Reviewerly/exHarmony huggingface dataset:
Efficient Asymmetric Encryption Scheme based on Elliptic Encryption Technology

Since the data to be transmitted by the smart grid contains a lot of secret information, it is necessary to encrypt the transmission of its data. Asymmetric encryption is the mainstream scheme of current encryption technology. Aiming at the current asymmetric encryption requires a large amount of calculation and storage, and the lack of integrity of encrypted messages, an elliptic curve encryption scheme with identity authentication is proposed. First, the use of elliptic curve encryption reduces the consumption of calculation and storage; then, a nine-stage encrypted message transmission scheme is proposed to ensure the integrity and confidentiality of the message; finally, through security analysis and The experiment simulation verifies the superiority of the scheme in this paper.
Fusion with GCN and SE-ResNeXt Network for Aspect Based Multimodal Sentiment Analysis

Aspect Based Multimodal Sentiment Analysis to determine the sentiment polarity of each aspect mentioned in multimodal posts or comments. However, many models fail to explain the related syntactic constraints and long-term word dependencies, as well as lack image feature extraction. In view of the above problems, we propose a Fusion with GCN and SE-ResNeXt Network(FGSN), which constructs a graph convolution network on the dependency tree of sentences to obtain the context representation and aspects words representation by using syntactic information and word dependency. Also, we use the pretrained network SE-ResNeXt101 to extract image features and generate visual representations. Moreover, the position attention and channel attention mechanism are used to obtain the image features. Finally, we fuse image features and text features to classify sentiment polarity. The experimental results show that our model performs well on the two datasets of TWITTER-2015 and TWITTER-2017.
Exploitation and exploration in text evolution. Quantifying planning and translation flows during writing

Writing is a complex process at the center of much of modern human activity. Despite appearing to be a linear process, writing conceals many highly non-linear processes. Previous research has focused on three phases of writing: planning, translation and transcription, and revision. While research has shown these are non-linear, they are often treated linearly when measured. Here, we introduce measures to detect and quantify subcycles of planning (exploration) and translation (exploitation) during the writing process. We apply these to a novel dataset that recorded the creation of a text in all its phases, from early attempts to the finishing touches on a final version. This dataset comes from a series of writing workshops in which, through innovative versioning software, we were able to record all the steps in the construction of a text. 61 junior researchers in science wrote a scientific essay intended for a general readership. We recorded each essay as a writing cloud, defined as a complex topological structure capturing the history of the essay itself. Through this unique dataset of writing clouds, we expose a representation of the writing process that quantifies its complexity and the writer's efforts throughout the draft and through time. Interestingly, this representation highlights the phases of "translation flow", where authors improve existing ideas, and exploration, where creative deviations appear as the writer returns to the planning phase. These turning points between translation and exploration become rarer as the writing process progresses and the author approaches the final version. Our results and the new measures introduced have the potential to foster the discussion about the non-linear nature of writing and support the development of tools that can lead to more creative and impactful writing processes.
Habitus as a subject matrix of a social game

The vital functions of modern human take place in the conditions of postmodern influence of informative society. On a gamification background of social processes, to availability of the impressions of various orientation and highly emotional force, wide variantness of social reference-points a brindle and obtrusive repetition of influences become the main signs of such influence. It results in supersaturating of cultural space different social models, roles and standards. Also actualize probability of human ecological destabilization, imposing artificial role models, human motive to the inheritance of erroneous social standards. A loss can become the result of it by the human of identity, development of depersonalization, panopticonization of social practices, distortion of democratic public principles. At the same time of practice, which are based on the role models of co-operative vital functions, show the public benefit and stipulate human existential success. Such success is determined by quality of mastering and translation by the subject of the role models purchased during life – standards of interaction, which have game genesis, game organization and maintenance. In the conditions of social game a habitus is formed and develops is fundamental mental individual education which comes forward as a 1) universal, matrix, general psychodynamic structure of subject, 2) psychological basis of individual, productive investigation of socialization, interiorization of social roles, referential role models, 3) subject matrix of social game as a public dynamics and vital co-operation 4) model which does possible the analysis of the internal world of individual, human activity and behavior, 5) main attributive systems genesis component of personality and social dynamics of human. A habitus of subject from a moment his birth constantly is in the center of general architecture and activity of the functional system of human. During subjects life a habitus is a primary and central integral systems genesis component, universal acceptor of action of human. A habitus in all situations is perfected and strengthens the programmatic influence and participation. The genesis and development of the habitus takes place in accordance with the fundamental structure and cycle of the game Situation/Role-game motivation/Role-game action/Post-reflection – a matrix suitable for ontogenetic branching in all existential situations of a person. The general psychological meaning and significance of habitus is that this concept is able to become a dynamic center, the center that sets the direction and rhythm of the subject’apos;s life, manages his socialization and development.
SEBD: A Stream Evolving Bot Detection Framework with Application of PAC Learning Approach to Maintain Accuracy and Confidence Levels

A simple supervised learning model can predict a class from trained data based on the previous learning process. Trust in such a model can be gained through evaluation measures that ensure fewer misclassification errors in prediction results for different classes. This can be applied to supervised learning using a well-trained dataset that covers different data points and has no imbalance issues. This task is challenging when it integrates a semi-supervised learning approach with a dynamic data stream, such as social network data. In this paper, we propose a stream-based evolving bot detection (SEBD) framework for Twitter that uses a deep graph neural network. Our SEBD framework was designed based on multi-view graph attention networks using fellowship links and profile features. It integrates Apache Kafka to enable the Twitter API stream and predict the account type after processing. We used a probably approximately correct (PAC) learning framework to evaluate SEBD’s results. Our objective was to maintain the accuracy and confidence levels of our framework to enable successful learning with low misclassification errors. We assessed our framework results via cross-domain evaluation using test holdout, machine learning classifiers, benchmark data, and a baseline tool. The overall results show that SEBD is able to successfully identify bot accounts in a stream-based manner. Using holdout and cross-validation with a random forest classifier, SEBD achieved an accuracy score of 0.97 and an AUC score of 0.98. Our results indicate that bot accounts participate highly in hashtags on Twitter.
Modeling and High-performance Trajectory Optimization of the Industrial Robot

Abstract Rapidity, accuracy and energy cost are the key performance indexes for of the work of industrial robot. In this paper, the shortest time and the minimum energy consumption are taken as the goal for the comprehensive optimization. The control vector parameterization method is used to reasonably refine the time grid of the optimization problem. Then, an efficient control strategy for non-uniform adaptive mesh refinement is proposed. According to different changing trends, time nodes are merged or inserted into the time grid. While reducing the number of discrete grids, it also ensures the accuracy of the solution. Taking the Manutec R3 industrial robot as the object, the trajectory optimization simulation calculation is carried out. According to the verification of the simulation computing, the proposed method can significantly improve the time grid refinement. Compared with the traditional CVP method, not only the calculation time is shortened, but also more accurate results can be obtained.
Digital Competence of Special Education Teachers: An Analysis from the Voices of Members of School Management Teams

The use of digital technologies is one of the fundamental resources to favour the inclusion of students with special educational needs. However, recent studies continue to show a lack of use by teachers during the development of their teaching practices, especially in the field of special education. This article aims to analyse the level of digital competence of special education teachers through the perceptions of the school management team in Andalusia (Spain). The information is obtained through interviews with 62 members of school management teams. The results suggest that the low level of training and digital competence of special education teachers is the reason why they do not make use of digital tools in their teaching practice, due to a lack of teacher awareness, as well as the non-existent or insufficient development of training activities that hinder such training. In conclusion, there is a need to improve the professional development of special education teachers in digital competencies, as well as greater institutional involvement through strategic plans that promote such training.
Evaluating the usability of a prototype gesture-controlled illuminative textile

In the field of human-computer interaction (HCI), recent years have witnessed an increasing interest in smart textiles with gesture recognition functions. This paper reports usability evaluations of a prototype gesture-controlled illuminative textile developed by the authors. This is an interactive woven POF textile system with computer vision-enabled mid-air gesture recognition. For the usability test, 25 participants (11 males and 14 females) aged between 19 and 56 (M = 30.85, SD = 10.8) were randomly recruited. The study was conducted by the means of the user experience questionnaire (UEQ) and the system usability scale (SUS). Results show that participants responded positively to our gesture-controlled illuminative textile in terms of its attractiveness, perspicuity and stimulation but were less positive about its efficiency. The results will be used to make targeted improvements to the existing product design.
3D shape reconstruction of Japanese traditional puppet head from CT images by graph cut and machine learning methods

In this study, we discuss the digital archiving of Japanese traditional puppets. We propose two methods for extracting the puppet head shape from computed tomography (CT) images. The first is the graph cut method, and the second is a machine learning method based on U-Net. According to the experimental results of the extraction of puppet heads from CT images, the U-Net-based method can extract puppet heads more accurately than the graph cut method. Moreover, the U-Net-based method can extract puppet heads with multiple materials. However, the extraction of metal parts is inaccurate because of metal artefacts in the X-ray CT images and insufficient learning data.
Generating Greenberger-Horne-Zeilinger states using multiport splitters

Symmetric multiport splitters are versatile tools in optical quantum information processing. They can be used for studying multiparticle scattering, studying distinguishability and mixedness, and also for the generation of multipartite entangled quantum states. Here, we show that N-photon N-mode Greenberger-Horne-Zeilinger (GHZ) states can be generated using symmetric multiport beam splitters. Varying the input states' internal degrees of freedom and post-selecting onto certain photon-number distributions allows the probabilistic generation of GHZ states with arbitrary photon numbers. We present two novel schemes, one for odd and one for even numbers of photons, to generate GHZ states using symmetric multiport splitters and compare them to a strategy utilizing a 2N-port network as well as the standard post-selection method.
Translating explicatures between Arabic and English

Abstract This article explores which areas of explicature identification (i.e., disambiguation, reference resolution, saturation, free enrichment, and ad hoc concept constructions) were more attended to in Arabic-English translation. Twenty translators were asked to render from one language into another 50 sentences whose logical form could be enriched by explicatures. Translators were found to attend more to the effects of disambiguation and reference resolution. A demarcation line was drawn between explicatures that called for completing overt logical forms (disambiguation and reference resolution) and those that prompted calculating pragmatic competence and meta-linguistic knowledge (saturation, free enrichment, and ad hoc concept constructions). Using relevance theory as a framework of analysis, we propose that completing overt logical forms is more rewarding and less costly for translators than computing pragmatic competence and meta-linguistic knowledge. The figures corroborated this assumption, which showed that explicature observance was positively correlated with educational attainment and years of experience.
A modified crow search algorithm based on group strategy and adaptive mechanism

As a swarm-based metaheuristic algorithm, the crow search algorithm (CSA) has attracted a lot of attention owing to its simplicity and flexibility. However, CSA tends to have low efficiency. To improve the optimization efficiency, this article proposes a modified version of CSA based on group strategy with an adaptive mechanism (GCSA). On this basis, crows are divided into multiple competing groups, and are assigned different roles and statuses. Then, the group strategy including different search modes is implemented to increase the solution diversity and search efficiency. Moreover, benefiting from the adaptive mechanism, the search range of crows changes in different stages to balance exploration and exploitation capabilities. To evaluate the performance of the proposed algorithm, 35 benchmark test functions (including 10 CEC2020 functions) and three engineering design problems are solved by GCSA and 11 other algorithms. The results prove that GCSA generally provides more competitive results than other metaheuristic algorithms.
Accurate few-shot object counting with Hough matching feature enhancement

Given some exemplars, few-shot object counting aims to count the corresponding class objects in query images. However, when there are many target objects or background interference in the query image, some target objects may have occlusion and overlap, which causes a decrease in counting accuracy.To overcome the problem, we propose a novel Hough matching feature enhancement network. First, we extract the image feature with a fixed convolutional network and refine it through local self-attention. And we design an exemplar feature aggregation module to enhance the commonality of the exemplar feature. Then, we build a Hough space to vote for candidate object regions. The Hough matching outputs reliable similarity maps between exemplars and the query image. Finally, we augment the query feature with exemplar features according to the similarity maps, and we use a cascade structure to further enhance the query feature.Experiment results on FSC-147 show that our network performs best compared to the existing methods, and the mean absolute counting error on the test set improves from 14.32 to 12.74.Ablation experiments demonstrate that Hough matching helps to achieve more accurate counting compared with previous matching methods.
To the problem of professional orientation of the population

Today, in the conditions of modern realities of the economy, a person is required to have a high level of professional readiness for its rapidly changing guidelines. The transition from the concept of «education for life» is updated to the concept of «education throughout life», while improving labor efficiency requires additional education, qualifications, as well as professional retraining, therefore, it is necessary to develop a system of high-quality professional guidance for the population of Russia, capable of promptly responding to the challenges of an economy focused on innovative development.
Normalized Attention Neural Network with Adaptive Feature Recalibration for Detecting the Unusual Activities Using Video Surveillance Camera

Over the past few years, surveillance cameras have become common in many homes and businesses.Many businesses still employ a human monitor of their cameras, despite the fact that this individual is more probable to miss some anomalous occurrences in the video feeds owing to the inherent limitations of human perception.Numerous scholars have investigated surveillance data and offered several strategies for automatically identifying anomalous occurrences.Therefore, it is important to build a model for identifying unusual occurrences in the live stream from the security cameras.Recognizing potentially dangerous situations automatically so that appropriate action may be taken is crucial and can be of great assistance to law enforcement.In this research work, starting with an MRCNN for feature extraction and AFR for fine-tuning, this architecture has a number of key components (AFR).To increase the quality of the features extracted by the MRCNN, the AFR replicas the inter-dependencies among the features to enhance the quality of the low-and high-frequency features extracted.Then, a normalized attention network (NAN) is used to learn the relationships between channels, which used to identify the violence and speeds up the convergence process for training a perfect.Furthermore, the dataset took real-time security camera feeds from a variety of subjects and situations, as opposed to the hand-crafted datasets utilized in prior efforts.We also demonstrate the method's capability of assigning the correct category to each anomaly by classifying normal and abnormal occurrences.The method divided the information gathered into three primary groups: those in need of fire protection, those experiencing theft or violence, and everyone else.The study applied the proposed approach to the UCF-Crime dataset, where it outperformed other models on the same dataset.
Design and implementation of eye tracking application for generation of augmented reality content on spatial see through display of remote and virtual control tower (RVT)

Abstract Airports are a fundamental node in the aviation system. The growth of this sector undergoes the evolution of the airports procedures and infrastructures. Air traffic control is considered to be one of the most important activity performed in an airport. This often involves numerous personnel, construction and maintenance costs etc. With the ongoing digitalisation process in various fields, the concept of remote and virtual control towers (RVT) has emerged owing to the innovation in this field. Technologies such as augmented reality (AR) have successfully paved their smooth way to bring in improvements to an RVT. The current work aims to enhance and improve the controller work in an RVT exploiting the adaption of virtual reality and AR systems. In particular, this concept is under investigation within the EU funded SESAR project: RETINA (Resilient Synthetic Vision for Advanced Control Tower Air Navigation Service Provision) and DTT (Digital Technologies for Tower). AR content generation by virtue of various display technologies are studied and, Optical Spatial See-through displays (O SSTDs) have been considered for its own advantages in developing this application. One of the necessary requirements of AR in this configuration is identified as the tracking of the operator’s point of view (eye-tracking), to provide virtual content consistent with its real position. Thus, this paper elaborates the design of specific eye-tracking system using Microsoft Kinect V2 for the virtual control tower application. The need to have binocular vision to use AR content is assessed and the interface has been tested with few subjects to evaluate the precision of the measurements detected through the proposed solution.
Design of Sweeping Robot Based on STM32 Single Chip Microcomputer

Abstract As a new product in the field of service robots, the sweeping robot has made some achievements, but there are still many key technical problems to be solved or improved. This paper focuses on the precise obstacle avoidance and highly sensitive execution of the sweeping robot, and designs a sweeping robot based on STM32 microcontroller. The infrared ranging and ultrasonic ranging are combined to achieve accurate data acquisition. When the measurement distance is relatively close, infrared ranging is used. Otherwise, the ultrasonic distance measurement is adopted. In addition, PID algorithm is used to speed up the response of the system, and the motion state of the sweeping robot is adjusted through prediction data to ensure the accuracy of STM32 execution. It provides reference for the development of sweeping robot industry.
Innovation and ICTs in ESL: Exploring the Role of the Podcast as a Learning Enhancing Tool in Innovation Projects

What we know as Information and Communication Technologies, or ICTs, encompasses all electronic devices and network systems that enable society to interact digitally. There is, however, a key difference between ICT and the Information Technology (IT), as can be seen from common definitions of ICTs, which often, if not always, include terms such as interacting, transmitting, or sending, hence: communication is key. This paper aims at presenting a classroom project proposal based on the application of ICTs (a podcast), which allows the objectives set out in the English as a Second or Foreign Language curriculum to be effectively incorporated during the teaching-learning period for students. This paper also aims to serve as a guide for other teachers of the English language who seek to effectively integrate the use of innovative methodologies into their lessons, based on the effective use of ICT in the classroom. Among the methodologies that can be implemented in the classroom with ICTs and innovative methodologies such as the flipped classroom, project-based learning, or game-based learning, which will be discussed in detail.