Reviewerly/exHarmony huggingface dataset:
Understanding the Utilization of Artificial Intelligence and Robotics in the Service Sector

Artificial Intelligence (AI) is increasingly posing a threat to human service jobs in several sectors. According to projections, AI will put a remarkable percent of service sector employment at risk, including a wide range of jobs. Since AI offers chances to improve service provision efficacy and enhance consumer engagement, it is expected that artificial intelligence and service robots will become widespread in many countries. It is anticipated that service provision tasks will be affected by automation and artificial intelligence in different ways. Regarding the replacement of jobs, it is envisaged that job designs will transform on a task-based rather than top-down basis, with simple cognitive and analytical tasks being performed first by service robots, and then complex emotional and social tasks are likely to be supported by robot-human collaboration. As a result, it is critical to assess artificial intelligence as it penetrates every aspect of our life, particularly in terms of consumer acceptance & use and the service industry, where it is perceived as a significant threat to service jobs. Depending on the predictions regarding the widespread applications of AI shortly and the change of job designs in the service sector, the evaluation of the current research in the area is critical for reducing the gap between practitioners and academic studies. Until far, most AI for service research has classified the service tasks and attempted to explain how the transformation of jobs can take place on a task basis. On the other hand, some research has conceptually discussed the potential benefits and drawbacks of AIs in the service industry. A stream of research has empirically measured user acceptance of different AI applications, their antecedents, and consequences. Hence, this chapter aims to synthesize and discuss the previous literature findings to have a broad understanding of the current research output. Some literature gaps, particularly in terms of human-robot interaction, have been identified, and avenues for future research have been emphasized. Finally, a roadmap for future research is presented.
Quantum Distributed Microgrid Control

To empower flexible and scalable operations, distributed control of multi-inverter microgrids, based on classical communication networks among distributed energy resources, has attracted considerable attention. Notwithstanding this, resilience of the current schemes on classical communication makes microgrids vulnerable to cyber attacks. Inspired by quantum properties of quantum bits, in this paper, we devise a novel synchronization mechanism. We extend the synchronization framework utilized in distributed control algorithms to networks of quantum systems. By employing the architecture of quantum network, security of the protocol can be enhanced. Test results on two representative ac and dc microgrids validate the efficacy and universality of the quantum distributed control.
ECoalVis: Visual Analysis of Control Strategies in Coal-fired Power Plants

Improving the efficiency of coal-fired power plants has numerous benefits. The control strategy is one of the major factors affecting such efficiency. However, due to the complex and dynamic environment inside the power plants, it is hard to extract and evaluate control strategies and their cascading impact across massive sensors. Existing manual and data-driven approaches cannot well support the analysis of control strategies because these approaches are time-consuming and do not scale with the complexity of the power plant systems. Three challenges were identified: a) interactive extraction of control strategies from large-scale dynamic sensor data, b) intuitive visual representation of cascading impact among the sensors in a complex power plant system, and c) time-lag-aware analysis of the impact of control strategies on electricity generation efficiency. By collaborating with energy domain experts, we addressed these challenges with ECoalVis, a novel interactive system for experts to visually analyze the control strategies of coal-fired power plants extracted from historical sensor data. The effectiveness of the proposed system is evaluated with two usage scenarios on a real-world historical dataset and received positive feedback from experts.
Visual Analysis of Neural Architecture Spaces for Summarizing Design Principles

Recent advances in artificial intelligence largely benefit from better neural network architectures. These architectures are a product of a costly process of trial-and-error. To ease this process, we develop ArchExplorer, a visual analysis method for understanding a neural architecture space and summarizing design principles. The key idea behind our method is to make the architecture space explainable by exploiting structural distances between architectures. We formulate the pairwise distance calculation as solving an all-pairs shortest path problem. To improve efficiency, we decompose this problem into a set of single-source shortest path problems. The time complexity is reduced from O(kn2N) to O(knN). Architectures are hierarchically clustered according to the distances between them. A circle-packing-based architecture visualization has been developed to convey both the global relationships between clusters and local neighborhoods of the architectures in each cluster. Two case studies and a post-analysis are presented to demonstrate the effectiveness of ArchExplorer in summarizing design principles and selecting better-performing architectures.
Experimental test of quantum causal influences within an instrumental process

We implement a binary-variable instrumental scenario on a photonic setup. Despite no Bell-like inequality exists for this model, we detect non-classical correlations by quantifying causal influences, which allows demonstrating discrepancies between quantum and classical predictions.
Interactive Visual Analysis of Structure-borne Noise Data

Numerical simulation has become omnipresent in the automotive domain, posing new challenges such as high-dimensional parameter spaces and large as well as incomplete and multi-faceted data. In this design study, we show how interactive visual exploration and analysis of high-dimensional, spectral data from noise simulation can facilitate design improvements in the context of conflicting criteria. Here, we focus on structure-borne noise, i.e., noise from vibrating mechanical parts. Detecting problematic noise sources early in the design and production process is essential for reducing a product's development costs and its time to market. In a close collaboration of visualization and automotive engineering, we designed a new, interactive approach to quickly identify and analyze critical noise sources, also contributing to an improved understanding of the analyzed system. Several carefully designed, interactive linked views enable the exploration of noises, vibrations, and harshness at multiple levels of detail, both in the frequency and spatial domain. This enables swift and smooth changes of perspective; selections in the frequency domain are immediately reflected in the spatial domain, and vice versa. Noise sources are quickly identified and shown in the context of their neighborhood, both in the frequency and spatial domain. We propose a novel drill-down view, especially tailored to noise data analysis. Split boxplots and synchronized 3D geometry views support comparison tasks. With this solution, engineers iterate over design optimizations much faster, while maintaining a good overview at each iteration. We evaluated the new approach in the automotive industry, studying noise simulation data for an internal combustion engine.
Cause typicality and the continued influence effect.

A large body of research has focused on whether and how readers update their knowledge of events when an initial piece of causal information is corrected. These studies have indicated that corrections can reduce, but do not eliminate, readers' reliance on the initial cause when drawing inferences or making decisions about the events (i.e., the continued influence effect). Additional studies suggest that supplementing a correction with an alternative cause can further reduce reliance on discredited initial causes. In three experiments, we interrogated the importance of cause typicality for the generalizable utility of these correction strategies by manipulating the typicality of initial and alternative causes. We found evidence that participants showed greater reliance on a typical than an atypical initial cause both before and after correction, but no consistent evidence that this typicality impacted the effectiveness of the correction. Furthermore, the typicality of the alternative causes used to supplement a correction did not seem to matter with respect to updating. These results highlight the importance that characteristics of an initial cause can have for event encodings and corrections, identifying critical boundary conditions for understanding the effects of corrections on knowledge revision and the continued influence effect. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
AutoXAI

A large number of XAI (eXplainable Artificial Intelligence) solutions have been proposed in recent years. Recently, thanks to new XAI evaluation metrics, it has become possible to compare these XAI solutions. However, selecting the most relevant XAI solution among all this diversity is still a tedious task, especially if a user has specific needs and constraints. In this paper, we propose AutoXAI, a framework that recommends the best XAI solution and its hyperparameters according to specified XAI evaluation metrics while considering the user's context (dataset, machine learning model, XAI needs and constraints). It adapts approaches from context-aware recommender systems on one side and strategies of optimization and evaluation from AutoML (Automated Machine Learning) on the other. Through two use cases, we show that AutoXAI recommends XAI solutions adapted to the user's needs with the best hyperparameters matching the user's constraints.
Differences in the degree of awareness of graduate students in Jordanian universities of the importance of using internet of things (IoT) technology in the educational process

This study aimed to identify the differences in the degree of awareness of graduate students in Jordanian universities of the importance of using IoT technology in the educational process based on the variable (university classification, academic level, and faculty classification). This study was conducted during the second semester of the academic year 2022-2021 AD. The descriptive survey method was used, and the study tool (the questionnaire) consisted of (54) items. It was divided into four categories (the concept of IoT technology, uses of IoT technology, benefits of IoT technology, and challenges of IoT technology). The study sample consisted of (439) male and female graduate students in Jordanian universities, who were chosen randomly. The results of the study showed that there were no statistically significant differences in the degree of students’ awareness due to the university classification variable, and that there were statistically significant differences due to the academic level variable in favor of the Master’s stage, as well as the presence of statistically significant differences due to the faculty classification and in favor of the humanities faculties.
Accelerating General-purpose Lossless Compression via Simple and Scalable Parameterization

The storage of multi-media data can benefit from the advancements in general-purpose lossless compression. The explosive growth of multi-media data volume in data centers demands a higher compression ratio and better compressors' run-time speed. However, recent deep-learning-based compressors with a high compression ratio usually build complicated dependencies on history symbols, leading to a long compression time. This paper investigates the behavior of historical symbols and finds an approximate order of importance. Namely, recent symbols have a substantially larger influence on the probability estimation of the next unknown symbol. This observation guides the designing of an interpretable structure for data compression, rather than learning implicitly from data like Recurrent Neural Network (RNN) and attention. Based on this observation, we disentangle the compression model into order learning and feature learning, which were fused in a large module in previous works. A parameterized ordered mask unit is established to learn the ordered importance of history symbols. A fast Multi-Layer Perceptron (MLP) network is designed for efficient feature learning. The proposed compressor can improve both compression performance and computational efficiency compared with transformer-based or RNN-based compressors. To further enhance computational efficiency, we propose a branch-MLP block to replace the original MLP layer. This block reduces the parameters and the FLOPs of the original MLP to a half, without sacrificing compression performance. Experiments on multi-media data demonstrate that our model improves the compression ratio by 10% on average across data domains while accelerating compression speed by 100% compared with the state-of-the-art. The source code and appendix are released at https://github.com/mynotwo/compressor_via_simple_and_scalable_parameterization.git.
MaMiCo: Macro-to-Micro Semantic Correspondence for Self-supervised Video Representation Learning

Contrastive self-supervised learning (CSL) has remarkably promoted the progress of visual representation learning. However, existing video CSL methods mainly focus on clip-level temporal semantic consistency. The temporal and spatial semantic correspondence across different granularities, i.e., video, clip, and frame levels, is typically overlooked. To tackle this issue, we propose a self-supervised Macro-to-Micro Semantic Correspondence (MaMiCo) learning framework, pursuing fine-grained spatiotemporal representations from a macro-to-micro perspective. Specifically, MaMiCo constructs a multiple branch architecture of T-MaMiCo and S-MaMiCo on a temporally-nested clip pyramid (video-to-frame). On the pyramid, T-MaMiCo aims at temporal correspondence by simultaneously assimilating semantic invariance representations and retaining appearance dynamics in long temporal ranges. For spatial correspondence, S-MaMiCo perceives subtle motion cues via ameliorating dense CSL for videos where stationary clips are applied for stably dense contrasting reference to alleviate semantic inconsistency caused by ''mismatching''. Extensive experiments justify that MaMiCo learns rich general video representations and works well on various downstream tasks, e.g., (fine-grained) action recognition, action localization, and video retrieval.
Virtual machine optimization setting of ship public computer platform

Ship public computer platform is a shared platform built for ship users by using public hardware resources and equipment. Users of public computer platform can directly call the hardware resources of public computer platform through network, virtual machine and other media to create their own virtual computing environment. With the development of computer technology and virtualization technology, ship public computing platform technology has also received extensive attention in the industry. Firstly, this paper introduces the key points of cloud computing technology. Based on cloud platform computing, this paper introduces the virtual machine design, database redundancy design, CPU and network optimization setting of ship public computer platform in detail. Finally, the virtual machine construction of ship public computer platform is realized based on VMware software platform, which has very important application significance.
Language Matters

CLARIN stands for "Common Language Resources and Technology Infrastructure".In 2012 CLARIN ERIC was established as a legal entity with the mission to create and maintain a digital infrastructure to support the sharing, use, and sustainability of language data (in written, spoken, or multimodal form) available through repositories from all over Europe, in support of research in the humanities and social sciences and beyond.Since 2016 CLARIN has had the status of Landmark research infrastructure and currently it provides easy and sustainable access to digital language data and also offers advanced tools to discover, explore, exploit, annotate, analyse, or combine such datasets, wherever they are located.This is enabled through a networked federation of centres: language data repositories, service centres, and knowledge centres with single sign-on access for all members of the academic community in all participating countries.In addition, CLARIN offers open access facilities for other interested communities of use, both inside and outside of academia.Tools and data from different centres are interoperable, so that data collections can be combined and tools from different sources can be chained to perform operations at different levels of complexity.The strategic agenda adopted by CLARIN and the activities
Secure QR Code Scanner to Detect Malicious URL using Machine Learning

Q-R codes are utilised for a variety of purposes, including accessing online web-pages and making a settlement. The Internet facilitates a wide range of illegal acts, including unsolicited e-marketing, financial embezzlement, and malicious distribution. Even though all the users identify the presence of Q-R codes visually, the information stored in those codes can only be accessed through an allocated Q-R code decoder. Q-R codes have also been shown to be used as an effective attack vector, For example techniques include social engineering, phishing, pharming, etc. Harmful codes are distributed under false pretences in congested areas, or malicious Q-R codes are pasted over current ones on billboards. Finally, consumers rely on decoder operating system to determine a random Q-R code is whether malicious or benign.For the purpose of this report, we consider the identification of malicious Q-R codes as a two-way classification problem in this research, and we test the effectiveness of many well-known M-L algorithms, including namely K-Nearest Neighbour, Random Forest, Binary LSTM and Support Vector Machine. This implies that the proposed method might be deemed an optimal and user-friendly QR code security solution. We created a prototype to test our recommendations and found it to be secure and usable in protecting users from harmful QR Codes.
A GAN Approach for Anomaly Detection in Spacecraft Telemetries

In spacecraft health management a large number of time series is acquired and used for on-board units surveillance and for historical data analysis. The early detection of abnormal behaviors in telemetry data can prevent failures in the spacecraft equipment. In this paper we present an advanced monitoring system that was carried out in partnership with Thales Alenia Space Italia S.p.A, a leading industry in the field of spacecraft manufacturing. In particular, we developed an anomaly detection algorithm based on Generative Adversarial Networks, that thanks to their ability to model arbitrary distributions in high dimensional spaces, allow to capture complex anomalies avoiding the burden of hand crafted feature extraction. We applied this method to detect anomalies in telemetry data collected from a simulator of a Low Earth Orbit satellite. One of the strengths of the proposed approach is that it does not require any previous knowledge on the signal. This is particular useful in the context of anomaly detection where we do not have a model of the anomaly. Hence the only assumption we made is that an anomaly is a pattern that lives in a lower probability region of the data space.
Identifying Shared Security Vulnerabilities and Mitigation Strategies at the Intersection of Application Programming Interfaces (APIs), Application-Level and Operating System (OS) of Mobile Devices

As mobile devices and applications continue to grow in popularity worldwide, so do the rise of cybersecurity attacks and threats to daily users of both Android and iOS devices on a widespread basis. These attacks not only target mobile devices at the application level but also at the Operating System (OS) device level and Application Programming Interface (API) level extending the Internet of Things (IoT). This paper will address the security vulnerabilities in Android Operating System at the intersection of APIs, device OS, and application-level vulnerabilities by identifying an intersection of commonalities to create mitigation strategies and best practices for securing mobile devices for organizations. The study recommends the adoption of processes of authentication on mobile devices and web-based log-ins as well as access control mechanisms that can mitigate the overlapping security challenges of APIs, mobile devices OS and applications using optimal resources to enhance security. This was achieved by analyzing the threats and vulnerabilities of API.
SleepCon: Sleeping Posture Recognition Model using Convolutional Neural Network

Recognition of sleep patterns and posture has sparked interest in various clinical applications. Sleep postures can be monitored autonomously and constantly to provide useful information for decreasing health risks. Existing systems mostly use images to train the model to learn based on many sensors. For example, a camera, pressure sensor, and electrocardiogram. In this study, a model (named as SleepCon) was designed using deep learning, which will have the capability to train with any threshold image obtained from any sensor. This paper presented a system where data was obtained from a camera installed on the top of a mattress. The camera located the movement of the body posture on the mattress while the subject was lying down on the mattress. In doing so, CNN and other pre-processed steps took place to collect data and then analyze the data to recognize different sleep postures. This model was stored for use in real-time applications. The system can recognize the three major postures, i.e., left, right, and supine. A real-time application is also developed and operates the stored SleepCon model through an accompanying desktop application for detecting the posture live. The accuracy of classification was greater than 90%, while the actual application accuracy was 100% after carrying out the experiment on the SleepCon model. Doi: 10.28991/ESJ-2023-07-01-04 Full Text: PDF
A graph neural network framework based on preference-aware graph diffusion for recommendation

Transforming user check-in data into graph structure data is a popular and powerful way to analyze users' behaviors in the field of recommendation. Graph-based deep learning methods such as graph embeddings and graph neural networks have shown promising performance on the task of point-of-interest recommendation in recent years. Despite effectiveness, existing methods fail to capture deep graph structural information, leading the suboptimal representations. In addition, they lack the ability of learning the influences of both global preference and user preference on the check-in behavior. To address the aforementioned issues, we propose a general framework based on preference-aware graph diffusion, named PGD. We first construct two types of graphs to represent the global preference and user preference. Then, we apply a graph diffusion process to capture the structural information of the generated graphs, resulting in weighted adjacency matrices. Finally, graph neural network-based backbones are introduced to learn the representations of users and POIs on weighted adjacency matrices. A learnable aggregation module is developed to learn the final representations from global preference and user preference adaptively. Extensive experiments on four real-world datasets demonstrate the superiority of PGD on POI recommendation, compared with the mainstream graph-based deep learning methods.