Reviewerly/exHarmony huggingface dataset:
Real-Time Path Planning for Robot Using OP-PRM in Complex Dynamic Environment

During task execution, the autonomous robots would likely pass through many narrow corridors along with mobile obstacles in dynamically complex environments. In this case, the off-line path planning algorithm is rather difficult to be directly implemented to acquire the available path in real-time. Hence, this article proposes a probabilistic roadmap algorithm based on the obstacle potential field sampling strategy to tackle the online path planning, called Obstacle Potential field-Probabilistic Roadmap Method (OP-PRM). The obstacle potential field is introduced to determine the obstacle area so as to construct the potential linked roadmap. Then the specific range around the obstacle boundary is justified as the target sampling area. Based on this obstacle localization, the effectiveness of the sampling points falling into the narrow corridors can be increased greatly for feasible roadmap construction. Furthermore, an incremental heuristic D* Lite algorithm is applied to search the shortest paths between the starting point and the target point on the roadmap. Simulation experiments demonstrate that the OP-PRM path planning algorithm can enable robots to search the optimal path fast from the starting point to the destination and effectively cross narrow corridors in complex dynamic environments.
Iterative Hard Thresholding Using Least Squares Initialization

An improvement of the typical iterative hard thresholding (IHT) algorithm is presented by means of least squares initialization. Unlike the initialization that assigns all zeros to the initial estimate, the linearized least squares estimate is instead adopted herein. Numerical examples are conducted under a sparse signal recovery problem. Performance of the proposed approach in terms of root-mean-square relative error (RMSRE), computational time, and memory consumption is compared to the typical IHT algorithm. It is shown in numerical example that a good initialization can deliver lower RMSRE, less computational time, and less memory requirement.
Learning global and local features using graph neural networks for person re-identification

Person re-identification (re-id) is the task of recognizing an individual across non-overlapping camera views. Some approaches only rely on extracting global appearance features from images and fail to consider people’s local body information (head, foot, body shape), which can be used as complementary. Other techniques combine local and global features but rely on external information such as pedestrian attributes or human pose to locate and align the local regions. This strategy increases the learning difficulty and is not efficient or robust to real-world scenarios. In this paper, we propose an end-to-end deep learning framework to overcome these limitations. Our method combines the global and local feature representations of a pedestrian and captures the body structural information by modeling the spatial relation of patches using graph neural networks. We also represent the relationships between probe–gallery pairs using a graph neural network and propose incorporating a scoring function to mine a correspondence for local regions. Experimental results on several datasets validate the effectiveness of the proposed method.
Text Vectorization Method Based on Concept Mining Using Clustering Techniques

With the incredible increase in the amount of text data, the need to develop efficient methods for processing and analyzing it increases. In this context, feature extraction from text is an urgent task to solve many texts mining and information retrieval problems. Traditional text feature extraction methods such as TF-IDF and bag-of-words are effective and characterized by intuitive interpretability, but suffer from the «curse of dimensionality», and they are unable to capture the meanings of words. On the other hand, modern distributed methods effectively capture the hidden semantics, but they are computationally intensive, and uninterpretable. This paper proposes a new concept-mining-based text vectorization method called Bag of weighted Concepts BoWC that aims to generate representations with low dimensions and high representational ability. BoWC vectorizes a document according to the concepts’ information it contains, where it creates concepts by clustering word vectors, then uses the frequencies of these concept clusters to represent document vectors. To enrich the resulted document representation, new weighting functions are proposed for concept weighting based on statistics extracted from word embedding information. This work is a development of previous research in which the proposed method was developed and tested on a text classification task. In this work, empirical tests were extended to include tuning the parameters of the proposed method and analyzing the effect of each on the efficiency of the method. The proposed method has been tested in two data mining tasks; document clustering and classification, with five various benchmark data sets, and it was compared with several baselines, including Bag-of-words, TF-IDF, Averaged word embeddings, Bag-of-Concepts, and VLAC. The proposed method outperforms most baselines in terms of the minimum number of features and maximum classification and clustering accuracy.
Exploring the viability of Conversational AI for Non-Playable Characters: A comprehensive survey

Following the surge in AI advancements in the gaming industry, there has also been a drastic shift in the very concept of Non-Playable Characters (NPCs) and the roles they play in games. Over the evolution of the gaming sphere, there has a been a shift of focus towards NPCs, primarily in RPG games. Game developers have realized the potential that well scripted NPCs can carry to deliver a much more refined and immersive experience. Despite the aforementioned, NPC interactions are still at their infancy when compared to the strides in development made on their surrounding aspects. Additionally, we also explore conversational AI, the successor to the conventional chatbot as many call it, an emerging domain that has applications stretching to a multitude of different fields. Conversational AI is essentially an extended branch of Machine Learning to develop language-based frameworks that allows users to naturally interact with machines, providing a more enhanced and refined user experience. In this review paper, we glance over evolution, methodologies and uses cases of both NPCs and conversational AI. Towards the latter half we discuss the viability of fusing the two domains to enrich and potentially redefine NPC interactions via the sea of functionalities offered by conversational AI.
Information Architecture to Support Engineering Education in the Era of Industry 4.0

In order to meet the demands of Industry 4.0, the education system faces the task of forming the image of a student, which can be solved by creating his digital twin. The article discusses the existing approaches to the application of this technology in education. In the field of informatization of the educational process, they will form a development vector of student's educational trajectory. The technology of creating a digital twin of a student and the tasks that can be solved with its application are considered.
Developing and Validating an Instrument for Measuring Teachers’ Informatization Teaching Ability in Primary and Secondary Schools in China for the Sustainable Development of Education Informatization

Teachers’ informatization teaching ability (TITA) is the core ability of teachers to engage in educational activities in the informatization environment. It is an essential indicator of the quality of education and affects teachers’ professional development in the technological age. To get a precise teachers’ informatization teaching ability scale and to measure TITA more accurately, the quality of existing scales needs to be improved. This study analyzed and generalized definitions, models and frameworks for TITA, proposed a four-dimension model (teachers’ informatization teaching metacognitive ability, informatization teaching design ability, informatization teaching implementation ability and informatization teaching evaluation ability), and developed the TITA scale. Three experts were involved in the content validity of the TITA scale process. A total of 403 valid questionnaires answered by Chinese primary and secondary school teachers were used to test the reliability and convergent validity of the scale. The results showed that the TITA scale has high reliability and good validity, and it can be used to evaluate TITA in future studies. The TITA scale also provided a theoretical framework to help teachers consider how to transfer knowledge and skills to students by various technologies more effectively.
Deep Learning-Based Real-Time Activity Recognition with Multiple Inertial Sensors

We propose a real-time activity recognition system based on data from multiple wearable inertial sensors. They are worn at the user’s right wrist, waist, and right ankle to collect acceleration and angular velocity data, which are then transmitted via Bluetooth to a computer. The data are used to train a convolutional neural network (CNN) model to recognize 13 types of daily activities. The trained model has been ported to Tensorflow Lite running on Raspberry Pi to enable edge processing. The latency for recognizing the first motion takes 2.6 seconds, and subsequent ones are 1.325 seconds. Experimental results show that among the six participants, our model achieves high accuracy of 98.62% and 99.84% for leave-one-out cross-validation and 10-fold cross-validation, respectively.
Deep Multi-Scale Residual Connected Neural Network Model for Intelligent Athlete Balance Control Ability Evaluation

Athlete balance control ability plays an important role in different types of sports. Accurate and efficient evaluations of the balance control abilities can significantly improve the athlete management performance. With the rapid development of the athlete training field, intelligent and automatic evaluations have been highly demanded in the past years. This study proposes a deep learning-based athlete balance control ability evaluation method through processing the time-series movement pressure measurement data. An end-to-end model structure is proposed, which directly analyzes the raw data and provides the evaluation results, which largely facilitates practical utilization. A multi-scale feature extraction scheme is employed, by exploring the learned features in different scales. A residual connected neural network architecture is further proposed. By using the short-cut connection, the deep neural network model can be more efficiently trained. Experiments on the real athlete balance control ability tests are carried out for validations. Through comparisons with different related methods, the results show the proposed deep multi-scale residual connected neural network model is well suited for the athlete balance control ability evaluation problem, and promising for actual applications in the real scenarios.
ICE-DEN: A click-through rate prediction method based on interest contribution extraction of dynamic attention intensity

The goal of click-through rate prediction is to predict the probability of users clicking on items, and the improvement of its performance is still one of the core tasks in e-commerce. With the development of online social networks, users have access to a large amount of historical behavior data. The use of historical data for extracting user interests is crucial to the improvement of the click-through rate prediction performance. However, most existing methods ignore the importance of dynamic attention intensity. Therefore, this paper proposes an interest enhancement method based on dynamic relationships called Interest Contribution Extraction (ICE). This method first obtains the dynamic interest intensity by considering the dynamic changes in interest. Then, an adaptive interest attention unit is constructed to collect users’ different interest and attention strengths for different candidate items. Based on the ICE method, a new click-through rate prediction model called the Deep Extraction Network based on Interest Contribution Extraction (ICE-DEN) is proposed. The model obtains a user’s low-dimensional features through the embedding layer. Mini-batch perception regularization and the Dice activation function are used to help train deep learning networks with a large number of parameters. The ICE-DEN model has achieved better prediction results than other models on three real datasets.
Ubiquitous Work Assistant: Synchronizing a Stationary and a Wearable Conversational Agent to Assist Knowledge Work

Recent research in Human-Computer Interaction for work has shown that conversational agents (CA) are beneficial for supporting focused work and well-being while at work. Knowledge workers struggle in maintaining focus, work schedule, and well-being. Typically, they rely on multiple tools and services for work productivity, scheduling tasks, and reminding breaks. With the goal of tackling these problems, we propose the concept of a ubiquitous work assistant (UWA), which consists of two components: a stationary CA (S-CA) and a wearable CA (W-CA). S-CA is meant to be placed on user’s work desk while W-CA is fixed on the user’s wrist. The UWA interface is distributed between S-CA and W-CA. We initiated our study by conducting semi-structured interviews with knowledge workers (N = 14). We identified their expectations from conversational agents (CAs) that would assist them in their daily work life. From the interview findings, we developed an UWA prototype that could assist users by briefing their daily schedule, monitoring their schedule, and reminding breaks. We conducted a lab study simulating a home-office environment. The findings of the study show that the knowledge workers see potential in the UWA system. Further, we discuss implications of distributed user interface (DUI) for UWA design.
New Ways of Working through Emerging Technologies: A Meta-Synthesis of the Adoption of Blockchain in the Accountancy Domain

In an attempt to deepen how the way of working is changing due to the digital transformation, this research aims at understanding the process by which individuals adopt blockchain technology in accountancy. We conducted a meta-synthesis of the qualitative literature on the topic of blockchain technology adoption in the context of accountancy. Drawing from 10 systematically selected qualitative studies, we analyzed the process of blockchain technology adoption in accountancy, with particular reference to the impacts on accounting professionals, in terms of individual attitudes and behaviors, as well as organizations. Our findings contribute to the existing literature in at least two ways. First, our research explores the topic of blockchain adoption in the accountancy domain and stresses the relevance of the use of that emerging technology by accounting professionals and organizations, as well as the main problems that could limit its adoption and use. Second, we provide an overview of the process of blockchain technology adoption with specific reference to the questions of “why” and “how” blockchain is (or is not) adopted by accounting professionals and organizations, in an effort to shed light on a critical issue that has yet to be explored in accountancy.
Cognition and Society: Prolegomenon to a Dialog

Cognitive ScienceVolume 46, Issue 6 e13162 Letter to the Editor Cognition and Society: Prolegomenon to a Dialog Thom Scott-Phillips, Thom Scott-Phillips Department of Cognitive Science, Central European UniversitySearch for more papers by this authorDaniel Nettle, Corresponding Author Daniel Nettle daniel.nettle@ncl.ac.uk Population Health Sciences Institute, Newcastle University Correspondence should be sent to Daniel Nettle, Population Health Sciences Institute, Newcastle University, Newcastle NE2 4HH, UK. E-mail: daniel.nettle@ncl.ac.ukSearch for more papers by this author Thom Scott-Phillips, Thom Scott-Phillips Department of Cognitive Science, Central European UniversitySearch for more papers by this authorDaniel Nettle, Corresponding Author Daniel Nettle daniel.nettle@ncl.ac.uk Population Health Sciences Institute, Newcastle University Correspondence should be sent to Daniel Nettle, Population Health Sciences Institute, Newcastle University, Newcastle NE2 4HH, UK. E-mail: daniel.nettle@ncl.ac.ukSearch for more papers by this author First published: 04 June 2022 https://doi.org/10.1111/cogs.13162 This article is part of the “Progress & Puzzles of Cognitive Science” letter series. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume46, Issue6June 2022e13162 RelatedInformation
Research on Quantum Annealing Integer Factorization Based on Different Columns

The majority of scholars believe that Shor’s algorithm is a unique and powerful quantum algorithm for RSA cryptanalysis, so current postquantum cryptography research has largely considered only the potential threats of Shor’s algorithm. This paper verifies the feasibility of deciphering RSA public key cryptography based on D-Wave, which is the second most effective RSA attack method after Shor’s algorithm. This paper proposes the influence of different column methods on the final integer factorization, puts forward a new dimension reduction formula, simplifies the integer factorization model based on quantum annealing, simulates it with the qbsolv quantum computing software environment provided by D-Wave, and factors the integer 1630729 (an 11-bit prime factor multiplied by an 11-bit prime factor). The research results show that choosing an appropriate number of columns and column width in the binary integer factorization multiplication table is very important for studying the optimization ability of the quantum annealing algorithm. In fact, Science, Nature, IEEE Spectrum, and the National Academies of Sciences (NAS) are consistent in asserting that the practical application of general-purpose quantum computers is far in the future. Therefore, although D-Wave computers were initially mainly purchased by Lockheed Martin, Google, etc., for purposes such as image processing, machine learning, combinatorial optimization, and software verification, post quantum cryptography research should further consider the potential of the D-Wave quantum computer in deciphering RSA cryptosystems in the future, and a discussion of this potential is one of the contributions of this paper.
Designing public VR installations:

Virtual Reality (VR) installations using head-mounted displays (HMD) are becoming increasingly popular in public spaces. However, VR's immersive nature engages only the HMD wearer and excludes everyone else in the public space, and there is little design knowledge of how to engage those not wearing an HMD. To address this, we draw from our experiences of having designed seven public VR installations to present a design space around the dimensions of "agency" and "interest" with four user engagement frames to articulate twelve different user roles. To guide designers to support all roles and to transition users between those roles, we complement the design space with a set of design tactics for public VR installations. We hope that these combined contributions will help designers engage more people with VR installations so that ultimately more people benefit from what VR has to offer.
JEDI: These aren't the JSON documents you're looking for...

The JavaScript Object Notation (JSON) is a popular data format used in document stores to natively support semi-structured data. In this paper, we address the problem of JSON similarity lookup queries: given a query document and a distance threshold τ, retrieve all documents that are within τ from the query document. Different from other hierarchical formats such as XML, JSON supports both ordered and unordered sibling collections within a single document which poses a new challenge to the tree model and distance computation. We propose JSON tree, a lossless tree representation of JSON documents, and define the JSON Edit Distance (JEDI), the first edit-based distance measure for JSON. We develop QuickJEDI, an algorithm that computes JEDI by leveraging a new technique to prune expensive sibling matchings. It outperforms a baseline algorithm by an order of magnitude in runtime. To boost the performance of JSON similarity queries, we introduce an index called JSIM and an effective upper bound based on tree sorting. Our upper bound algorithm runs in O(nτ) time and O(n+τ log n) space, which substantially improves the previous best bound of O(n2) time and O(n log n) space (where n is the tree size). Our experimental evaluation shows that our solution scales to databases with millions of documents and JSON trees with tens of thousands of nodes.