Reviewerly/exHarmony huggingface dataset:
Fast and Accurate Single Image Super- Resolution via Enhanced U-Net

Recent studies have demonstrated the strong ability of deep convolutional neural networks (CNNs) to significantly boost the performance in single image super-resolution (SISR).The key concern is how to efficiently recover and utilize diverse information frequencies across multiple network layers, which is crucial to satisfying super-resolution image reconstructions.Hence, previous work made great efforts to potently incorporate hierarchical frequencies through various sophisticated architectures.Nevertheless, economical SISR also requires a capable structure design to balance between restoration accuracy and computational complexity, which is still a challenge for existing techniques.In this paper, we tackle this problem by proposing a competent architecture called Enhanced U-Net Network (EUN), which can yield ready-to-use features in miscellaneous frequencies and combine them comprehensively.In particular, the proposed building block for EUN is enhanced from U-Net, which can extract abundant information via multiple skip concatenations.The network configuration allows the pipeline to propagate information from lower layers to higher ones.Meanwhile, the block itself is committed to growing quite deep in layers, which empowers different types of information to spring from a single block.Furthermore, due to its strong advantage in distilling effective information, promising results are guaranteed with comparatively fewer filters.Comprehensive experiments manifest our model can achieve favorable performance over that of state-of-the-art methods, especially in terms of computational efficiency.
Telugu Word Image Retrieval using Deep learning Convolutional Neural Networks

Telugu word image retrieval (TWIR) is a still challenging task due to the structure complexity of Telugu word image. An efficient TWIR system can be implemented by a holistic representation of word image that comprises of every possible extracted feature. Further, it is also required to retrieve more relevant word images even there is a noisy query word image. Here, it is proposed an efficient TWIR system that utilizes deep learning convolutional neural networks (DL-CNN) to extract the feature map from the query and database word images. In addition, principal component analysis (PCA) is employed to compute the principal features form the feature map and pairwise hamming distance is considered as a similarity metric to retrieve most relevant Telugu word images from the database. Extensive simulation analysis disclosed that proposed TIWR system obtained a superior performance over conventional TIWR systems in terms of mean average precision (mAP) and mean average recall (mAR).
A Novel Impurity Measuring Technique for Decision Tree Learning in Machine Learning

Decision tree classification is one of the most powerful data classification techniques in machine learning, data mining, big data analytics and split functionality is a crucial and inherently associated integral part of the decision tree learning. Many split similarity measures are proposed to determine the best split attribute and then partitioning the node data in decision tree learning accordingly. A new impurity measuring based split technique called (IMDT) for decision tree learning is proposed in this paper and it is used in obtaining experimental results. Many UCI machine learning dataset are employed in experimentation. The algorithm C4.5 is the most using data classification algorithm. The results obtained with the proposed approach are outperformed than the many existing decision tree classification algorithms in particular C4.5 decision tree algorithm.
Social Media as the Next Trend in Social Business Marketing Social Media as the Next Trend in Solar Business Marketing

In today’s era, everyone have online presence. Someone will have more presence, someone will have less presence. Someone will be on mail and Facebook only and someone will be on everything from mail to Facebook, Twitter, Instagram, Linkedin etc. For Business, they are the perfect target. One can reach as many as potential customers and leads through social media all over the world which they can never achieve through any other media. Social media can be interactive and informative. New generation is highly active in social media at the same time they are highly aware of the growing concerns like global warming. New generation knows the importance of change and they encourage any noble cause all over the world. Even though solar business are present for a very long time but now only it is gaining importance after growing concerns towards our Mother Nature. To reach maximum target customers, it is very important for the Solar business to take leverage of Social media. Posting an article, or blog or uploading a video is not enough, if you are not targeting the correct viwers and you are not converting your viwers into leads. In this article, we would understand that how solar business can get fullest from social media and how to increase your customer base. Social media may sound like informal way to serve the customers but it is a powerful relationship builder for the company. Social media is very important to inspire and build strong relationship with the potential customers
Straggler Problem Tail Latancy in Distributed network

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Performance Evaluation of Genetic Algorithm &amp; Fuzzy Logic for Portfolio Optimization

Teaching-learning based optimization (TLBO), biogeography-based optimization (BBO) and fuzzy multi-objective linear programming (FMOLP) are compared in this paper for portfolio optimization. A hybrid approach has been adopted for this comparative study which is a combination of a few methods, such as investor topology, cluster analysis, analytical hierarchy process (AHP) and optimization techniques. Return, risk, liquidity, coefficient of variation (CV) and AHP weighted scores are used as the objective function for optimization.
STUDENT LEARNING IN A RAPID FEEDBACK ENVIRONMENT

The research presented in the paper examines how instructor policies related to the use of feedback in a university course can improve student learning.Using MS Excel assignments and a rapid feedback computerized grading system, we examine two specific course policies.The first of these is whether to allow students more than one attempt at the same assessment, using feedback from the first attempt to inform student work on subsequent attempts.The second involves whether the highest or the average score across multiple attempts is recorded in the grade book.We find that the student learning, measured as performance on a comprehensive final exam, improves when students are allowed multiple attempts on other course assignments.Additionally we find that student learning improves when the instructor records the average score instead of the highest score in course grade book for assignments with multiple attempts.
Determinative Factors of Students’ Reading Interest at Community Education Units in Bandung

This research based on Indonesian Act about National Education System, UU RI No.20 Tahun 2003 article 26 section (4): non-formal education units consist of courses, training organizations, learning communities, community learning centre, majelis taklim, and so forth. The general objective of this research was to observe the factors determining the students’ reading interest at community education units in Bandung. Quantitative and qualitive approach or mix method was applied to this research. Qualitative approach was used to describe the empiric condition and the program implementation. The quantitative approach was applied to find out the determinative factors of students’ reading interest. The data were collected from observation, interview, questionnaire, and documentation study which were applied to 267 data sources. The result showed that some units have used computer-based system, such as SLIMS, INLIST, OPAC, blog, and website. The implementation of the system increased the numbers of visitor. The determinative factors affecting the reading interest are divided into two, which are internal factor (motivation, interest) and internal factor (jobs, group, environment, and interpersonal relationship). Similar jobs create similar goals, so the learning community is created.
Breast Cancer Detection, Diagnosis, and Prediction

The early detection, diagnosis, prediction, and treatment of breast cancer are challenginghealthcare problems. This study focuses on outlining the traditional and trending techniques used for breast cancer detection, diagnosis, and prediction, including trending noninvasive, nonionizing, and biomarker genetic techniques.In addition, a Computer Aided Detection (CAD) is introduced to classify benign and malignant tumors in mammograms. This CAD system involves three steps. First, the Region of Interest (ROI) that includesthe tumor is identified using a threshold-based method. Second, a deep learning Convolutional Neural Network (CNN) processes the ROI to extract relevant mammogram features. Finally, a Support Vector Machine (SVM) classifier is used to decode two classes of mammogram structures (i.e., Benign (B), and Malignant (M) nodules). The training processes and implementations were carried out using 2800 mammogram images taken from the Curated Breast Imaging Subset of DDSM (CBIS-DDSM). Results have shown that the accuracy of CNN-SVM system achieves 85.1% using AlexNet CNN. Comparison with related work shows the promise of the proposed CAD system
Big Data Virtualization and Visualization on Cloud

Data virtualization is the procedure of combining data from different sources of information to develop a solo, logical and virtual view of facts so that it can be accessed by front-end resolutions such as applications, dashboards and portals without having to know the data's exact storingsite. Several organizations ride multiple types of database management systems, such as Oracle and SQL servers, which do not work fine with one another. Therefore, enterprises face new challenges in data integration and storage of huge amounts of data. With data virtualization, commercial handlers are able to get real time and consistent information speedily, which supports them to take foremost corporate decisions. The process of data virtualization involves abstracting, transforming, federating and delivering data from unequal sources. The key objective of data virtualization technology is to deliver a single point of access to the data by aggregating it from a wide range of data sources.
Spectral reflectance features of kimberlites and carbonatites; the key to remote sensing for exploration

Recent advances in the spectral and spatial resolution of airborne and spaceborne multispectral scanning devices provide new opportunities for using remote sensing techniques in kimberlite and carbonatite exploration.To evaluate their remote sensing potential, laboratory diffuse reflectance spectra were recorded in the 0.4-/um to 2.5-A»n wavelength range for naturally weathered surfaces of kimberlites from southern Africa and for carbonatites from South Africa and North America.Mineralogy was confirmed by petrographic and/or x-ray diffraction analysis.Characteristic absorption features of minerals are derived from either electronic or vibrational processes.The principal electronic processes result from energy level transitions in a crystal field or from the formation of valence and conduction energy bands.In the near-infrared range, vibrational processes yield overtones and combination tones of the fundamental modes of anion groups and water.
Optimization of Shadow Detection and Removal using Multilevel Thresholds and Improved Artificial Bee Colony Algorithm

Shadow Detection and removal from images is a challenging task in visual surveillance and computer vision applications. The appearance of shadows creates severe problems. There are various methods already exists but scope in this area is wide and open. In this paper, Optimization of Shadow Detection and Removal using Improved Artificial Bee Colony Algorithm (IABC) is proposed. The proposed method uses edge map, multilevel thresholds, masking, boundaries evaluation and, IABC algorithm. First data pre-processing is applied to find the correlation between the pixels then three level low, medium and high value of thresholds and the corresponding value of masking and boundaries are calculated to accurately differentiate pixels as foreground. The edge response, curvature, gradient are applied to find the true location of boundaries. Finally, IABC has been applied for detecting the shadow and median filter is used to remove the shadow. The results show improvement as compared to other existing methods.
Organization of students’ research work in the conditions of providing professional orientation of the educational process

The article considers the organization of research work of future engineers-teachers in the system of professional training of IT specialists through computer environment design. The contradictions between the usual system of training and the individual creative direction of the future professional activity of the specialist are analysed. The authors studied the concept of the computer environment, considered its information, software, and didactic component. The practical aspects of the organization of educational and research activities of future specialists, the specialty of which requires further research, in particular using the computer environment as an object of design, are revealed. Its intellectual properties are considered and it is proved that they are a reaction to human actions. The model of knowledge representation in the process of designing a computer environment is described and it is proved that the use of a network model in the form of a graph is the best to reflect the meaning of a holistic image. Thanks to the network model, the course in the discipline is created by a certain structure of modules, which consist of topics that contain tasks. This presentation of data is inherent in the semantic network, which is a combination of ideas and knowledge retention of the entire learning process in an individual educational trajectory. Thus, it is possible to adjust it depending on the level of effectiveness of the student's mastering the material. The informational, program and didactic components of the computer environment are substantiated, the basic requirements to the organization of research work of students from the point of view of designing where flexibility is the basic property of a system of management, in particular in questions of realization of various forms of occupations on professional training are revealed. An example of software implementation of a computer environment with an intuitive interface for working with information content is described. It is substantiated that the proposed principles of organization of research work can also be applied in the organization of research work of students of physical culture and sports profile.
23. Designs—an overview

This chapter summarizes the discussion of the law on registered and unregistered designs. The Design Directive, as implemented in the revised version of the Registered Designs Act 1949, has put in place a modern system of registered design protection that does away with the aesthetical requirements of the old 1949 Act and only attempts to exclude technical matters that should, in principle, be dealt with by patent rather than design law. The continued enforcement of a domestic unregistered design right that was supposed to fit in seamlessly with a system of registered rights that no longer exists is no longer necessary. The overlap of protection creates confusion and an undue duplication of rights.
Digital Degradation

Interviews with more than 100 people whose records appear online show how the ability to manage digital punishment is directly tied to a person’s familiarity with technological systems and their faith in bureaucracy. Instead of confronting the government or the criminal justice system, many people engage in digital avoidance, afraid that any attempts will only make the problem worse. This intersection between the criminal justice system and technology reproduces social inequality at the speed of the internet, disproportionately impacting people who have less access to and command over digital technologies. This chapter discusses the qualities of digital punishment, the strategies people who are experiencing digital punishment deploy to deal with their online stigma, and an explanation for why many people choose to engage in digital avoidance rather than try to have their online record removed. Rooted in theories of the digital divide and the disparate impact of big data technologies, the chapter concludes with a discussion of how digital punishment challenges long-held theories of criminal stigma, desistance, and rehabilitation.
Engineering Major Development of the "Automobile Service Excellence Engineer Education and Training Program"

The excellent engineer training program is an important step for China to transform from a large-scale engineering education country to a strong engineering education country.This is a solid guarantee for the strength of China's higher education.It aims to cultivate a large number of high-quality engineers and technicians with high innovation ability to meet the needs of economic and social development.The training of automotive service excellence engineers should not only be limited to campuses and classrooms, but also conform to the development concept of quality education, broaden educational channels and promote the construction of the second classroom.This paper mainly expounds the construction focus and significance of the second classroom, and focuses on the important role that the second classroom plays in the training mechanism of excellent engineers, discusses the research reform strategy of the existing training mechanism
Decision Tree Based Algorithm for Removal of Different Probability Salt and Pepper Noise in Images

A Decision tree based algorithm for the removal of equal and unequal probability salt and pepper noise in images is proposed. The algorithm aims to address one solution for different salt and pepper noise models. The proposed algorithm operates on an image using fixed 3*3 window. The decision tree based algorithm classifies pixel into noisy or not based on the decision and replaces it with mean of neighbours or unsymmetrical trimmed median or unsymmetrical trimmed midpoint. The algorithm exhibit excellent noise elimination capability at high noise densities in terms of quantitative and qualitative perspective. The algorithm was found to exhibit good noise removal characteristics for three noise models
Utilizing Reinforcement Learning to Continuously Improve a Primitive-Based Motion Planner.

of the Hybrid A* algorithm. The framework is tested on a navigation task using a nonlinear F-16 model. The framework autonomously added 91 motion primitives to the primitive library and reduced average path cost by 21.6 seconds, or 35.75% of the original cost. The learned primitives are applied to an obstacle field navigation task, which was not used in training, and reduced path cost by 16.3 seconds, or 24.1%. Additionally, two heuristics for the modified Hybrid A* algorithm are designed to improve effective branching factor.
Preface

FAIRNESS 2019 was the sixth edition in a series of workshops designed to bring together excellent international young scientists with research interests focused on physics at FAIR (Facility for Antiproton and Ion Research) and was held on May 20-24 2019 in Arenzano, Italy. The topics of the workshops cover a wide range of aspects in both theoretical developments and current experimental status, concentrated around the four scientific pillars of FAIR. FAIR is a new accelerator complex with brand new experimental facilities, that is currently being built next to the existing GSI facility close to Darmstadt, Germany. The spirit of the conference is to bring together young scientists, e.g. young non-tenured scientists, postdocs and advanced PhD students to present their work, to foster active informal discussions and build up networks. Every participant in the meeting with the exception of the organizers gives an oral presentation, and all sessions are followed by a long discussion period. During the talks, questions are anonymously collected in a box to stimulate discussions. The broad physics program at FAIR is reflected in the wide range of topics covered by the workshop: □ Atomic and plasma physics, biophysics, material sciences and applications □ Nuclear structure, astrophysics and reactions □ Physics of hot and dense nuclear matter, QCD phase transitions and critical point □ Hadron Spectroscopy, Hadrons in matter and Hypernuclei □ Experimental programs APPA, CBM, HADES, PANDA, NUSTAR, as well as BES, NICA and the RHIC beam energy scan For these different areas one invited speaker was selected to give a longer introductory presentation. The write-ups of the talks presented at FAIRNESS 2019 are the content of this issue of Journal of Physics: Conference Series and have been refereed according to the IOP standard for peer review. This issue constitutes therefore a collection of the forefront of research that is dedicated to the physics at FAIR. June 2019, Organizers of FAIRNESS 2019: Tetyana Galatyuk, Andreas Bauswein, Marco Destefanis, Claudia Ratti, and Laura Tolos. Support for holding the conference was provided by:
BENFORD'S LAW APPLIED TO PRECINCT LEVEL ELECTION DATA

This paper attempts to determine whether precinct level election data conforms with Benford's Law.In order to evaluate whether election data conform with Benford's Law, we constructed a two-part test.The first test assesses whether election data correlates with Benford's law.If the election data under study is found to correlate with Benford's law, we then subject it to the Kolmogorov-Smirnov test, which is used to evaluate more rigorous conformance with Benford's law and which aids in forensic analysis.We conclude that the frequency pattern of the first digits of the precinct level elections under study correlate strongly with the pattern predicted by Benford's Law of first digits.We also conclude, however, that the correlation is not strong enough for definitive forensic analysis.
Edge Preserving Fuzzy Filter for Suppression of Impulse Noise in Images

Impulse noise in images culminates to the loss of valuable information. This paper proposes an efficient fuzzy filter for suppression of impulse noise in images (EFFSIN). Firstly, impulse noise is effectively detected by the proposed algorithm with the utilization of Fuzzy C-Means (FCM) in two stages.Only legitimate pixels are restored after fair distiction of noisy pixel from edge pixel. Proper restoration of noisy pixel following effective detection by proposed work leads to low miss detection(MD) and false alarm (FA) rates. Simulation results depict the efficacy of the proposed filter with reference to peaksignal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).