Reviewerly/exHarmony huggingface dataset:
CSMOTE: Contrastive Synthetic Minority Oversampling for Imbalanced Time Series Classification

The class imbalanced classification is pervasive in real-world applications for a long time, such as image, tabular, textual, and video data analysis. The imbalance issue of time series data attracts especial attention recently, with the development of the Industrial Internet. The Oversampling method is one of the most popular techniques, which usually heuristically re-establishes the balance of the dataset, i.e., interpolation or adversarial generative technology for minority class instances augmentation. However, the high dimensional and temporal dependence characteristics pose great challenge to time series minority oversampling. To this end, this paper proposes a Contrastive Synthetic Minority Oversampling (CSMOTE) for imbalanced time series classification. Specifically, we assume that the minority class example is composed of its peculiar private information and common information shared with majority classes. According to the variational Bayes technology, we encode this information into two separated Gaussian latent spaces. The minority class synthetic instances are generated from the combination of private and common representation draws from the two latent spaces. We evaluate CSMOTE’s performance on five real-world benchmark datasets, and it outperforms other oversampling baselines in most of the cases.
Plot and game techniques of disciplining junior schoolchildren in choreographic activity

We consider the diagnostic and methodological aspects of disciplining junior schoolchildren in choreographic activity. In accordance with the selected criteria and indicators of a criterion-diagnostic toolkit we develop a system of plot-game techniques for disciplining junior schoolchildren in choreographic activity. The concrete scientific level of work is provided by game theory, which considers it as a cultural phenomenon, as an activity that occurs at a certain stage of ontogenesis; theories and concepts of personality individuality, ideas about a person as a subject of activity and life; theories of developmental learning; modern theories and concepts of personality-oriented education and upbringing. The results of the study can be used by teachers of additional education of artistic orientation working with children of primary school age to develop methodological materials and recommendations.
Matrix Factorization in Latent Semantic Indexing

Matrix factorizations are methods used to factorize a matrix into a product of two or more matrices. Matrix factorizations are used to reduce the dimension of a data set that help in reducing the computational time. In this project, we study how Singular Value Decomposition (SVD) and Non-Negative Matrix Factorization (NMF) are applied in Latent Semantic Indexing (LSI). LSI is a search algorithm where a set of documents is returned based on the keywords searched by the user. The performance of the two types of matrix factorizations are compared while applying them in LSI.
Density-Guided Locality Consensus for Fast Feature Matching

Establishing correct feature correspondences from two images of the same scene or target is a critical prerequisite in various applications. This letter proposes a simple but novel method to efficiently remove outliers from a putative match set, which is performed on searching potential inliers that can well preserve local topology structure. To this end, we provide a mathematical formulation and its closed-form solution for fast optimization. To construct local structure more accurately, we introduce a density-guided strategy, which enables our method to distinguish inliers and outliers more easily, thus largely enhancing the matching performance. Extensive experiments on both rigid and nonrigid datasets demonstrate the superiority of our method against the state of the art in both accuracy and efficiency.
Comparison and proof of the fastest way of the sorting algorithm by functional programming in Erlang

Typically, refactoring are expressed in high-level, general purpose programming languages, which are impractical to verify because they are too complex to have tractable formal semantics definitions. Refactoring can be defined in various formats at various abstraction levels, the sorting algorithm is the most important and powerful in most or all programs language and database, the mighty importance of the sorting towards data mining that needs ordered the datasets elements to be fast particularly in preprocessing data. Methods : The challenges side incur of this paper summed up development the sort algorithm many times, using different refactoring ways as sequentially method sometimes with list comprehension moreover recursive listing, further with parallel computing, Implementation and execute different methods for the sorting algorithm code on several different computers with different in CPU to achieve the best rate of pure results for implementation under the Erlang code. Proposed solution : The purpose of this paper is to conduct a comparison between methods by evaluate the runtime execution prepared code that writing under Erlang functional programming, the research effort comparative analysis between the approaches adopted many member of the sort algorithm and proposed a new technique that works ordered the list by minimum elements with recursive the list comprehensive. Result: then this paper will a prove the Quick Sort technique is the fastest compared with the others.
An Evaluation Dataset Construction Approach for Task-Oriented Dialogue

Aiming to construct an evaluation dataset for task-oriented dialogues under slot filling task, this paper proposes a dataset construction approach based on two optimized data augmentation techniques named back-translation annotation synchronization and slot substitution. These optimized techniques perform well in reducing error annotations introduced by data augmentation and help maintain the style and difficulty of the original dataset. Besides, these techniques can be easily implemented by leveraging commercial interfaces and executing automated scripts, making the approach especially suitable for evaluation dataset construction. In experiments, MultiWOZ 2.0 was utilized as the benchmark dataset to generate new samples. The newly generated dialogues have lower error rate in annotations, and show the same evaluation capability as the original data, which verifies the feasibility of the construction approach and the effectiveness of two optimization methods.
A Scalable CNN Accelerator with FPGA On-board Resource Optimization for Semantic Segmentation

In this paper, a scalable hardware accelerating system deployed on FPGA that can efficiently accelerate various types of convolution for semantic segmentation networks is designed. A hierarchical multiple line buffer structure is proposed to flexibly construct convolution dataflow and utilize FPGA on-chip resources. The accelerating system is validated on both ZYNQ series high performance SoC and XC7A100T board with a RISC-V core. The results show that the proposed system can be easily implemented and migrated to different FPGA platforms with high computation efficiency.
A Multi-Resolution Deep Forest Framework with Hybrid Feature Fusion for CT Whole Heart Segmentation

Cardiac medical image segmentation plays an important role in the diagnosis and clinical treatment of cardiovascular diseases. However, due to the variability of cardiac anatomy and the ambiguity between cardiac substructures, it is still difficult to quickly segment the entire heart from medical images. Most of the current researches utilize neural network structure to perform whole heart segmentation. Although good segmentation accuracy has been achieved, it usually requires a long training time. This paper aims to build a new whole heart segmentation model based on Deep Forest, called Multi-Resolution Deep Forest Framework(MRDFF), which performs segmentation through two stages. In the first stage, the heart region is located by rough binary classification, and similarity screening is used to reduce redundancy. The second stage subdivides the heart substructures based on the results of the first stage and uses multi-scale fusion to achieve high segmentation accuracy. The experimental results conducted on the public data set MM-WHS show that under the same training data and configuration, our model can be trained in only 4.5 hours, which is about 1/2 of the training time of neural network models, and can reach the accuracy not lower than neural network models, which shows the feasibility and efficiency of our model. The code will be made publicly available at https://github.com/xufeixf/MRDFF.
Anti-Phishing Game-Based Training: An Experimental Analysis of Demographic Factors

AbstractThe purpose of this quantitative experimental study was to analyze and measure how age, gender, education, and previous training affected an individual’s ability to learn during an anti-phishing game. While game-based anti-phishing training has been demonstrated to be a promising training method, there is a lack of research indicating what demographic performs best when using this method. In addition, there is also a gap in the literature comparing game-based training with a more traditional type of training such as a video. In this study, 481 participants participated in a survey that consisted of a pretest, an anti-phishing game or a short anti-phishing video, and lastly a posttest. The results revealed that over a large demographic, a short video was more effective than the anti-phishing game and no individual demographic factor had a significant impact on the game’s effectiveness. However, additional analysis indicated that individuals with specific combinations of demographic factors had an improved response to game-based training
Application of Firefly Algorithm in Scheduling

Metaheuristics offer a high-quality solution in a short period of time for real-world situations; they are a fascinating field of research that has made significant advances in solving problems. Bio-inspired Swarm Intelligent (SI) algorithm is represented in different studies' umbrellas, where inspiration and principles of biological evolution develop a new way and robust techniques in computing. The paper's primary purpose is to apply the Firefly Algorithm process in scheduling. The study experimented and tested the efficiency of the developed objective function based on the scheduling policy of RLMH. The experiment focused on the five different iterations and two values for attractiveness. Based on the findings that FA generates different results with different values of attractiveness and iterations. Comparing attractiveness shows a significant improvement in β =2 in generating best solutions, but not the fireflies' movements and time execution. The experiment achieved 66.37% of the objective function.
UAV Taxiing Control Based on Linear Active Disturbance Rejection Control

In order to research the ability of the UAV to correct deviation during the front three-wheel taxiing phase and establish accurate taxi model of the drone, this paper takes a certain UAV with nose-wheel landing gear as the research object. This paper conducts a complete dynamic analysis of its ground model, improves the ground force analysis and aerodynamic analysis, builds a 6-demention model which contains a PID controller and adds a LADRC controller in Matlab for verifying the anti-disturbance ability of LADRC of the UAV in the model. The result shows that compared with PID controller, LADRC can effectively suppress heading deviation caused by the disturbance, and the drone can be more quickly aligned to the heading for the UAV rolling under disturbance.
Research on UAV Route Planning Technology Based on Evolutionary Algorithm

Due to the complex flight environment, many constraints of UAV, and strong coupling among various factors, route planning has become a challenging problem in UAV mission planning. Aiming at the complexity of route planning, this paper studies the environment model, UAV constraints and route planner. Finally, based on the evolutionary algorithm, the paper plans the optimal or feasible route to meet the objectives of the route planning.
Radar active jamming recognition based on time-frequency image classification

Flexible active radar jamming has become one of the main threats in modern electronic warfare. Aiming at the problem of classifying and identifying active jamming patterns of towed decoy radar, this paper proposes a set of methods for classifying and identifying jamming time-frequency images based on deep learning. The content mainly includes: using short-time Fourier transform to obtain time-frequency images of six kinds of active interference under different interference signal ratios. Using ResNet network to realize the classification and recognition of different interferences. Through simulation analysis, the algorithm can still obtain better results under low interference-to-noise ratio.
Explainable Fuzzy Texture Words Model for Automatic Bone Fracture Identification

The examination and interpretation of x-ray images is a time-consuming process that requires manual examination of bones for a fracture diagnosis. In this study, we propose a novel feature extraction and classification model for the automatic detection of bone fractures, named Explainable Fuzzy Texture Words (EFTW). The advantage of EFTW, in comparison with other conventional classification methods is that it is explainable, which means that it can explain why an x-ray image is classified as fracture/non-fracture, depending on the existence of a bone fracture. The proposed model utilizes a novel way to express the textural feature vector extracted from x-rays images, using dictionaries of textural words, based on the similarity between the feature vector and the words of the dictionaries. In addition, fuzzy logic is exploited for the quantification of the similarity, using linguistic values. The proposed model is investigated on a real datasets that include 300 x-ray bone images of upper and lower extremity. The results show that EFTW outperforms several state-of-the-art methods, while providing the advantage of explainability that contributes efficiently to the reduction of diagnostic errors, as well as, the increase of the radiologists’ productivity.
Evaluating Data-Parallel Distributed Training Strategies

Deep neural networks (DNNs) require distributed training strategies to deal with large data sizes. TensorFlow is one of the most widely used frameworks that support distributed training. Among the TensorFlow training strategies, the mirrored strategy (MS) and multi-worker mirrored strategy (MWMS) follow synchronous distributed model training; the parameter server strategy (PSS) implements asynchronous methods for training. In this work, we analyze the performance of distributed training strategies in synchronous and asynchronous settings by implementing LSTM and MLP on two industrial time-series datasets from network management systems (NMS) and Google cluster trace (GCT), and Inception-v3 model on CIFAR-10 image dataset. The strategies were compared by empirically evaluating loss, accuracy, training time, and scalability.
Methods for Domain Adaptation of Automated Systems for Aspect Annotation of Customer Review Texts

Today, social media and instant messengers are a widely used information channel that has a powerful impact on a public opinion. Therefore, the rapid identification of the main topics and generalized content of a set of texts becomes an important task. In fact, the problem arises of the aspect-based annotation of texts, interrelated on some of the topics presented in them. A set of texts can contain data for a variety of semantic categories. Therefore, it is of interest to obtain annotations for categories automatically extracted from texts. This task essentially depends on specific subject areas. Therefore, the issue of quick and effective adaptation of existing models to new domains is highly relevant. This paper proposes a hybrid method of aspect-oriented analysis and text annotation based on data extracted from both common dictionaries and domain-oriented unstructured texts. The introduced characteristic functions and numerical metrics make it possible to assess the significance of individual terms within the entire domain. An algorithm for the categorization of texts is proposed, based on the selection of semantic clusters in a domain semantic graph. A method for highlighting the most significant text fragments included in the annotation, based on statistical data, is proposed. The results of experiments are presented, which makes it possible to evaluate the quality of the algorithms.
RecGraph: Graph Recovery Attack using Variational Graph Autoencoders

Graph-structured data contains a lot of sensitive information about individuals. In order to protect users&#x2019; privacy, many anonymization mechanisms for graph-structured data are proposed. However, one common drawback of these mechanisms is that they only consider to hide the local characteristics, such as the degree of nodes or their neighbors. They lack the consideration for the nodes&#x2019; attribute features and the features of potential global graph structure, which leads to the failure of these mechanisms to provide sufficient security.To address this shortcoming, we propose RecGraph, a framework for graph recovery attack based on variational graph autoencoders. We use RecGraph to perform graph recovery attack on three real social network datasets, and compare it with five existing baselines, to prove the effectiveness of our method. We also evaluate the privacy wastage after performing the graph recovery attack using RecGraph to demonstrate the serious security risks faced by the existing graph anonymization mechanisms.
Nearly Reversible Image-to-Image Translation Using Joint Inter-Frame Coding and Embedding

Image-to-image translation tasks which have been widely investigated with generative adversarial networks (GAN) aim to map an image from the source domain to the target domain. The translated image can be inversely mapped to the reconstructed source image. However, existing GAN-based schemes lack the ability to accomplish reversible translation. To remedy this drawback, a nearly reversible image-to-image translation scheme where the reconstructed source image is approximately distortion-free compared with the corresponding source image is proposed in this paper. The proposed scheme jointly considers inter-frame coding and embedding. Firstly, we organize the GAN-generated reconstructed source image and the source image into a pseudo video. Furthermore, the bitstream obtained by inter-frame coding is reversibly embedded in the translated image for nearly lossless source image reconstruction. Extensive experimental results and analysis demonstrate that the proposed scheme can achieve a high level of performance in image quality and security.
Multi-Dimension Aware Back Projection Network For Scene Text Detection

Recently, scene text detection based on deep learning has progressed substantially. Nevertheless, most previous models with FPN are limited by the drawback of sample interpolation algorithms, which fail to generate high-quality up-sampled features. Accordingly, we propose an end-to-end trainable text detector to alleviate the above dilemma. Specifically, a Back Projection Enhanced Up-sampling (BPEU) block is proposed to alleviate the drawback of sample interpolation algorithms. It significantly enhances the quality of up-sampled features by employing back projection and detail compensation. Further-more, a Multi-Dimensional Attention (MDA) block is devised to learn different knowledge from spatial and channel dimensions, which intelligently selects features to generate more discriminative representations. Experimental results on three benchmarks, ICDAR2015, ICDAR2017- MLT and MSRA-TD500, demonstrate the effectiveness of our method.
Portraits of Typical Accounts in Ethereum Transaction Network

Ethereum is the largest blockchain system supporting Turing-complete smart contracts. In recent years, we have witnessed its boom and popularity in various applications. However, since users use pseudonyms in Ethereum, it is hard to know the true identity behind an account. Meanwhile, a large number of cyber-crimes in Ethereum emerged and have been reported. Therefore, it is an important task to analyze the transaction behavior of accounts in Ethereum and conduct account portraits based on the honest and public information which is transaction records. Although facing the anonymity challenge of blockchain, it makes this task possible that some Ethereum analysis platforms provide ground truth by classifying accounts into specific types. However, prior work tried to dig out features of one certain account type but lack of a comparative analysis of multi-class account types. In this paper, we model the partial Ethereum transaction data as a transaction network, then portray the characteristics of six types of accounts in Ethereum according to the obtained labels from both transaction statistics perspective and network structure perspective. Moreover, we adopt a Graph Convolutional Network (GCN)-based model to distinguish different kinds of accounts to verify the effectiveness of the properties we choose. The experimental results show that our model performs well in classifying various types of accounts in Ethereum.
MMReLU: A Simple and Smooth Activation Function with High Convergence Speed

Activation functions have a major effect on deep networks’ performance. In past few years, there is an increasing interest in the construction of novel activation functions. In this paper, we introduced a novel non-monotonic activation function, named Moreau Mish Rectified Linear Unit (MMReLU). It’s simple, efficient, and robust, comparing with Mish, ReLU, and etc. The experimental results on several classical datasets demonstrate that MMReLU outperforms its counterparts in both convergence speed and accuracy. We show that the capacity of neural networks could be enhanced by MMReLU without changing the network structure, especially the convergence speed.
Three Challenges of Artificial Intelligence for Antitrust Policy and Law

The following text addresses the challenges that the increasing use of artificial intelligence, in particular smart algorithms that collect and process large amounts of data for internet gatekeepers (i.e. the largest online service providers), poses for competition protection. The analysis focuses on three areas: the potential clash between competition protection and consumer (privacy) protection that may be caused by the push for sharing and portability of client data in order to open up online markets; then the issue of super-dominance caused by internet gatekeepers escaping both their competitors and effective control due to the massive deployment of AI; and finally, the issue of the algorithmic price collusion that seemingly turns some existing competition protection paradigms on their head. These three challenges are critically analysed with regard to their reflection in the literature and in the existing decision-making practice of competition authorities.
The Recognition and the Automatic Translation of Dative Verbs

This paper aims at studying French dative verbs extracted from Jean Dubois and Françoise Dubois Charlier’s LVF dictionary. In this database, dative verbs are classified according to three semantic-syntactic categories (D1, D2, D3) and fifteen syntactic subcategories (D1a, D2b, D3a…). In this study, we seek an automatic recognition of dative verbs and also a French-Arabic automatic translation using the NooJ platform. The automatic recognition phase is problematic because the majority of dative verbs are used in different contexts and placed in different syntactic-semantic constructions.