Reviewerly/exHarmony huggingface dataset:
Perceptual resemblance and the communication of emotion in digital contexts

Abstract Online communication has created new ways to express emotions, including emoji and reaction GIFs. Emoji are often discussed as signs for meaning-making, adding emotional tone to communication. Reaction GIFs express emotions and attitudes in a playful manner. This study shows that through the lens of cognitive pragmatics, these phenomena are not distinct. Both are cases of non-verbal communication pointing to the communicator’s emotional state. Drawing on relevance-theoretic notions of the showing-meaning continuum and perceptual resemblance , along with relevance-theoretic analyses of metaphor and irony, I argue that emoji and reaction GIFs provide clues to ostension and communicate emotions by virtue of perceptual resemblance between what they represent and the communicator’s emotional state. I will also argue that both emoji and GIFs can involve echoic use of language, enabling the communicator to convey their attitude.
Traffic Flow Prediction and Application of Smart City Based on Industry 4.0 and Big Data Analysis

For smart city traffic flow prediction in the period of big data and industry 4.0, the prediction accuracy is low, the prediction is difficult, and the prediction effect is different in different geographical locations. This paper proposes a smart city traffic communication forecast based on Industry 4.0 and big data analysis application. Firstly, this paper theoretically explains the application scenario of urban traffic fault text big data and analyzes the characteristics of related problems, especially the fault problems. Secondly, the AC traffic prediction algorithm is studied, and the application analysis of PVHH, IDT, and Ford–Fulkerson algorithms is applied, respectively. Finally, the above three algorithms are used to predict and analyze traffic flow.
Footsteps in the fog: Certificateless fog-based access control

The proliferating adoption of the Internet of Things (IoT) paradigm has fuelled the need for more efficient and resilient access control solutions that aim to prevent unauthorized resource access. The majority of existing works in this field follow either a centralized approach (i.e. cloud-based) or an architecture where the IoT devices are responsible for all decision-making functions. Furthermore, the resource-constrained nature of most IoT devices make securing the communication between these devices and the cloud using standard cryptographic solutions difficult. In this paper, we propose a distributed access control architecture where the core components are distributed between fog nodes and the cloud. To facilitate secure communication, our architecture utilizes a Certificateless Hybrid Signcryption scheme without pairing. We prove the effectiveness of our approach by providing a comparative analysis of its performance in comparison to the commonly used cloud-based centralized architectures. Our implementation uses Azure – an existing commercial platform, and Keycloak – an open-source platform, to demonstrate the real-world applicability. Additionally, we measure the performance of the adopted encryption scheme on two types of resource-constrained devices to further emphasize the applicability of the proposed architecture. Finally, the experimental results are coupled with a theoretical analysis that proves the security of our approach.
Stabilize deep ResNet with a sharp scaling factor $$\tau$$

We study the stability and convergence of training deep ResNets with gradient descent. Specifically, we show that the parametric branch in the residual block should be scaled down by a factor $$\tau =O(1/\sqrt{L})$$ to guarantee stable forward/backward process, where L is the number of residual blocks. Moreover, we establish a converse result that the forward process is unbounded when $$\tau >L^{-\frac{1}{2}+c}$$ , for any positive constant c. The above two results together establish a sharp value of the scaling factor in determining the stability of deep ResNet. Based on the stability result, we further show that gradient descent finds the global minima if the ResNet is properly over-parameterized, which significantly improves over the previous work with a much larger range of $$\tau$$ that admits global convergence. Moreover, we show that the convergence rate is independent of the depth, theoretically justifying the advantage of ResNet over vanilla feedforward network. Empirically, with such a factor $$\tau$$ , one can train deep ResNet without normalization layer. Moreover for ResNets with normalization layer, adding such a factor $$\tau$$ also stabilizes the training and obtains significant performance gain for deep ResNet.
An empirical study of incorporating syntactic constraints into BERT-based location metonymy resolution

Abstract Metonymy resolution (MR) is a challenging task in the field of natural language processing. The task of MR aims to identify the metonymic usage of a word that employs an entity name to refer to another target entity. Recent BERT-based methods yield state-of-the-art performances. However, they neither make full use of the entity information nor explicitly consider syntactic structure. In contrast, in this paper, we argue that the metonymic process should be completed in a collaborative manner, relying on both lexical semantics and syntactic structure (syntax). This paper proposes a novel approach to enhancing BERT-based MR models with hard and soft syntactic constraints by using different types of convolutional neural networks to model dependency parse trees. Experimental results on benchmark datasets (e.g., ReLocaR , SemEval 2007 and WiMCor ) confirm that leveraging syntactic information into fine pre-trained language models benefits MR tasks.
Digital Competencies, critical thinking and innovation: Systematic Mapping

Technology is an extension of our body and is present in our daily lives, which has led to a narrow mindset that equates digital transformation with access to technology and the use of digital resources, believing that passive consumption of online content will contribute to a digital mindset. Digital transformationis not just about access to technology, but also about understanding and using technology to solve problems, and critical thinking is key to driving innovation. This study systematically maps the outcomes of educational practices that contribute to the development of digital competencies. Twenty-six articles related to the research topic were identified, which show that there is an absence of studying innovation as an effect of the appropriation of digital skills.
Coordinated Control of a Collaborative 6R Robotic Arm With a Virtual Twin and Augmented Reality for Engineering Education

This work describes the direct and inverse kinematics mathematical framework, the development procedure, and data communications interface for the implementation of an augmented reality version of a six degrees of freedom (6-DOF) cobot (collaborative robot) under the Unity platform. The kinematic equations for the inverse kinematic control of the Techman® cobot are presented. The procedure to achieve communication between the virtual robot and the physical robot is described, that is, the communication link from the TM5-900 robot with the application in Unity. A safe environment was created to manipulate the collaborative robot and to avoid collisions to validate the applicability of the augmented reality functionality. The virtual twin with its enhanced visualization based on augmented reality can also run-on mobile devices either locally or remotely accessed. The developed platform has diverse potential applications and in particular is suitable for engineering education and training in robotics.
Virtual Reality for Teacher Training

This chapter discusses the use of virtual reality (VR) in the training of preservice secondary education teachers in Spain as an integral part of their learning process. The authors propose some premises from which to design a training program to improve preservice teachers' communicative competence and their ability to manage conflict impacting the classroom climate. First, it explains the experiential and experimental potential of a virtual learning environment (VLE), its ability to create personalized virtual worlds, as well as the possibility to generate insightful instant feedback and feedforward. Finally, an example of a prototype scenario designed on this conceptual basis is provided. Furthermore, the chapter presents an overview of an educational proposal to implement this experiential immersive opportunity for preservice teachers to interact and manage disruptive situations in a safe and reliable environment conducive to the development of key communicative competences and strategies to turn conflict into a learning opportunity.
An Immersive Virtual Reality Training Environment for CBRN Procedures

First responders are exposed to numerous hazards and must be well prepared to act under dangerous conditions. This is especially true for operators working in the domain of CBRN (chemical, biological, radiological, nuclear), who need high-quality training to avoid fatal errors and ensure the success of operations. However, CBRN practice sessions can reproduce only approximated, low-fidelity versions of real scenarios, whereas highly detailed simulations should be preferred. To address this issue, the authors developed a prototype of an immersive virtual reality (VR) training platform in cooperation with CBRN experts from the Italian Air Force. The devised platform is an improved version of an existing tool and offers a digital training ground where operators can learn CBRN procedures and test their abilities in a realistic virtual environment without facing the risks normally associated with real hazardous scenarios.
Exploring the didactic-psychological features of the dialogical discourse patterns in an online classroom context

Abstract From the communicative-cognitive point of view, it is necessary to look at dialogic discourse as an active phase of the transition of language skills to speech skills. In this regard, discourse is considered as the articular form of consciousness consisting of a set of knowledge that motivates the speech activity of the interviewees. This empirical study draws on recent developments in dialogic approaches to learning and teaching and explores the relationship between the dialogic discourse pattern and improvement of the students’ participation and learning in an online EFL context. Developments in the dialogic teaching from two perspectives, namely didactic and psychological are reviewed and necessary preconditions and rules for the teachers to have a dialogical discourse pattern as an approach to classroom interaction are considered. It is assumed that when clearly defined, rules for a dialogic discourse pattern can lead to the development of students’ overall performance and mainly speaking skills.
ANALISIS KESULITAN BELAJAR MEMBACA PERMULAAN PADA SISWA KELAS II SDN 3 CILANGKAP

The problem in this study is the low ability of students in reading at the beginning of class II SDN 3 Cilangkap. With that the aim of this study is to analyze learning difficulties in reading the beginning. The method used in this research is descriptive qualitative. Data collection techniques using test techniques, observation, and interviews. The results of the study obtained data from 4 people, there were 2 students who were not fluent in reading, including difficulty in recognizing letters, arranging syllables into words, and difficulty distinguishing almost the same letter forms. Then 2 more people began to be able to show fluency in reading. This can be seen when they begin to be able to recognize letters, arrange syllables into words, and are able to distinguish letters that are almost the same at the time of spelling. Of these difficulties, there are factors that cause SDN 3 Cilangkap students to experience difficulty in reading at the start, these factors include intellectual, psychological, and family factors. Based on the explanation above, it can be concluded that there are 4 students who have problems in reading the beginning, while the contributing factors are intellectual, psychological, and family factors.
«THE IONOSPHERE»: A POSTER AT THE EXHIBITION«EARTH WITHIN THE UNIVERSE»

“The Ionosphere”, a poster at the Earth Within the Universe exhibition of the Earth Science Museum, reveals the specifics of the structure and dynamics of the complex system of the Earth’s ionosphere. Processes occurring in the ionosphere are reflected in the poster.
A Technique for Discovering Similarities between Texts Based on Extracting Features from the Text

The discovery of the similarity between two texts is very important and useful in many applications.The similarity between texts is the core research area of dataset, data warehouse, and data mining.This paper provides a framework that gives a similarity between two input texts based on pattern recognition and the use of approximate string matching; there is a weight that affects the proportion of similarity.The search compares the similarity of two texts without adherence to the grammar or the use of synonyms or meanings of words.Preliminary results showed the benefit of extracting some of the features in the discovery of the similarity between the texts.
A Heuristic Approach to the Game of Sylver Coinage

Sylver Coinage is a zero-sum terminating game, making the search for an optimal strategy very enticing. Many of the challenges that existed with creating computer programs to play games like Chess and Go exist for Sylver Coinage as well. However, unlike Chess and Go, working towards finding an optimal strategy in the game of Sylver Coinage presents some new and interesting challenges. We attempt to make some headway on the problems associated with finding a strategy for Sylver Coinage using several heuristic algorithms employed by bots to play the game.
Chess Endgame Compression via Logic Minimization

Chess endgame tables encode perfect information to inform heuristic search and permit error-free play once the root position is within them. We introduce a new approach to their minimization, and demonstrate better probe performance than the state-of-the-art.
Optimal $$L_{2}$$ Color Transfer Based on Adaptive Morphological Reconstrution

In view of the problems of poor color classification, poor hierarchy and more artifacts in traditional color transfer methods, this paper proposes an adaptive color transfer method combining morphological segmentation and optimal $$L_{2}$$ divergence. Firstly, the adaptive segmentation matrix of the reference image and the content image is obtained by the morphological segmentation algorithm, and the useless regions of the reference image and the content image are filtered out, and the regions with strong color sense are retained. Secondly, by calculating the probability density, the mapping relationship between the probability density function of the reference image and the content image is obtained, and the color transfer is carried out by TPS algorithm. Finally, a large number of experimental results show that the results obtained by the proposed method can better maintain the structural sense of the content image and the color sense of the reference image.
Using Modern Information and Communication Technologies for Intelligent Capitalization of Cultural Resources

The chapter presents the concepts and technology of virtual exhibitions, with a particular emphasis on the changes brought on by the current COVID-19 pandemic Modern information and communication technologies are used to promote a new paradigm for facilitating the access to cultural heritage collections, by implementing virtual exhibitions, in the form of web applications, accessible online, as well as native mobile applications for mobile devices. A practical example is presented. The resource-based view (RBV) concept is used to determine the strategic resources that cultural institutions can exploit to achieve sustainable competitive advantage.
Manifold Learning Algorithm Based on Constrained Particle Swarm Multi-objective Optimization

Manifold learning algorithm is that the local optimum gradually approaches the global optimum, especially the complicated manifold learning problem used in big data dimensionality reduction needs to find an optimization method to adjust k-nearest neighbors and extract dimensionality. Therefore, we intend to use the constrained particle swarm multi-objective optimization algorithm to solve the constrained multi-objective optimization problem model composed of multi-information objectives and multi-user requirements (constraints), and the design is based on the Lebesgue measure constraint processing technology particle swarm Manifold learning algorithm of multi-objective optimization algorithm to improve the calculation accuracy of popular learning algorithms.
A Many-Objective Anomaly Detection Model for Vehicle Network Based on Federated Learning and Differential Privacy Protection

With the rapid development of Internet of things, vehicle network has become the focus of smart city construction. However, due to the lack of encryption and authentication mechanism, the vehicle network is vulnerable to malicious attacks and intrusion, which poses a threat to the driver’s life safety and data privacy. To solve this problem, this paper proposes a method combining federal learning and differential privacy protection to protect the privacy of drivers. A many-objective anomaly detection model based on Federated learning is constructed. The model optimizes the accuracy, loss, privacy protection degree and communication cost in the federated training model at the same time. The model can obtain better accuracy and privacy protection with less loss and communication cost. The model is solved by many-objective evolutionary algorithm, and some solutions are selected as training parameters, and satisfactory results are obtained. The effectiveness of the proposed method is verified.
Evaluating Interpretability Methods for DNNs in Game-Playing Agents

There is a trend in game-playing agents to move towards an Alpha-Zero-style architecture, including using a deep neural network as a model for evaluating game positions. Model interpretability in such agents is problematic. We evaluate the applicability and effectiveness of several saliency-map-based methods for improving the interpretability of a deep neural network trained for evaluating game positions, using the game of Breakthrough as our testbed. We show that the more applicable methods provide insights into the importance of the different game pieces and other domain-dependent knowledge learned by the model.
A Survey of Linear Value Function Approximation in Reinforcement Learning

In reinforcement learning, when the state space is enormous or infinite, it is not feasible to find the exact value for each state in the memory. A common way to tackle this problem is to adopt linear value function approximation technique. In this paper, we review some commonly used linear value function approximation algorithms, which divided into two classes. The first class is the so-called on-policy algorithms that mainly include temporal difference and least-squares-based methods. The second is the off-policy algorithms that use two strategies to solve the exploration-exploitation problem. From the perspective of algorithm design, on-policy is simple, but it does not take both exploration and exploitation into consideration when collecting data, while the off-policy can well balance the relation between exploration and exploitation. In the end, we first present some experiments to demonstrate the usefulness of linear value function approximation technique in the continuous and high dimensional scenes, and then in-class and out-class experiments are respectively conducted to further illustrate the performance of different approximation algorithms.
Chinese Text Clustering Algorithm Based on Multi-agent Optimization System

In this paper, we propose a global-to-local searching-based Binary Particle Swarm Optimization (GSBPSO) based on Binary Particle Swarm Optimization (BPSO). The GSBPSO, which enables the particle swarm algorithm to have strong global search capability in the early stage of the algorithm and strong local search capability in the late stage of the algorithm. For text clustering, this paper first uses document frequency for feature coarse selection, then GSBPSO algorithm for feature reselection to further reduce feature redundancy, and finally uses Spherical K-means (SKM) algorithm for final clustering of text. The simulation experiments of Chinese text clustering algorithm based on GSBPSO particle swarm algorithm and SKM using Chinese dataset from Fudan University show that the GSBPSO algorithm can compress the high-dimensional and sparse text feature matrix with a compression ratio of 47%. By clustering the text matrices before and after feature selection separately, the experiments show that the F-value and NMI values of the clustering algorithm are improved to different degrees on the dataset after feature reselection by GSBPSO algorithm.
AES-based encoding and decoding images using MATLAB

This paper mainly deals with mixed-image secret writing techniques. From the point of view of information security, recording secret images are extremely important. Security is the most complex aspect of networks and network applications. The web and networked applications are growing in the blink of an eye, which increases the importance and, therefore, the value of the information transmitted through the web or alternative media. Encryption can be related to improve image security by scanning pixels. Encryption will protect privacy on its own. It works in a wide variety of fields, such as network communications, medical photography, and military communications. Due to the natural selection of images, such as high duplication of information and the ability of mass dissemination of information, image encryption is different from the text. Text encryption is the conversion of information into an encoded cipher code that cannot be easily decrypted by outsiders.
Techniques of migration in live virtual machine and its challenges

Cloud computing is the on-demand availability of computer system resources. Most technology industries are moving to the cloud. Cloud structures can be costly for users. Virtualization is used in cloud computing that helps the cloud at a low cost. Migrating virtual machines (VMs) helps to manage computation. Migration of virtual machines is a core feature of virtualization. The technique of migrating a running virtual machine from one physical host to another with minimal downtime is called "live virtual machine migration." This paper discusses the migration technique, i.e., migration before and after copying, and also issues related to live migration. This paper presents a better approach to the VM migration method and future challenges by differentiating from the previous live VM migration method.