Reviewerly/exHarmony huggingface dataset:
Lossless Compression of Medical Images based on the Differential Probability of Images

Lossless compression is crucial in the remote transmission of large-scale medical image and the retainment of complete medical diagnostic information. The lossless compression method of medical image based on differential probability of image is proposed in this study. The medical image with DICOM format was decorrelated by the differential method, and the difference matrix was optimally coded by the Huffman coding method to obtain the optimal compression effect. Experimental results obtained using the new method were compared with those using Lempel–Ziv–Welch, modified run–length encoding, and block–bit allocation methods to verify its effectiveness. For 2-D medical images, the lossless compression effect of the proposed method is the best when the object region is more than 20% of the image. For 3-D medical images, the proposed method has the highest compression ratio among the control methods. The proposed method can be directly used for lossless compression of DICOM images.
Субъектность отечественной технической интеллигенции в осуществлении задач информатизации

The&nbsp;article deals with the problems of solving urgent problems facing the Russian society from the methodological position of subjectivity of the Russian technical intelligentsia. Based on empirical data obtained by the author as a result of a survey of the expert community of technical intelligentsia (April 2019), the author shows the main priorities of innovative development, including informatization, problems faced by representatives of the technical intelligentsia in their activities, as well as measures necessary to create a modern information and innovation environment.
Biogen Int'l GmbH v. Banner Life Scis. LLC United States Court of Appeals of the Federal Circuit, 2020 956 F.3d 1351

Biotechnology Law ReportVol. 39, No. 4 As presented by Chris HolmanBiogen Int'l GmbH v. Banner Life Scis. LLC United States Court of Appeals of the Federal Circuit, 2020 956 F.3d 1351Edited for Biotechnology Law Report by CHRISTOPHER M. HOLMAN*Published Online:17 Aug 2020https://doi.org/10.1089/blr.2020.29188.cmhAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 39Issue 4Aug 2020 InformationCopyright 2020, Mary Ann Liebert, Inc., publishersTo cite this article:Biogen Int'l GmbH v. Banner Life Scis. LLC United States Court of Appeals of the Federal Circuit, 2020 956 F.3d 1351.Biotechnology Law Report.Aug 2020.324-327.http://doi.org/10.1089/blr.2020.29188.cmhPublished in Volume: 39 Issue 4: August 17, 2020Online Ahead of Print:July 9, 2020PDF download
The efficiency of greedy best response algorithm in road traffic assignment

In this work, we investigate the problem of the integer road traffic assignment. So, we model the interaction among the road users sharing the same origin-destination pair, as a symmetric network congestion game. We focus on Rosenthal's results to guarantee the existence of a pure Nash equilibrium (PNE). Then, we study the behaviour of an algorithm based on greedy best response (GBR) in finding PNE. In previous studies, the efficiency of GBR to compute a PNE of a symmetric network congestion game in series-parallel networks is proved. In our work, another approach is used to demonstrate its efficiency in more general networks. It is shown that the non-series parallel networks can be classed into two types. The conditions that make GBR succeeds for each type is then drawn. The advantage of GBR-algorithm is that provides a better approximation of the equilibrate assignment, since it is integer.
Software tools for analysis and synthesis of stochastic systems with high availability (XII)

The article proceeds the thematic cycle dedicated to analysis and synthesis for mean square criteria software tools for vector nonstationary (e.g. shock disturbances) linear stochastic systems with high availability (StSHA). Methodological support is based on Haar wavelet expansions (WLE) and wavelet canonical expansions (WLCE). Sect.1 is dedicated to problems statements. General basic algorithm is given in Sect. 2. Algorithms for StSHA with linear parameters and additive noises based on WLE and WLCE are described in Sect.3. Software tools in MATLAB are considered in Sect. 4. For typical shock disturbances bank of algorithms is developed. Complex of examples is given in Sect.5. Some generalizations are presented.
Image Inpainting Applied to Art: Completing Escher’s Print Gallery

This extended abstract presents the first stages of a research in inpainting suited for art reconstruction. We introduce M.C Escher’s Print Gallery lithography as a use case example. This artwork presents a void on its center and additionally, it follows a challenging mathematical structure that needs to be preserved by the inpainting method. We present our work so far and our future line of research.
Facial Feature Crop Detection Based on Haar Feature and AdaBoost Training

The advance of technology has created a demand on face detection, which is the first phase in face recognition, one of the important biometric technologies. In this research, the Facial Feature Crop Detection Based on Haar Feature and Adaboost Training framework is presented. The framework’s objective is to detect face crops and each facial feature’s crop like eye, mouth and nose crops; the framework depends on using Haar-like features and the AdaBoost learning algorithm. This algorithm has been chosen according to its computational effectiveness and easiness. The presented framework was tested on a dataset containing 500 colour images of faces collected from the FERET database. The training achieved 100% accuracy and 98.8% achieved for the testing set.
US academy to eject harassers

Some 85% of the 2300 members of the US National Academy of Sciences have voted in favour of expelling any member who commits “egregious violations” of its code of conduct, including for proven cases of sexual harassment.
Investigating Information: A Qualitative Analysis of Expert Designers’ Information Representation and Structuring Behaviors

Abstract Information organization and utilization are integral to the design and development of creative ideas. However, navigating this often complex information space can be challenging, even for experienced designers. Therefore, deep analysis of how expert designers utilize and organize information is needed to provide qualitative insights into their information organization strategies. To address this, four professionals in the software design and development field were recruited for individual 3-hour design sessions. They were asked to generate ideas for a design challenge (reducing distraction-based pedestrian accidents) using information sheets specifically developed to contain different types of information, as identified by prior work. Results reveal individual differences in information approach and categorization, although these were motivated by similar underlying patterns of evaluating the relevance of information for its ability to inform the project constraints, resources or (user) requirements. Designer experience and use of design processes and knowledge transfer tools enhanced their ability to turn information into insights.
Higher Dimensional Data Access and Management with Improved Distance Metric Access for Higher Dimensional Non-Linear Data

Distance metrics for different kinds of data we daily use is the common approach for indentifying the insights of information and identifying the noisy and resolving the information with different scenarios and rules. The methodology imposed here is different in kinds of rules and the information we provide to the knowledge machine is the most important and considerable thing in designing and implementing distance metrics. The contradictory data is mixed information which the dataset includes is having the irrelevant information with the relevant information and identifying the novel thing from the information gathered. The information and the data gathered will be in the form of different formats of data and the most frequent thing we use is to make the clusters. In this article machine learning applications and the different data mining distance metric algorithms will be discussed and the information passed to the machine will be the ultimate and the dataset making is the quite challenging. In machine learning implementation the path of identifying the concept behind the everything to be predicted. The prediction works when the data is accurate and the information we get from the different repositories. All the data captured is not genuine and the combination of different such kind repositories make the contradicting data. The usage the additional distance metrics to manipulate and calculate the relation between the variables or the features we are considering. The machine learning is the finite mechanism which can help the researchers to identify the relationships between the variables or there is chance to find the inner relations among this kind of contradictory information. Contradictory data can help to identify the inner relations which can’t be identified with normal distance metrics and here used a little advanced and succeeded in reaching the optimal result.
Normalization and Outlier Removal in Class Center-Based Firefly Algorithm for Missing Value Imputation

Abstract Missing data is one of the factors often causing incomplete data in research. Data normalization and missing value handling were considered major problems in the data pre-processing stage, while classification algorithms were adopted to handle numerical features. Furthermore, in cases where the observed data contains outliers, the missing values’ estimated results are sometimes unreliable, or even differ greatly from the true values. This study aims to proposed combination of normalization and outlier removal’s before imputing missing values using several methods, mean, random value, regression, multiple imputation, KNN, and C3-FA. Experimental results on the sonar dataset show normalization and outlier removal’s effect in these imputation methods. In the proposed C3-FA method, this produced accuracy, F1-Score, Precision, and Recall values of 0.906, 0.906, 0.908, and 0.906, respectively. Based on the KNN classifier evaluation results, this value outperformed the other five (5) methods. Meanwhile, the results for RMSE, Dks, and r obtained from combining normalization and outlier removal’s in the C3-FA method were 0.02, 0.04, and 0.935, respectively. This shows that the proposed method is able to reproduce the real values ​​of the data or the prediction accuracy and maintain the distribution of the values ​​or the distribution accuracy.
A Multi-Agent Bio- Inspired System to Map Learners with Learning Resources using Clustering Based Personalization

The work makes an examination of clustering methods in a multi-agent system which is fully decentralized. This has the goal of grouping agents that have similar data or objectives as in the case of traditional clustering. But, this adds to some more additional constraints wherein the agent will have to be in the same place as opposed to being collected within a centralized database. For doing this, it will connect to agents within a random network and will search for them in a peer-to-peer based fashion for the other agents that are similar. The primary aim here was to tackle the basic problem in clustering on the Internet scale thus creating methods where the agents may be grouped thus forming coalitions. For the purpose of investigating the decentralized approaches and their feasibility, the work presents the K-means clustering, the multi-agent Firefly Algorithm, (FA) and the Differential Evolution (DE). This is done for a reasonable number to times and will be surprisingly good. The results of the experiment prove that the multi-agent firefly clustering has better performance compared to that of a multi-agent K-Means clustering or a multi-agent DE clustering.
Ensemble Based Hybrid Recommender Systems

In the past few years, the advent of computational and prediction technologies has spurred a lot of interest in recommendation research. Content-based recommendation and collaborative filtering are two elementary ways to build recommendation systems. In a content based recommender system, products are described using keywords and a user profile is developed to enlist the type of products the user may like. Widely used Collaborative filtering recommender systems provide recommendations based on similar user preferences. Hybrid recommender systems are a blend of content-based and collaborative techniques to harness their advantages to maximum. Although both these methods have their own advantages, they fail in ‘cold start’ situations where new users or products are introduced to the system, and the system fails to recommend new products as there is no usage history available for these products. In this work we work on MovieLens 100k dataset to recommend movies based on the user preferences. This paper proposes a weighted average method for combining predictions to improve the accuracy of hybrid models. We used standard error as a measure to assign the weights to the classifiers to approximate their participation in predicting the recommendations. The cold start problem is addressed by including demographic data of the user by using three approaches namely Latent Vector Method, Bayesian Weighted Average, and Nearest Neighbor Algorithm.
Cross-chain Exchange by Transaction Dependence with Conditional Transaction

&lt;div&gt;Cross-chain exchange swaps assets among different blockchains, which facilitates cooperation among blockchains. A cross-chain exchange contains several transactions from different blockchains. It requires to synchronize asset transfers in those associated blockchains to avoid partial transfers. Current cross-chain methods are divided into two main types. One locks the assets first and transfers the frozen asset to receivers later. The second one is the two-phase or three-phase transaction commit protocol. Those methods require at least two steps, which makes those blockchains coherent to synchronize at different time(steps). Serialization is required as the second step has to wait for the completion of the first step, and even some steps in the same stage are required to be serialized. Meanwhile, their implements are either by smart contracts or special blockchain structures/roles. Smart contract based methods require to pre-deploy a smart contract and cannot change dynamically. Special structure or role-based methods force associated blockchains to have those special requirements. In this paper, we propose a new cross-chain exchange model based on the dependence of associated transactions, which is expressed inside a transaction and can change when sending transactions. It saves the step to lock the asset or perform a pre-commit and has no special requirements for blockchains or pre-deployment of smart contracts. The simulation results show the proposed model exchanges asset effectively among different blockchains.&lt;/div&gt;
Getting Started

If you’re interested in browser automation through Node.js and understanding the various methods that can be employed to effectively automate user actions on your web site, this book is for you. WebdriverIO is a fast-growing automation tool that is hard to ignore as Node.js/JavaScript technologies assert their dominance in front-end and back-end web development.
The Effects of Human Capital on Organizational Innovation in Abu Dhabi Government

Today, human capital plays a significant role in developing the innovation process of organizations and for that reason it is an important element of any successful organization. The aim of this study is to examine the impact of three factors associated with human capital namely knowledge, skill, and attitudes towards organizational innovation. To achieve this aim, this study adopted a quantitative survey by collecting data from employees of public sector in Abu Dhabi. Descriptive and inferential statistics was used to analyse the quantitative data. The result of this study shows that human capital has a direct and positive influence towards organizational innovation through high degree of knowledge and skills as well as positive attitude. This study tested three factors mentioned above twords organziational innovation in UAE. Therefore, the author recommends the leaders in public organizations in UAE to focus on the factors associated with human capital in particular knowledge, skill, and attitude of employees. The outcome of this study revealed that the skills and knowledge of human capital are one of the pillars of this vision is transforming the UAE into competitive knowledge economy through innovation and creativity. Moreover, it is highly recommended to test the moderation effect of transformational leadership on this relationship in future studies to understand how the behavior of leaders support or weakening the impact of human capital on organizational innovation.
Variable Precision Depth Encoding for 3D Range Geometry Compression

This paper presents a novel method for accurately encoding 3D range geometry within the color channels of a 2D RGB image that allows the encoding frequency&#x2014;and therefore the encoding precision&#x2014;to be uniquely determined for each coordinate. The proposed method can thus be used to balance between encoding precision and file size by encoding geometry along a normal distribution; encoding more precisely where the density of data is high and less precisely where the density is low. Alternative distributions may be followed to produce encodings optimized for specific applications. In general, the nature of the proposed encoding method is such that the precision of each point can be freely controlled or derived from an arbitrary distribution, ideally enabling this method for use within a wide range of applications.
Software Quality

Every software component has a quality; it may be low quality, high quality, or somewhere in between. Its level of quality will reflect how well the software component meets the needs of the organization using it. This chapter covers software quality and how it can be evaluated.
Detection of Class Regularity with Support Vector Machine methods

One of the most factor that affects the achievement and learning motivation of students is a conducive classroom environment. It can be seen from the student's regularity in the class. Teachers can determine whether the class is adequate or not by monitoring the class condition through video. The research tries to apply the extraction of imagery and sound features by using the Centroid extraction method and the MFCC along with classifying the regular or irregular classrooms with the SVM methods which are taken by video installed in a classroom. The video will be split into image data and sound data. The process of image data starts with reading the input, then it goes to the stages of preprocessing, segmentation with K-Means, morphology, and the most important part is to get information before it is classified by the SVM method to get its class regularity. The sound frequency will be extracted by the MFCC method and then it is classified by the SVM method to get the class noise. The results of this research get an accuracy value of 78% in the linear kernel and 70% in the polynomial kernel. This research uses 50 test data consisting of 25 regular data and 25 irregular data taken directly through video recording. These results prove that the SVM method has given good classification results for regular and irregular classes.
A Virtual Immersion in Urban Sustainability: Past, Present and Future

Immersive virtual reality and 360° video can be used for academic purposes with success. This article summarizes the design, implementation, and evaluation of the use of these two tools at Tecnológico de Monterrey. The objective of using immersive learning was to give university students a comprehensive view of urban sustainability in ancient times and make a comparison with the current civilizations, and the projections for the future. For the virtual travels of the past and present, the tool used was 360° video while for the future it was virtual reality. Two 360° videos were designed of Sumerian and Roman civilizations, in specific of the cities of Ur (Mesopotamia) and Pompeii. These two urban designs were contrasted with today’s design of the cities of Buenos Aires, São Pablo, Hong Kong, and Bangkok. For the future, Google Earth allowed a tour at street-level of the European Green Capitals. At the end of the course the students answered a survey that showed a 91% of satisfactory opinions due to immersive learning.
Spain Case Study: Covid-19 and Censorship during the State of Alarm

This Paper analyzes the constitutional coverage of the Right to Information during the state of alarm in Spain
Automated Feature Extraction from UML Images to Measure SOA Size

Enormous development has been experiences in the field of text and image extraction and classification. This is due to large amount of image data that is generated as a result of document sharing for collaborative software development and electronic storage of design documents. One of the recent technique for analyzing large dataset and discover underlying patterns is Deep learning technique. Deep learning is a branch of Machine learning inspired by human brain functionality for the purpose of analyzing unstructured data including images, sound and text. Unified Model Language (UML) is an architectural design which provides developers with a view of software components and scope. UML contain texts and notations which are mostly analyzed and interpreted manually for the purpose of system implementation and scope or size measurement. Consequently, manual processing of electronic design artifacts is prone to bias, errors and time consuming. Various researchers have attempted to automate the process of reading and interpreting design artifacts but still there is a challenge due to varying style of designing these artifacts. This study propose an automatic tool based on existing deep learning algorithms including ResNet50 CNN to read UML interface and sequence diagrams images to detect UML arrows, EAST test detector to detect text, Tesseract OCR with Long Short-Term Memory (LSTM) to recognize text and Multi-class Support Vector Machine to classify text for the purpose of measuring Service Oriented Architecture size. We subjected the tool to accuracy tests which returned encouraging results.