Reviewerly/exHarmony huggingface dataset:
Transfer non-stationary texture with complex appearance

Texture transfer has been successfully applied in computer vision and computer graphics. Since non-stationary textures are usually complex and anisotropic, it is challenging to transfer these textures by simple supervised method. In this paper, we propose a general solution for non-stationary texture transfer, which can preserve the local structure and visual richness of textures. The inputs of our framework are source texture and semantic annotation pair. We record different semantics as different regions and obtain the color and distribution information from different regions, which is used to guide the the low-level texture transfer algorithm. Specifically, we exploit these local distributions to regularize the texture transfer objective function, which is minimized by iterative search and voting steps. In the search step, we search the nearest neighbor fields of source image to target image through Generalized PatchMatch (GPM) algorithm. In the voting step, we calculate histogram weights and coherence weights for different semantic regions to ensure color accuracy and texture continuity, and to further transfer the textures from the source to the target. By comparing with state-of-the-art algorithms, we demonstrate the effectiveness and superiority of our technique in various non-stationary textures.
<i>Retracted</i> : Optimizing the convolutional network layers using the Viola–Jones framework and ternary weight networks

In this paper, we have presented a method of combining the weight optimizations from ternary weight networks with a set of custom features inspired by the Viola–Jones Object Detection Framework to improve a network's performance. By replacing the first convolutional layer in a network with a function using a custom set of filters, we can reduce the number of operations required to compute the layer's outputs. These filters are composed of four rectangles, labeled as either positive or negative, within the filter space. When combined with an integral image, these filters allow for more efficient computations of feature values than a standard convolutional layer. When using our custom filters of shape 5 × 5, the layer only requires 64% of the operations that a standard convolutional layer need. This reduction only increases as the filter sizes get larger. Empirically, we have found that these layers' real-world execution times behave as theoretically calculated and that their optimizations do not lead to significant drop inaccuracy. During testing, we evaluated our proposed optimization on two different data sets with two different network architectures.
Effects of Noise on Machine Learning Algorithms Using Local Differential Privacy Techniques

Noise has been used as a way of protecting privacy of users in public datasets for many decades now. Differential privacy is a new standard to add noise, so that user privacy is protected. When this technique is applied for a single end user data, it's called local differential privacy. In this study, we evaluate the effects of adding noise to generate randomized responses on machine learning models. We generate randomized responses using Gaussian, Laplacian noise on singular end user data as well as correlated end user data. Finally, we provide results that we have observed on a few data sets for various machine learning use cases.
Analytical network process method for badminton athlete’s scholarship recommendations

Abstract Pathways admissions line independent achievement sports especially badminton was held in UIN Sunan Gunung Djati Bandung aimed to help athletes who want to proceed to a higher level and change the paradigm of athletes on the importance of continuing education to a higher level in accordance with the specified criteria. The process of input in determining the acceptance still uses old system by Microsoft Excel that sometimes causes confusion. To help determine an independent candidate of badminton accomplishment line, it is then required a decision support system. The Method that can be used for deciding support system by using Analytical Network Process (ANP). Using this method, the calculation was done by doing a comparison between the criteria and alternatives then produce supermatrics, then do the weighting process will determine the athletes who deserve to UIN Sunan Gunung Djati in Bandung. This research used 9 prospective registrants (alternate) as a sample after doing comparison calculation manually and the application of the method itself then acquired relative error of 0%. So the system has been made is said to be worth used relative error less than 50%.
Globalization, Governance and the Green Economy in Sub-Saharan Africa: Policy Thresholds

This study assesses how globalization modulates the effect of governance on CO2 emissions in sub-Saharan African countries. The empirical evidence is based on Generalized Method of Moments. The minimum level (or negative threshold) of FDI required for it to interact with political stability and contribute towards the green economy is 45% of GDP, while 90% of GDP is the maximum level (or positive threshold) required for trade to complement “voice & accountability” in mitigating CO2 emissions. 76 % of GDP and 80 % of GDP are respectively negative trade thresholds for government effectiveness and economic governance. The corresponding negative trade thresholds for the rule of law, corruption-control and institutional governance are respectively, 230% of GDP, 63.5% of GDP and 106.5% of GDP. Actionable openness policy thresholds are provided to inform policy makers on how governance interacts with globalization to promote the green economy.
Multi user multi keyword searchable encryption scheme supporting user authorization

In the big data environment of cloud computing, searchable encryption enables users to selectively access ciphertext data while improving data security. The existing multi-keyword-based encryption schemes have great shortcomings in terms of too large trapdoors and not supporting dynamic authorization of multiple users. Based on this, this paper proposes a multi-user multi-keyword searchable encryption scheme that supports user authorization. This solution supports the data owner to authorize users who access the data, and realizes that the authorized user sends the request for accessing the data to the data owner. Finally, the data owner generates a trapdoor and sends it to the server, and the server sends the result to the user after retrieval. In addition, this solution supports multi-users to dynamically add or withdraw users to ensure data security. This scheme proves that IND-CKA is safe and has the security of keyword trapdoor.
Deep Learning Based Search and Recommendation of Literature in Wanfang Knowledge Base

Classifying Chinese text is a hot but difficult topic in natural language processing field. In this work, we aim to quickly recommend the required documents for users via searching keywords in a massive literature database. We use "Wanfang Data Knowledge Service Platform Journal Document User Behavior Log Data" as the object to research and build a text classifier based on deep learning techniques. It is proposed to perform data cleaning and data extraction for user browsing behavior logs and user download behavior logs. Firstly, Chinese word segmentation and keyword extraction are carried out on the document title information, and the keyword data set and document keyword data set based on the document name are constructed. Finally, using the traditional deep learning model and convolutional neural network model to build a text classifier for training and classification, as a model for users to search for keywords recommended for literature. Experimental results show that the constructed model is able to effectively classify and recommend documents with the users' search keywords, and extract keywords from document names in the construction model. The proposed model TEXTCNN V2 obtains a value for accuracy, precision and F-1 score is 0.9132, 0.9220 and 0.9154, respectively.
Analysis of Intelligent application software testing and evaluation technology

Aiming at the problem of intelligent application software evaluation, this paper analyzes several common defect location technologies: defect location technology based on coverage, defect location technology based on program slice, defect location technology based on model and algorithm evaluation method. Aiming at the deep learning algorithm model based on convolutional neural network commonly used in intelligent application software, the corresponding test method is proposed. This paper compares the advantages of intelligent application software testing with traditional software testing, and discusses the challenges and development direction of intelligent application software testing in the future.
A Video Analyzing Method for Competitive Rifle Shooting

In this paper, we propose a video analysis method for scientific training of rifle shooting. Rifle target shooting is one of the Olympic games, it is a competition aiming at a 45.5mm target of 10m away. Due to the small number of players, computer-based training assistance has not been provided. When trying to analyze the movement of the rifle shooter's form using video analysis, unnecessary movements are detected as noises. If the noise is removed by the conventional video processing filters, the feature points that are originally required will be removed also. In this paper, we propose a noise removal method using machine learning. Experimental results show that the proposed method has an accuracy of about 90%.
DSN: A Fast Stereo Disparity Estimation Network based on Deformable Convolution

In recent years, the stereo matching method based on the convolutional neural network has been greatly developed and achieved accurate disparity estimation results. However, the high precision stereo disparity estimation method is often slow in reasoning, so it cannot meet the requirements of real-time scene. Moreover, this type of method has a large number of parameters and is therefore not friendly to resource-constrained embedded devices. In this paper, a novel 2D cost aggregation module based on deformable convolution is constructed based on AnyNet, and a lightweight stereo matching network named DSN is constructed accordingly. We measured performance on SceneFlow and KTTTI2015 dataset. Experiments show that our method achieves the approximate inference time and parameter quantity of AnyNet, and the accuracy of disparity estimation is much better than AnyNet, achieving a better tradeoff between performance and time.
An Approach to Supporting Militia Collaboration in Disaster Relief Through a Digital Environment

The continuously increasing frequency of natural disasters makes an efficient organization of relief missions an important target for public safety. In Austria, the regular armed forces, militia units as their reserve, and different civil first responder organizations support such missions. All of them represent important stakeholders for the restoration of public safety. Disaster relief missions need to fulfill demanding requirements and high standards, especially when considering (military) information security. Several digital processes during and after such operations already satisfy these norms, whereas preparatory ones still require improvement. The literature survey included in this paper is therefore intended to provide the foundation for further digitalization of disaster recovery operations by supporting collaboration between a variety of stakeholders. A typical collaboration stretches over several dimensions. This paper focuses on discussing the dimension communication support. The literature survey identifies the requirements, and is followed by a description of a prototype. Interested organizations can readily adopt or adapt the presented results for disaster recovery missions in Austria.
System Component-Level Self-Adaptations for Security via Bayesian Games

Security attacks present unique challenges to self-adaptive system design due to the adversarial nature of the environment. However, modeling the system as a single player, as done in prior works in security domain, is insufficient for the system under partial compromise and for the design of fine-grained defensive strategies where the rest of the system with autonomy can cooperate to mitigate the impact of attacks. To deal with such issues, we propose a new self-adaptive framework incorporating Bayesian game and model the defender (i.e., the system) at the granularity of components in system architecture. The system architecture model is translated into a Bayesian multi-player game, where each component is modeled as an independent player while security attacks are encoded as variant types for the components. The defensive strategy for the system is dynamically computed by solving the pure equilibrium to achieve the best possible system utility, improving the resiliency of the system against security attacks.
Design and Implementation of Remote Multimedia Physical Education Teaching System Based on Mobile Terminal

With the rapid development of modern information technology, the development of Internet technology has deeply influenced people's life style and economic activities, and has also changed the traditional education mode. College physical education informatization is a teaching method that combines Internet technology with college physical education. It has the advantages of high efficiency, intelligence and versatility, and can quickly transmit physical education information to students through the network. Smart classroom makes full use of the network education platform, integrates high-quality educational information resources, extends the course teaching from in-class to out-of-class, and changes the traditional passive learning into independent inquiry learning, which fundamentally changes the learning style. Under the background of diversified information technology, our education has been fully developed. This paper analyzes the influence of mobile learning on college physical education and the application of digital resources in college physical education, so as to explore the design and implementation method of remote multimedia physical education system based on mobile terminal.
Research on the Construction of Library Chinese Information Resources Based on Citation Analysis

This paper uses the citation analysis method to analyze the Chinese citations of the Chinese core journal articles published by the personnel of the unit in the past ten years, revealing the protection of different types of Chinese collection resources, in order to provide corresponding references for the construction of library resources.
Position-Aware Safe Boundary Interpolation Oversampling

The class imbalance problem is characterized by the unequal distribution of different class samples, usually resulting in a learning bias toward the majority class. In the past decades, kinds of techniques have been proposed to alleviate this problem. Among those approaches, one promising method, interpolation-based oversampling, proposes to generate synthetic minority samples based on selected reference data, which can effectively solve the skewed distribution of data samples. However, there are several unsolved issues in interpolation-based oversampling. Existing methods often suffer from noisy synthetic samples due to improper data clustering and unsatisfactory reference selection. In this paper, we propose the position-aware safe boundary interpolation oversampling algorithm (PABIO) to address such issues. We firstly introduce a combined clustering algorithm for minority samples to overcome the shortage of clustering methods which are only distance-based or density-based. Then a position-aware interpolation-based oversampling algorithm is proposed for different minority clusters. Especially, we develop a novel method to leverage the majority class information to learn a safe boundary for generating synthetic points. The proposed PABIO is evaluated on multiple imbalanced datasets classified by two base classifiers: support vector machine (SVM) and C4.5 decision tree classifier. Experimental results show that our proposed PABIO outperforms other baselines among benchmark datasets.
Using Data mining for GEDCO customers' data to increase the collection of electricity bills in Gaza Strip

The Gaza Governorate Electricity Distribution Company (GEDCO) is an economic service company operating with a special financial liability and owned by the Palestinian National Authority and local authorities.The unpaid bills are one of the company's biggest operating obstacles that obstructs money processing. The ever-increasing number of unpaid invoices has caused constant problems with the company's cash flow.In this paper, Data mining techniques and neural networks techniques were implemented over the data warehouses of GEDCO. Decision tree machine learning algorithm is used to classify the customers according to their commitment to payment, as well as to extract the categories for each class of customers. The neural networks are used to train the machine to extract a threshold for consumption and amperes. The threshold extracted for each subscriber according to his history of consumption and payment, to help him stay committed to payment.The results concluded that consumption should not exceed some value, based on the address of the subscribers, the number of amperes and the history of payment.The study that will be recommended to GEDCO, should improve their data warehouses to include more features and categories of their databases especially the subscribers’ occupations and level of income.
Cryptocurrencies as Pension Fund Components: Smart Move or Drinking the Kool-Aid?

This study investigates whether cryptocurrencies can be considered a viable addition to pension funds. Using the regulatory setting of Switzerland, it is assessed whether adding crypto-components to a standard pension fund portfolio has positive effects on the fund’s risk and return figures. The empirical data supports the notion that cryptocurrency components may well increase the yield of a pension fund portfolio, yet this enhancement of yield comes at slightly higher risk levels. This increase in risk can be mitigated by adding an actively managed crypto-component to the portfolio rather than a passive investment product. The paper contributes to the ongoing debate in the area of financial innovations on the purpose and solidity of cryptocurrencies as an asset class.
Tracking Mixed Bitcoins

Mixer services purportedly remove all connections between the input (deposited) Bitcoins and the output (withdrawn) mixed Bitcoins, seemingly rendering taint analysis tracking ineffectual. In this paper, we introduce and explore a novel tracking strategy, called Address Taint Analysis, that adapts from existing transaction-based taint analysis techniques for tracking Bitcoins that have passed through a mixer service. We also investigate the potential of combining address taint analysis with address clustering and backward tainting. We further introduce a set of filtering criteria that reduce the number of false-positive results based on the characteristics of withdrawn transactions and evaluate our solution with verifiable mixing transactions of nine mixer services from previous reverse-engineering studies. Our finding shows that it is possible to track the mixed Bitcoins from the deposited Bitcoins using address taint analysis and the number of potential transaction outputs can be significantly reduced with the filtering criteria.
Focusing on the "14th Five-Year Plan"’: Analysis of Some Problems of Teachers’ Ethics in Physical Education Teachers Based on Data Visualization

2020 is not only the closing year of the 13th Five-Year Plan (2016-2020), but also the year of the 14th Five-Year Plan. This paper uses the methods of literature, mathematical statistics and other research methods to sort out the relevant research results of "physical education teachers' ethics" during the 13th Five-Year Plan, and summarizes the common problems existing in the teachers' ethics of physical education in China. The results show that there are some problems such as lack of sense of mission, lack of professional self-identity, lack of humanistic care, weak sense of lifelong learning, lack of rigorous teaching and poor academic consciousness.
Object-oriented methods of analyzing the parameters of interaction of speech and music language

The article considers the methodological principles of analyzing the parameters of interaction of speech and music language in the context of object-oriented methods of Astafiev&amp;rsquo;s intonation theory, Yavorsky&amp;rsquo;s concept of music speech, the views on and ideas about the intonation basis of speech of Bonfeld, Medushevsky, Aranovsky and Nazaikinsky, and the new system of interaction of speech and music in the works of Helmut Lachenmann and&amp;nbsp; Luciano Berio. Music language and speech are the phenomena allowing for various intersections, complementarities and mutual interferences. These factors manifest themselves in various aspects spotlighting particular sides of these parameters characterizing speech and music language. Since there&amp;rsquo;s no unified methodology for analyzing these parameters at the moment, it is reasonable to study a music composition as a text (in a broad sense) using various methods of analysis. The author suggests considering the parameters of interaction of speech and music language in terms of object-oriented methods. In the works of composers, music and speech have different forms of interaction and, consequently, need to be studied via different analytical approaches. Comprehensive consideration of a piece of music as a text (by means of various approaches) allows considering it as an intercultural universum representing the code of the epoch. Based on the peculiarities of interpretation of voice as an instrument, and speech as music, and the opportunities it gives, the author formulates the classification of creative methods and principles and the system of analysis which can be used in musicology.&amp;nbsp; &amp;nbsp;
GazeWheels: Recommendations for using wheel widgets for feedback during dwell-time gaze input

Abstract We present GazeWheels: a series of visual feedback methods for dwell-based gaze input in the form of a wheel that is filled gradually until target selection. We evaluate three variations: Resetting, Pause &amp; Resume and Infinite GazeWheel, and study how dwell duration and visual feedback position (co-located vs remote) impact performance. Findings from a user study (N = 19) show that Infinite and Pause &amp; Resume GazeWheels are error prone but significantly faster than Resetting GazeWheel even when including error correction time. We conclude with five design recommendations.
Elliptical Shape Recovery from Blurred Pixels Using Deep Learning

In this paper, we study the problem of ellipse recovery from blurred shape images. A shape image is a continuous-domain black and white (binary-valued) image in which the points of the same color form a shape. We assume to have a digitized version of the shape image which is a sampled and blurred version of the image using a 2D kernel (the point spread function); the resulting pixels may also be corrupted by additive noise. Our goal in this work is to recover the original continuous-domain image based on the available pixels when the shape image is an ellipse. Our approach is to represent an ellipse as the zero-level-set of a bivariate polynomial of degree 2 and estimate the involved 6 polynomial coefficients based on a deep neural network. Our model is trained end to end on a wide range of blurring setups with varying noise levels. Besides, the network is trained to recover the ellipse even when the available noisy pixels cover only a part of the ellipse. Simulation results validate the performance of the proposed method and indicate its superiority compared to the state of art methods.