Reviewerly/exHarmony huggingface dataset:
Image Colorization Based on Texture by Using of CNN

Colorization is a challenging task of adding colors to a given grayscale image. Related palettes can keep better consistency of color in an image and provide sharper results. This paper presents a novel technique that based on input's uniform LBP texture to generate multiple color palettes, while other methods based on reference images, words, or other subjective elements. By combing the generated color palettes, a given grayscale image can be colorized. The proposed Texture2Color model consists of two convolutional neural networks: the texture-to-palette generation networks and the palette-based colorization networks. The former network adopts the visual features and texture of the given gray image to generate colorful palettes. The latter one blends the generated palettes and grayscale images to get a plausible colorful image. Compared with the basic palette generation benchmark, the proposed method can provide a more realistic palette and achieve better performance.
Collaborative filtering recommendation algorithm based on variational inference

Purpose The purpose of this paper is to alleviate the problem of poor robustness and over-fitting caused by large-scale data in collaborative filtering recommendation algorithms. Design/methodology/approach Interpreting user behavior from the probabilistic perspective of hidden variables is helpful to improve robustness and over-fitting problems. Constructing a recommendation network by variational inference can effectively solve the complex distribution calculation in the probabilistic recommendation model. Based on the aforementioned analysis, this paper uses variational auto-encoder to construct a generating network, which can restore user-rating data to solve the problem of poor robustness and over-fitting caused by large-scale data. Meanwhile, for the existing KL-vanishing problem in the variational inference deep learning model, this paper optimizes the model by the KL annealing and Free Bits methods. Findings The effect of the basic model is considerably improved after using the KL annealing or Free Bits method to solve KL vanishing. The proposed models evidently perform worse than competitors on small data sets, such as MovieLens 1 M. By contrast, they have better effects on large data sets such as MovieLens 10 M and MovieLens 20 M. Originality/value This paper presents the usage of the variational inference model for collaborative filtering recommendation and introduces the KL annealing and Free Bits methods to improve the basic model effect. Because the variational inference training denotes the probability distribution of the hidden vector, the problem of poor robustness and overfitting is alleviated. When the amount of data is relatively large in the actual application scenario, the probability distribution of the fitted actual data can better represent the user and the item. Therefore, using variational inference for collaborative filtering recommendation is of practical value.
Data Integration: A Real-Time, Participant-Driven, and Visually Supported Method

We introduce a method in which instant data visualization facilitates real-time data integration and involves participants in data interpretation. The results of quantitative research (e.g., electronic card sorting) are represented visually (e.g., in a dendrogram) and fed back to research participants in follow-up focus group conversations. The visualized quantitative results are reviewed and discussed by participants. The visual display of the quantitative results is annotated with qualitative feedback generated by participants that explains, enriches, or challenges the quantitative results. We apply our method in a card sorting study of Fédération Internationale de Football Association’s (FIFA) stakeholders. An approach that facilitates real-time data integration that is participant driven and visually supported is the unique contribution of this article to mixed methods research.
Horizontal gene transfer for recombining graphs

Abstract We introduce a form of neutral horizontal gene transfer (HGT) to evolving graphs by graph programming (EGGP). We introduce the $$\mu \times \lambda$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>μ</mml:mi><mml:mo>×</mml:mo><mml:mi>λ</mml:mi></mml:mrow></mml:math> evolutionary algorithm (EA), where $$\mu$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>μ</mml:mi></mml:math> parents each produce $$\lambda$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>λ</mml:mi></mml:math> children who compete only with their parents. HGT events then copy the entire active component of one surviving parent into the inactive component of another parent, exchanging genetic information without reproduction. Experimental results from symbolic regression problems show that the introduction of the $$\mu \times \lambda$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>μ</mml:mi><mml:mo>×</mml:mo><mml:mi>λ</mml:mi></mml:mrow></mml:math> EA and HGT events improve the performance of EGGP. Comparisons with genetic programming and Cartesian genetic programming strongly favour our proposed approach. We also investigate the effect of using HGT events in neuroevolution tasks. We again find that the introduction of HGT improves the performance of EGGP, demonstrating that HGT is an effective cross-domain mechanism for recombining graphs.
Authorship verification of opinion articles in online newspapers using the idiolect of author: a comparative study

Many approaches have been introduced to solve the authorship verification problem, including the use of machine learning techniques. These techniques proved to be effective in detecting a person’s distinctive way of speaking or writing. The main aim of this study was to show that every writer has an idiolect which is presented through the use of several types of stylometric features unique to individual authors. For this purpose, 120 online opinion articles written by non-native speakers of English were chosen from four newspapers published in the Arab world, while 145 articles written by native speakers of English were taken from other four newspapers. All of these articles were classified and compared using the SMO and MLP algorithms via a tool called ‘JStylo’. The proposed framework achieved a competitive performance with an accuracy of 80% using the SMO classifier. The results of the study indicate that each author has an individual style of writing (idiolect), and that idiolect is not shown by one group of writers better than the other, namely the native and non-native authors.
Autonomous System for a Racing Quadcopter

In this paper, we present a methodology to make an autonomous drone fly through a sequence of gates only with on-board sensors. Our work is a solution to the AlphaPilot Challenge, proposed by the Lookheed Martin Company and the Drone Racing League. First, we propose a strategy to generate a smooth trajectory that passes through the gates. Then, we develop a localization system, which merges image data from an on-board camera with IMU data. Finally, we present an artificial vector field based strategy used to control the quadcopter. Our results are validated with simulations in the official simulator of the competition and with preliminary experiments with a real drone.
Serious Game for Blockchain Education Purposes (using Proof-of-Work Consensus of Bitcoin)

The blockchain-based system is widely being studied and implemented for dealing with various real problems from securing money transactions, data sharing in healthcare, and supply chain in businesses. In this study, we study a conceptual framework of a serious game for teaching how the mining process occurs in the blockchain using the proof-of-work consensus. The purpose of this game is to educate blockchain newcomers or developers the importance of mining within a short time, how the consensus works, the increasing problem complexity, and how coins can be obtained as the result of the mining. We believe that this conceptual game can contribute to the development of blockchain technology in general.
Integrating Domain Ontologies in an MDA-Based Development Process of e-Health Management Systems at the CIM Level

We all know that the development of e-Health management applications goes through several steps, as hence, several stakeholders are involved in the development of these applications. Therefore, it is recommended to define a common vocabulary to facilitate the understanding of the proposed business processes and requirements between these stakeholders in the early stages of this process. On the other hand, the model driven architecture (MDA) is defined by the object management group (OMG) in order to enhance the development process of applications. This architecture is based on three models: CIM, PIM, and PSM; covering all stages of the software development life cycle. The CIM is the first level in this process, which represents a computation independent model. It describes the basic business and requirements analysis, as well as the application domain model. In light of this level, the MDA based approach is presented in this article integrating domain ontologies as CIM model using ODM standard. The process starts with the ODM model representing an eHealth domain ontology, then we transform it into a Class diagram by applying the QVTo transformation engine.
Discriminative Neural Network for Hero Selection in Professional <i>Heroes of the Storm</i> and <i>DOTA 2</i>

Multiplayer online battle arena (MOBAs) games are one of the most popular types of online games. Annual tournaments draw large online viewership and reward the winning teams with large monetary prizes. Character selection prior to the start of the game (draft) plays a major role in the way the game is played and can give a large advantage to either team. Hence, professional teams try to maximize their winning chances by selecting the optimal team composition to counter their opponents. However, drafting is a complex process that requires deep game knowledge and preparation, which makes it stressful and error-prone. In this article, we present an automatic drafter system based on the suggestions of a discriminative neural network and evaluate how it performs on the MOBAs <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Heroes of the Storm (HotS)</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DOTA 2</i> . We propose a method to appropriately exploit very heterogeneous data sets that aggregates data from various versions of the games. Drafter testing on professional games shows that the actual selected hero was present in the top three determined by our drafting tool 30.4% of the time for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HotS</i> and 17.6% for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DOTA 2</i> . The performance obtained by this method exceeds all previously reported results.
The Arab Image in Spanish Social Media: A Twitter Sentiment Analytics Approach

Intercultural relations scholars have recently focused on investigating the impact of the news coverage and the media on society in general. However, although social media have become a valuable resource for mining opinions, there are no previous studies investigating the Arab image sentiments expressed on social media. This study fills this gap through the analysis of a random sample of 26,905 tweets dealing with the Arab image in Spanish tweets. To conduct the analysis, an expert-predefined lexicon of around 6,800 seed adjectives was used. Descriptive analysis detected a generally positive sentiment towards the Arab image while partitioning around medoids (PAM) clustering suggested that it is possible to cluster the tweets into four distinct clusters. Thus, it seems that the Arab image represents a complex construction. By analysing opinions expressed on social media towards the Arab image, this research adds breadth and depth to the debate over such an under-represented area.
Comparison of Priori and FP-Growth Algorithms in Determining Association Rules

Collection of transaction data on the sale of goods of a minimarket, which is increasing every day, is often treated only as a record, causing data to accumulate in a database. Data mining can help the decision-making process quickly, making it possible to manage the information contained in transaction data into new knowledge. The purpose of this study was to analyze the data of sales transactions of goods by comparing the Apriori algorithm and the FP-Growth algorithm, to determine the association rules based on consumer purchase patterns with association techniques that seek several frequent itemsets and proceed with the establishment of Association Rules. The research utilizes primary data in the form of sales transactions from February to March 2018. The results of analyzing goods sales transaction data using Apriori algorithm and FP-Growth algorithm by setting a minimum support value of 4% and a minimum value of confidence of 19% is to produce a number of rules different associations where the Apriori algorithm produces 11 rules while FP-Growth produces 10 rules but has the final association value (the same and the execution time required by the FP-Growth algorithm is faster with 0.5 seconds than the Apriori algorithm which takes 0.6 seconds. This information very useful for managing the layout of items close together and allows designing marketing concepts to also provide stock.
Deep code operation network for multi-label image retrieval

Deep hashing methods have been extensively studied for large-scale image search and achieved promising results in recent years. However, there are two major limitations of previous deep hashing methods for multi-label image retrieval: the first one concerns the flexibility for users to express their query intention (so-called the intention gap), and the second one concerns the exploitation of rich similarity structures of the semantic space (so-called the semantic gap). To address these issues, we propose a novel Deep Code Operation Network (CoNet), in which a user is allowed to simultaneously present multiple images instead of a single one as his/her query, and then the system triggers a series of code operators to extract the hidden relations among them. In this way, a set of new queries are automatically constructed to cover users’ real complex query intention, without the need of explicitly stating them. The CoNet is trained with a newly proposed margin-adaptive triplet loss function, which effectively encourages the system to incorporate the hierarchical similarity structures of the semantic space into the learning procedure of the code operations. The whole system has an end-to-end differentiable architecture, equipped with an adversarial mechanism to further improve the quality of the final intention representation. Experimental results on four multi-label image datasets demonstrate that our method significantly improves the state-of-the-art in performing complex multi-label retrieval tasks with multiple query images.
Design Log Management System of Computer Network Devices Infrastructures Based on ELK Stack

Device monitoring is an important thing to manage networks. Information-related network state or condition can be gathered through the device monitoring for administrators to take decisions regarding occurred events. Logs can be useful information to monitor network devices. Network administrator of Diponegoro University needs a centralized the logs, so that can receive, manage, and analyze logs. This research identifies functional requirements of the log management system. DSR Method was used to design topology and software of the log management system. The next step is implementation of the topology, software, and application. The last step is testing the system and log management application. The results show that collecting centralized logs and processing these logs into information in the form of dashboards using ELK Stack application successfully implemented. The dashboard resulted by ELK Stack Application will be implemented on the web application using PHP programming language and Code Igniter framework. The test results show that system can receive logs and group the log according to the device location and the severity level of the log.
Automatic Pointer Meter Reading Based on Machine Vision

Pointer meters in power systems are wildly used. Although smart meters gradually replace pointer meters to conveniently acquire measurement, digitalization of some special meters have not been available. The paper presents a new method based on machine vision to detect pointer meter reading automatically. Two phases of training and testing are proposed in this method. In the training phase, a template of meter feature points on a training image is extracted. In the testing phase, the template matching performs on a testing image to find feature points. Then, an elliptic arc fitting based on the feature points to generate a 1-D signal. Finally, the 1-D signal is used to locate the pointer and the reading result is obtained. Differing with existing methods, the proposed method performs effectively and efficiently automatic meter reading without using Hough transformation.
Fake Currency Detection

Fake currency notes are increasing day by day, in order to overcome this we proposes a very helpful and efficient system to detect the fake currency. For detecting the fake currency note is done by counting the number of interruptions in the thread line. For predicting the note is real or fake on the basis of number of interruptions. If the number of interruption is zero, if it is real note otherwise it is fake. And also we calculate the entropy of the currency notes for the efficient detection of fake currency note. MATLAB software is used to detect the fake currency note.
Path Planning for a Multi-robot System with Decentralized Control Architecture

In this chapter we have studied the path planning problem of a group of autonomous Wheeled Mobile Robots in a very dynamic workspace. The work focuses on the combination of artificial potential field approach with the decentralized architecture to coordinate the movements of robots. The technique implemented is adapted to solve the path planning problem for a multi-robot system. So, the problem of coordination of robots at the intersection zone is solved by the use of the priority allocation method. We also used the neighborhood detection technique to reduce the influence area of each robot and to optimize the time of calculation. The solution is tested and simulated with Matlab/Simulink.
Small Clues Tell: a Collaborative Expansion Approach for Effective Content-Based Recommendations

Content-based recommendation techniques usually require a large number of training examples for model construction, which however may not always be available in many real-world scenarios. To address the training data availability constraint common to the content-based approach, we develop a collaborative expansion-based approach to expand the size of training examples, which could lead to improved content-based recommendations. We use a book rating data set collected from Amazon to evaluate our proposed method and compare its performance against those of two salient benchmark techniques. The results show that our method outperforms the benchmark techniques consistently and significantly. Our method expands the size of training examples for a focal customer by leveraging the available preferences of his or her referent group, and thereby better supports personalized recommendations than existing techniques that solely follow content-based or collaborative filtering, without incurring costs to identify, collect, and analyze additional information. This study reveals the value and feasibility of collaborative expansion as a viable means to increase training size for the focal customer and thus address the training data availability constraint that seriously hinders the performance of content-based recommender systems.
AccUDNN: A GPU Memory Efficient Accelerator for Training Ultra-Deep Neural Networks

With the implementation of mainstream DL frameworks, scarce GPU memory resource is the primary bottleneck that hinders the trainability and training efficiency of ultra-deep neural networks (UDNN). Prior memory optimization works focus on removing the trainability restriction but leave the training efficiency out of consideration. To fill the gap, we present "AccUDNN", an accelerator that aims to make full use of finite GPU memory resource to speed up the training process of UDNN in this paper. AccUDNN mainly includes two modules: memory optimizer and hyperparameter tuner. Memory optimizer develops a novel performance-model guided dynamic swap out/in strategy to meet trainability first and further remedy the efficiency degradation in other swapping strategies. Then, a hyperparameter tuner is designed to explore the efficiency-optimal minibatch size and the matched learning rate after applying the dynamic swapping strategy. Evaluations demonstrate that AccUDNN cuts down the GPU memory requirement of ResNet-152 from more than 24GB to 8GB. In turn, given 12GB GPU memory budget, the efficiency-optimal minibatch size can reach 4.2x larger than Caffe and finally improve the scaling efficiency (speedup) of 8 GPUs' cluster by 1.9x.
Neural Networks for Cryptocurrency Evaluation and Price Fluctuation Forecasting

Today, there is a growing number of digital assets, often built on questionable technical foundations. We design and implement neural networks in order to explore different aspects of a cryptocurrency affecting its performance, its stability as well as its daily price fluctuation. One characteristic feature of our approach is that we aim at a holistic view that would integrate all available information: First, financial information, including market capitalization and historical daily prices. Second, features related to the underlying blockchain from blockchain explorers like network activity: blockchains handle the supply and demand of a cryptocurrency. Lastly, we integrate software development metrics based on GitHub activity by the supporting team. We set two goals: First, to classify a given cryptocurrency by its performance, where stability and price increase are the positive features. Second, to forecast daily price tendency through regression; this is of course a well-studied problem. A related third goal is to determine the most relevant features for such analysis. We compare various neural networks using most of the widely traded digital currencies (e.g. Bitcoin, Ethereum and Litecoin) in both classification and regression settings. Simple Feedforward neural networks are considered, as well as Recurrent neural networks (RNN) along with their improvements, namely Long Short-Term Memory and Gated Recurrent Units. The results of our comparative analysis indicate that RNNs provide the most promising results.
Applying Blockchain Technology to Develop Cross-Domain Digital Talent

To decrease the talent shortage in the digital economy industry and fill the gap between academics and industry, this study establishes an innovative model to cultivate interdisciplinary talent that links the resources in the academic and industrial fields. To monitor and manage the learning performance and training history throughout the process, this study creates a Talent Management System (TMS), which uses blockchain technology to record 698 interns' data on their online learning history, project outcomes, and certification of completion. With TMS, research institutions, schools, and corporations can access the digital resumes of interns more efficiently, thus reducing the cost of management and recruiting and ensuring the authentication of resumes.