Reviewerly/exHarmony huggingface dataset:
Realization of interactive animation creation based on artificial intelligence technology

Abstract In film and television animation works, animated characters are the soul and core of the work. The behavior, language expression, and emotional expression of animated characters play an important role in the expression of the animation theme and content. Aiming at the problem that the mobile animation system can only add and change actions for a single virtual character, and the characters cannot interact with each other, this paper analyzes the technical principles, technical characteristics, and application scope of human–computer interaction (HCI), taking sensors as the research object. An algorithm for separating the human body from the background environment in the depth image is proposed. Through the calculation of the depth value, the calculation results are compared, and the target human body and the background are effectively separated. In the depth data processing, the algorithm of judging the pixel offset value is used to identify the body part, and a sensor‐based HCI system is designed. The depth‐of‐field data map acquired by the sensor is used to identify human body parts and determine actions, thereby realizing HCI based on action recognition. Simulation test results show that the effective rate of the system is 80%, and the design of animated characters can be put into the visualization stage. Using the algorithm in this paper, the physical signs of the animated characters can be quickly identified, so that the next action of the animation can be more clearly captured. Has a certain practical value.
Research on K-Means Clustering Algorithm Based on Improved Genetic Algorithm

Genetic algorithm is the simulation of the natural biological evolution principle, through the selection, crossover, mutation three genetic operators to complete the evolution and inheritance of nature. The advantage of genetic algorithm is its strong ability of global optimization. However, genetic algorithm has premature phenomenon, which makes the algorithm produce sub optimal solution prematurely. In this paper, adaptive strategy is introduced. In the process of algorithm implementation, crossover probability and mutation probability are adjusted dynamically, and population evolution speed is automatically adjusted to ensure that the algorithm finally obtains the global optimal solution.
Assessment of Mixed-Reality Devices for Production Engineering

The article presents the results of works aimed at evaluation of Mixed Reality equipment for various industrial applications. The technical parameters and the existing limitations of the 15 MR devices that appeared on the market in the last two years was analyzed and compared. As a result, an overall equipment ranking was developed, as well as an expert assessment in terms of their use in industry in supporting operation and maintenance processes of machines and devices, interactive trainings and product and process design. Due to the lack of data, certificates and recommendations from equipment manufacturers, the authors recommend planning and conducting experiments aimed at determining the range of possible working conditions for selected devices. The limits of their use by employees related to the frequency and severity of adverse effects should be considered as well.
APF-based Control for Obstacle Avoidance in Smart Electric Wheelchair Navigation

In this paper, we propose an autonomous and real-time navigation module that drives a smart electric wheelchair (SEW) to the desired target, along with its ability to avoid obstacles in a dynamic 3D environment. To avoid obstacles, we use a remote sensing technology (Lidar) to provide real-time mapping of the environment. The generated map is fed to the virtual obstacle avoidance control algorithm. To control the SEW to the target, we use an algorithm based on the artificial potential field (APF) method to generate the desired trajectory and simultaneously guarantee its efficiency and continuity. Our algorithm is specially adapted and based on the idea of virtual forces, the one of attraction generated by reaching the desired target and the one of rejection caused by the detected obstacles. The SEW behaviour is obtained by the sum of the force of attraction and all the repulsion forces at a given position. The simulation results in a corridor with obstacles that confirm the viability of the proposed APF algorithm. It can be used effectively to plan the trajectory of intelligent wheelchairs and can be applied in real-time scenarios.
Arbiter: Improved Smart City Operations through Decentralized Autonomous Organization

Smart cities have emerged as one of the most promising applications of cyber-physical systems (CPS), carrying the potential to serve the various interests of the public and private sectors at large. However, contemporary smart city infrastructure commonly uses heavily-centralized network architectures, reducing failure resilience and application flexibility. This centralization also imposes high barriers to entry for public access, limiting usage and oversight opportunities. To address these limitations, we describe Arbiter, a novel fog- and edge-based communication architecture based on the concept of a Decentralized Autonomous Organization (DAO). Arbiter aims to improve the socioeconomic equity of the local citizenry by (1) acting as a management layer for citywide CPS assets, (2) providing a compliance layer for managing human capital, and (3) offering a data protection layer to ensure that citizens retain full control of their personal data. We then analyze in detail the technical, socioeconomic, and ethical implications of Arbiter, and contextualize its role in the modern smart city.
Evaluation of using VAR (Virtual Augmented Reality) based educational games in Islamic kindergartens in Semarang City

Based on the current condition of educational games, the development is so fast. One indicator of development is that PAUD children are busy playing games while their parents are busy. It is ]an opportunity to make educational games adjusted to the level of development of PAUD children. It improves the psychomotor skills of PAUD children because this product displays virtual and augmented reality that attracts children. Therefore it increases PAUD children’s learning motivation. The study employed the ADDIE R & D model (Analysis, Design, Develop, Implementation, & Evaluation). Material experts obtained product validation by 91%, media experts by 87%, and teacher responses by 95%. It means that this educational game product is suitable for use in learning in the PAUD class. According to the first-year results, the product has been appropriately improved according to kindergarten or early childhood thinking level.
VSpSR: Explorable Super-Resolution via Variational Sparse Representation

Super-resolution (SR) is an ill-posed problem, which means that infinitely many high-resolution (HR) images can be degraded to the same low-resolution (LR) image. To study the one-to-many stochastic SR mapping, we implicitly represent the non-local self-similarity of natural images and develop a Variational Sparse framework for Super-Resolution (VSpSR) via neural networks. Since every small patch of a HR image can be well approximated by the sparse representation of atoms in an over-complete dictionary, we design a two-branch module, i.e., VSpM, to explore the SR space. Concretely, one branch of VSpM extracts patch-level basis from the LR input, and the other branch infers pixel-wise variational distributions with respect to the sparse coefficients. By repeatedly sampling coefficients, we could obtain infinite sparse representations, and thus generate diverse HR images. According to the preliminary results of NTIRE 2021 challenge on learning SR space, our team ranks 7-th in terms of released scores.
Stance Identification by Sentiment and Target Detection

Stance detection has attracted attention for several years. Previous work focuses mainly on a supervised topic-specific setting which requires labeled data for each individual topic. In this paper, we discuss the characteristics of different types of topics, and the interaction among sentiment, target, and stance in a sentence. We propose an approach without the need of stancelabeled data to identify stance incorporating the findings of their interaction. The proposed approach is topic independent and can be applied to individual topics flexibly. Furthermore, we evaluate our method on the SemEva1-2016 dataset for detecting stance in tweets, which contains six topics of two different types. Experimental results show that our approach is promising even when stance-labeled data is not available.
Malicious Encrypted Traffic Identification Based on Four-Tuple Feature and Deep Learning

With the increasing popularity of traffic encryption protocols such as SSL/TLS, attacks based on encrypted traffic are more and more rampant, so does the need to inspect all SSL traffic. At present, the methods based on feature engineering and the methods based on deep learning and representation learning are the research hot-spots. In this paper, a new method of encrypted malicious traffic identification is proposed, which is based on deep learning and four- tuple feature. The unit of traffic identification is flow four-tuple. We extract 3 types of features which are statistical feature, handshake byte stream feature, and application data size sequence feature. We design different deep learning models to deal with various features and work together in traffic identification. We used the CTU Malware dataset to experiment. The results show that the accuracy of our method can reach 98.31%, which is better than that of other methods using four-tuple as the unit of flow identification and experimenting on the CTU Malware dataset.
An Emotional Topic Recommendation Chat Assistant

Mobile technology advances such as online chat or messaging apps were created to make communication easier and more convenient. However, the absence of tone and non-verbal cue, and lack of warmth, etc. in online communication results in shallow interactions, less demand for communications, and could cause negative effects on social relations. Despite a growing body of research about the design and use of recommender systems in online chat scenarios, existing work has paid a little attention to help interactions and social relations between users of messaging Apps. In this paper, we present a chat assistant trying to address this problem by recommending positive and negative emotional topics to users during online chat. As the result, users can take the recommended topics into account during the conversation to have more positive emotional connections and social relations. Our preliminary experiment shows an acceptable accuracy of the recommendation system design in different conversation scenarios.
Pragmatic extension in computer-mediated communication: The case of ‘#’ and ‘™’

There is a growing body of work discussing the evolving functions of computer-mediated communication (CMC) cues including emoticons, emojis, memes, punctuation and GIFs. Two such cues are the hashtag (#) and the trademark operator (™). Using a corpus of public Reddit comments from 10 distinct ‘subreddits’, I analyze the usage of the trademark operator across different online communities. I demonstrate that the trademark operator use is undergoing a shift to take on characteristics previously identified by Zappavigna (2018) to correspond with the hashtag. In addition to having shared metadiscursive properties, these operators have overlapping functions including construing evaluation, ambient affiliation, and critique. One possible motivation for the use of these symbols is the indexing of community membership, since the use of these contextually defined operators assumes a degree of shared common ground. I argue that the overlapping characteristics extend to a broader class of CMC cues that I refer to as artificial operators.
Synthesize-It-Classifier: Learning a Generative Classifier through Recurrent Self-analysis

We show the generative capability of an image classifier network by synthesizing high-resolution, photo-realistic, and diverse images at scale. The overall methodology, called Synthesize-It-Classifier (STIC), does not require an explicit generator network to estimate the density of the data distribution and sample images from that, but instead uses the classifier’s knowledge of the boundary to perform gradient ascent w.r.t. class logits and then synthesizes images using the Gram Matrix Metropolis Adjusted Langevin Algorithm (GRMALA) by drawing on a blank canvas. During training, the classifier iteratively uses these synthesized images as fake samples and re-estimates the class boundary in a recurrent fashion to improve both the classification accuracy and quality of synthetic images. The STIC shows that mixing of the hard fake samples (i.e. those synthesized by the one-hot class conditioning), and the soft fake samples (which are synthesized as a convex combination of classes, i.e. a mixup of classes [36]) improves class interpolation. We demonstrate an Attentive-STIC network that shows iterative drawing of synthesized images on the ImageNet dataset that has thousands of classes. In addition, we introduce the synthesis using a class conditional score classifier (Score-STIC) instead of a normal image classifier and show improved results on several real world datasets, i.e. ImageNet, LSUN and CIFAR 10.
Problematising Transparency Through LARP And Deliberation

Information technology is increasingly designed to increase information transparency as a way to increase trust. However, it can be hard to comprehend and anticipate the social implications of information visibility, people’s competing expectations of transparency versus privacy, and the role of enabling technologies. We devised Live Action Role Play (LARP) as a means for people to explore the potential and consequences of transparency, within a social context of daily transactions related to food, fashion, and finance. Through the process of deliberation, we enabled participants to critically assess their co-created LARP experience and articulate their transparency expectations for design considerations. We report on insights into their expectations and perceived limitations of information technology in delivering transparency, including social measures required to realise the full potential of technology, as well as transparency, while minimising unintended consequences.
Image encryption algorithm using DNA pseudo-symbols and chaotic map

There have been developed image encryption algorithm using chaotic map and DNA pseudo-symbols sequence gained on the basis of real DNA symbols. In the suggested algorithm, the address for the selecting of DNA symbols sequence from Gene Bank, encoding rule of the DNA symbols, also the initial parameters of the chaotic map are determined on the secret key basis. Image pixels modification is based on the numerical values of the chaotic points sets coordinates obtained with the chaos play description of the DNA pseudo-symbols and the transference of pixels is based on displacement table constructed with the chaotic map.
Towards Embodied Interaction for Geospatial Energy Sector Analytics in Immersive Environments

We present a tool for immersive analysis of spatial energy data. The tool is built on ImAxes, which was designed for immersive analytics of abstract data in virtual environments, and extends it with a geospatial layer that allows for iterative and collaborative sensemaking. We developed new interaction techniques that enable the user to freely choose data variables, combine them into charts, and use filters and data lenses to create interactive visualisations grounded in a map. Fusion between various separate data sources can take place directly in the hands of the humans who need to understand and explore it. In this paper, we describe the system development and present a use case scenario with data from Australia's National Energy Analytics Research Program.
Team R00 at Mowjaz Multi-Topic Labelling Task for Arabic Articles

This paper describes the winning system for the Mowjaz Multi-Topic Labelling Task. The goal of the task is to classify articles based on their topics and predict multiple topics in one article. The proposed system is an ensemble model that consists of six BERT-Based models trained on different versions of the dataset. It achieved an F1-Micro score of 0.886 and an Accuracy score of 0.843 on the validation data. It also achieved an F1-Micro score of 0.8595 on the Test data, which led to ranking the model 1st in the Mowjaz Multi-Topic Labelling leaderboard. The current research work discusses the pre-trained language models used for the experimentation that led to the proposed system and shows the models' performances on the Arabic Articles dataset.
Distribution Estimation Based Pseudo-Feature Library Generation For Few-Shot Image Classification

Due to the high cost of labeled data acquisition, few-shot learning has attracted great attention in recent years. The biased estimation of class distribution from a few labeled samples hinders the model’s performance. Some existing methods generate samples or features by a learning module or network. In this paper, a distribution-based pseudo-feature library generation method is proposed, and it follows a simple distribution modeling hypothesis. The base class features is introduced to better estimate the novel class distribution. Furthermore, a patch-level pseudo-feature library is adversarially generated to reinforce the training of the classifier. The proposed method significantly improves the model performance for the few-shot image classification task without introducing additional training parameters. Our method ranks first in the ICME 2021 Few-Shot Learning for Vehicle Footprint Recognition Challenge, demonstrating its effectiveness.
Outlier detection algorithm based on density and distance

Abstract In order to solve the problem that the existing outlier detection algorithm is difficult to detect the one-dimensional integer data set with uneven frequency distribution and uniform distance distribution and low accuracy, the advantages of density outlier detection and distance outlier detection can be combined. An outlier detection algorithm DAD (Density and Distance) based on density and distance was proposed. In order to improve the possibility of outlier sample distance, the algorithm can define the weight distance; introduce the global density outlier factor combined with the weight distance as the relative distance, and use the cutting edge strategy to quickly cut the outliers based on the minimum spanning tree. Then, an artificial data set was used to test the algorithm. Experimental results showed that the algorithm had a good outlier detection accuracy in dealing with data sets with uneven frequency distribution and uniform distance distribution.
Image Retrieval Based on Texture Using Local Binary Pattern and Local Phase Quantization

As a broad area of field and applications, texture analysis from the area of remote sensing to biomedical imaging, image inpainting, etc., for each area of these, it requires some raw data image to extract some meaningful features that define the characteristics of an image. In this manner, the content-based image retrieval (CBIR) plays a vital role in finding the similarity of images to query image. Either number of work had been done in these directions, but there is still the scope of work to be done. With the continuation on this, analysing the different research papers on texture-based feature extraction especially on local binary pattern (LBP) and local phase quantization (LPQ), here this paper tries to analyse and compare the efficiency of image retrieval with respect to their texture feature vector dataset of images with the help of different texture feature extraction techniques, especially LBP and LPQ. This paper is focused on the LBP and LPQ techniques and its comparative analysis.
Small Selectivities Matter: Lifting the Burden of Empty Samples

Every year more and more advanced approaches to cardinality estimation are published, using learned models or other data and workload specific synopses. In contrast, the majority of commercial in-memory systems still relies on sampling. It is arguably the most general and easiest estimator to implement. While most methods do not seem to improve much over sampling-based estimators in the presence of non-selective queries, sampling struggles with highly selective queries due to limitations of the sample size. Especially in situations where no sample tuple qualifies, optimizers fall back to basic heuristics that ignore attribute correlations and lead to large estimation errors. In this work, we present a novel approach, dealing with these 0-Tuple Situations. It is ready to use in any DBMS capable of sampling, showing a negligible impact on optimization time. Our experiments on real world and synthetic data sets demonstrate up to two orders of magnitude reduced estimation errors. Enumerating single filter predicates according to our estimates reveals 1.3 to 1.8 times faster query responses for complex filters.
Tools for organizing teachers-students interaction using version control systems

The article describes an approach to organizing the teacher-students interaction in programming courses using the Git version control system. In order to select the most suitable and affordable system for educational needs a comparative analysis of different Git repository management systems was carried out. Based on the experience of various educational institutions that use version control systems in their courses, the advantages and disadvantages of using these systems in teaching were identified. Taking into account the existing problems, a software solution was developed based on the GitLab system. As part of this solution, a method is proposed for organizing the work of a teacher and students in disciplines that use version control systems. This approach implies using both GitLab and additional system, which serves as a manager for Git repositories and is designed to facilitate the work of the teacher and administrator by automating the tasks they perform. The main purpose of the article is a detailed description of this approach: limiting permissions to both teachers and students, GitLab organization and functionality, a list of use cases for each user. The article also presents common workflows of the additional system, its main entities and their relationships and an overview of the features that the system provides.
Webshell Detection Technology Based on Deep Learning

In this paper, we use a Deep Learning technique called Long Short Term Memory (LSTM) recurrent neural networks to detect Webshell which is a kind of trojan scripts written by hackers and causes great security risks to web servers. We mainly use deep learning theory to intelligently extract the characteristics of the opcode sequences of malicious codes written by PHP and study the classification model. In this paper, we compile PHP files into opcode sequences, build Webshell detection model by using LSTM, which also includes word embedding conversion, multi-layer LSTM structure and so on. The trained single-layer model finally shows over 95% accuracy for detecting Webshells. However, accuracy of multi-layer models is reduced on the contrary. The double-layer model shows 93% accuracy and the triple-layer model even shows lower than 90% accuracy. It turns out that more layers is not always better probably due to gradient vanishing or overfitting caused by multiple layers. The result indicates that single-layer model may perform best on Webshell detection.