Reviewerly/exHarmony huggingface dataset:
Problematizing Human-Centred Design: Notes on Planet- Oriented Design

This article is part of the discussion about what to do with regard to the Anthropocene and how to project design in a direction oriented towards caring for the Planet. Overcoming the environmental crisis implied by the notion of Anthropocene involves questioning or, rather, redesigning the culture of design in terms of its ontological, methodological, and ethical suppositions. Specifically, this article proposes analytic displacements in order to problematize the hegemonic paradigm of user-centered design, opening the discussion up to other ways of being and world-making. To that end, we focus on the question of how to deploy planet-oriented design. We develop four points: bringing design down to Earth, situating design, decelerating design and intersectionalities in design. This article is meant to contribute to expanding this research agenda of design for transitions, focusing on the need for design that fosters more careful and ethical cohabitation on a damaged Planet.
MODERATING ROLE OF ONLINE TRUST ON THE RELATIONSHIP BETWEEN SERVICE DYNAMICS, INFORMATION AWARENESS, CITIZEN SATISFACTION TO E-GOVERNMENT SERVICES AND CONTINENCE INTENTION: A CASE OF UAE

The purpose of this research is to examine the trends in citizen satisfaction for e-government services as observed through service usefulness, service ease of use, information awareness and service quality. Furthermore, this study also investigates the impact of citizen satisfaction towards e-government services on E-government service continuance intention. Additionally, the moderating impact of online trust on the relationship between exogenous and endogenous variables is also observed. A sample of 364 questionnaires was observed as valid enough for the purpose descriptive statistics, correlational matrix and demographic analysis as well. Additionally, this research has applied two step approach while considering the measurement model assessment and structural model assessment. The study findings through measurement model confirms that there is no problem for the model validity and reliability along with other fit indices. After examining the measurement model, structural model is applied through Smart PLS-SEM approach for both direct and moderating effect. The study results confirm that there is a significant and positive impact of e-service ease of use, information awareness, e-service quality, and online trust on citizen satisfaction to e-government services. Furthermore, the study findings confirm that there is a significant moderating effect of online trust on the relationship between e-service usefulness-citizen satisfaction, between e-service ease of use-citizen satisfaction, between information awareness-citizen satisfaction, and between e-service quality-citizen satisfaction. The study findings would be great support to various stakeholders specifically to those who are responsible for managing and providing e-government services for better citizen satisfaction. Besides, some limitations like missing of cross departmental comparison, consideration of only UAE region, and data collection through survey questionnaire only. Future studies are highly recommended to address these limitations.
Theatre Majors and Immersive Technology: An Interview with HP's Joanna Popper

In this interview, E.B. Hunter, Assistant Professor of Drama at Washington University in St. Louis and Director of the Fabula(b) Theatre + New Media Lab interviews Joanna Popper, Global Head of VR for HP (formerly Hewlett Packard) about ways in which theatre students can become involved in the emerging immersive technology industry.
Are Current Task-oriented Dialogue Systems Able to Satisfy Impolite Users?

Task-oriented dialogue (TOD) systems have assisted users on many tasks, including ticket booking and service inquiries. While existing TOD systems have shown promising performance in serving customer needs, these systems mostly assume that users would interact with the dialogue agent politely. This assumption is unrealistic as impatient or frustrated customers may also interact with TOD systems impolitely. This paper aims to address this research gap by investigating impolite users' effects on TOD systems. Specifically, we constructed an impolite dialogue corpus and conducted extensive experiments to evaluate the state-of-the-art TOD systems on our impolite dialogue corpus. Our experimental results show that existing TOD systems are unable to handle impolite user utterances. We also present a data augmentation method to improve TOD performance in impolite dialogues. Nevertheless, handling impolite dialogues remains a very challenging research task. We hope by releasing the impolite dialogue corpus and establishing the benchmark evaluations, more researchers are encouraged to investigate this new challenging research task.
A PAC-Bayesian Generalization Bound for Equivariant Networks

Equivariant networks capture the inductive bias about the symmetry of the learning task by building those symmetries into the model. In this paper, we study how equivariance relates to generalization error utilizing PAC Bayesian analysis for equivariant networks, where the transformation laws of feature spaces are determined by group representations. By using perturbation analysis of equivariant networks in Fourier domain for each layer, we derive norm-based PAC-Bayesian generalization bounds. The bound characterizes the impact of group size, and multiplicity and degree of irreducible representations on the generalization error and thereby provide a guideline for selecting them. In general, the bound indicates that using larger group size in the model improves the generalization error substantiated by extensive numerical experiments.
Implementation of the Hindmarsh–Rose Model Using Stochastic Computing

In this paper we present a successful implementation of the Hindmarsh–Rose model within a SC environment. The merits of the proposed approach are design simplicity, due to stochastic computing, and the ease of implementation. Simulation results showed that the approximation achieved is equivalent to introducing a noise source into the original model. A study for the level of noise introduced, according to the number of bits in the stochastic sequence, has been performed. Additionally, we demonstrate that such an approach, even though it is noisy, it reproduces the behaviour of biological systems, which are intrinsically noisy. It is also demonstrated that a speedup of x2 compared to biological systems is easily achievable, with a very small number of gates, thus paving the road for the in silico implementation of large neuron networks.
How to Measure Agreement, Consensus, and Polarization in Ordinal Data

There are different measures to capture agreement, consensus, concentration, dispersion, and polarization in ordinal data. To better understand how different measures work in practice, we compare consensus scores across specific situations: constructed cases, simulated data where we know the underlying distribution, and empirical data. Researchers have addressed the ‘problem’ of measuring agreement, consensus, and polarization in ordinal data in many ways across many subfields; we highlight the similarities as well as the distinctive features of these approaches. In many cases, the different measures are highly correlated, but the choice of method can lead to substantially different conclusions in situations that are difficult for non-mathematical users to predict. Unless researchers prioritize specific properties, we recommend a combination of measures and graphics to qualitatively examine the distribution.
Analysis and Implementation of Augmented Reality Using Markerless and A-Star Algorithm (Case Study: Gedung Kuliah Umum ITERA)

Institut Teknologi Sumatera is a public university in the province of Lampung. Institut Teknologi Sumatera (ITERA) has many buildings, including Gedung Kuliah Umum (GKU). GKU is the largest and widest lecture building in ITERA. GKU has four floors, where each floor has many rooms in it with different functions in each room. As the largest building in ITERA, GKU is often used for various events, including CPNS exams, new student admissions, or for visits from other campuses. Due to the size of this building, this allows visitors from outside ITERA to GKU to experience problems in terms of time to ask questions and difficulty finding various spaces in the GKU Building. This research uses Augmented Reality technology to help make it easier for visitors from outside ITERA to find space quickly and precisely. In its development using several tools, including the ARWaKit SDK. This framework is used on devices with the IoS operating system. In its implementation, it requires a camera on a smartphone to capture existing images and convert them into cyberspace. In the ARWayKit framework, Azure Spatial Anchors have been used which can be used to carry out the mapping process as a markerless method and to optimize the distance from the user's position to the destination location, the a-star algorithm is used. The results obtained from the Variation-2 test were 91.6%.
Key features of digital library management system (DLMS) for developing digital libraries: An investigation from LIS practitioners in Pakistan

The main objectives of the study were to identify key features of the digital library management system (DLMS) for developing and managing digital libraries, to reveal the satisfaction level of the library professionals in using digital library management system, and the problems being encountered in using these systems. The current study was quantitative in nature as quantitative measures were applied through a survey research strategy. Keeping in view the nature of the study, a purposive sampling technique was used to collect data from the target librarians who had been involved in operating digital library systems for building digital libraries in their respective libraries. Descriptive and inferential statistical tests were used to analyze the data. Findings revealed that in most of the university libraries in Pakistan, free and open source software (FOSS) had been implemented. DSpace was the most widely used software by library professionals in Pakistan. Key features of the digital library software included a user-friendly interface, customization, reliability and security, use of metadata standards (Dublin Core), web-based, advanced searching, consultancy and technical support, sharing e-books to devices (mobile, tabs), full-text searching, use of Boolean operators, and access control. The study recommended that library professionals of universities should be careful in planning library digitization and they should consider the above features before implementing any digital library software in their libraries.
DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models

With recent advancements in diffusion models, users can generate high-quality images by writing text prompts in natural language. However, generating images with desired details requires proper prompts, and it is often unclear how a model reacts to different prompts or what the best prompts are. To help researchers tackle these critical challenges, we introduce DiffusionDB, the first large-scale text-to-image prompt dataset totaling 6.5TB, containing 14 million images generated by Stable Diffusion, 1.8 million unique prompts, and hyperparameters specified by real users. We analyze the syntactic and semantic characteristics of prompts. We pinpoint specific hyperparameter values and prompt styles that can lead to model errors and present evidence of potentially harmful model usage, such as the generation of misinformation. The unprecedented scale and diversity of this human-actuated dataset provide exciting research opportunities in understanding the interplay between prompts and generative models, detecting deepfakes, and designing human-AI interaction tools to help users more easily use these models. DiffusionDB is publicly available at: https://poloclub.github.io/diffusiondb.
Short Communication: The Wasserstein distance as a dissimilarity metric for comparing detrital age spectra, and other geological distributions

Distributional data such as detrital age populations or grain size distributions are common in the geological sciences. As analytical techniques become more sophisticated, increasingly large amounts of distributional data are being gathered. These advances require quantitative and objective methods, such as multidimensional scaling (MDS), to analyse large numbers of samples. Crucial to such methods is choosing a sensible measure of dissimilarity between samples. At present, the Kolmogorov-Smirnov (KS) statistic is the most widely used of these dissimilarity measures. However, the KS statistic has some limitations such as high sensitivity to differences between the modes of two distributions, and insensitivity to their tails. Here we propose the Wasserstein-2 distance (W2) as an additional and alternative metric for use in geochronology. Whereas the KS-distance is defined as the maximum vertical distance between two empirical cumulative distribution functions, the W2-distance is a function of the horizontal distances (i.e., age differences) between observations. Using a variety of synthetic and real datasets we explore scenarios where W2 may provide greater geological insight than the KS statistic. We find that in cases where absolute time differences are not relevant (e.g., mixing of known, discrete age peaks), the KS statistic can be more intuitive. However, in scenarios where absolute age differences are important (e.g., temporally/spatially evolving sources, thermochronology, and overcoming laboratory biases) W2 is preferable. The W2-distance has been added to the R package IsoplotR, for immediate use in detrital geochronology and other applications. The W2 distance can be generalised to multiple dimensions, which opens opportunities beyond distributional data.
A Hybrid Phishing Detection System Using Deep Learning-based URL and Content Analysis

Phishing attacks are one of the most preferred types of attacks for cybercriminals, who can easily contact a large number of victims through the use of social networks, particularly through email messages. To protect end users, most of the security mechanisms control Uniform Resource Locator (URL) addresses because of their simplicity of implementation and execution speed. However, due to sophisticated attackers, this mechanism can miss some phishing attacks and has a relatively high false positive rate. In this research, a hybrid technique is proposed that uses not only URL features, but also content-based features as the second level of detection mechanism, thus improving the accuracy of the detection system while also minimizing the number of false positives. Additionally, most phishing detection algorithms use datasets that contain easily differentiated data pieces, either phishing or legitimate. However, in order to implement a more secure protection mechanism, we aimed to collect a larger and high-risk dataset. The proposed approaches were tested on this High-Risk URL and Content-Based Phishing Detection Dataset that only contains suspicious websites from PhishTank. According to experimental studies, an accuracy rate of 98.37 percent was achieved on a more realistic dataset for phishing detection.
Location-aware deep learning-based framework for optimizing cloud consumer quality of service-based service composition

&lt;span&gt;The expanding propensity of organization users to utilize cloud services urges to deliver services in a service pool with a variety of functional and non-functional attributes from online service providers. brokers of cloud services must intense rivalry competing with one another to provide quality of service (QoS) enhancements. Such rivalry prompts a troublesome and muddled providing composite services on the cloud using a simple service selection and composition approach. Therefore, cloud composition is considered a non-deterministic polynomial (NP-hard) and economically motivated problem. Hence, developing a reliable economic model for composition is of tremendous interest and to have importance for the cloud consumer. This paper provides “A location-aware deep learning framework for improving the QoS-based service composition for cloud consumers”. The proposed framework is firstly reducing the dimensions of data. Secondly, it applies a combination of the deep learning long short-term memory network and particle swarm optimization algorithm additionally to considering the location parameter to correctly forecast the QoS provisioned values. Finally, it composes the ideal services need to reduce the customer cost function. The suggested framework's performance has been demonstrated using a real dataset, proving that it superior the current models in terms of prediction and composition accuracy.&lt;/span&gt;
Policy regularization for legible behavior

Abstract In this paper we propose a method to augment a Reinforcement Learning agent with legibility. This method is inspired by the literature in Explainable Planning and allows to regularize the agent’s policy after training, and without requiring to modify its learning algorithm. This is achieved by evaluating how the agent’s optimal policy may produce observations that would make an observer model to infer a wrong policy. In our formulation, the decision boundary introduced by legibility impacts the states in which the agent’s policy returns an action that is non-legible because having high likelihood also in other policies. In these cases, a trade-off between such action, and legible/sub-optimal action is made. We tested our method in a grid-world environment highlighting how legibility impacts the agent’s optimal policy, and gathered both quantitative and qualitative results. In addition, we discuss how the proposed regularization generalizes over methods functioning with goal-driven policies, because applicable to general policies of which goal-driven policies are a special case.
Quantum Fourier addition simplified to Toffoli addition

Quantum addition circuits are considered being of two types: (1) Toffoli-adder circuits which use only classical reversible gates (controlled-not and Toffoli), and (2) QFT-adder circuits based on the quantum Fourier transformation. We present a systematic translation of the QFT addition circuit into a Toffoli-based adder. This result shows that QFT addition has fundamentally the same fault-tolerance cost (e.g., T count) as the most cost-efficient Toffoli adder: instead of using approximate decompositions of the gates from the QFT circuit, it is more efficient to merge gates. In order to achieve this, we formulated circuit identities for multicontrolled gates and apply the identities algorithmically. The employed techniques can be used to automate quantum circuit optimization heuristics.
AI-Generated Face Image Identification with Different Color Space Channel Combinations

With the rapid development of the Internet and information technology (in particular, generative adversarial networks and deep learning), network data are exploding. Due to the misuse of technology and inadequate supervision, deep-network-generated face images flood the network, and the forged image is called a deepfake. Those realistic faked images launched a serious challenge to the human eye and the automatic identification system, resulting in many legal, ethical, and social issues. For the needs of network information security, deep-network-generated face image identification based on different color spaces is proposed. Due to the extremely realistic effect of deepfake images, it is difficult to achieve high accuracy with ordinary methods for neural networks, so we used the image processing method here. First, by analyzing the differences in different color space components in the deep learning network model for face sensitivity, a combination of color space components that can effectively improve the discrimination rate of the deep learning network model is given. Second, to further improve the discriminative performance of the model, a channel attention mechanism was added at the shallow level of the model to further focus on the features contributing to the model. The experimental results show that this scheme achieved better accuracy in the same face generation model and in different face generation models than the two compared methods, and its accuracy reached up to 99.10% in the same face generation model. Meanwhile, the accuracy of this model only decreased to 98.71% when coping with a JPEG compression factor of 100, which shows that this model is robust.
The Key Strategies for Measuring Employee Performance in Companies: A Systematic Review

Businesses must maintain their ability to adapt in order to remain competitive in today’s ever-evolving industry. They must collect and evaluate data continuously to make educated decisions and take appropriate measures. As a result, since the turn of the 20th century, managers’ preferred areas of interest have been connected to the achievement of their companies. This systematic review aimed to assess the most important methodologies for measuring employee performance in organizations. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) technique, the researchers carried out a systematic review on a specific topic. A thorough review of the literature was carried out, with a particular emphasis on articles, research papers, and other materials deemed relevant to the primary approaches for staff performance measurement and their associated factors and other relevant resources. The systematic review findings revealed that collaboration initiatives had positive and statistically significant standard-size effects on employee performance. The use of performance measurement attempts to identify the strengths and weaknesses of employees and to motivate them to improve their performance. Employee performance measurement demonstrates a favorable relationship between performance evaluation and employee inspiration. Performance evaluation schemes should be designed to provide a good treatment perspective of other teammates and the worker’s own aims.
Dynamic Path Planning for the Differential Drive Mobile Robot Based on Online Metaheuristic Optimization

Mobile robots are relevant dynamic systems in recent applications. Path planning is an essential task for these robots since it allows them to move from one location to another safely and at an affordable cost. Path planning has been studied extensively for static scenarios. However, when the scenarios are dynamic, research is limited due to the complexity and high cost of continuously re-planning the robot’s movements to ensure its safety. This paper proposes a new, simple, reliable, and affordable method to plan safe and optimized paths for differential mobile robots in dynamic scenarios. The method is based on the online re-optimization of the static parameters in the state-of-the-art deterministic path planner Bug0. Due to the complexity of the dynamic path planning problem, a metaheuristic optimization approach is adopted. This approach utilizes metaheuristics from evolutionary computation and swarm intelligence to find the Bug0 parameters when the mobile robot is approaching an obstacle. The proposal is tested in simulation, and well-known metaheuristic methods are compared, including Differential Evolution (DE), the Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The dynamic planner based on PSO generates paths with the best performances. In addition, the results of the PSO-based planner are compared with different Bug0 configurations, and the former is shown to be significantly better.