LSOE employs a quadratic error norm to constrain the distance between gradients and orientation vectors. The estimated orientation is then used to indicate the wrapped phase quality, which is in terms of a weight mask. The weight mask is calculated by post-processing, including a bilateral filter, STDS, and numerical relabeling. Simulation results show that the proposed method can work in a scenario in which the noise variance is 1.5. Comparisons with the four WLS phase unwrapping methods indicate that the proposed method provides the best accuracy in terms of segmentation mean error under the noisy patterns.
Transparent forwarders

In this paper, we revisit the open DNS (ODNS) infrastructure and, for the first time, systematically measure and analyze transparent forwarders, DNS components that transparently relay between stub resolvers and recursive resolvers. Our key findings include four takeaways. First, transparent forwarders contribute 26% (563k) to the current ODNS infrastructure. Unfortunately, common periodic scanning campaigns such as Shadowserver do not capture transparent forwarders and thus underestimate the current threat potential of the ODNS. Second, we find an increased deployment of transparent forwarders in Asia and South America. In India alone, the ODNS consists of 80% transparent forwarders. Third, many transparent forwarders relay to a few selected public resolvers such as Google and Cloudflare, which confirms a consolidation trend of DNS stakeholders. Finally, we introduce DNSRoute++, a new traceroute approach to understand the network infrastructure connecting transparent forwarders and resolvers.
Learning Optimal Decision Sets and Lists with SAT

Decision sets and decision lists are two of the most easily explainable machine learning models. Given the renewed emphasis on explainable machine learning decisions, both of these machine learning models are becoming increasingly attractive, as they combine small size and clear explainability. In this paper, we define size as the total number of literals in the SAT encoding of these rule-based models as opposed to earlier work that concentrates on the number of rules. In this paper, we develop approaches to computing minimum-size “perfect” decision sets and decision lists, which are perfectly accurate on the training data, and minimal in size, making use of modern SAT solving technology. We also provide a new method for determining optimal sparse alternatives, which trade off size and accuracy. The experiments in this paper demonstrate that the optimal decision sets computed by the SAT-based approach are comparable with the best heuristic methods, but much more succinct, and thus, more explainable. We contrast the size and test accuracy of optimal decisions lists versus optimal decision sets, as well as other state-of-the-art methods for determining optimal decision lists. Finally, we examine the size of average explanations generated by decision sets and decision lists.
Joint Multimedia Event Extraction from Video and Article

Visual and textual modalities contribute complementary information about events described in multimedia documents.Videos contain rich dynamics and detailed unfoldings of events, while text describes more high-level and abstract concepts.However, existing event extraction methods either do not handle video or solely target video while ignoring other modalities.In contrast, we propose the first approach to jointly extract events from video and text articles.We introduce the new task of Video MultiMedia Event Extraction (VM 2 E 2 ) and propose two novel components to build the first system towards this task.First, we propose the first self-supervised multimodal event coreference model that can determine coreference between video events and text events without any manually annotated pairs.Second, we introduce the first multimodal transformer which extracts structured event information jointly from both videos and text documents.We also construct and will publicly release a new benchmark of video-article pairs, consisting of 860 video-article pairs with extensive annotations for evaluating methods on this task.Our experimental results demonstrate the effectiveness of our proposed method on our new benchmark dataset.We achieve 6.0% and 5.8% absolute F-score gain on multimodal event coreference resolution and multimedia event extraction.
Fusion of Data and Expert Knowledge for Fault Tree Reliability Analysis of Cyber-Physical Systems

Reliability analysis of cyber-physical systems have benefitted substantially from the introduction of a range of technology enablers. Internet of things (IoT), advanced computing architectures and digital platforms are among the new technologies that are enhancing the data collection and analytics perceptions in the era of Industry 4.0. Fault tree modelling and failure analysis of systems have been traditionally performed using exhaustively expert knowledge. However, nowadays cyber-physical systems are equipped with sensors and meters, enabling reliability analysis to become more automated and less human-dependent. There have been approaches that fully depend on data that utilized these new developments. However, completely ignoring human cognitive capabilities and expert knowledge causes a great loss of information, which might only be compensated by collecting large amounts of data that is costly in many aspects, and sometimes even impossible. In this paper we discuss how and to what extend expert knowledge can be fused or combined with data to learn fault trees of cyber-physical systems. We, furthermore, point out the gap in availability of systematic methods for fusing data with expert knowledge for the purpose of reliability analysis of cyber-physical systems. Results of an initial simulation study indicate that hybrid reliability analysis of a system increases the accuracy and is less tedious.
Blockchain for IoT-Based Cyber-Physical Systems (CPS): Applications and Challenges

Cyber-Physical System (CPS) enables to combine the physical objects with computing and storage capabilities to have data exchange in an interconnected network of systems and objects. Blockchain is a recently distributed computing paradigm that provides a promising solution for modern CPS application. It forms an underpinning technique for CPS that offers strong added value to industrial IoT (IIoT)Industrial IoT (IIoT), fault-tolerant, reliable, secure, and efficient computing infrastructure. The inherent integration of consensus algorithms and distributed storage with advanced security protocols provides powerful solutions for CPS applications. BlockchainBlockchains in CPSs/IoT ensure secure and saved information for different industrial applications and achieve a means of adaptability, process, and operation protection, for example, in manufacturing, transportation, health care, and energy applications. This chapter will provide extensive technical background for blockchain in IoT-based CPS. Applications, opportunities, and challenges for the combination of CPS, IoT, and blockchain were presented.