Reviewerly/exHarmony huggingface dataset:
Emotion detection on Greek social media using Bidirectional Encoder Representations from Transformers

The widespread proliferation of online social networks has resulted in the creation of huge amounts of data related to, among other things, the expression of opinion and sentiment about literally all aspects of everyday life. In this respect, various tools have been developed by interested parties (companies, individuals) that monitor the social media pulse with respect to various topics (products, persons, organizations, etc) in order to detect the stance, either positive or negative, and the overall emotion in the textual content of social media posts and comments. Despite the success of machine learning models for related tasks in popular languages (e.g. English), little progress has been made in under-represented languages, such as Greek. Based on this reality, in this work, we capitalize on the use of Bidirectional Encoder Representations from Transformers (BERT) architectures for emotion detection in social media textual content written in the Greek language. For this purpose, a relevant corpus is initially collected and annotated. Then, two pre-trained BERT models for the Greek language are employed, one of which has been proposed by the authors of the current work in previous publications. Both models are further trained on the collected corpus and are subsequently used in architectures that classify short social media texts in one or more emotion classes. The obtained results indicate that the BERT-based models outperform other approaches, especially those based on emotion lexicons.
Improved topography reconstruction of volume scattering objects using structured light

The use of structured light projection enables the reconstruction of three-dimensional topography of surface reflecting objects. However, if the investigated object exhibits volume scattering, the obtained topography is erroneously caused by light undergoing volume scattering inside the object. In this theoretical study, we investigate these errors using Monte Carlo simulations. Additionally, a method is proposed to correct the errors by quantifying the light propagation in the scattering object based on the radiative transfer equation. Reconstructed surfaces with a small spatial variation of topography can be quickly corrected using a local correction method that depends only on the directions of the incident and detected light relative to the surface. For surfaces that show a large spatial variation of the surface geometry, another approach is introduced by simulating the light propagation in the whole scanned three-dimensional object using graphics processing unit (GPU)-accelerated Monte Carlo simulations. A cylindrical object and an incisor tooth are, exemplarily, investigated. The results show a major improvement in the reconstructed topography due to the correction with the proposed methods.
Traffic image haze removal based on optimized retinex model and dark channel prior

GPS monitoring systems and the development of driverless vehicles are almost inseparable from camera images. The images taken by traffic cameras often contain certain sky areas and noise, the traditional dark channel prior (DCP) algorithm easily produces color distortion and halo effect, when processing the hazy traffic images with sky and high brightness areas. An optimized Retinex model and dark channel prior algorithm (ORDCP) is proposed in this paper. Firstly by adjusting the calculation method of dark channel image, the proportion of dark channel is improved; Then, the transmittance image is corrected and smoothed by guided filtering and mean filtering. Finally, the Retinex model is fused to save the details.ORDCP corrects the inaccurate calculation of scene transmittance value in DCP algorithm,and modifies some dehazing problems, such as the loss of details, halo effect, contrast and color distortion,etc. Using information entropy (IE) as the objective evaluation index, combined with the subjective evaluation, it is concluded that the algorithm proposed in this paper can effectively retain the detailed information of the image, and eliminate the halo effect. Meanwhile, it meets the visual characteristics of human eyes better, and has some practicality and applicability in traffic control and intelligent detection.
DOES COVID-19 DRIVE ROBOT ACCEPTANCE? AN EXPLORATORY STUDY OF SERVICE ROBOT IN HOSPITALITY

Purpose – The purpose of this study is to explore the acceptance of robots as social distancing agents and to understand how guests may respond to the application of service robots in a hospitality setting as a way to achieve a zero-COVID-19 travel experience. This study contributes to the current knowledge in the area of service robot application by providing a better insight of, and guests response to, service robot operation in hotels. Design/Methodology/Approach – To obtain information from participants, the semi-structured interview method was used. articipants were hotel guests who had stayed in hotels where robots performed human tasks. Data were analysed using thematic analysis. Findings – The findings suggest that robots are perceived as effective social distancing agents even though the participants experienced instances of robot incompetency during their stay at a hotel with robotics-based services. Participants also believe that with improved smart robot services, hotels can resume operations and guests can stay in hotels during the pandemic period without unnecessary worries. Originality – In light of the findings, some future research directions are suggested for researchers to further understand and explore the wider application of robotics in social distancing
A novel hyperchaotic map and its application in fast video encryption

Abstract By the combined feedback of exponential, cubic and sinusoidal nonlinearity, a hyperchaotic map is constructed, which has the distinct features as providing two unipolar hyperchaotic sequences and large area of hyperchaotic orbit. A fast video encryption algorithm that adopted the permutation-diffusion-permutation strategy was developed consequently based on the hyperchaotic system. Each frame image obtained from the video stream is encrypted by the algorithm in real time. To accelerate the encryption, the frame image are encrypted in parallel. Experiment results and security analysis confirm the good security, robustness, and effectiveness of the encryption algorithm.
Kinodynamic on-orbit inspection path planning for full-coverage inspection in close proximity of space structures

In this paper, a kinodynamic inspection path planner, called Random Kinodynamic Inspection Tree algorithm, is presented to perform in-close proximity full-coverage on-orbit inspection for the space structures, under kinematic/dynamic motion constraints in deep space environments. Inspection, as one of the key elements of proximity operations, would make a significant breakthrough in on-orbit servicing and, therefore, space exploration missions. The presented approach combines a novel coverage planning scheme with a kinodynamic motion planner to quickly and effectively solve the inspection path planning problem while at the same time handling both holonomic and non-holonomic constraints of the environment and the inspector robot. Our planner avoids the previous decoupled two steps method, which usually has been used to solve coverage planning problems, namely the Art Gallery and Traveling salesman problems, which are difficult or infeasible to be applied to a robot with differential constraints working in a high-dimensional environment. By contrast, our planner employs the Rapidly Exploring Random Tree algorithm as an asymptotically-optimal sampling-based technique in cooperation with a Linear Quadratic Minimum Time controller to generate an optimal and smooth inspection trajectory for any space structures, given complete knowledge of the structure and inspector's dynamics. The algorithm guarantees probabilistic completeness. Simulations are provided as a validation of the achieved inspection performance.
GM-PHD Filter Based Sensor Data Fusion for Automotive Frontal Perception System

Advanced driver assistance systems and highly automated driving functions require an enhanced frontal perception system.The requirements of a frontal environment perception system cannot be satisfied by either of the existing automotive sensors.A commonly used sensor cluster for these functions consists of a mono-vision smart camera and automotive radar.The sensor fusion is intended to combine the data of these sensors to perform a robust environment perception.Multi-object tracking algorithms have a suitable software architecture for sensor data fusion.Several multi-object tracking algorithms, such as JPDAF or MHT, have good tracking performance; however, the computational requirements of these algorithms are significant according to their combinatorial complexity.The GM-PHD filter is a straightforward algorithm with favorable runtime characteristics that can track an unknown and time-varying number of objects.However, the conventional GM-PHD filter has a poor performance in object cardinality estimation.This paper proposes a method that extends the GM-PHD filter with an object birth model that relies on the sensor detections and a robust object extraction module, including Bayesian estimation of objects' existence probability to compensate for drawbacks of the conventional algorithm.
COVID and ICT in Primary Education: Challenges Faced by Teachers in the Basque Country

This article focuses on the digital competency of primary school teachers in the Basque Country Autonomous Region (Spain) during the COVID-19 pandemic. The temporary closure of schools forced teachers to adapt face-to-face teaching-learning processes in order to operate remotely, making use of technology infrastructure and digital resources. While several educational policies have been developed with the aim of incorporating Information and Communication Technologies into schools, including the “Escuela 2.0” programme, research has shown that there is a lack of Information and Communication Technology training for teachers. The main objective of this study is to further understand the challenges faced by primary school teachers in adapting to remote teaching due to the COVID-19 during stay-at-home lockdown restrictions. An ad hoc quantitative questionnaire was used, which was validated in a trial by eight experts. A total of 1069 primary school teachers participated in the survey. The main results show the participants’ emphasis on their effort to reorganise and redesign subjects and their willingness to make use of information and communication technology infrastructure and resources. However, a high percentage of teachers reported that either they lacked information and communication technology training or considered themselves self-taught in these skills, and expressed interest in lifelong learning programmes in Google Classroom, Google Sites and Google Meet applications, among others.
About r-primitive and k-normal elements in finite fields

In 2013, Huczynska, Mullen, Panario and Thomson introduced the concept of k-normal elements: an element $$\alpha \in {\mathbb {F}}_{q^n}$$ is k-normal over $${\mathbb {F}}_q$$ if the greatest common divisor of the polynomials $$g_{\alpha }(x)= \alpha x^{n-1}+\alpha ^qx^{n-2}+\cdots +\alpha ^{q^{n-2}}x+\alpha ^{q^{n-1}}$$ and $$x^n-1$$ in $${\mathbb {F}}_{q^n}[x]$$ has degree k, generalizing the concept of normal elements (normal in the usual sense is 0-normal). In this paper we discuss the existence of r-primitive k-normal elements in $${\mathbb {F}}_{q^n}$$ over $${\mathbb {F}}_{q}$$ , where an element $$\alpha \in {\mathbb {F}}_{q^n}^*$$ is r-primitive if its multiplicative order is $$\frac{q^n-1}{r}$$ . We provide many general results about the existence of this class of elements and we work a numerical example over finite fields of characteristic 11.
A Self-Representation Method with Local Similarity Preserving for Fast Multi-View Outlier Detection

With the rapidly growing attention to multi-view data in recent years, multi-view outlier detection has become a rising field with intense research. These researches have made some success, but still exist some issues that need to be solved. First, many multi-view outlier detection methods can only handle datasets that conform to the cluster structure but are powerless for complex data distributions such as manifold structures. This overly restrictive data assumption limits the applicability of these methods. In addition, almost the majority of multi-view outlier detection algorithms cannot solve the online detection problem of multi-view outliers. To address these issues, we propose a new detection method based on the local similarity relation and data reconstruction, i.e., the Self-Representation Method with Local Similarity Preserving for fast multi-view outlier detection (SRLSP). By using the local similarity structure, the proposed method fully utilizes the characteristics of outliers and detects outliers with an applicable objective function. Besides, a well-designed optimization algorithm is proposed, which completes each iteration with linear time complexity and can calculate each instance parallelly. Also, the optimization algorithm can be easily extended to the online version, which is more suitable for practical production environments. Extensive experiments on both synthetic and real-world datasets demonstrate the superiority of the proposed method on both performance and time complexity.
Displacement monitoring of a pedestrian bridge using 3D digital image correlation

Using computer vision methods for monitoring the behaviour of structures in urban environments, especially if it is intended to be a long term operation, requires the vision system and the preparation of the structure to be set up with minimal disruption to the surroundings and the use of the structure itself. With this concept in mind, 3D digital image correlation was used to evaluate the deflection caused by a static load on a pedestrian bridge, using cameras installed by windows in different floors of an adjacent building. The measurements were performed using IP cameras designed for video surveillance and security applications, capturing the bridge's surface from above. Rather than preparing the surface with a custom speckle pattern, the DIC processing was performed using the pattern appearing naturally due to the irregularities on the surface. A 2D DIC test was performed simultaneously with an industrial camera capturing the same section of the bridge from a horizontal position inside a lower floor of the same building. The results from the 3D and 2D tests were in close agreement with each other and with the expected displacement profile.
Ensemble Stego Selection for Enhancing Image Steganography

In this paper, we propose an enhancing steganographic scheme by random generation and ensemble stego selection. Different from existing steganography that only focuses on distortion function designing, our scheme considers both distortion model and optimized stego generation. In specific, for given cover, we firstly train an universal steganalyzer to calculate its gradient map, which is referenced to randomly adjust cost distribution of this cover. Multiple candidate stegos are sequentially generated by combining adjusted cost and syndrome trellis coding. Furthermore, we build an ensemble selection mechanism to effectively determine the candidate that is closest to the statistical characteristics of cover image as the final steganographic image. Comprehensive experiments demonstrate that compared to existing state-of-the-art schemes, our scheme can significantly boost the anti-steganalysis capability.
Virtual and Augmented Reality in Manufacturing Companies in Slovakia

Virtual and augmented reality technologies are gaining a stable presence in many industries, while supporting the efficiency and productivity of individual business processes. Education, virtual prototyping, production system design, remote maintenance, digital twin scenario simulations, digital tutorials are areas in which these technologies have found their place and at the same time record visible development. These are solutions that shorten product development time, reduce prototyping costs, streamline training and assembly processes, and can be used as a decision support tool through simulations of various production scenarios. Slovakia, as a pre-developed country, has the ambition to implement modern technologies in industry and education. Reflecting on this strategy, the aim of our paper was to quantify and analyze the level of knowledge and interest in these technologies in Slovak manufacturing companies.
Authenticated and Prunable Dictionary for Blockchain-Based VNF Management

Network function virtualization is a key enabling technology in future wireless networks for flexible and efficient sharing of network resources. Due to the increasing heterogeneity of network resource providers, a blockchain-based distributed architecture is a promising solution to enable reliable and transparent virtualized network function (VNF) management. However, since on-chain storage and computation are costive, it becomes a challenging task to achieve efficient VNF management with blockchain. In this paper, we first introduce a consortium blockchain for collaborative VNF management among network resource providers. Then, we propose an authenticated VNF dictionary that can be stored as a succinct authenticator on blockchain to support rich VNF query functionalities and efficient verifications of query results. Moreover, we design a dictionary pruning strategy to securely generate a compact authenticator for a given query, which reduces unnecessary memory accesses of the original dictionary when VNF queries are represented as arithmetic circuits. Finally, we conduct extensive experiments with a consortium blockchain network. The experimental results demonstrate that our pruning strategy is efficient for both on-chain and off-chain VNF management.
Adaptive Label Propagation for Group Anomaly Detection in Large-Scale Networks

This paper concentrates on group anomalies in general large-scale networks. Existing algorithms on group anomalies mainly focus on homogeneous or bipartite networks, and thus are difficult to apply to heterogeneous networks directly. Moreover, these algorithms follow the non-overlapping hypothesis of groups implicitly, which is improper in many scenarios. For example, fraud users in Alibaba E-commerce platform may join more than one organization at the same time. In this paper, we introduce a novel algorithm called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Adaptive Label Propagation (ALP)</i> to solve these problems. ALP is designed based on label propagation (LP) frameworks, for the reason that LP-based frameworks are simple in thought and easy to scale. ALP is able to find overlapping groups by label propagation with belonging coefficients, and can be applied to heterogeneous networks for its design of adaptive neighbor weighting. Assigning different weights to neighbors in label propagation is a challenging task. Inspired by the combinatorial multi-armed bandit mechanism, ALP views the neighbors of each node as arms to be selected, and iteratively updates their weights by evaluating their expected rewards in following iterations. Experiments are conducted on four real-world networks (including two bipartite ones and two heterogeneous ones). The results show that LP-based methods are effective for detecting group anomalies, and the comparison results with several state-of-the-art label propagation based community detection methods show the effectiveness of the proposed method.
An Empirical Investigation of Agile Information Systems Development for Cybersecurity

Cybersecurity has been identified as a major challenge confronting the digital world, neglecting cybersecurity techniques during software design and development increases the risk of malicious attacks. Thus, there is a need to make security an integral part of the agile information system development process. In this exploratory study, we empirically explore the agile security practices adopted by software developers and security professionals. Data was collected by conducting ten semi-structured interviews with agile practitioners from seven companies in the United Kingdom (UK). The study was conducted between August–November 2020. An approach informed by grounded theory was used for data analysis including Open coding, Memoing, Constant comparison and Theoretical saturation. The security practices identified in this study were categorized into roles, ceremonies and artefacts and mapped onto the different phases of the Software Development Lifecycle (SDLC). We discovered practitioners use five artefacts: security backlog documentation, software security baseline standards, security test plan templates, information security and security audit checklists; and that there are more artefacts than roles and ceremonies. Also, while most practitioners rely on automated tools for software security testing, only one practitioner mentioned conducting security tests manually. These practices that we have identified comprise a novel taxonomy which form the main research contribution of this paper.
Human Abnormal Activity Detection in the ATM Surveillance Video

Automatic teller machine (ATM) installation is increased in recent years, due to the financial needs and convenience of the customers. However, the ATM machine installation increases the criminal activities such as attacks on customer, snatching of customer money also increased which affects the bank customer's security. Detecting and localizing the human anomalous activities inside the ATM room ARE the most challenging area which received much research attention. Video surveillance system aimed at monitoring the human behaviours by learning patterns of normal and abnormal activities. Any event/activity which deviates from the regular activity is identified as abnormal/anomalous. We propose computer vision-based human abnormal activity recognition system which will efficiently detect the abnormal activities happening at the ATM room. We conducted the experiment to assess the proposed system performance with existing methods our system achieves better accuracy with less error rate when varying the bin size.
How to conduct descriptive statistics online: A brief hands-on guide for biomedical researchers

Background: Descriptive statistics is the first step of data analysis. In biomedical researches, inferential statistical tests are invariably conducted after descriptive statistical tests for getting a summary of the data. Many resource-limited settings may not have dedicated software for carrying out these tests. Aim: This article aimed to provide a brief technical guide about the conduct of descriptive statistics with visualization that can be done without any dedicated statistical software package. Methods: We searched for online tools that provide free service for the conduct of descriptive statistics. The example data were fabricated for the conduct of the test online. The visualization of the data (i.e., figures) was explained in brief, wherever necessary. Results: We described the method to graph and summarize the data using a pie chart, frequency table, stem and leaf display, histogram, frequency polygon, box plot, bar chart, stacked bar chart, line graph, dot plot, central tendency, variance, quantile-quantile plot, scatter plot, and Venn diagram. All these tests and visualization were done online without any installed dedicated software package. Conclusion: This article provides a brief technical guide for conducting common descriptive statistical tests online. Researchers in any resource-limited settings may use these services to summarize and visualize the data online from public domain websites.