Reviewerly/exHarmony huggingface dataset:
Double-edged perspectives on service robots: working with robots and robots’ future career impacts

This study investigates the perspectives of undergraduate tourism and hospitality students on working with robots and the influence of the widespread use of robots on future careers. Accordingly, interviews were conducted with thirty students. The findings include two main categories: working with robots in the tourism and hospitality industry (advantages of working with robots, disadvantages of working with robots, and willingness to work with or implement robots) and future career impacts of the widespread use of robots (threatening human employment, reducing the motivation toward working in the industry, unfair competition between humans and robots, negative psychological impact/feeling of being less skilled than robots, and giving up/changing the industry). This research contributes to the literature by revealing the dimensions of working with robots and the future career impacts of the widespread use of robots. A model of future career impacts of the widespread use of robots was also proposed.
Explaining short text classification with diverse synthetic exemplars and counter-exemplars

Abstract We present xspells , a model-agnostic local approach for explaining the decisions of black box models in classification of short texts. The explanations provided consist of a set of exemplar sentences and a set of counter-exemplar sentences. The former are examples classified by the black box with the same label as the text to explain. The latter are examples classified with a different label (a form of counter-factuals). Both are close in meaning to the text to explain, and both are meaningful sentences – albeit they are synthetically generated. xspells generates neighbors of the text to explain in a latent space using Variational Autoencoders for encoding text and decoding latent instances. A decision tree is learned from randomly generated neighbors, and used to drive the selection of the exemplars and counter-exemplars. Moreover, diversity of counter-exemplars is modeled as an optimization problem, solved by a greedy algorithm with theoretical guarantee. We report experiments on three datasets showing that xspells outperforms the well-known lime method in terms of quality of explanations, fidelity, diversity, and usefulness, and that is comparable to it in terms of stability.
Robust Satellite Image Classification with Bayesian Deep Learning

Image-based object detection and classification are essential for satellite-based monitoring, which spans multiple safety-critical engineering applications. Meanwhile, state-of-the-art deep learning significant improves the accuracy for image classification tasks thus has been deployed in various scenarios. However, it&#x2019;s well-known deep learning-based image classifiers are vulnerable to small perturbations along specific directions, known as adversarial attacks. These attacks are exceptionally effective to fool image classifiers. In extreme cases, merely one pixel&#x2019;s change can lead to a attacker-desired wrong prediction label. In this work, we show that deep learning with Bayesian formulation can extend the deep learning adversarial robustness by a large margin, without the need of adversarial training. Moreover, we show that the stochastic classifier after the deterministic CNN extractor has sufficient robustness enhancement rather than a stochastic feature extractor before the stochastic classifier. This advises on utilizing stochastic layers in building decision-making pipelines within a safety-critical domain. Additionally, we show that the Bayesian posterior can act as the safety precursor of ongoing malicious activities towards a deployed image classification system. This leads to the detection of adversarial samples in cybersecurity. With lots of potentials, we leave them as the future studies.
Detecting collusive spammers on e-commerce websites based on reinforcement learning and adversarial autoencoder

The collusive spamming behavior on e-commerce websites seriously affects the purchase decisions of consumers and disrupts the fair competition order among merchants. To address this problem, many approaches have been proposed to detect collusive spammers on e-commerce websites. However, the existing approaches rely on hand-crafted features for detecting collusive spammers, which is costly and time-consuming. With this limitation in mind, we propose a collusive spammer detection approach based on reinforcement learning and adversarial autoencoder. First, we model the given dataset as a user-product bipartite graph that is treated as the agent's interactive environment and use the modified reinforcement learning algorithm to obtain candidate groups. Second, we exploit the Doc2Vec model to generate the embedding of each candidate group and devise an adversarial autoencoder-based one-class classification model for detecting collusive spammers. The experimental results on the real-world review datasets show that the proposed approach has better detection performance than the state-of-the-art methods.
CVSS-based Vulnerability and Risk Assessment for High Performance Computing Networks

Common Vulnerability Scoring System (CVSS) is intended to capture the key characteristics of a vulnerability and correspondingly produce a numerical score to indicate the severity. Important efforts are conducted for building a CVSS stochastic model in order to provide a high-level risk assessment to better support cybersecurity decision-making. However, these efforts consider nothing regarding HPC (High-Performance Computing) networks using a Science Demilitary Zone (DMZ) architecture that has special design principles to facilitate data transition, analysis, and store through in a broadband backbone. In this paper, an HPCvul (CVSS-based vulnerability and risk assessment) approach is proposed for HPC networks in order to provide an understanding of the ongoing awareness of the HPC security situation under a dynamic cybersecurity environment. For such a purpose, HPCvul advocates the standardization of the collected security-related data from the network to achieve data portability. HPCvul adopts an attack graph to model the likelihood of successful exploitation of a vulnerability. It is able to merge multiple attack graphs from different HPC subnets to yield a full picture of a large HPC network. Substantial results are presented in this work to demonstrate HPCvul design and its performance.
How Safe You Are on Social Networks?

Millions of individuals use Twitter, one of the most prominent social media sites, to exchange information, broadcast tweets, and follow other users. Twitter being an open application programming interface is vulnerable to attacks from fake accounts. Fake accounts are primarily used for advertising and marketing, defamation of an individual, consumer data acquisition, increasing fake blog or website traffic, sharing wrong information, online fraud, and control. Fake accounts are disruptive to both users and service providers, thus it's critical to recognize and filter out such information on social media. This paper presents a technique for detecting fake Twitter accounts using the feature set mapped to Twitter’s rules and policies to flag suspicious accounts. To choose the apt subset of features from the original feature space, feature selection techniques such as information gain and correlation were used. Users have been classified using a Logistic Regression classifier with the detection accuracy of 93.5 percent after being trained on the data of about 1,00,000 Twitter users. Furthermore, an algorithm for classifying suspicious users as Fake profiles has been presented. The results of the experiments show that the suggested model outperforms competitive state-of-the-art research in the majority of circumstances.
A Color Image Encryption Algorithm Based on Chaotic Map and Discrete Wavelet Transform

The security of multimedia data such as audio, video, and images has become a vast research subject due to the fast progress of multimedia technologies. The proposed study aims to develop a novel chaotic logistic map-based image encryption technique for multidimensional color images. Preprocessing is conducted initially in the proposed technique by down sampling the plaintext image. Instead of encrypting the whole color picture after preprocessing, encrypt each component independently (R, G, and B). To add extra unpredictability to the cipher text image, several chaotic mathematical equations are applied. To demonstrate the strength of the suggested work, certain statistical analysis and experimental results are performed, revealing that the new method outperforms the current ones.
A GIS-Based Fuzzy Multiclassification Framework Applied for Spatiotemporal Analysis of Phenomena in Urban Contexts

In this research, we propose a GIS-based framework implementing a fuzzy-based document classification method aimed at classifying urban areas by the type of criticality inherent or specific problems highlighted by citizens. The urban study area is divided into subzones; for each subzone, the reports of citizens relating to specific criticalities are analyzed and documents are created, and collected by topic and by temporal extension. The framework implements a model applied to the multiclassification of the documents in which the topic to be analyzed is divided into categories and a dictionary of terms connected to each category is built to measure the relevance of the category in the document. The framework produces, for each time frame, thematic maps of the relevance of a category in a time frame in which a subzone of the study area is classified based on the classification of the corresponding document. The framework was experimented on to analyze and monitor over time the relevance of disruptions detected by users in entities that make up urban areas, such as: roads, private buildings, public buildings and transport infrastructures, lighting networks, and public green areas. The study area is the city of Naples (Italy), partitioned in ten municipalities. The results of the tests show that the proposed framework can be a support for decision makers in analyzing the relevance of categories into which a topic is partitioned and their evolution over time.
An architecture for mission coordination of heterogeneous robots

Robots can potentially collaborate to execute a variety of tasks in the service robots domain. However, developing applications of service robots can be complex due to the high level of uncertainty and required level of autonomy. We aim at contributing an architecture for the development of applications, capable of coordinating multi-robot missions, and that promotes modifiability and seamless integration of independently developed components. In this work, we introduce MissionControl: an ensemble-based architecture to coordinate missions of heterogeneous robots to autonomously form coalitions. MissionControl comprises a component model and a runtime environment. The component model specifies how the system can be extended for different robot’s behaviors and environments. The runtime environment provides the processes required for coordinating the execution of missions at runtime. We evaluated MissionControl in a simulated environment in the healthcare domain. We randomly generated 81 scenarios with uncertainty in the robots’ initial configurations. Then, each scenario was executed 8 times (i.e. 648 runs), where we evaluated the feasibility and efficiency of MissionControl for autonomously forming coalitions against a baseline approach that uses a random robot allocation. Statistical hypotheses testing yielded that MissionControl was able to achieve higher success rates while reducing the required time to conclude a mission, when compared to a baseline approach. We also perform an evaluation of the key quality attributes of the architecture, i.e. modifiability and integrability. MissionControl demonstrated itself able to coordinate multi-robot missions by autonomously assigning missions. Despite the error-prone robotic mission environment and demanding computational resources, MissionControl led to a significant increase in the success rate, while also decreasing the time required to conclude robotic missions when compared to a baseline approach.
Factors Impacting Students’ Creativity-related Self-efficacy in an Undergraduate Makerspace-based Course

The need to cultivate creativity in engineering education calls for opportunities for students to exercise freedom in proposing and pursuing projects aligned with their interests. This paper presents insights into an undergraduate makerspace-based course in terms of factors affecting students&#x2019; creativity-related self-efficacy. We conducted a survey on students who come from an engineering and science background to gain their opinions about the impact of this course on enhancing their creativity. To establish if there is a significant difference in the students&#x2019; creativity, we performed the non-parametric Wilcoxon signed-rank test comparing the first and second survey, with results showing that there is a statistically significant increase in students&#x2019; creativity-related self-efficacy. There was a general increase for all the items, especially students&#x2019; perceptions toward the relevance of the course, the conduciveness of the learning environment, opportunities to make and learn from mistakes, and the resourcefulness of their team. Results obtained via quantitative statistical analysis was backed up by qualitative analysis that employed text mining techniques such as automatic key phrase extraction and sentiment analysis on the open-ended responses and the reason(s) for the Likert-scale answer choice. In addition, we used the Spearman&#x2019;s rho to report correlations between Likert-scale items and determine the variables that are significantly and positively correlated with the creativity-related self-efficacy construct. A multivariate regression model was then constructed to observe the extent to which each highly correlated variable impacts creativity-related self-efficacy; of which, a sense of relevance appears to have the largest effect. Through gaining insights into the factors that may impact students&#x2019; creativity-related self-efficacy, this study contributed to a deeper understanding on how this important attribute could be developed through a makerspace-based university course.
Depth-aware gaze-following via auxiliary networks for robotics

Gaze-Following aims to predict the gaze target of a subject within an image, and information on orientation and depth greatly improves this task. However, previous methods require additional datasets to obtain depth or orientation information, leading to cumbersome training or inference processes. To this end, we propose an end-to-end depth-aware gaze-following approach that incorporates depth and orientation information without additional datasets. Our approach identifies a primary task, gaze-following, supervised by true labels from the gaze-following dataset and two auxiliary tasks, scene depth estimation and 3D orientation estimation, supervised by generated pseudo labels. Intermediate auxiliary features are integrated into the primary task network as implicit information. We propose a residual filter module for screening useful information that can enhance gaze-following prediction performance. Extensive experiments on GazeFollow and VideoAttentionTarget show that our approach achieves state-of-the-art results (0.120 Ave. Dist. achieved on GazeFollow and 0.104 L2 Dist. achieved on VideoAttentionTarget). Finally, we apply our approach to a real robot for understanding human attention and intention. Compared to the previous depth considered gaze-following method, our method saves half of the computation time.
Analysis of the View Factors in Rooftop PV Solar

In solar PV fields, solar photovoltaic panels are typically arranged in parallel rows one after the other. This arrangement introduces variations in the distribution of solar irradiance over the entire field, compared to measurements made at meteorological weather stations and data obtained from solar radiation databases. This is due to the difference in the view factors between the rows of the solar PV field and a single reference surface, as well as the presence of shade on rear rows and in the space separating the rows. These phenomena combined will reduce the energy yield of a solar PV field. Accurate estimation of solar radiation on solar fields requires knowledge of the sky, ground, and rear surface of the preceding row view factors, and an estimation of the time and space occupied by the row’s shadow. Prior literature has addressed this issue using two-dimensional (2D) techniques such as Crossed-Strings Method, which this study proved to be inaccurate particularly in the case of Rooftop solar PV fields. This study uses a novel three-dimensional (3D) analytical and numerical analysis to determine the view factors associated with solar fields using hourly solar irradiance data acquired from Solar-GIS for the period 2007-2020, including global, beam, and sky diffuse irradiance components on horizontal plane. The study uses both isotropic and anisotropic transposition analyses to determine solar irradiance incident on the solar field with varying tilt angles of solar panels and distance separating the rows.
The “Precedent World” of F.M. Dostoevsky in the Socio-Cultural and “Playing Ground” of Modern Media

Precedent cultural signs from the citation source Russian classical literature are in wide demand in the modern media space, including both professional and social media. This article studies the problem of identifying the reasons for this demand and the functioning features of these specific cultural signs in canonical and transformed forms in the context of the reception of works by F.M. Dostoevsky in modern media texts. The article also raises the question of the nature of transformations of textbook precedent signs functioning as the authors evaluation of current social events. The aim of this research is to identify two trends that are characteristic of both professional and social media. On the one hand, it is the trend to transform various precedent phenomena (precedent onyms, utterances, situations) in order to create a comic effect and a language game. On the other hand, it is the trend to comprehend the content of a cultural sign for the argumentation of the authors position. The material of the research is constituted by media texts of different genres and with varying socio-political connotation which contain precedent phenomena from the F.М. Dostoevsky source sphere and which are published in Russian print media, as well as posted on social media. These media texts were obtained using the manual sampling method and with the help of the Russian National Corpus. The research made it possible to present the precedent world of F.M. Dostoevsky reflected in media sources in proper linguistic terms (from the point of view of the nature of linguistic transformations and their interaction with the context) and in linguoethical terms (from the point of view of the ethical correctness of free use of textbook signs of high culture). The analysis resulted in the identification of various ways of foregrounding precedent cultural signs in different media genres. It also helped establish that the replication of precedent cliches in the digital media environment leads to devaluation of the content of a cultural sign, while the socio-political analytical press, on the contrary, shows demand for its message, its social and philosophical content, which is relevant for understanding the events taking place in the country and in the world, and the eternal questions of life.
Application of Table Tennis Ball Trajectory and Rotation-Oriented Prediction Algorithm Using Artificial Intelligence

The present work aims to accelerate sports development in China and promote technological innovation in the artificial intelligence (AI) field. After analyzing the application and development of AI, it is introduced into sports and applied to table tennis competitions and training. The principle of the trajectory prediction of the table tennis ball (TTB) based on AI is briefly introduced. It is found that the difficulty of predicting TTB trajectories lies in rotation measurement. Accordingly, the rotation and trajectory of TTB are predicted using some AI algorithms. Specifically, a TTB detection algorithm is designed based on the Feature Fusion Network (FFN). For feature exaction, the cross-layer connection network is used to strengthen the learning ability of convolutional neural networks (CNNs) and streamline network parameters to improve the network detection response. The experimental results demonstrate that the trained CNN can reach a detection accuracy of over 98%, with a detection response within 5.3 ms, meeting the requirements of the robot vision system of the table tennis robot. By comparison, the traditional Color Segmentation Algorithm has advantages in detection response, with unsatisfactory detection accuracy, especially against TTB's color changes. Thus, the algorithm reported here can immediately hit the ball with high accuracy. The research content provides a reference for applying AI to TTB trajectory and rotation prediction and has significant value in popularizing table tennis.
CSCAHHO: Chaotic hybridization algorithm of the Sine Cosine with Harris Hawk optimization algorithms for solving global optimization problems

Because of the No Free Lunch (NFL) rule, we are still under the way developing new algorithms and improving the capabilities of the existed algorithms. Under consideration of the simple and steady convergence capability of the sine cosine algorithm (SCA) and the fast convergence rate of the Harris Hawk optimization (HHO) algorithms, we hereby propose a new hybridization algorithm of the SCA and HHO algorithm in this paper, called the CSCAHHO algorithm henceforth. The energy parameter is introduced to balance the exploration and exploitation procedure for individuals in the new swarm, and chaos is introduced to improve the randomness. Updating equations is redefined and combined of the equations in the SCA and HHO algorithms. Simulation experiments on 27 benchmark functions and CEC 2014 competitive functions, together with 3 engineering problems are carried out. Comparisons have been made with the original SCA, HHO, Archimedes optimization algorithm (AOA), Seagull optimization algorithm (SOA), Sooty Tern optimization algorithm (STOA), Arithmetic optimizer (AO) and Chimp optimization algorithm (ChOA). Simulation experiments on either unimodal or multimodal, benchmark or CEC2014 functions, or real engineering problems all verified the better performance of the proposed CSAHHO, such as faster convergence rate, low residual errors, and steadier capability. Matlab code of this algorithm is shared in Gitee with the following address: https://gitee.com/yuj-zhang/cscahho.
Designing public financial management systems: exploring the use of chatbot-assisted case studies

IMPACTThe design of public financial management (PFM) systems requires the exploration of the problem space before solution options are generated. Case studies are often employed to teach the design of PFM systems, but conventional forms of delivery of case study materials fail to help develop the skills needed to explore the problem space. This article investigates the use of chatbot-assisted case studies as a way to stimulate students’ efforts to pose questions about features of the problem scenario.