Reviewerly/exHarmony huggingface dataset:
Criminal Intelligence Management System and Security in Kenya: A Case Study of Kirinyaga County

Law enforcement agents require Criminal Intelligence about their immediate and that of the wider environment in which they operate in order to guarantee their safety and that of others, they need to keep track of internal and external threats.It is a combination of these internal and external threats that develop the concept of security.The purpose of this study was to look at Criminal Intelligence management system and security in Kenya.The objective of the study was to; assess the extent to which integrated criminal intelligence management system is used in Kenya.The study targeted Kirinyaga County from where the findings were realized.The study employed a descriptive survey design targeting various levels of Intelligence Management in Kirinyaga County.The levels comprised of County, Sub county and Ward security and intelligence committees.The Total target Population of the group was 79 people.However, for practical reasons the research sampled 55 persons.An interview schedule and individual questionnares were used to collect data.Prior to engaging in actual data collection a pilot study was carried out in Kiambu County which had population with similar characteristics with Kirinyaga county to test validity and reliability of the instruments.Data collected was analyzed both quantitatively and qualitatively.The data was then described in statistics from where it was captured in frequencies and percentages arrived at through excel computer package.The results were then presented using tables and charts from where the discussions were based and findings drawn.The finding from the study revealed that criminal intelligence management system did not have proper control mechanisms to gather actionable information to be able to fully curb crime.Other findings were that the system was riddled with some challenges which included lack of trust among the various agencies, bureaucracies in disseminating the intelligence to the operations teams among others.The main recommendation of the study is that there is urgent need to put in place criminal intelligence plan detailing information gathering, collation and sharing, proper vetting and joint training of all the officers handling intelligence be undertaken so as to achieve synergy at various levels and have enabling legislations to address issues of autocracy.
Pharma Tech Sols., Inc. v. LifeScan, Inc. United States Court of Appeals of the Federal Circuit, 2019 942 F.3d 1372

Biotechnology Law ReportVol. 39, No. 1 As presented by Chris HolmanPharma Tech Sols., Inc. v. LifeScan, Inc. United States Court of Appeals of the Federal Circuit, 2019 942 F.3d 1372Edited for Biotechnology Law Report by CHRISTOPHER M. HOLMAN*Published Online:5 Feb 2020https://doi.org/10.1089/blr.2020.29160.cmhAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 39Issue 1Feb 2020 InformationCopyright 2020, Mary Ann Liebert, Inc., publishersTo cite this article:Pharma Tech Sols., Inc. v. LifeScan, Inc. United States Court of Appeals of the Federal Circuit, 2019 942 F.3d 1372.Biotechnology Law Report.Feb 2020.69-76.http://doi.org/10.1089/blr.2020.29160.cmhPublished in Volume: 39 Issue 1: February 5, 2020PDF download
The paper describes methods of studying the texture properties of rocks from photographs of a whole core using variograms and matrices of co-occurrence. The practical goal of our research is to obtain curves based on images of rocks; for example, core photographs, microresistivity formation images (FMI), and X-ray computerized tomography (CT), which make it possible to segment images depending on the difference of textural characteristics of different areas of the image. Such texture attributes…

Abstract Summary Variograms and Co-Occurrence Matrices in Problems of Description of Lithotypes and Modeling of Petrophysical Properties of Rocks from Whole Core Images
Word Sense Based Hindi-Tamil Statistical Machine Translation

Corpus based natural language processing has emerged with great success in recent years. It is not only used for languages like English, French, Spanish, and Hindi but also is widely used for languages like Tamil, Telugu etc. This paper focuses to increase the accuracy of machine translation from Hindi to Tamil by considering the word's sense as well as its part-of-speech. This system works on word by word translation from Hindi to Tamil language which makes use of additional information such as the preceding words, the current word's part of speech and the word's sense itself. For such a translation system, the frequency of words occurring in the corpus, the tagging of the input words and the probability of the preceding word of the tagged words are required. Wordnet is used to identify various synonym for the words specified in the source language. Among these words, the one which is more relevant to the word specified in source language is considered for the translation to target language. The introduction of the additional information such as part-of-speech tag, preceding word information and semantic analysis has greatly improved the accuracy of the system.
Design and Optimization of a Pruning-Efficient DCNN Inference Accelerator

This paper presents the pruning-efficient architecture of DCNN inference accelerators. The sparse representation with the row-bank structure can compress and decompress the pruned DCNNs with little hardware overhead. The hardware-aware pruning flow is also proposed for our architecture. The experiment results show that our pruned model can be accelerated 1.2× to 2× faster than others with higher accuracy at the same sparsity levels. Besides, our pruning-efficient accelerator is more effective than the row-stationary accelerator. With a slight accuracy drop, the proposed accelerator can perform ResNet-50 2.7× to 4.5× faster while reducing the energy-delay-product (EDP) by 71% to 88%. Therefore, DCNN acceleration can be achieved with the co-optimization of both accelerator design and structure pruning for the trade-off among model accuracy, latency, and energy consumption.
RhumbLine: Plectrohyla Exquisita — Spatial Listening of Zoomorphic Musical Robots

Contending with ecosystem silencing in the Anthropocene, RhumbLine: Plectrohyla Exquisita is an installation-scale instrument featuring an ensemble of zoomorphic musical robots that generate an acoustic soundscape from behind an acousmatic veil, highlighting the spatial attributes of acoustic sound.Originally conceived as a physical installation, the global COVID-19 pandemic catalyzed a reconceptualization of the work that allowed it to function remotely and collaboratively with users seeding robotic frog callers with improvised rhythmic calls via the internet-transforming a physical installation into a web-based performable installation-scale instrument.The performed calls from online visitors evolve using AI as they pass through the frog collective.After performing a rhythm, audiences listen ambisonically from behind a virtual veil and attempt to map the formation of the frogs, based on the spatial information embedded in their calls.After listening, audience members can reveal the frogs and their formation.By reconceiving rhumb lines-navigational tools that create paths of constant bearing to navigate space-as sonic tools to spatially orient listeners, RhumbLine: Plectrohyla Exquisita functions as a new interface for spatial musical expression (NISME) in both its physical and virtual instantiations.
Active Shape Model based Correlative Assessment of Statistical Parameters to Detect Cataract

The lens opacity acquired due to clouding of eye lens causing blindness is called a cataract, its detection is challenge. Based on positional occurrence of opacity in a lens it is categorized as nuclear sclerotic, posterior sub-capsular, and cortical cataract. Excessive use of steroids medicines, smoking, ageing, or eye injury lens cause cataract. Gradual and progressive growth of opacity is occurring in lens due to agglomeration of water and protein molecules. The maximum reflected rays form object, that focusing at retina lead to clear understanding of object image. Opacity inside lens causes refraction, reflection and diffraction of reflected light from object. This affects interpretation of image due to poor retinal reception. The patient may be advised treatment or change in lifestyle to prohibit the further growth of cataract or to improve vision. Researches are suggesting methods for detection of cataract from lens images by analysis of color, structural, and optical parameters. The research work presented uses wide slit illumination lens images for analysis to detect cataract. Due to elliptical shape of lens proposed system prefers active shape model with region properties to segment lens structure. Mean pixel value and uniformity are parameters used to distinguish lens without cataract from lens with cataract. The statistical parameters are dependent on probability of pixel values. Hence, using these parameters, system can be made invariant to source illumination. Correlation is performed between parameter vector of input image, and that of known images from database to identify and classify cataract type.
Tunisia – 2019 Presidential Election Results First Round September 15th

Africa Research Bulletin: Political, Social and Cultural SeriesVolume 56, Issue 9 p. 22438B-22438C Internal Developments Tunisia – 2019 Presidential Election Results First Round September 15th First published: 15 October 2019 https://doi.org/10.1111/j.1467-825X.2019.09071.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume56, Issue9October 2019Pages 22438B-22438C RelatedInformation
An Effectual Homomorphic Tag Based Block for Dynamic Provable Data Possession Framework Based on Block Tagging the Cloud File

Storing the data in cloud helps in satisfying the demand of data access at anyplace, anytime. In cloud storage, users authenticate whether the data has been stored to the cloud storage server correctly. In order to enhance the storage provision, an Effectual Homomorphic Tag based Block for Dynamic Provable Data Possession (EHTB-DPDP) framework has been designed. This framework checks for the data integrity in the cloud storage server. The existing PDP, DPDP schemes were analyzed and the drawbacks encountered in those systems have been reframed using the proposed methodology. The major benefit of the proposed EHTB-DPDP is that it offers an effectual dynamic provable data possession and data integrity. This scheme spotlights on the integrity of the remote data by reducing data storage space, so that users can retrieve data efficiently. This security enhancement is achieved by the block tagging methodology. In addition, converting the variable block size to the fixed block size using hash function is also investigated. The feasibility of the scheme is proved by aanlyzing the security and the performance
Copy-Move Image Forgery Detection using Adhoc Algorithm

It is easy make fake images by making use of editing software. It has become an effortless job to put together or detach some attributes from an image. Validation of digital images is very essential. Identifying the fake image is the crucial topic. In order to identify tampered image active and passive detection methods are used. An image can be tampered by using image splicing, copy-move, and retouching. In particular, copy-move attack is considered in this paper. It is essential to find out whether the image is tampered or not. An effective method for detecting forged image is proposed which uses adhoc algorithm. In this algorithm there is no need of original image as it compares the similar pixels in the given image. Clusters which are larger than block size are pulled out. Similar clusters are extracted using some similarity function. The method successfully detects the forged parts in the image and saves the forged image in JPEG format. The performance is measured on various images.
FRBR and Serials: An Overview and Analysis

SUMMARY. This article provides an overview of the Functional Requirements for Bibliographic Records (FRBR) with an emphasis on issues of interest to serialists. The basics of the Entity-Relationship (E-R) model are used to introduce the aspects of FRBR which can be used to model serial publications. Special attention is paid to the attributes and relationships of the article (the individual contribution in a periodical) and the article’s relationship to its “aggregated” work (the issue and the journal). This article includes a short discussion of the serial &quot;work&quot; and of outstanding issues in the application of FRBR to serial works. [Article copies available for a fee from The Haworth Document Delivery Service: 1800-HAWORTH. E-mail address: &lt;docdelivery@haworthpress.com &gt; Website: &lt; https://www.HaworthPress.com &gt;]
OPTIMIZATION OF THE PERFORMANCE OF MULTI-ARM SURGICAL ROBOT AND CONSTRUCTION OF VIRTUAL SIMULATION SYSTEM

To optimize the performance of the multi-arm surgical robot and apply it in the virtual simulation system, so as to achieve better surgical results, in this research, by analyzing the joint and position characteristics of the robotic arm for multi-arm surgical robot, the variables that need to be optimized are determined, and the operating space is divided.According to the performance index of the multi-arm surgical robot, the global isotropic function, multi-arm cooperation function, and multi-arm maximum distance function are constructed, and a comprehensive optimization algorithm combining particle swarm optimization algorithm with gaussian process is proposed.Based on this, the virtual simulation system is constructed, the weight distribution of the subspace is set, and the iterative experiment is carried out in the virtual simulation system to compare the optimization speed and accuracy of the proposed comprehensive optimization algorithm with the particle swarm optimization algorithm, so as to verify the effectiveness and rationality of the proposed algorithm.The results show that with the increase of iteration times, the values of global isotropic optimization function and the multi-arm cooperation function increase gradually, the efficiency of the multi-arm surgical robot also increases, and the proposed algorithm is effective and feasible.And the higher the set weight, the larger the simulated cooperative surgical space, which can be used as a surgical hotspot for the robotic arm positioning.The total value of the integrated objective optimization function obtained by the proposed algorithm is larger than that of the particle swarm optimization algorithm, with higher precision and fewer iterations.Without considering the weight of the subspace, it is impossible to guarantee that the multi-arm surgical robot has a high efficiency in the hot spot area.Therefore, the effectiveness of the multi-arm surgical robot can be optimized according to the algorithm proposed in this study, so as to achieve a good surgical effect and provide guidance for clinical use in the future.
The CODATA-RDA Data Steward School

Given the expected increase in demand for Data Stewards and Data Stewardship skills it is clear that there is a need to develop training, education and CPD (continuous professional development) in this area. In this paper a brief introduction is provided to the origin of definitions of Data Stewardship. Also it notes the present tendency towards equivalence between Data Stewardship skills and FAIR principles. It then focuses on one specific training event – the pilot Data Stewardship strand of the CODATA-RDA Research Data Science schools that by the time of the IDCC meeting will have been held in Trieste in August 2019. The paper will discuss the overall curriculum for the pilot school, how it matches with the FAIR4S framework, and plans for getting feedback from the students. Finally, the paper discuss future plans for the school, in particular how to deepen the integration between the Data Stewardship strand with the Early Career Researcher strand.
Spectral and Graph Clustering

The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.
Forschung: Vakuum in Forschung und Praxis 1/2019

Vakuum in Forschung und PraxisVolume 31, Issue 1 p. 11-18 Forschung Forschung: Vakuum in Forschung und Praxis 1/2019 First published: 20 February 2019 https://doi.org/10.1002/vipr.201970104AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume31, Issue1February 2019Pages 11-18 RelatedInformation
Quantum Geometric Transformations

Geometric transformations are basic operations in image processing. This chapter describes geometric transformations of images and videos. These geometric transformations include two-point swapping, symmetric flip, local flip, orthogonal rotation, and translation.
PerLS 2020: 4th International Workshop on Pervasive Smart Living Spaces – Welcome and Committees

The workshop on pervasive living spaces (PerLS) is in its 4th year.The workshop was started as a platform for researchers and developers in pervasive computing, working to create smart living spaces for human living.The workshop acknowledges significant developments that have taken place in the use of 'things' in human activities.PerLS aims to bring together researchers and practitioners with an interest in pervasive computing intended for the smart home, smart car and/or other smart living spaces.The goal of PerLS is to identify concepts, theories, and methods applicable to smart living spaces, as well as, system-oriented issues related to the design and implementation of such pervasive systems.Due to the private nature of the pervasive smart living space, particular attention will be paid to perspectives concerning risk evaluation, user-interaction, threat-modeling, and methods for enhancing security and privacy.The scope of the workshop is directed, but not limited, to such issues.Pervasive computing is moving beyond the personal devices to everyday 'things' with embedded technology getting smaller and more powerful than ever before.There have been significant activities and new developments in creating and using smart spaces for use by humans.We would like to acknowledge the valuable contributions of the program committee members during the review process.We are very
Effective Image Co-Segmentation using Modified Higher Order Algorithm

A young interactive photograph co segmentation algorithm the usage of possibility estimation then greater method electricity optimization is proposed because of extracting frequent foreground objects beside a crew concerning related images. Our approach introduces the greater method clique’s, power into the co-segmentation optimization process successfully. A region-based possibility fixity method is preceding celebrated in conformity with provide the formerly skills because our higher kilter strength function. Then, a current co-segmentation electricity characteristic the usage of higher kilter cliques is developed, who may efficaciously co-segment the foreground objects with vast appearance editions beyond a crew about photographs among complicated scenes. Both the quantitative or qualitative pilot results over consultant datasets exhibit that the rigor on our co-segmentation consequences is lots higher than the cutting-edge co-segmentation methods.
Taiwan’s Ability to Reduce the Transmission of COVID-19: A Success Story

Taiwan is known for its effective responses to COVID-19, with only 799 confirmed cases by the end of 2020. Based on the previous research, this article identifies three major technical approaches used in Taiwan to prevent the community spread of COVID-19: (1) Digital fence and entry quarantine system to track close contacts and force 14 days of in-home quarantine; (2) Evolving face mask distribution policy and system to ensure fair allocation of the limited face mask resources; and (3) Open-source software co-developed by the government and tech community to share real-time COVID-19 related information and conduct location history based contact tracing. The combat against COVID-19 in Taiwan is a success in digital governance, with great synergy between the government and every citizen.
USING OF MULTILAYER AND CONVOLUTIONAL NEURAL NETWORKS FOR RECOGNITION OF HAND-PRINTED LETTERS OF THE AZERBAIJANI ALPHABET

The implementation of information technologies in various spheres of public life dictates the creation of efficient and productive systems for entering information into computer systems. In such systems it is important to build an effective recognition module. At the moment, the most effective method for solving this problem is the use of artificial neural and convolutional networks. This paper is devoted to a comparative analysis of the recognition results of handwritten characters of the Azerbaijani alphabet using neural and convolutional neural networks. The results of numerical experiments are given.
Predicting Inpatient Status for the next 30/60/90 Days with Machine Learning

In this paper, we report the development of machine learning techniques which can help hospital authorities assess a patients' medical condition and also calculate the probability of readmission of the patient as inpatient, and thus identify patients with higher risks for readmissions. Factor Analysis is performed on patient data to understand the severity of mental health, and Random Forest models are used to determine the probability of a patient becoming an inpatient for the next 30/60/90 days from their last visit to the physician’s office. The Random Forest model fits the data with an overall OOB Error rate of 3.69% and an accuracy of 97.65%. The accuracy on the test data was 96.11%. A web application is also developed to provide a user-friendly interface for physicians and administrators to interact with and obtain relevant information for a given patient and or a group of patients. The web application affords physicians additional inputs to assist in their diagnosis and administrators, a window into anticipating and preparing for future patient needs.