Reviewerly/exHarmony huggingface dataset:
ANDROID-BASED LEARNING MEDIA DEVELOPMENT FOR CHAPTER 2D AND 3D ANIMATION

The Students of 2D and 3D Animation subjects class XI Multimedia SMK Negeri 1 Sijunjung have difficulty in understanding learning materials. Absence of learning media that support learners' learning outcomes in 2D and 3D Animation subjects makes it difficult for learners to understand the subject matter. This research aims to develop a valid and practical android-based learning medium on the subjects of 2D and 3D Animation. The research method used Research and Development (R&amp;D) with ADDIE (Analyze-Design-Development-Implement-Evaluate) model. The results of the study are: (1) media validation results obtained with an average of 0.77 with the category "valid" (2) material validation results obtained with an average of 0.85 with the category "valid" (3) the practicality of teachers obtained with a percentage of 89% with the category "very practical" (4) the practical results of learners obtained with a percentage of 94% with the category "very pragmatic. Keywords: Media, Animation, Android, R&amp;D, ADDIE
Accelerate and secure serverless networks with QUIC

In serverless computing [3], cloud providers manage responsibility for all server-related tasks, including both hardware resource allocation and software runtime preparation. Cloud tenants are thus free to simply focus on designing discrete stateless functions and orchestrate them together for their high-level business logic.
Text removal network based on comprehensive loss evaluation

This paper proposes a text removal model, Text Remove Network (TRNet), which achieves an unprecedented clearing effect of picture text. The network uses a jump-connected U-net structure to encode and decode the generator, so as to obtain clearer and sharper texture details of the original image. To solve the problem of color distortion, the generator removes the batch normalization layer and uses ELUs as the activation layer of all convolutional layers. Through the comprehensive loss, which include reconstruction, content, style, total variation, and structural similarity (SSIM) loss, we can clear the image text and preserve the image information of the background, which solves the problem of incomplete text removal and loss of background texture. The local discriminator is used to evaluate the local consistency of the text erasure area. In a text elimination experiment on a synthetic dataset and the ICDAR 2013 dataset, this method had a good effect on foreground text erasure and background authenticity restoration. Experiments on a comprehensive dataset of real documents also showed good results. To achieve targeted removal of sensitive text information on pictures, we collected datasets based on real and synthetic documents, and experimental results were satisfactory. Compared to current and classic algorithms, our text removal algorithm performs best in Image Quality Assessment (IQA).
Real-time multi-view background matting for 3D light field video

Matting is a method to extract foreground objects of arbitrary shape from an image. In the field of 3D display, matting technology is of great significance. Through the study of this technology, we can extract high-quality target foreground, and then reduce unnecessary stereo matching calculation and improve the effect of 3D display. This paper primarily studies the human target in 3D light field, and proposes a real-time multi-view background matting algorithm based on deep learning. Three-dimensional video live broadcast puts forward high requirements for the real-time performance of the matting algorithm. We pre-compose a group of multi-view images taken at the same time into a multi-view combined image. The network directly carries on the background matting to the multi-view combined image and outputs a group of foreground images at one time. Because the background of the multi-view combined image is not holistic, a pre-photographed background picture without human is added to the input to assist the network for learning. In addition, we add a channel subtraction module to help the network better understand the role of the original image and background image in the matting task. The method in this paper is tested on our multi-view data set. For pictures with different background complexity, it can run about 65 frames per second and maintain a relatively stable accuracy. The method can efficiently generate multi-view matting results and meet the requirements of 3D video live broadcast.
3D Path Planning based on MMVO

Aiming at the problems of difficult parameter determination and poor quality of crawling path planning, this paper proposes a 3D path planning method which uses the complex field to model the environment and the modified Multi-Verse Optimizer algorithm (MMVO) to optimize the gravitational factor of the virtual potential field. On the basis of ameliorating the potential field navigation function, the complex number is used to carry the information of elevation and obstacle area in the environment, so as to tackle the problem of large data scale and low efficiency of path planning in traditional environment modeling. In order to enhance the quality of path planning model, fitness function is designed and Multi- Verse Optimizer algorithm (MVO) is improved from the aspect of travel distance rate, which can solve the problems of slow convergence speed and easy to fall into local extremum in the process of path planning parameter selection of common optimization algorithms, and get rid of the limitations of artificial potential field itself. The simulation results show that the path planning method based on MMVO artificial potential field has better modeling effect and can reduce the complexity of the model, which verifies the effectiveness, progressiveness and real time performance of the method.
Efficient elimination of Skolem functions in $$\text {LK}^\text {h}$$

We present a sequent calculus with the Henkin constants in the place of the free variables. By disposing of the eigenvariable condition, we obtained a proof system with a strong locality property—the validity of each inference step depends only on its active formulas, not its context. Our major outcomes are: the cut elimination via a non-Gentzen-style algorithm without resorting to regularization and the elimination of Skolem functions with linear increase in the proof length for a subclass of derivations with cuts.
MC-CapsNet : Low-level Pooling and High-level Fusion of Multi-columns Capsule Network

Capsule network is a new type of neural network structure. The biggest feature of capsule network is “vector in vector out”, which replaces the previous “scalar in scalar out”. The pristine capsule network completely discards the pooling layers and uses a convolutional kernel size of 9 × 9 to increase the perceptual field However, since the original capsule network has only two feature extraction layers, it cannot extract high level abstract features of objects, which are very important for image classification. Moreover, the original capsule network completely discards the use of pooling layers. Based on the above problems, we propose a multi-column capsule network (MC-CapsNet) with low-level pooling and high-level fusion. Unlike the traditional capsule network, MC-CapsNet has the following features: (a)MC-CapsNet consists of two columns of capsule network: the primary column of capsule network extracts high-level impalpable features; the other column of capsule network extracts subordinate shallow features (b)Pooling operation is added to the initial two convolutional layers of the two columns of capsule network (c)Using the high-level theoretical features extracted from the first capsule network to fuse features with the low-level surface layers extracted from the second capsule network to achieve feature complementarily (d)We add the elements of the digital capsule layers in the two-column capsule network to increase the probability of object features being detected by the capsule. The experimental results show that the classification accuracy of MC-CapsNet reaches 87.96% and 94.42% on the CIFAR10 and FashionMNIST datasets.
An Empirical Study of the Impact of Field Features in Learning-to-rank Method

In learning-to-rank for information retrieval, a ranking model is usually learned using features that are extracted from the different fields of the documents and naively combined. However, such a conventional way to learn a ranking model does not accurately reflect the utility and contribution of the fields and may also risk joining highly correlated features from different fields. It lacks an empirical analysis of how field-grouped features determine or influence the performance of the ranking models learned. In this paper, we classify features by the fields they are extracted from and investigate the role of using field-grouped features in the learning-to-rank method, particularly to see whether using field-grouped features leads to a different and better performance than using the naively combined feature list. Our experiments, on two large scale publicly available learning-to-rank benchmark datasets, show that ranking models learned using field-grouped features have competitive advantages over the models learned using a naively combined feature list, and that aggregation results of different fields present a better performance. These results suggest that learning ranking models using field-grouped features can be useful to obtain more effective performances in learning-to-rank methods.
I2DE: Improved Interval Differential Evolution for Numerical Constrained Global Optimization

Several hybrid approaches have been proposed to solve numerical constrained optimization problems. In this paper we present an Improved Interval Differential Evolution (I2DE) that uses structural information of the instance during the optimization process. We extend the math operations supported by a multi-interval core implementation that allows pruning infeasible solutions by using local consistency techniques and a backtrack-free local search. Furthermore, we propose a reformulation of interval evolutionary mutation strategies. A comprehensive experimental analysis is conducted over COCONUT and CEC2018 competition benchmarks and indicates that the hybridization between metaheuristics and constraint programming significantly improves the quality of the solutions. The experimental evaluation shows that our black-box version of I2DE outperformed several state-of-the-art solvers.
Developing the platform model for problem solving of automated machine learning

Abstract The article discusses the current state of technologies for automated machine learning. The development trends and the nature of the distribution model - MLaaS - are defined. There is highlighted a number of problems of automating the machine learning process, such as: excessive simplification and specialization of tools, vagueness of implemented processes, lack of flexibility in the infrastructure hardware, using closed algorithms. As a partial or complete solution to them, we have proposed the architecture, consisting of separate modules: models, hybridizer, learning algorithms module, testing module, user support module, and a theoretical framework. The main feature of the given architecture is its modularity, transparency and encapsulation of components. Each module is described as a separate element, implemented as an independent microservice. The paper describes the benefits of applying the given approach to the implementation of automated machine learning systems, the need to implement the given or similar standards. For each of the modules, its purposes, the tasks it solves and the implemented functionality, as well as the data necessary for the functioning and their sources are described. A general diagram showing the flows of information exchange between modules is presented. The main scenarios for the resulting system operation, as well as ways of interacting with it and the result of its operation - the generated model - are described.
Batterfly: Battery-Free Daily Living Activity Recognition System through Distributed Execution over Energy Harvesting Analog PIR Sensors

In recent years, the use of energy harvesting (EH) sensors has led to the proposal of activity sensing systems that are easy to install and do not require maintenance such as battery replacement. In this study, we aim to construct a system that can not only sense but also recognize activities of daily living (ADLs) using only power generated by EH sensors. To achieve this goal, in this paper, we propose a fully EH-based ADL recognition system called Batterfly, which consists of EH analog PIR sensor nodes that can operate with indoor light, continuously senses human movement, and recognizes daily activities through machine learning. We applied the distributed execution method of the activity recognition model with five sensor nodes to five types of activities by five participants, and found that the system could recognize them with an average accuracy of 63.59%, comparable to the performance of the centralized model running on a gateway.
Log-Gabor filter based DT-CWT and Moth Flame-Lion optimization algorithm for image denoising with deep belief networks

The quality of reconstructed images utilizes the Dual Tree Complex Wavelet Transform (DT-CWT) that depends on the output of coefficients as well as filter coefficients. Conventional Low Pass (LP) filters and High Pass (HP) filters produce annoying DC components which hinders DT-CWT performance. Further thresholding of output coefficients results in neglecting significant image details. Hence there is a need to improve filter performance and design alternate ways to substitute thresholding. In this context, application of Log Gabor filter in DT-CWT is studied. In order to minimize thresholding problems, a new scheme of optimizing filter coefficients using nature inspired hybrid meta-heuristic Moth Flame-Lion (MFL) algorithm is proposed. Here DT-CWT of noisy image is initially found by employing Log Gabor filters. The coefficients of filters are then optimized by MFL algorithm, the objective function being maximization of Peak Signal to Noise Ratio (PSNR). Final DT-CWT is performed using the optimized coefficients which results in de-noised image. Further the algorithm is also used for optimizing weights in Deep Belief Network (DBN) in the training process. Satellite, texture and medical images were used for carrying out the experiments. The efficiency of the algorithm is found by comparing its performance metrics such as PSNR and computation time with that of other algorithms. Both experimental as well as numerical results indicate better performance of the algorit hm in image de-noising.
Review on adopting concept extraction in weak signals detection in competitive intelligence

The dynamic nature of competition in the business environment makes a company’s ability to secure future change more critical to its survival. Consequently, efficient exploitation of valuable intel is globally acknowledged as an essential foundation of competitive advantage, leading to Competitive Intelligence. Besides, one of the crucial keys to successful competitive information securing is studying the future. Thus, predicting what may happen in the uncertain future is a leading-edge technology leading to an extensive need for foresight analysis. Foresight study uses various methods to recognize future developments and make plans that anticipate possible future changes. One of the leading techniques used in foresight is detecting and understanding Weak Signals. But knowing the nature of these signals, automatically scanning them is still considered a difficult task. For this, we examine the Concept Extraction technique as a main step to detect weak signals from documents automatically. In this paper, we will explain the concept extraction methods used so far, and we present in detail all the main methods and approaches and their application in detecting weak signals.
Boosting Graph Embedding on a Single GPU

Graphs are ubiquitous, and they can model unique characteristics and complex relations of real-life systems.Although using machine learning (ML) on graphs is promising, their raw representation is not suitable for ML algorithms.Graph embedding represents each node of a graph as a d-dimensional vector which is more suitable for ML tasks.However, the embedding process is expensive, and CPU-based tools do not scale to real-world graphs.In this work, we present GOSH, a GPU-based tool for embedding large-scale graphs with minimum hardware constraints.GOSH employs a novel graph coarsening algorithm to enhance the impact of updates and minimize the work for embedding.It also incorporates a decomposition schema that enables any arbitrarily large graph to be embedded with a single GPU.As a result, GOSH sets a new state-of-the-art in link prediction both in accuracy and speed, and delivers high-quality embeddings for node classification at a fraction of the time compared to the state-of-the-art.For instance, it can embed a graph with over 65 million vertices and 1.8 billion edges in less than 30 minutes on a single GPU.
Linear Concept Approximation for Multilingual Document Recommendation

In this paper, we proposed Linear Concept Approximation, a novel multilingual document representation approach for the task of multilingual document representation and recommendation. The main idea is in creating representations by using mappings to align monolingual representation spaces using linear concept approximation, that in turn will enhance the quality of content-based Multilingual Document Recommendation Systems. The experimental results on JRC-Acquis have shown that our proposed approach outperformed traditional methods on the task of multilingual document recommendation.
Modelling and Prediction of Cardiac Dysrhythmia

The paper aims to successfully categorize the condition of cardiac dysrhythmia into fourteen different variants and conditions. Although not all are threatening and vicious, some of them pose serious threat to the heart. The project aims to make use of machine learning algorithms that are developed for the purpose of detecting these dysrhythmias. The project makes an effort to improve upon the previously existing methods and algorithms applied by improving the accuracy of detection. After a thorough and intensive study of related work in the field and after many attempts, the project has managed to achieve higher accuracy than the previously existing algorithms. This has lead us to draw a comparison between the success of the project and its inspiration with respect to the same model and algorithm. The positive results of the project indicate that the project is highly capable of modelling, predicting and classifying the cardiac dysrhythmias into its respective category. The project hopes to find expression in the medical field and assist the doctors to create an impact in the people’s lives and improve general civil health and well-being.
Research on the Value Core and Practice Path Selection of Curriculum Ideology Based on Education Cloud Platform

The progress of science and technology and the development of computer technology have led us into the information age, and information education has become an inevitable trend in the development of Chinese education. "Curriculum ideology" points to a new concept of ideology, which is an inevitable choice for higher education to realize the whole process and all-round education. The reform of higher vocational curriculum ideology is based on moral education, with the goal of realizing students' all-round growth, emphasizing the organic combination of moral education and intellectual education in the process of education, which is an important measure to solve the "isolated island" dilemma of higher vocational curriculum ideology, build a "great ideology" collaborative education pattern, and train socialist successors who are responsible for national rejuvenation. The emergence of educational cloud platform provides a direction for the reform of the teaching mode of "ideology", and the integration of educational resources is realized by using educational cloud platform to carry out online teaching of "ideology". This paper analyzes the core essence and internal mechanism of "Curriculum ideology" based on educational cloud platform, and puts forward the practical path of synergy effect of curriculum ideology.
Synthesis of four new neuro-statistical tests for testing the hypothesis of independence of small samples of biometric data

Abstract The paper considers the analysis of small samples according to several statistical criteria to test the hypothesis of independence, since the direct calculation of the correlation coefficients using the Pearson formula gives an unacceptably high error on small biometric samples. Each of the classical statistical criteria for testing the hypothesis of independence can be replaced with an equivalent artificial neuron. Neuron training is performed based on the condition of obtaining equal probabilities of errors of the first and second kind. To improve the quality of decisions made, it is necessary to use a variety of statistical criteria, both known and new. It is necessary to form networks of artificial neurons, generalizing the number of artificial neurons that is necessary for practical use. It is shown that the classical formula for calculating the correlation coefficients can be modified with four options. This allows you to create a network of 5 artificial neurons, which is not yet able to reduce the probability of errors in comparison with the classical formula. A gain in the confidence level in the future can only be obtained when using a network of more than 23 artificial neurons, if we apply the simplest code to detect and correct errors.
Assessing the Connectedness between Proof of Work and Proof of Stake/Other Digital Coins

Major cryptocurrencies such as bitcoin and etherium rely on the computationally expensive and energy inefficient Proof of Work (PoW) consensus mechanism to validate transactions and secure their networks. In response to such concerns digital coins that implement more energy efficient algorithms, e.g. Proof of Stake (PoS), have started to grow in popularity and some PoW based coins are planning to switch to PoS. We investigate linkages and transmission of price shocks across fourteen PoW and PoS/Other powered digital assets. PoW cryptocurrencies appear to be more strongly connected within the network of digital coins than are PoS/Other digital currencies. On average PoW coins export more uncertainty to other cryptocurrencies, while assets in both groups import similar levels of risk. PoS/Other cryptocurrency stakeholders need to be aware of the impact that PoW cryptocurrencies can exert on the riskiness of their assets.