Reviewerly/exHarmony huggingface dataset:
On Clones and Comments in Production and Test Classes: An Empirical Study

While many OO class features have been explored in detail, little work in the past has explored the characteristics of test classes. In this exploratory paper, we investigate traits of these classes which we believe demonstrate distinct differences between test classes and production classes. We explore differences between the number of clones, the number of comments and the maintainability of each class type. We believe that developers unjustifiably treat test classes as “second-class citizens” and we would therefore expect test classes to contain a) far more clones, b) a far lower (i.e., poorer) comment density and, c) worse maintainability levels. Six open-source systems were used as a basis of the work. Results showed that all three suppositions were indeed supported; this suggests the need to explore test classes in more detail and to remedy this clear problem that contributes to the level of technical debt.
Deep Separation of Direct and Global Components from a Single Photograph under Structured Lighting

Abstract We present a deep learning based solution for separating the direct and global light transport components from a single photograph captured under high frequency structured lighting with a co‐axial projector‐camera setup. We employ an architecture with one encoder and two decoders that shares information between the encoder and the decoders, as well as between both decoders to ensure a consistent decomposition between both light transport components. Furthermore, our deep learning separation approach does not require binary structured illumination, allowing us to utilize the full resolution capabilities of the projector. Consequently, our deep separation network is able to achieve high fidelity decompositions for lighting frequency sensitive features such as subsurface scattering and specular reflections. We evaluate and demonstrate our direct and global separation method on a wide variety of synthetic and captured scenes.
Adversarially-learned Image Transfer Model for Multi-content Disentanglement

This paper discusses the multi-content disentanglement issue in unsupervised image transfer model. Image transfer based on generative model such as VAE1 or GAN2 can be defined as mapping data from source domain to target domain. Existing disentanglement methods have focused on separating elements of latent vector to distinguish content and style information from an image. However, since it has focused on extracting information from all pixels, it is hard to perform image transfer while controlling specific contents. To solve this problem, image transfer which is able to control a specific content disentanglement has been suggested recently. In this paper, by adapting the disentanglement concept to control various specific contents in a image, we propose a suitable architecture for image transfer task such as adding or subtracting multiple contents. In addition, we also propose an adversarially-learned auxiliary discriminator to further improve the quality of synthesized images from the multi-content disentanglement method. Based on the proposed method, we can generate images by controlling two contents from the CelebA dataset, and prove that we can attach specific content more clearly with auxiliary discriminator.
Facebook Debate on Sea Watch 3 Case: Detecting Offensive Language Through Automatic Topic Mining Techniques

Tontodimamma, Alice Del Gobbo, Emiliano Russo, Vanessa Sarra, Annalina Fontanella, LaraOver the years, there has been growing concern about the disproportionate use of hate speech on social media platforms. In this paper, we present a text analysis for detecting abusive language in Italian messages on Facebook, surrounding the debate over the migrant-rescue ship, Sea Watch 3, and its captain Carola Rackete. The study data consists of more than 130,000 posts retrieved from two pages relating to Matteo Salvini, the leader of the Italian Lega political party, and from the official Facebook pages of five Italian newspapers. To explore the presence of offensive and hatred expressions in the corpus and to establish to what extent social users’ language differs, depending on the type of Facebook pages analysed, we ran a topic model based on Latent Dirichlet Allocation. We have complemented this approach with tools from semantic network analysis.
An Adaptive Normalized Google Distance Similarity Measure for Extractive Text Summarization

No doubt, for each clustering algorithm running improper similarity calculation, that can lead to reduce the clustering accuracy. Hence, several applications that employ such algorithm are affected negatively and generate improper results. In a previous work, we found that employing the Normalized Google Distance (NGD) similarity measure to cluster document's sentences for text summarization problem is unreasonable; since NGD was basically designed to work with large databases. On the other hand, a term-weighting approach is used widely to define document's contents. In this paper, a term-weighting approach is integrated with the NGD similarity measure to adopt the latter from being able to work in small database (single document). Differential Evolution (DE) algorithm is used to train and test the proposed method. The DUC2002 dataset is preprocessed and used as a test bed. The results showed that our proposed method could outperform the previous work in terms of F-score evaluation measure as well as outperformed the standard baseline methods Microsoft Word and Copernic Summarizer.
Automated Machine Learning using Evolutionary Algorithms

As the global data quantity already follows an exponential trend, machine learning has become present in every application, creating a great demand for general knowhow, be it data scientists or computer scientists with related knowledge. Currently, the demand for work to be done surpasses the offer of such professionals, thus automatic solutions have to be found. The classical machine learning process involves data engineering, model selection, and hyperparameter tuning for the chosen model. Due to the highly repetitive nature of trial and error of these tasks, automation can play a big role in optimizing time spent on them. Automated Machine Learning comes to help the process by adding different optimization techniques that help data scientists be more productive and achieve similar or better results in a shorter time. This paper provides a novel approach to Automated Machine Learning using Evolutionary Algorithms and proves its performance by presenting top results in benchmark tests.
Development of Machine Learning Applications for Chemical Objects

Abstract Machine learning is a method used to create programs that can learn from data or applications. In contrast to ordinary static computer programs, machine learning programs are programs designed to be able to learn on their own. Machine learning technology has been widely applied in various fields, including in chemistry. Currently the introduction of basic chemical objects is a study that has many concepts that are easy for beginners to understand and imagine, such as atomic structure and molecular shape in chemical bonds. So that an alternative learning media is needed in order to improve understanding of the concept of chemical objects interactively. Machine learning technology is expected to provide a more realistic interaction and is an advancement of a promising technological method that can motivate users to engage in a more active learning system.
An Analysis and Measurement of Website Quality Using WebQual 4.0 and Importance Performance Analysis (IPA) Method (A Case Study of Kemiriamba Village Brebes)

Abstract Based on the Law of the Republic of Indonesia number 6/2016 regarding villages, it is said that regional governments are obliged to develop village information systems in implementing e-government towards good governance. In Indonesia, e-government continues to increase in number, but it is not accompanied by quality, including the Kemiriamba Village website. This study was aimed to measure the quality of the Kemiriamba Village website using the Webqual 4.0 method and Importance Performance Analysis (IPA) with 4 (four) instruments, namely Usability Quality, Information Quality, Service Interaction Quality and Visual Quality. This study is descriptive with a quantitative approach. The questionnaire data were 132 and were processed with SPSS software. The results of the analysis and measurement stated that the suitability level of the Kemiriamba Village website was 96.63%, and the average value of the gap was negative, which was -0.11, which meant that the performance level of the Kemiriamba Village website still did not meet user satisfaction and expectations, especially in Service Interaction Quality. The main priority that needs to be corrected and improved immediately is the website attribute; it should have a good reputation. Moreover, the questions, suggestions and complaints need to be processed as promised. The results of this study suggested the Kemiriamba Village website to immediately make improvements and development in order to become a qualified website towards good governance.
Profit Optimization for Multiuser Video Transmission over Mobile Networks

Abstract With the increasing demand for high quality mobile video, how to receive higher quality video through mobile network with lower payment is a becoming an emerging problem. Existing approaches for Dynamic Adaptive Streaming over HTTP (DASH) face tremendous challenges when applying to video streaming in devices with different resolutions. In this paper, we propose a comprehensive profit optimization model with consideration of the cost of Content Provider, traffic payment, QoE factors and the relationship between device resolution and video quality. For ensuring the optimal profit of Content Provider and End Users at same time, we formulate both of their profit as a hybrid-objective optimization model. By solving the model, the optimum bitrates and the resulting profit can be obtained through our proposed algorithm. The simulation results demonstrate that the proposed method increases the average profit of End Users in 10%-15% compared with existing approaches.
Robotic Arm for Extricate Operation in Bore well

The demand for water is piled up. In order to meet the basic amenities bore wells are dug. Bore wells are left unwrapped after usage leading to increase in number of accidents. The main intention of the paper is to redeem the victim in case of untimely accidents of falling inside the bore well. The system avail in rescuing the child from bore well by the use of Robotic arm controlled by Arduino which includes the additional features such as air quality sensor to detect the toxic gases, temperature sensors which is used to measure the temperature and a miniature camera that is used to monitor the live status of the child inside the bore well. The proposed system will easily rescue the child without major injury.
ON PROBLEMS OF -CONNECTED GRAPHS FOR

Abstract A connected graph G is $\mathcal {CF}$ -connected if there is a path between every pair of vertices with no crossing on its edges for each optimal drawing of G . We conjecture that a complete bipartite graph $K_{m,n}$ is $\mathcal {CF}$ -connected if and only if it does not contain a subgraph of $K_{3,6}$ or $K_{4,4}$ . We establish the validity of this conjecture for all complete bipartite graphs $K_{m,n}$ for any $m,n$ with $\min \{m,n\}\leq 6$ , and conditionally for $m,n\geq 7$ on the assumption of Zarankiewicz’s conjecture that $\mathrm {cr}(K_{m,n})=\big \lfloor \frac {m}{2} \big \rfloor \big \lfloor \frac {m-1}{2} \big \rfloor \big \lfloor \frac {n}{2} \big \rfloor \big \lfloor \frac {n-1}{2} \big \rfloor $ .
Research On The Method Of Combining Educational Resources Under The Background Of Internet+

The traditional educational reform resource merging method directly mines relevant resources based on keywords and then further processes the resources, resulting in excessively high repetition rate and incomplete rate of merging resources. Aiming at the above problems, the method of combining educational reform resources under the background of Internet+ is proposed. Use web crawlers to collect educational reform resources in the Internet, and transfer the crawled resource metadata to the server under the SOAP transmission mechanism. Use the hash function to delete the duplicate resource metadata and establish the objective function of education reform resource allocation. Solve the objective function, get the optimal resource allocation plan, and complete the research on the method of resource reform in education reform. The experimental results show that, compared with the traditional resource structuring method, after applying the proposed education reform resource structuring method to process resources, there are no incomplete resources and the proportion of duplicate resources is at most 3.7%, which can effectively improve the effect of education reform and is feasible.
Efficient Simulation of Loop Quantum Gravity — A Scalable Linear-Optical Approach

We unite loop quantum gravity (LQG) with linear-optical quantum simulation, by designing linear-optical quantum circuits which mimic scattering amplitudes of LQG. Our design efficiently computes LQG amplitudes, which are classically intractable.
Comparative visual gaze analysis for virtual board games

We introduce an approach for the visual analysis of eye movement data from two people playing competitive virtual board games. Our approach provides methods to temporally synchronize and spatially register gaze and mouse recordings from two eye tracking devices. Analysts can examine such fused data visually with a combination of techniques: attention maps and gaze plots as well as a temporal summary of the distance between gaze positions and mouse events of the two players. We show different game scenarios from the competitive game Go, which is especially complex for analyzing strategies of individual players, to demonstrate our methods. In general, our visual analysis approach can provide analysts with insights into strategies, learning processes, and means of communication between people.
Research on three-step accelerated gradient algorithm in deep learning

Gradient descent (GD) algorithm is the widely used optimisation method in training machine learning and deep learning models. In this paper, based on GD, Polyak's momentum (PM), and Nesterov accelerated gradient (NAG), we give the convergence of the algorithms from an initial value to the optimal value of an objective function in simple quadratic form. Based on the convergence property of the quadratic function, two sister sequences of NAG's iteration and parallel tangent methods in neural networks, the three-step accelerated gradient (TAG) algorithm is proposed, which has three sequences other than two sister sequences. To illustrate the performance of this algorithm, we compare the proposed algorithm with the three other algorithms in quadratic function, high-dimensional quadratic functions, and nonquadratic function. Then we consider to combine the TAG algorithm to the backpropagation algorithm and the stochastic gradient descent algorithm in deep learning. For conveniently facilitate the proposed algorithms, we rewite the R package ‘neuralnet’ and extend it to ‘supneuralnet’. All kinds of deep learning algorithms in this paper are included in ‘supneuralnet’ package. Finally, we show our algorithms are superior to other algorithms in four case studies.
Research on the oriented teaching method of computer network foundation course

Abstract In recent development of modern education reform, it is imperative to accelerate high-level education structure adjustment, and the trend from local undergraduate universities transformed to the ones which accord application and technology -type development being the general consensus in the industry. These universities took the demanding of application technology as orientation,definited the subject objection and optimized the teaching method, focused on the combination of theory &amp; practice teaching methods.This article is mainly about the computer network technology application application oriented design concept in teaching.Convey useful simplification of theory to the students and they can learn in order to practise.
Motion deblurring method based on Improved DeblurGAN

Generative Adversarial Networks(GANs) is a generation model that learns data distribution through the mutual against between generator network and discriminantor network.It has the advantage of generating clear and sharp samples, and has made progress in the application of image super-resolution and image repair. DeblurGAN solves the problem of end-to-end image deblurring by using conditional Generative Adversarial Networks(cGANs).In order to obtain better deblurring effect, this paper proposes an improvement based on DeblurGAN.Firstly, the method in this paper uses DenseBlock to replace the ResBlock in DeblurGAN, and adds two skip-connections. Finally, depthwise separable convolution is used to replace the common convolution block in the network, so as to reduce the network model, reduce parameters and accelerate the convergence speed of the network. The loss function uses the perceptual loss to ensure content consistency between the generated image and the clear image.
Dynamical entropic analysis of scientific concepts

In the present era of information, the problem of effective knowledge retrieval from a collection of scientific documents becomes especially important for continuous scientific progress. The information available in scientific publications traditionally consists of bibliometric metadata and its semantic component such as title, abstract and text. While the former having a machine-readable format usually used for knowledge mapping and pattern recognition, the latter designed for human interpretation and analysis. Only a few studies use full-text analysis, based on carefully selected scientific ontology, to map the actual structure of the scientific knowledge or uncover similarities between documents. Unfortunately, the presence of common (basic) concepts across semantically unrelated documents creates spurious connections between different topics. We revise the known method based on the entropic information-theoretic measure used for selecting basic concepts and propose to analyse the dynamics of Shannon entropy for more rigorous sorting of concepts by their generality.
Using Interval-Valued Fuzzy Sets for Recommending Groups in E-Learning Systems

To obtain required effects of Web-based learning process, teaching environment should be adjusted to student needs. Differentiation of the environment features can be received by grouping learners of similar preferences. Then each new student, who joins the community, should obtain the recommendation of the group of colleagues with similar characteristics. In the paper, we consider using fuzzy logic for modeling student groups. As the representation of each group, we assume fuzzy numbers connected with learner attributes ranked according to their cardinality. Recommendations for new students are determined taking into account similarity of their dominant features and the highest ranked attributes of groups. The presented approach is examined, for students described by learning style dimensions. The method is evaluated on the basis of experimental results obtained for data of different groups of real students.
Neural Networks Based on Latent Dirichlet Allocation For News Web Page Classifications

Any popular news website in our modern life, offering details to millions of users every day. Although computer technology continues to grow, the number of disease data is rising. How to structure the document to enable data recognition dynamically has become one of the main challenges for sophisticated web services. Traditional systematic classification of news text requires not only a lot of human and financial assets but it also hardly accomplishes fast classification function. In this work, we introduce a new method relying on both the Latent Dirichlet Allocation and the Neural Networks that are used in the Arabic document classification. Our approach adopts the Vector Space Model to interpret documents in applications for the text classification. In this process, the text is represented as a term vector; n-grams. These methods can not distinguish semantic or textual content; this results in considerable space for features and semantic losses. In this research, the new proposal utilizes a “topics” sampled as text characteristics by the Latent Dirichlet Allocation method. Effectively it eliminates the problems described. We have extracted important themes (topics) of all the texts. Each theme is identified by a different descriptor distribution, and then each text is depicted on the vectors of certain themes. Our experiments indicate that the proposed solution is capable of achieving high efficiency with an accuracy rate of 85.11% for the Arabic text classification task.
PLAY ME! Influencing Game Decisions through Suggestions made by Augmented Characters

During physical games, we love to socially interact with other players through bluffing or giving them hits. This work aims to enrich AR characters by adding a suggestive behavior to them intended to playfully influence game decisions. In a user study, we evaluated such behaviors presented as body postures by animated card characters using an AR trading card game. Our results indicate that AR characters can indeed influence the player’s game decisions through postures that encourage or discourage to play a certain card. Our approach enriches the game design space, can make the game more interesting, and finally adds a social component to the game.
A Short Communication on Hematology

Hematology (also spelled haematology) is the branch of medicine concerned with the study of the cause, prognosis, treatment, and prevention of diseases related to blood. The blood plays an essential roles in human health
A Dynamic Pricing Model for Virtual Machines in Cloud Environments

The following topics are dealt with: cloud computing; learning (artificial intelligence); optimisation; resource allocation; Internet of Things; business data processing; Web services; security of data; neural nets; cryptography.
Model-driven engineering and usability evaluation of self-adaptive user interfaces

Enes Yigitbas is a Senior Researcher at Paderborn University. In 2019, he got his doctoral degree in Computer Science under the advisory of Prof. Dr. Gregor Engels. During his doctorate, his research work was linked to various industrial projects in the Software Innovation Campus Paderborn (SICP). In this context, he was significantly involved in the implementation and management of various projects related to model-driven and human-centered software engineering, self-adaptive software systems, and human-computer interaction. His work on engineering adaptive user interfaces received a late-breaking results award at the ACM SIGCHI Symposium on Engineering Interactive Computing Systems. In addition to project and teaching activities, he is actively involved in various committee activities as well as serves as a reviewer for various international conferences and journals in the areas of Software Engineering and HCI. In his thesis, supervised by Prof. Dr. Gregor Engels (Paderborn University), he focused on the engineering and usability evaluation of self-adaptive user interfaces. Self-adaptive user interfaces (SAUIs) have been promoted as a solution for context variability due to their ability to automatically detect context changes and adapt to the current context-of-use at runtime. In the course of his thesis work, he developed a model-driven engineering approach which supports modeling, transformation, and execution of self-adaptive user interfaces. Furthermore, a novel on-the-fly usability testing solution for SAUIs has been introduced which allows to evaluate the end-user satisfaction of SAUIs by combining context monitoring together with collection of instant user feedback.