Reviewerly/exHarmony huggingface dataset:
Effect of learning self-efficacy of Chinese calligraphy students on innovative behavior

Objectives This study attempted to investigate the effect of learning self-efficacy on innovative behavior in Chinese calligraphy students.&#x0D; Methods For the study, a survey was conducted on 213 students for a total of six weeks from the end of October to mid-December 2022 with college students majoring in 4-year calligraphy in A City, Hanam Province, China. To verify the research hypothesis, frequency analysis, exploratory factor analysis, reliability analysis, and correlation analysis were conducted using SPSS Statistics 22. And independent sample t-test and one-way ANOVA were conducted to find out the differences in variables according to general characteristics, and regression analysis was conducted to verify hypotheses through the concept of measurement.&#x0D; Results TAs a result of the old study, first, it was found that there was a significant difference in the gender of college students majoring in calligraphy. Second, it was found that learning self-efficacy and difficulty preference had a positive (+) effect on creative thinking ability. Third, it was found that learning self-efficacy had a positive (+) effect on empathy ability. Fourth, it was found that learning self-efficacy and difficulty preference had a positive (+) effect on adaptation efforts. Therefore, it was found that learning self-efficacy had a partial effect on innovation behavior.&#x0D; Conclusions TSince learning self-efficacy and innovation behavior perceived by Chinese calligraphy college students have a partial influence, it suggests that various educational support systems should be developed in universities that can improve learning self-efficacy and innovation behavior.
A formal semantics for agent distribution and fault tolerance in Jason

This article provides a formal specification of the distribution and fault-tolerance mechanisms of eJason.The eJason programming language is an extension to the agent-oriented programming language Jason that introduces native support for the transparent distribution of agents as well as fault-tolerance mechanisms.This formal semantics is presented from a multiagent system perspective.It unambiguously describes both the possible evolution of the distributed multiagent system over time and the different instruments for fault detection and fault recovery, hence exposing their strengths.This specification may serve as a reference for researchers interested in the inclusion of similar mechanisms in agentoriented programming languages.The formal semantics has been mechanized through an (open-source) implementation written in Prolog, which implements both the standard Jason operational semantics, along with the new rules for distribution and fault-tolerance introduced in this article.
Image stitching method based on improved optimal seam

In order to improve the running time of image stitching and remove the phenomenon of ghosting, this paper proposes an image stitching method based on the improved optimal seam line.First, the two input images are preprocessed using a median filter algorithm.Next, image registration is performed using the ORB algorithm. Finally, the best seam method based on the Canny operator is used.The experimental results show that when stitching images, whether for large scale or small scale images, the algorithm in this paper has faster running speed and better stitching effect.
Indoor object segmentation based on YOLACT++

Intelligent Manufacturing originates from the research of artificial intelligence, which can not only reduce operating costs, but also improve product quality, and has become the way to cultivate new advantages in forging international competition in manufacturing. With the transformation of traditional manufacturing to intelligent manufacturing, the course of intelligent manufacturing should also be different from the traditional teaching mode, but through the survey, it has been found that the course of intelligent manufacturing still has a single teaching mode, insufficient innovative guidance for students and insufficient combination of theory and practice. In order to solve the issues mentioned above, the Project-Analysis-Evaluation (PAE) structure is proposed in this paper, and it is combined with constructivist theory and analysed through the study of indoor object instance segmentation detection. The YOLACT++ algorithm is pre-trained on SUNRGBD dataset and applied to indoor environment detection in this paper.
Detecting Collusive Agents by Trust Measures in Social IoT Environments: A Novel Reputation Model

In the Internet of Things, smart objects can build multidimensional and context-sensitive network infrastructures potentially rich of social interactions. Smart objects can be associated with software agents to boost social interactions and realizing complex and sophisticated forms of collaboration of objects with both other objects and people. In such a scenario, there exists the possibility to interact with unreliable partners exposing agents to the risks deriving by malicious behaviors. To mitigate these risks, Trust and Reputation Systems can be adopted to provide each agent with appropriate trustworthiness measures about the potential counterparts in order to select the best ones. In this context, our contribution consists of (i) a method to preliminarily identify the best candidates as malicious in order to consider them as pre-untrusted entities and (ii) a novel effective reputation model able to detect collusive malicious agents without introducing collateral effects with respect to the reputation scores of honest agents.
Evaluation of Topology Description Models in Road Network Formats

Topology is a particular feature of road networks.Topology refers to properties, such as the connection of roads, but not only through their axes or reference lines, but also at the level of lanes.The map topology of road networks does not currently have procedures that can be expressed in mathematical formulas (or only to a minimal extent), but can only be implemented by algorithms.Our research, therefore, aimed at observing such topological regularities and then developing a method of investigation, by constructing rules and additional algorithms.To test this work, we used synthetic and real data, focusing on the case of map content embodied in four formats.In this paper, we present the test methods for the data models, the results of our test runs and finally, we point out that topological checks are extensively justified to determine the quality of the produced map.In the future we plan to develop an automatic correction mechanism based on this.
Learning to Transmit with Provable Guarantees in Wireless Federated Learning

We propose a novel data-driven approach to allocate transmit power for federated learning (FL) over interference-limited wireless networks. The proposed method is useful in challenging scenarios where the wireless channel is changing during the FL training process and when the training data are not independent and identically distributed (non-i.i.d.) on the local devices. Intuitively, the power policy is designed to optimize the information received at the server end during the FL process under communication constraints. Ultimately, our goal is to improve the accuracy and efficiency of the global FL model being trained. The proposed power allocation policy is parameterized using graph convolutional networks (GCNs), and the associated constrained optimization problem is solved through a primal-dual (PD) algorithm. Theoretically, we show that the formulated problem has a zero duality gap and, once the power policy is parameterized, optimality depends on how expressive this parameterization is. Numerically, we demonstrate that the proposed method outperforms existing baselines under different wireless channel settings and varying degrees of data heterogeneity.
SGT: Session-based Recommendation with GRU and Transformer

Session-based recommendation aims to predict user preferences based on anonymous behavior sequences. Recent research on session-based recommendation systems has mainly focused on utilizing attention mechanisms on sequential patterns, which has achieved significant results. However, most existing studies only consider individual items in a session and do not extract information from continuous items, which can easily lead to the loss of information on item transition relationships. Therefore, this paper proposes a session-based recommendation algorithm (SGT) based on Gated Recurrent Unit (GRU) and Transformer, which captures user interests by learning continuous items in the current session and utilizes all item transitions on sessions in a more refined way. By combining short-term sessions and long-term behavior, user dynamic preferences are captured. Extensive experiments were conducted on three session-based recommendation datasets, and compared to the baseline methods, both the recall rate Recall@20 and the mean reciprocal rank MRR@20 of the SGT algorithm were improved, demonstrating the effectiveness of the SGT method.
Flying Names: Manipulating Name Tags in XR Social Environments for Target Selection Tasks

Recent developments in Extended Reality (XR) have led to the creation of various Virtual Reality (VR) social applications that allow small groups of people to interact and participate in various activities. However, these applications are not suitable for larger-scale social scenarios. One major challenge is the lacking of effective solutions for target selection because existing methods for occluded-target selection do not apply well to humans. In this paper, we propose a method called "Flying Names" to improve decision-making accuracy during target selection for both VR and Augmented Reality (AR) social environments by manipulating the heights of name tags on the fly and connecting an auxiliary line from a name tag to its highlighted owner. Our user study results indicate that this proposal effectively improves the accuracy of identifying the actual target person and is better than using either one or none of manipulating heights and connecting lines.
A Learner-Verifier Framework for Neural Network Controllers and Certificates of Stochastic Systems

Abstract Reinforcement learning has received much attention for learning controllers of deterministic systems. We consider a learner-verifier framework for stochastic control systems and survey recent methods that formally guarantee a conjunction of reachability and safety properties. Given a property and a lower bound on the probability of the property being satisfied, our framework jointly learns a control policy and a formal certificate to ensure the satisfaction of the property with a desired probability threshold. Both the control policy and the formal certificate are continuous functions from states to reals, which are learned as parameterized neural networks. While in the deterministic case, the certificates are invariant and barrier functions for safety, or Lyapunov and ranking functions for liveness, in the stochastic case the certificates are supermartingales. For certificate verification, we use interval arithmetic abstract interpretation to bound the expected values of neural network functions.
Encryption and decryption algorithm based on the Latin groupoid isotopes

This paper studies encryption and decryption algorithm, using isotopes of Latin groupoid. Cryptographic algorithms are computationally intensive processes which consume large amount of CPU time and space during the process of encryption and decryption. The goal of this paper is to study the encryption and decryption algorithm with the help of the concept of Latin groupoid and notion of isotopes. The proposed algorithm is safe in the implementation process and can be verified without much difficulty. An example of encryption and decryption based on the atin groupoid and the concept of isotopy is examined.
Evolving Flying Machines in Minecraft Using Quality Diversity

Minecraft is a great testbed for human creativity that has inspired the design of various structures and even functioning machines, including flying machines. EvoCraft is an API for programmatically generating structures in Minecraft, but the initial work in this domain was not capable of evolving flying machines. This paper applies fitness-based evolution and quality diversity search in order to evolve flying machines. Although fitness alone can occasionally produce flying machines, thanks in part to a more sophisticated fitness function than was used previously, the quality diversity algorithm MAP-Elites is capable of discovering flying machines much more reliably, at least when an appropriate behavior characterization is used to guide the search for diverse solutions.
How the Move Acceptance Hyper-Heuristic Copes With Local Optima: Drastic Differences Between Jumps and Cliffs

In recent work, Lissovoi, Oliveto, and Warwicker (Artificial Intelligence (2023)) proved that the Move Acceptance Hyper-Heuristic (MAHH) leaves the local optimum of the multimodal cliff benchmark with remarkable efficiency. With its O (n3) runtime, for almost all cliff widths d, the MAHH massively outperforms the Θ(nd) runtime of simple elitist evolutionary algorithms (EAs). For the most prominent multimodal benchmark, the jump functions, the given runtime estimates of O(n2mm-Θ(m)) and Ω(2Ω(m)), for gap size m ≥ 2, are far apart and the real performance of MAHH is still an open question.
Agile Software Development in a Remotely Working Geographically Distributed Team: A Systematic Review

In recent years, the distribution of software development has gained widespread popularity. Among the obvious rea-sons are the wish of companies to save production costs and reduce the time to market. Besides this, the latest years COVID-19 pandemic resulted in the transition of many software development projects into a geographically distributed environment where most employees work remote-ly. Using Agile practices in such a setup brings a lot of new challenges. This paper presents a systematic literature re-view of the studies that discuss the use of Agile methods in globally distributed projects, particularly when team members work remotely or from home. Using our search criteria, we found 307 papers, of which 20 were relevant to our research. The results outlined in this review will help re-searchers and practitioners in understanding the challenges involved in using Agile methods by geographically distributed remotely working teams and figure out how to tackle these challenges to increase the efficiency of project management.
Succinct Data Structures for SP, Block-Cactus and 3-Leaf Power Graphs

We design succinct encodings of series-parallel, block-cactus and 3-leaf power graphs while supporting the basic navigational queries such as degree, adjacency and neighborhood optimally in the RAM model with logarithmic word size. One salient feature of our representation is that it can achieve optimal space even though the exact space lower bound for these graph classes is not known. For these graph classes, we provide succinct data structures with optimal query support for the first time in the literature. For series-parallel multigraphs, our work also extends the works of Uno et al. (Disc. Math. Alg. and Appl., 2013) and Blelloch and Farzan (CPM, 2010) to produce optimal bounds.
Modelling cloud computing adoption barriers for Indian SMEs' supply chain using TISM and MICMAC analysis

The implementation of CC can have an impact on the performance of small and medium-sized enterprises by changing the process of designing, producing, delivering, and discarding products. CC is advantageous to emerging countries, particularly India, and requires a comprehensive description for proper understanding and application in business. The aim of this paper is to identify the impediments to CC adoption from the perspective of Indian SMEs. The present research work is to identify the barriers to adopting CC in Indian SMEs using extensive literature and the response obtained from experts. The second main objective of this research is to develop an interpretive hierarchy of CC adoption barriers using TISM technique and categorise these identified barriers into different clusters using MICMAC analysis. This paper identifies four driving barriers named: 1) lack of government support; 2) low understanding of CC; 3) limitation of IT system; 4) lack of connectivity.
On-Site Maintenance Services by Using Web 3.0 and React JS

Abstract: This paper explores the impact of Web 3.0 and semantic web technologies on business, highlighting the potential for new business models to emerge. Web 3.0 represents a paradigm shift from an information-centric approach to a knowledge-centric one, allowing people and machines to connect, evolve, share and use knowledge on an unprecedented scale. The integration of semantic technologies in Web 3.0 has the potential to drive significant improvements in efficiency, cost reduction, enhanced effectiveness, and introduce new functionalities that were previously unfeasible. Through an analysis of the role and impact of Web 3.0 in business, this paper identifies nine potential business models that have emerged as a result of semantic web technologies. Preventive Maintenance also may cause unneeded repair of parts that still have remaining useful life. Condition Based Maintenance (CBM) is a proactive maintenance approach that takes into account the real conditions of the parts using sensors and then offers.
Full body video-based self-avatars for mixed reality: from E2E system to user study

Abstract In this work, we explore the creation of self-avatars through video pass-through in mixed reality (MR) applications. We present our end-to-end system, including custom MR video pass-through implementation on a commercial head-mounted display (HMD), our deep learning-based real-time egocentric body segmentation algorithm, and our optimized offloading architecture, to communicate the segmentation server with the HMD. To validate this technology, we designed an immersive VR experience where the user has to walk through a narrow tile path over an active volcano crater. The study was performed under three-body representation conditions: virtual hands, video pass-through with color-based full-body segmentation, and video pass-through with deep learning full-body segmentation. This immersive experience was carried out by 30 women and 28 men. To the best of our knowledge, this is the first user study focused on evaluating video-based self-avatars to represent the user in a MR scene. Results showed no significant differences between the different body representations in terms of presence, with moderate improvements in some Embodiment components between the virtual hands and full-body representations. Visual Quality results showed better results from the deep-learning algorithms in terms of the whole body perception and overall segmentation quality. In this study, we provide some discussion regarding the use of video-based self-avatars and some reflections on the evaluation methodology. The proposed E2E solution is in the boundary of the state-of-the-art, so there is still room for improvement before it reaches maturity. However, this solution serves as a crucial starting point for MR applications where users can feel immersed and interact with their own bodies.
Kerberos authentication protocol implementation based on DHR atypical construct

As a commonly used identity authentication protocol, the Kerberos authentication protocol uses a symmetric password system, which has significant efficiency advantages but also brings risks such as password guessing and replay attacks. Some existing research combines public key cryptography with Kerberos to greatly enhance its security, but the large amount of computation required for public-private key cryptography leads to lower efficiency. This paper proposes a solution that uses the DHR non-typical construction to provide dynamism and randomness to the Kerberos authentication protocol to improve its security while minimizing efficiency losses. Experimental results show that the improved protocol can resist attacks while ensuring efficiency.
Emotion Detection on Social Media

Abstract: We proposed combine approach of BiLSTM and CNN to capture the emotional information from the words we are going to train BiLSTM model to get the semantic information from the words and parallelly we will be training CNN to get emotional information from the words. We will be using FastText word vector method for obtaining vectors from words unlike Word2Vec it operates at more minute level with character n grams. CNN and RNN are more efficient in finding the patterns out of large dataset. Due to the Word2Vec it opened entirely new approach to NLP. LSTM algorithms are quite popular in NLP field there were used in many emotion detection model due to their ability to find semantic meaning of words but they fail to capture information about the words. CNN models are quite fascinating and due to their ability to find the complex pattern among the images they are getting lot of attention in NLP. But, they are subjected to many limitations as network is designed to handle single dimension so it became vague how they are going to generalizes multi class prediction.
All‐Optical Nonlinear Neuron Based on Metallic Quantum Wells

Abstract Optical nonlinear neuron, as an essential implementation method for optical interconnection, could greatly promote the development of optical neuron networks, which appears to be a promising alternative to electronic neural networks that are limited by computing speed and the end of Moore's law. However, restricted by weak nonlinearities, the modulation performance of optical nonlinear neuron networks is much lower compared to their electronic counterparts. Therefore, an effectual all‐optical nonlinear neuron is proposed by combining a double‐ring resonator with field‐enhancement and metallic quantum wells (MQWs) with a large Kerr nonlinear response. Based on the large and ultrafast Kerr nonlinearity provided by MQWs, the device can achieve a modulation extinction ratio of 18.78 dB and a transition rate of 13 GW cm −2 per 3 dB. The work may provide a new route for integrated, ultrafast, and high‐efficient optical nonlinear neurons for on‐chip neuron networks.
A Case Study in Tablet-Based Augmented Reality Vocabulary Acquisition

Augmented Reality (AR) is a novel technology capable of embedding digital objects a user's experience of reality via a smartphone, tablet, or head-mounted display. AR offers interesting pedagogical applications and research possibilities in the field of SLA, particularly in the deliberate study of vocabulary. Such applications also offer implications for distance learning. This case study examined a 25-year-old Japanese language teacher in her deliberate acquisition of three sets of 15 vocabulary items selected from the first 1,000 most frequent words of Basque. A word card study method was compared with two AR vocabulary study methods. One AR study method involved the study of physical objects and their signifying Basque vocabulary items within a context-independent scene which was unknown to the participant. The second AR study method similarly involved the study of physical objects and their signifying Basque vocabulary items within a context-dependent scene which was known to the learner. In terms of vocabulary retention as measured with meaning recognition, form recognition, and form recall tests, both AR methods of study appeared to be as effective if not more so than word cards. The study of vocabulary items represented by physical objects in a contextualized environment with the use of a tablet-based AR application was beneficial in leveraging the recall of meaning and form of those items. During reflective interviews regarding the use of AR the participant indicated that she found the novel method of vocabulary study to be engaging and motivating. The findings of this study also indicate that mobile AR applications have potential pedagogical uses relevant to distance education practices.