Reviewerly/exHarmony huggingface dataset:
A Toolkit to Evaluate and Characterize the Collaborative Process in Scenarios of Remote Collaboration Supported by AR

Remote collaboration using Augmented Reality (AR) has enormous potential to support collaborators that need to achieve a common goal. However, there is a lack of tools for evaluating these multifaceted contexts, involving many aspects that may influence the way collaboration occurs. Therefore, it is essential to develop solutions to monitor AR-supported collaboration in a more structured manner, allowing adequate portrayal and report of such efforts. As a contribute, we describe CAPTURE, a toolkit to instrument AR-based tools via visual editors, enabling rapid data collection and filtering during distributed evaluations. We illustrate the use of the toolkit through a case study on remote maintenance and report the results obtained, which can elicit a more complete characterization of the collaborative process moving forward.
Improve PBFT Based on Hash Ring

Aiming at the problems of practical Byzantine fault tolerance (PBFT) algorithm, such as high communication complexity, frequent switching views because of Byzantine node become primary nodes and random selection of primary node, HR-PBFT algorithm is proposed. First, the HR-PBFT algorithm uses a hash ring to group nodes, which ensures the randomness and fairness of the grouping. Then, a dual-view mechanism is used in the consensus process, where the first layer node maintains the primary view and the second layer node maintains the secondary view to ensure the proper operation of the algorithm. Finally, the Byzantine node determination mechanism is introduced to evaluate the node status according to the node behavior in the consensus process, improve the reliability of primary node selection, and reduce the frequency of view changes. The experimental results show that the optimized HR-PBFT algorithm can effectively improve the problem of the sharp increase in the number of communications caused by the increase in the number of nodes in the network and prevent frequent view changes.
Augmenting security for electronic patient health record (ePHR) monitoring system using cryptographic key management schemes

Security plays a major role in most of the fields including pharmaceutical field. Authorization and Authentication are the key concepts in supporting notable areas of the cyber-health world. HIPAA's (Health Insurance Portability and Accountability Act) ultimate focus is to preserve the privacy of the health records of an individual without disclosing it and preventing the data from unauthorized access. A complaint key management solution is applied over the patient's health records to reduce the risk factor while engaging with cryptographic mechanisms. Though there are many existing cryptographic algorithms such as Elliptic curve cryptography, Elgammal's key exchange algorithm which provides security to the access of patient's health records, the proposed key management solution will overlay the same variant of security to the Electronic Health Records (EHR). This paper provides the countermeasures for improving security and suggests a key recovery mechanism for the protection of keys used in the security mechanism.
Associations between vocal emotion recognition and socio-emotional adjustment in children

The human voice is a primary channel for emotional communication. It is often presumed that being able to recognize vocal emotions is important for everyday socio-emotional functioning, but evidence for this assumption remains scarce. Here, we examined relationships between vocal emotion recognition and socio-emotional adjustment in children. The sample included 141 6- to 8-year-old children, and the emotion tasks required them to categorize five emotions (anger, disgust, fear, happiness, sadness, plus neutrality), as conveyed by two types of vocal emotional cues: speech prosody and non-verbal vocalizations such as laughter. Socio-emotional adjustment was evaluated by the children's teachers using a multidimensional questionnaire of self-regulation and social behaviour. Based on frequentist and Bayesian analyses, we found that, for speech prosody, higher emotion recognition related to better general socio-emotional adjustment. This association remained significant even when the children's cognitive ability, age, sex and parental education were held constant. Follow-up analyses indicated that higher emotional prosody recognition was more robustly related to the socio-emotional dimensions of prosocial behaviour and cognitive and behavioural self-regulation. For emotion recognition in non-verbal vocalizations, no associations with socio-emotional adjustment were found. A similar null result was obtained for an additional task focused on facial emotion recognition. Overall, these results support the close link between children's emotional prosody recognition skills and their everyday social behaviour.
Film and TV Animation Production Based on Artificial Intelligence AlphaGd

The dual attributes of artificial intelligence AlphaGd production, combined with modern social culture, industrial economy, and other factors, show rich connotation and extension in the development process of film and television animation production from the perspective of artificial intelligence AlphaGd. This paper first discusses and analyzes the practical significance and points out that AI AlphaGd has powerful functions, can greatly improve the sense of animation, and can effectively promote the technological innovation of animation production, then introduces the role modeling ideas of AI AlphaGd, and describes the modeling steps. Finally, the application of AI AlphaGd works produced after the use of artificial intelligence AlphaGd is more lifelike and specific in both the embodiment of the character image and the basic picture effect, which can make the audience have a more comfortable and perfect viewing experience. After summarizing, this paper puts forward the shortcomings of perspective artificial intelligence AlphaGd. Artificial intelligence may be useful in key decision-making processes, but it may negate the need for humans to be involved in these discussions. In view of these shortcomings, further research and discussion are made to accumulate new experience for product development and production from the perspective of artificial intelligence AlphaGd.
Academic Library Video Services: Charting a Post-COVID Course

The vast majority of academic libraries offer streaming video services to support teaching and research, and demand for this already popular format from faculty and students increased when libraries closed their physical locations during the COVID-19 pandemic. Despite its popularity, many users are unfamiliar with copyright and terms of use for video content. Use of streaming video is further complicated by its availability on a variety of platforms using different licensing models. This case study shows how the Penn State University Libraries (PSUL) helped librarians, faculty, and students navigate access to video content for remote instruction during the pandemic. The examples of training library staff, educating faculty and students, and streamlining internal processes can be adapted by all academic libraries to provide greater access to video content. Demand for streaming video will continue even when libraries return to full in-person operations, so these approaches will continue to be relevant.
Is Artificial Intelligence Customer Service Satisfactory? Insights Based on Microblog Data and User Interviews

A growing number of sectors are delivering customer services powered by artificial intelligence (AI) instead of humans, with evidence indicating labor cost reduction and efficiency improvement. However, it would be worthwhile to examine the extent to which consumers are satisfied with AI service agents. In two studies based on an analysis of 17,673 Weibo data (Study 1) and 33 interviews (Study 2), we constructed a pair of theoretical models of consumer attitudes toward AI services: a sentiment model and an evaluation model. The results from Weibo data analysis showed that consumers display a stronger negative attitude toward AI customer service than toward their human counterparts. Complaints regarding AI customer service is mainly about its poor problem-solving ability, while untimely response and lack of human touch also dissatisfy customers. Whether consumers offer positive feedback mainly depends on voice traits and service attitudes. The results from the interviews confirm an overall negative attitude of consumers toward AI customer service. Consumers also recognize AI customer service agents as human like and social interaction stress relieving. Taken together, these findings reveal Chinese customers' attitudes toward AI service solutions and provide concrete suggestions for the development and upgrade of AI customer services.
Quantum software testing: State of the art

Abstract Quantum computing is expected to exponentially outperform classic computing on a broad set of problems, including encryption, machine learning, and simulations. It has an impact yet to explore on all software lifecycle's processes and techniques. Testing quantum software raises a significant number of challenges due to the unique properties of quantum physics—such as superposition and entanglementand the stochastic behavior of quantum systems. It is, therefore, an open research issue. In this work, we offer a systematic mapping study of quantum software testing engineering, presenting a comprehensive view of the current state of the art. The main identified trends in testing techniques are (1) the statistic approaches based on repeated measurements and (2) the use of Hoare‐like logics to reason about software correctness. Another relevant line of research is reversible circuit testing, which is partially applicable to quantum software unitary testing. Finally, we have observed a flourishing of secondary studies and frameworks supporting testing processes from 2018 onwards.
Evolution of topics and hate speech in retweet network communities

Twitter data exhibits several dimensions worth exploring: a network dimension in the form of links between the users, textual content of the tweets posted, and a temporal dimension as the time-stamped sequence of tweets and their retweets. In the paper, we combine analyses along all three dimensions: temporal evolution of retweet networks and communities, contents in terms of hate speech, and discussion topics. We apply the methods to a comprehensive set of all Slovenian tweets collected in the years 2018-2020. We find that politics and ideology are the prevailing topics despite the emergence of the Covid-19 pandemic. These two topics also attract the highest proportion of unacceptable tweets. Through time, the membership of retweet communities changes, but their topic distribution remains remarkably stable. Some retweet communities are strongly linked by external retweet influence and form super-communities. The super-community membership closely corresponds to the topic distribution: communities from the same super-community are very similar by the topic distribution, and communities from different super-communities are quite different in terms of discussion topics. However, we also find that even communities from the same super-community differ considerably in the proportion of unacceptable tweets they post.
Experimental realization of quantum controlled teleportation of arbitrary two-qubit state via a five-qubit entangled state

Quantum controlled teleportation is the transmission of the quantum state under the supervision of a third party. This paper presents the theoretical and experimental results of an arbitrary two-qubit quantum controlled teleportation scheme, in which the sender Alice only needs to perform two Bell state measurements and the receiver Bob can perform an appropriate unitary operation to reconstruct the arbitrary two-qubit states under the control of the supervisor Charlie. The operation process of the scheme is verified on the IBM quantum experience platform, and the accuracy of the transmitted quantum state is further checked by performing quantum state tomography. Meanwhile, a good fidelity is obtained by using the theoretical density matrix and the experimental density matrix. A sequence of photonic states is introduced to analyze the possible intercept–replace–resend, intercept–measure–resend, and entanglement–measure–resend attacks on this scheme. The results proved that our scheme is highly secure.
Secure E-Commerce Scheme

E-commerce security has recently been an emerging topic due to the escalation in credit card fraud and stolen user accounts. In general, the security bridge and privacy leakage occur on the side of e-commerce companies due to various factors such as flaws in the design of their storage systems. The stored information of users increases the risk on privacy bridge and to remedy such risks e-commerce companies are forced to make costly investments. The security threats also enforce the development of robust security protocols and methods in digital commerce systems. The current protocols and methods generally bring extra communication and computation costs to all parties involving in the e-commerce system and the security risk on the side of e-commerce companies still remains. In this paper, we propose a Secure E-commerce Scheme (SES) which alleviates the security threats on the side of e-commerce companies and reduces communication costs for all parties. The proposed secure e-commerce protocol, SES, is implemented, analyzed and compared to two well-known schemes; Secure Electronic Transaction (SET) and 3D Secure.
ConvFusion: A Model for Layer Fusion in Convolutional Neural Networks

The superior accuracy and appealing universality of convolutional neural networks (CNNs) as a generic algorithm for many classification tasks have made the design of energy efficient CNN accelerators an important topic in both academia and industry. Of particular interest in the design and use of CNN accelerators is the scheduling of the computational workload, which can have a major impact on the quality of the final design. The many inherently independent operations in CNNs result in a vast scheduling space however, rendering the selection of the optimal schedule(s) non-trivial. To aid in this complex task, this work introduces a generic mathematical cost model of the external memory accesses, internal memory footprint, and compute load for CNN execution schedules. The model enables fast exploration of the scheduling space, including loop tiling, loop reordering, explicit data transfer scheduling, recomputation, and, crucially, layer fusion, which recently has attracted interest as a method to reduce external memory accesses. An accompanying open source tool is released to perform schedule space exploration for CNNs using the introduced cost model. Leveraging the code generation capabilities of this tool the proposed model is validated on six real world networks, demonstrating that layer fusion can reduce the external memory accesses by more than two orders of magnitude compared to the best non-fused schedules. Confusing at first glance however, a high-level energy analysis shows that the practical benefits of layer fusion may be overestimated if other parts of the system are not tuned accordingly.
Multi-robot co-operation for stick carrying application using hybridization of meta-heuristic algorithm

The paper offers a novel technique for resolving the multi-robot cooperation for stick carrying applications. The problem addresses the computation of a collision-free optimal path from a predefined initial position to target position during transportation of stick or object cooperatively by robot pair in the multi-robot environment. The stick carrying application has been resolved by embedding the modified Q-learning into the hybrid process of an improved version of particle swarm optimization and intelligent water drop algorithm. In the present context, modified Q-learning generates the best solution for particle swarm optimization and particle swarm optimization is upgraded through the perception of cubic spline and generate the optimal position in the successive iteration using intelligent water drop algorithm and also enhances the intensification and diversification capability of particle swarm optimization. The proposed hybrid algorithm computes the collision-free subsequent position for each robot pair by avoiding the obstacles in its path, avoiding the trapping at the local optima, improving the convergence speed, optimizing the path distance for every pair of robots, energy usage and path smoothness both in the static and dynamic environment’s. The validation of the proposed hybrid algorithm has been verified and checked the robustness of the algorithm through computer simulation and real robots through Webots simulator. Further, the efficiency of the proposed algorithm has been verified by comparing the result obtained proposed algorithm and its competitor algorithms and comparing the result of the proposed algorithm with the existing state-of arts. The comparison result shows that the proposed algorithm is superior to its competitor algorithms and state of arts for different matrices.
Pointer-type instrument positioning method of intelligent inspection system for substation

Robot intelligent inspection is widely used in the positioning of various pointer instruments in power, petroleum, chemical, and other industries. Aiming at the technical problems of poor adaptability, poor real-time performance, and low positioning accuracy of the pointer instrument positioning method in the existing substation intelligent inspection robot system, we propose a simple and effective pointer instrument positioning detection algorithm. The algorithm first extracts locally adaptive regression kernels (LARK) features of the input image, and the dimension of the LARK feature is reduced using the principal components analysis algorithm. Then, the template image is slid in the input image, the cosine similarity is used as an evaluation index, and the Fourier transform is used to accelerate the convolution operation in the cosine similarity calculation. Finally, the accelerated-KAZE algorithm is used to extract the feature points of the pointer-type instrument area image and the template image, and the statistical method of grid motion was used to eliminate the wrong matching points. The remaining matching points were processed by random sample consensus algorithm, and the homography matrix was obtained. The image registration was completed by the homography matrix, and the pointer-type instrument region positioning was realized. The experimental results show that the proposed method has good adaptability, strong real-time performance, and high accuracy of pointer-type instrument positioning.
Extracting fictional truth from unreliable sources

A fictional text is commonly viewed as constituting an invitation to play a certain game of make-believe, with the individual sentences written by the author providing the propositions we are to imagine and/or accept as true within the fiction. However, we can’t always take the text at face value. What narratologists call ‘unreliable narrators’ may present a confused or misleading picture of the fictional world. Meanwhile, there has been a debate in philosophy about ‘imaginative resistance’ in which we resist imagining (or even accepting as true in the fiction) what’s explicitly stated in the text. But if we can’t take the text’s word for it, how do we determine what’s true in a fiction? The chapter proposes an account of fiction interpretation in a dynamic setting (a version of Discourse Representation Theory (DRT) with a mechanism for opening, updating, and closing temporary ‘workspaces’) and combines this framework with belief revision logic.
Development and validation of a confined space rescue training prototype based on an immersive virtual reality serious game

Due to the inherently hazardous nature of the confined space environment and a lack of effective rescue training on on-site personnel attempting the rescue of fellow workers in a confined space, accidents or even multiple fatalities may happen. For this reason, improving the skill set of rescuers is of vital importance. Immersive virtual reality serious games (IVR SG) have been shown to be effective and are now widely used in safety training. However, there is little research on its application in confined space rescue training. To bridge this gap, we have developed a confined space rescue training system (called Rescue Success) based on IVR SG, and then tested its effectiveness in a real environment. We carried out a single-blind, randomized comparative experiment to study and compare the learning effectiveness of using IVR SG compared to an E-lecture in terms of behavioral skills, knowledge acquisition, self-efficacy, and simulation-induced sickness. Our results show that both training methods significantly improve the participant's rescue behavioral skills, knowledge, and self-efficacy. Overall, IVR SG performed better than an E-lecture in all aspects except simulation-induced sickness, where there is no difference between the two methods.
Mutual-Learning Improves End-to-End Speech Translation

A currently popular research area in end-toend speech translation is the use of knowledge distillation from a machine translation (MT) task to improve the speech translation (ST) task.However, such scenario obviously allows only a one way transfer, limiting the overall effectiveness of the approach by the performance of the pre-trained teacher model.Therefore, we pose that in this respect knowledge distillationbased approaches are sub-optimal.We propose an alternative-a trainable mutual-learning scenario, where the MT and ST models are collaboratively trained and are considered as peers, rather than teacher/student.This allows us to improve the performance of end-to-end ST more effectively than with a teacher-student paradigm.As a side benefit, performance of the MT model also improves.Experimental results show that in our mutual-learning scenario, models can effectively utilise the auxiliary information from peer models and achieve compelling results on MuST-C datasets.