Reviewerly/exHarmony huggingface dataset:
Studying interaction density in co-design sessions involving spatial augmented reality

The participation of end-users in co-design sessions has always been challenging. Despite a long history of trials and research, finding the best way to go about this form of collaboration in early innovation phases is still an open question for many companies. At the cutting edge of innovation, design agencies are becoming increasingly involved in co-design today. Direct face-to-face collaboration with end-users is a key factor for the success of co-design, and ICT solutions must therefore allow active involvement of all participants and their direct interaction. This paper reports an original evaluation of a Spatial Augmented Reality (SAR) environment dedicated to supporting co-design sessions. It investigates collaborative interactions and explores the role of design artefacts in the collaborative process. Observations of a conventional co-design situation are compared with those of sessions assisted by SAR technology. These two contrasting environments reveal both similarities and differences. The results show that within the SAR-supported environment there is an increase in end-users’ interaction density within the design activities. We observed that providing tangible artefacts augmented by digital content increased the density of interactions and encouraged greater sharing in co-design meetings.
What Affects the Acceptance and Use of Hotel Service Robots by Elderly Customers?

Against the realistic backdrop of the COVID-19 pandemic and an aging population, emerging robot technology provides a new path for the development of high-quality hotel service. However, little is known about elderly customers’ acceptance and use of hotel service robots. This study explores factors that affect elderly customers’ acceptance and use of hotel service robots. From the perspective of perception and emotion, based on the technology acceptance model and quality service theory, a hotel service robot acceptance model was constructed for this study, and a structural equation model was used to analyze the data from 218 interviews. The results show that empathy, perceived value, perceived usefulness and perceived ease of use directly affected the elderly customers’ intention to use robots. Perceived trust indirectly affected the use intention through perceived usefulness and perceived ease of use. This study provided a theoretical basis for user behaviors regarding hotel service robots and provided guidance for the research and development of hotel service robots and the marketing promotion of hotel managers, which would promote the healthy development of service robots and related industries, such as the hotel service industry.
A Multi-Layer Holistic Approach for Cursive Text Recognition

Urdu is a widely spoken and narrated language in several South-Asian countries and communities worldwide. It is relatively hard to recognize Urdu text compared to other languages due to its cursive writing style. The Urdu text script belongs to a non-Latin cursive family script like Arabic, Hindi and Chinese. Urdu is written in several writing styles, among which ‘Nastaleeq’ is the most popular and widely used font style. A gap still poses a challenge for localization/detection and recognition of Urdu Nastaleeq text as it follows modified version of Arabic script. This research study presents a methodology to recognize and classify Urdu text in Nastaleeq font, regardless of the text position in the image. The proposed solution is comprised of a two-step methodology. In the first step, text detection is performed using the Connected Component Analysis (CCA) and Long Short-Term Memory Neural Network (LSTM). In the second step, a hybrid Convolution Neural Network and Recurrent Neural Network (CNN-RNN) architecture is deployed to recognize the detected text. The image containing Urdu text is binarized and segmented to produce a single-line text image fed to the hybrid CNN-RNN model, which recognizes the text and saves it in a text file. The proposed technique outperforms the existing ones by achieving an overall accuracy of 97.47%.
Invariant Configuration-Space Bubbles for Revolute Serial-Chain Robots

This letter adapts the invariant-set motion planner (ISMP) for robot motion planning. We derive control invariant subsets of configuration-space bubbles lifted into the state-space. The resulting sets guarantee collision avoidance since they are both constraint admissible and control invariant. We present a command governor that enforces the positive invariance of the constraints in closed-loop. This governor can be used to transform any nominal tracking controller into a constraint enforcing controller. We use these control invariant sets to quantify a relationship between velocity and control authority that enables collision avoidance. We demonstrate our invariant-sets through an illustrative numerical example.
Mobilizing assistance through complaints in digital skills courses for adults

Within a rapidly digitalizing society, it is important to understand how the learning and teaching of digital skills play out in situ, especially among the elderly, an often marginalized user group. This paper will focus on elderly participants engaged in the process of learning digital skills in adult education courses. In video recordings from adult education centres in Finland and Germany, we explore how students mobilize their teachers’ assistance upon encountering a problem with their mobile devices (smartphones, laptops or tablets). Prior research on social interaction has shown that assistance can be recruited through a variety of verbal and embodied formats. In this specific educational setting, participants can use complaints about their digital skills or the mobile devices in order to obtain help. Utilizing multimodal conversation analysis, we will explore two basic sequence types involving students’ complaints, discuss their cross-linguistic characteristics, and reflect on their link to this educational setting and digital devices.
Deciphering emoji variation in courts: a social semiotic perspective

Abstract Emojis are increasingly being used as digital evidence in courts due to the miscommunication and misinterpretation arising from the high variability of their usage and interpretation. Emojis in courts have been extensively researched in extant studies, but relatively little attention has been paid to the emoji variation phenomena in Chinese courts. Through an empirical qualitative content analysis of the court judgments in China and the United States and some supplementary materials, this study posits that an emoji’s meaning in courts can be subject to the following six categories of variations: i. variation across platforms, including devices, operating systems, software programs and clients; ii. temporal variation; iii. variation in court cases under different rules of evidence; iv. variation in individual participants; v. variation across social groups; and vi. linguistic-cultural variation. From a social semiotic perspective, emojis as dynamic signs have great meaning potentials, making their meanings context-dependent and interpreter-dependent. For this reason, it is suggested that legal professionals untangle and weave historical, social, cultural and legal contexts into the interpretation of an emoji’s meaning. Moreover, a probe into the contextualized configuration of emojis can offer practical insights into the interpretation of emoji-bearing texts in judicial decision-making as well as the admissibility and investigation of digital evidence in courts.
Web and Social Media Analytics

The World Wide Web (WWW) has witnessed a plethora of changes in the last decade. The new-age business enterprises also have realized the potency of clubbing the decisions with data for enhanced customer and shareholder value. Data in any form is usable to organizations for tracking, analysis, and reporting. Today, an overwhelming amount of data is available on the WWW and social media platforms. Web analytics refers to the analysis of usage data from the web to trace certain interaction patterns of users on a particular website. Social media analytics is a method of using digital texts like customer reviews in discussion forums, blogs, and various social media platforms, classifying them as positive, negative, or neutral. These are useful in market research and competitive intelligence. Sentiment analysis enables marketers to learn the emotions of customers, be it good ones or the bad ones. Often, customers also hint on how marketers can make the product or service better for them. The data analytics industry is growing with a surplus of techniques, platforms, and career opportunities in all fields of businesses. This chapter bestows an outline of web and social media analytics.
Conflict-Free Joint Sampling for Preference Satisfaction through Quantum Interference

Collective decision making is vital for recent information and communications technologies. In our previous research, we mathematically derived conflict-free joint decision making that optimally satisfies players' probabilistic preference profiles. However, two problems exist regarding the optimal joint decision-making method. First, as the number of choices increases, the computational cost of calculating the optimal joint selection probability matrix explodes. Second, to derive the optimal joint selection probability matrix, all players must disclose their probabilistic preferences. Now, it is noteworthy that explicit calculation of the joint probability distribution is not necessarily needed; what is necessary for collective decisions is sampling. This study examines several sampling methods that converge to heuristic joint selection probability matrices that satisfy players' preferences. We show that they can significantly reduce the above problems of computational cost and confidentiality. We analyze the probability distribution each of the sampling methods converges to, as well as the computational cost required and the confidentiality secured. In particular, we introduce two conflict-free joint sampling methods through the quantum interference of photons. The first system allows the players to hide their choices while satisfying the players' preferences almost perfectly when they have the same preferences. The second system, where the physical nature of light replaces the expensive computational cost, also conceals their choices under the assumption that they have a trusted third party.
The influencing factors of digital health passport adoption and acceptance during COVID-19 in Saudi Arabia

Background The COVID-19 outbreak has left a destructive trail around the world in terms of deaths, travel restrictions, trade deficits, and an economy that is collapsing, including job losses, real estate, health benefit loss, and a decrease in the quality of access to care and services in almost all sectors, as well as generally in the overall quality of life. The successful development of COVID-19 vaccines may hasten the acceleration of global post-pandemic recovery by vaccinating residents, with a particular focus on important groups, in order to decrease secondary transmission. This will facilitate the easing of enforced restrictions on global and local travel, the tourism industry, education sectors, and other aspects of social life. Vaccinating residents may also help reduce the risk of secondary transmission. The efforts that Saudi Arabia made to control the epidemic were outstanding on all fronts and in all spheres, including the health, education, commerce, and tourism industries, among others. Objective The purpose of this research was to investigate the elements that influence a traveler's decision to acquire and use a digital health passport (DHP), which was introduced by the Tawakkalna application in Saudi Arabia at the COVID-19 conference. Methods The technology acceptance model (TAM) and the information system success model (ISSM) were the primary theoretical frameworks that guided this investigation. The terms “perceived ease of use” (PEOU), “perceived usefulness” (PU), “information quality” (IQ), “service quality” (SQ), and “net benefit” (NB) were applied in order to investigate the user's acceptance and use of the DHP, as well as how it contributes to the facilitation of traveling and public perception toward using the DHP. Results In order to assess the validity of the proposed model and its four assumptions, a survey was sent through social media platforms to get responses from nationals and residents of Saudi Arabia. The SPSS program was used to evaluate a total of 103 replies that were considered valid. Following the completion of the study, the findings revealed that PEOU, PU, IQ, SQ, and NB all had favorable impacts on the use of DHP. Conclusion PEOU, PU, IQ, and SQ have a significant relationship with NB that affects the public's acceptance and use of DHP. This study has established validity and reliability while testing the relationship between the variables suggested in the research model.
A Hybrid Quantum Image-Matching Algorithm

Image matching is an important research topic in computer vision and image processing. However, existing quantum algorithms mainly focus on accurate matching between template pixels, and are not robust to changes in image location and scale. In addition, the similarity calculation of the matching process is a fundamentally important issue. Therefore, this paper proposes a hybrid quantum algorithm, which uses the robustness of SIFT (scale-invariant feature transform) to extract image features, and combines the advantages of quantum exponential storage and parallel computing to represent data and calculate feature similarity. Finally, the quantum amplitude estimation is used to extract the measurement results and realize the quadratic acceleration of calculation. The experimental results show that the matching effect of this algorithm is better than the existing classical architecture. Our hybrid algorithm broadens the application scope and field of quantum computing in image processing.
Open-Set Recognition Model Based on Negative-Class Sample Feature Enhancement Learning Algorithm

In order to solve the problem that the F1-measure value and the AUROC value of some classical open-set classifier methods do not exceed 40% in high-openness scenarios, this paper proposes an algorithm combining negative-class feature enhancement learning and a Weibull distribution based on an extreme value theory representation method, which can effectively reduce the risk of open space in open-set scenarios. Firstly, the solution uses the negative-class sample feature enhancement learning algorithm to generate the negative sample point set of similar features and then compute the corresponding negative-class sample feature segmentation hypersphere. Secondly, the paired Weibull distributions from positive and negative samples are established based on the corresponding negative-class sample feature segmentation hypersphere of each class. Finally, solutions for non-linear multi-class classifications are constructed by using the Weibull and reverse Weibull distributions. Experiments on classic open datasets such as the open dataset of letter recognition, the Caltech256 open dataset, and the CIFAR100 open dataset show that when the openness is greater than 60%, the performance of the proposed method is significantly higher than other open-set support vector classifier algorithms, and the average is more than 7% higher.
Multi-Task Learning Model for Kazakh Query Understanding

Query understanding (QU) plays a vital role in natural language processing, particularly in regard to question answering and dialogue systems. QU finds the named entity and query intent in users' questions. Traditional pipeline approaches manage the two mentioned tasks, namely, the named entity recognition (NER) and the question classification (QC), separately. NER is seen as a sequence labeling task to predict a keyword, while QC is a semantic classification task to predict the user's intent. Considering the correlation between these two tasks, training them together could be of benefit to both of them. Kazakh is a low-resource language with wealthy lexical and agglutinative characteristics. We argue that current QU techniques restrict the power of the word-level and sentence-level features of agglutinative languages, especially the stem, suffixes, POS, and gazetteers. This paper proposes a new multi-task learning model for query understanding (MTQU). The MTQU model is designed to establish direct connections for QC and NER tasks to help them promote each other mutually, while we also designed a multi-feature input layer that significantly influenced the model's performance during training. In addition, we constructed new corpora for the Kazakh query understanding task, namely, the KQU. As a result, the MTQU model is simple and effective and obtains competitive results for the KQU.
Privacy Protection Practice for Data Mining with Multiple Data Sources: An Example with Data Clustering

In the age of data, data mining provides feasible tools with which to handle large datasets consisting of data from multiple sources. However, there is limited research on retrieving statistical information from data when data are confidential and cannot be shared directly. In this paper, we address this problem and propose a framework for performing data analysis using data from multiple sources without revealing true values for privacy purposes. The proposed framework includes three steps. First, data custodians individually mask data before publishing; then, the masked data collection is used to reconstruct the density function of the original dataset, from which resampled values are generated; last, existing data mining techniques are applied directly to the resampled data. This framework utilises the technique of reconstructing an original density function from noise-masked data using the moment-based density estimation method, which plays an essential role. Simulation studies show that the proposed framework performs well; analysis results from the resampled data are comparable to those of the original data when the density of the original data is estimated well. The proposed framework is demonstrated in data clustering analysis using the example of a real-life Australian soybean dataset. Results from the k-means algorithms with two and three fitted clusters are presented to show that cluster analysis using resampled data can well replicate that of the original data.
The Stable Artist: Steering Semantics in Diffusion Latent Space

Large, text-conditioned generative diffusion models have recently gained a lot of attention for their impressive performance in generating high-fidelity images from text alone. However, achieving high-quality results is almost unfeasible in a one-shot fashion. On the contrary, text-guided image generation involves the user making many slight changes to inputs in order to iteratively carve out the envisioned image. However, slight changes to the input prompt often lead to entirely different images being generated, and thus the control of the artist is limited in its granularity. To provide flexibility, we present the Stable Artist, an image editing approach enabling fine-grained control of the image generation process. The main component is semantic guidance (SEGA) which steers the diffusion process along variable numbers of semantic directions. This allows for subtle edits to images, changes in composition and style, as well as optimization of the overall artistic conception. Furthermore, SEGA enables probing of latent spaces to gain insights into the representation of concepts learned by the model, even complex ones such as 'carbon emission'. We demonstrate the Stable Artist on several tasks, showcasing high-quality image editing and composition.
Path Optimization of Welding Robot Based on Ant Colony and Genetic Algorithm

While the process of intelligent industrial production is accelerating, the application scope of welding robots is also expanding. For the purpose of reducing the work efficiency and time consumption of the welding robot, the ACO is used for the shortest distance and the GA is used for the shortest time fixed-point path trajectory optimization. The application of parameter optimization and random disturbance factor in the ACO increases the global search performance of the algorithm. In the shortest time trajectory optimization, the B-spline curve interpolation method and the GA are combined to carry out the segmental optimization processing. Simulation experiments show that the optimization strategy of ACO can increase the iterative calculation efficiency and path optimization performance of the algorithm. At the same time, the robot with optimized genetic algorithm has smaller fluctuations in joint angle and angular velocity in the simulated welding task, and the optimization algorithm takes 17.6 s less than the traditional particle swarm algorithm and 11 s less than the single <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow> <mi>A</mi> </mrow> <mrow> <mo>∗</mo> </mrow> </msup> </math> algorithm. The experiments confirmed the performance of the ACO-GA for the path optimization of the welding robot, and research can provide a scientific path optimization reference for the welding task of the industrial production line.
PiP-X: Funnel-Based Online Feedback Motion Planning/Replanning in Dynamic Environments

We propose an online single-query sampling-based feedback motion re-planning algorithm using finite-time invariant sets, “funnels". We combine concepts from nonlinear systems analysis, sampling-based motion planning, and graph-search methods to create a single framework that enables feedback motion planning/replanning for general nonlinear dynamical systems in a dynamic workspace. We introduce a novel graph data structure to represent a network of volumetric funnels, enabling the use of quick graph-replanning techniques. The use of incremental search techniques and a pre-computed library of motion-primitives ensure that our method can be used for quick on-the-fly rewiring of controllable motion plans in response to changes in the environment. We validate our approach on a simulated 6DOF quadrotor platform operating in a maze, and random forest environment.
Impact of Information Sharing on Supply Chain Performance through Supplier Quality Management, Supply Chain Agility, and Supply Chain Innovation

The study investigated the impact of information sharing on supply chain performance through supplier quality management, supply chain agility, and supply chain innovation. The data collection used a question­naire designed with a 7-point Likert scale in GoogleForm and printed format and distributed through social media, email, WhatsApp, and mail posts. Of the 266 population, as many as 112 responses were received and were considered valid for further processing. The data analysis used the partial least square technique (PLS) utilizing SmartPLS. The analysis showed that information sharing directly affects supplier quality mana­gement, supply chain agility, and supply chain innovation. Furthermore, supply chain agility, supplier quality management, and supply chain innovation directly affect operational performance. The exciting result of this study is the indirect effect of information sharing on operational performance through the mediating role of supply chain agility, supplier quality management, and supply chain innovation. This result offers solutions to practitioners to pursue information sharing, supplier quality management, supply chain agility, and supply chain innovation in improving operational performance. The main theoretical contribution of this research is the mediating role of supply chain agility, supply chain innovation, and supplier quality management toward the relationship between information sharing and operational performance.