Reviewerly/exHarmony huggingface dataset:
Strengthening Community Policing Culture and Practice in Nigeria for Effective Crime Prevention and Detection

With the growing insecurity in the country, many commentators are beginning to question the provision of the 1999 Nigerian constitution, Chapter 2, Section 14 (2)(b), which stipulates that “the security and welfare of the people shall be the primary purpose of government”. In Nigeria today, virtually 50 percent of headline news relates to some form of insecurity affecting almost every part of the country. From the Boko Haram insurgencies in the North East, to Herdsmen – Farmers crisis in the middle belt, to banditry and kidnapping in the North West. From South-South region battling militant agitation, to South-East security menace of separatist agitation, kidnapping and armed robbery. Of recent is the issues of kidnapping and banditry surfacing on highways within the South West region, one would rightly say that the centralized form of Policing structure being operated in Nigeria has completely failed. Hence, agitation for institutionalising the concept of Community Policing in Nigeria. Community Policing is a concept that emphasises proactive measures – preventing the act of crime through intelligence and community participation, rather than reactive policing. It is also principled on partnership and decentralisation of powers for effective crime fighting.
Spoofing Detection and Countermeasure in Automatic Speaker Verification System using Dynamic Features

This present paper aims to extract robust dynamic features used to spoofing detection and countermeasure in ASV system. ASV is a biometric person authentication system. Researchers are aiming to develop spoofing detection and countermeasure techniques to protect this system against different spoofing attacks. For this, replayed attack is considered, because of very common accessibility of recording devices. In replay spoofing, the speech utterances of target (genuine) speakers are recorded and played against ASV system for gaining access unauthorizedly. For this purpose, as a first step, different dynamic features will be extracted for each speech sample. For feature extraction MFCC, LFCC, and MGDCC feature extraction techniques are used. As a second step, a classifier is used to classify whether the given speech sample is genuine or not. As a classifier, GMM and universal background model is used. In this present work, GMM based ASV system and Countermeasure systems using different feature extraction techniques are developed, and the performance of the methods is evaluated using EER and t- DCF. Basing on the performance values, the best feature extraction technique is selected.
Protecting Composite IoT Server by Secure Secret Key Exchange for Xen Intra Virtual Machines

Challenges presented by ever increasing volumes of smart objects are huge in terms of security and privacy of data generated out of these objects. These challenges range from the usage scenario with other objects and dedicated servers which act as a middleman among various networks such as internet and wireless sensor network. In this paper, attempt has been made to highlight a simple use case of a composite server in a XEN-based virtualised system. Security threats like sniffing and spoofing are isolated and analysed with respect to a novel key transfer method between host and guest operating system in XEN-based virtualised system. In addition, secure and trust model to cater the said security threats like sniffing and spoofing is also presented. The performance of the proposed system in terms of CPU usage and network bandwidth is also shown.
Feature Selection us ing Normalized Weight Method f or Tamil Text Classification

The Feature Selection process simplify the Tamil text classification work at present we are in the information age, in this period all the applications has great growth in the domain of World Wide Web, so regional language like Tamil materials such as web pages, e-mails, e-books, and digital data has grown enormously so the retrieval of this Tamil digital document is more wanted by Tamil Document searcher. For quick retrieval of needed Tamil digitized documents among the millions of Tamil web documents, these documents should be classified by content according to their classes. The Tamil Text classification is a background work for many Tamil NLP applications such as query response, information extraction, information summarization, etc. the implementation of text categorization is very important in the information retrieval field. The text categorization assigns a document an appropriate category from a predefined group of categories. Tamil Text Classification classifies the documents based on Tamil text in a Document. Tamil language words are very rich in morphology and hence Tamil language consists of very large set of word forms. So it is important to reduce the features of Tamil text. This paper discusses about Feature selection Using Normalized weight from the huge set of key words from the preprocessed corpus. The Feature selection done by Term Weighting (TF*IDF) normalized method is reducing the size of the key word list which is very useful for training and testing Tamil text classification algorithms.
Introduction

The introduction outlines key discourses of sound through an examination of music reviews, recording technology advertisements, and sound engineering literature. Listening is theorized both from the perspective of production (evaluating and imagining music and sound in the process of shaping it) and from the perspective of reception (reviews and user discussions). A quality-oriented mode of listening is outlined, and it is argued that music listening is, for the most part, characterized by a certain aesthetic attitude toward sound, even when music listening is strongly connected to, for instance, social identification and everyday activities.
Trust Issues in Cloud Computing

Internet users are increasing at a logarithmic scale. Users are producing huge amount of data everyday across the world which is creating huge amount of data traffic. In this context data capturing , data analysis and the analysis sending it back to the stake holders is order of the day. In this scenario cloud plays an important role in big business. When cloud is playing an important role, users are using the cloud by generating data they are actually sending that data to the cloud. Here instantaneous information transformation from users to the cloud is one of the important component that is everyday happening today. Whatever data that is being produced should reside in the cloud. Whatever data which is available in the cloud should be available to the user back. This is the basic requirement as far as the users and maintainers of the cloud is concerned. In this context Trust plays an important role. In this paper we will address all the issues related to Trust, which are to be considered when someone wants to develop a trust model for cloud environment..
Enhancement Image Intensity of HSV Color Space

One of factor that affects technology in face detecting or recognizing is illumination. Poor lighting can cause difficulty to the system to recognize face. Although it is over exposure or under exposure. By doing color image processing, it supports the system to detect face in a poor lighting condition. This research used lighting intensity normalization method to increase face detection performance. This method can normalize the light intensity especially on the face lighting. We normalize the light intensity through HSV color space. HSV color space has saturation which is amount of light in the image. The method proceed saturation in image to increase face detection performance. Total number of faces we had tested is 286 faces, the system detect 243 faces before intensity normalization proceed. Whereas, after normalization process, it detects more faces which is 279 faces. As we can see, the percentage improvement before to after intensity normalization is 84.97% to 97.55%. This is 12.58% improvement. We can say this method helps face detection to increase it performance.
Watermarking for Images using Alphanumeric Technique

In the current daily routine the sharing of digital content is increasing in large scale through social media. Every individual is sharing his/her activities like photos, message, etc. through social media (i.e. WhatsApp, Facebook, etc.) to other persons without authentication of all these data and through this general activity of people some people are doing fraud to them or misuse of these data or illegally showing to other people without his concern. To ensure the authentication of digital contents this paper proposes methodology to prove the ownership by Digital watermarking and Intellectual Property Right (IPR). This paper explains the concepts of IPR and proposed alphanumeric watermarking technique for authentication of images and its protection from the fraud or misuse. Digital Watermarking has become one of the research areas in many fields from more than decades. This paper gives the methodology of embedding and extraction techniques for images to prove the authentication of images when required its demand. The result of this proposed technique is shown very helpful to society at a globe.
Analisis Aspek Kompetensi Individu dalam Duplikasi Nomor Rekam Medis di Puskesmas

Duplication of medical record number is one of the problems in medical record section. Duplication that occurred in the Labruk Kidul Public Health Center was 10,3% consist of 7,6% duplicate one medical record number owned by some head of families, and 2,7% duplicated one head of family with some medical record numbers. The purpose of this research is to analyze the priority causes of duplication medical record of numbers in the Labruk Kidul Public Health Center Lumajang. It was a qualitative research and the data collection techniques used interviews,and observations. The subject of this research was registration officer, head of the medical record and head of the public health center. The results showed that the cause of duplication from individual competency factors was that the officer could not make KIUP, the officer did not write the. The solution is to socialize the system of numbering and additional time and labor in finding duplicate medical record number and merging files.
Artificial Neural Network for Rainfall Prediction Base on Historical Rainfall Data by Day

Increasingly erratic rainfall has a significant effect on agriculture, and health.One field of agriculture that utilizes rainfall patterns is paddy fields.Rice farmers need a calculation of rainfall to determine the process of hatching and planting rice, especially for farmers who have rainfed rice fields.While in the health sector, rainfall greatly determines the growth of mosquitoes which can lead to dengue fever.The health office needs calculations to anticipate the number of people affected by dengue fever.In this study two data modeling were made in predicting rainfall in the Banyumas area, one data model or scenario using weather data other than rainfall such as temperature, humidity, wind speed, duration of solar radiation.While scenario two only uses historical rainfall data per day.In making models and testing data using the Artificial Neural Network (ANN) algorithm.The results showed that scenario two was better than scenario one after being evaluated using the Root Mean Square Error (RMSE).The RMSE obtained in scenarios one and two are 21,667 and 20,448, respectively.It can be concluded that to predict rainfall in the Banyumas area it is better to use historical rainfall data per day compared to using other weather data.
BHGE changes name to Baker Hughes Company

Baker Hughes, a GE company (BHGE) has changed its name to Baker Hughes Company.
Image Manipulation

Think you have a keen eye for spotting tampered images? Now what if the image has been manipulated so well that the average person is easily fooled? Neural networks can aid in finding subtle features of images and identify which ones are authentic and which ones have been modified. This is called image forensics.
A Tool for Analyzing Software Requirements Document Quality

The quality of a Software Requirements Specification (SRS) is measured in terms of quality properties such as completeness, conciseness, consistency and understandability. In general, evaluation of the SRS quality is done manually during review sessions. The evaluation process, however, is hugely dependent on the expertise of human experts i.e. the reviewers. In fact, the judgment of the human experts could also be inconsistent due to various factors including experience, knowledge and domain. The objectives of this study are to (1) identify feasible rules to measure SRS quality; and (2) help requirements engineer to improve their SRS quality. In this study, we analyzed SRS quality properties from the literature and identified quality factors that are feasible to be automated. From here, we identified two types of properties that are (1) requirements sentence quality (RSQ) and (2) requirements document quality (RDQ). For each of the type, its relevant quality indicators were identified. From here, rules on how to identify the quality indicators were further investigated and documented. As a case study, we implemented SRS Quality-Checker tool concept for demonstrating how the rules were implemented to measure the SRS quality
Euler Movement Firefly Algorithm and Fuzzy Kernel Support Vector Machine Classifier for Keystroke Authentication

User authentication can be successfully employed using keyboard typing patterns which is a form of behavioural biometrics. This modern method is highly analyzed for static authentication which refers to typing of fixed texts like ‘password’ and ‘pin numbers’. Most of the methods with respect to keystroke dynamics are restricted to the study of user’s activity involving fixed text. The formulated work concentrates on the investigation of the log of the user activity focused on the keyboard usage within the computer system through free text which refers to typing of texts throughout the login session. The Buffalo dataset is used in User Profiling Similarity Measurement (UPSM) stage and to recognize the time slice of the users, Euler Movement Firefly Algorithm (EMFA) is utilized. The typing behaviour is formulated in the form of time series in User Profiling Continuous Keystroke Authentication (UPCKA). Moreover the progression is made to user’s Continuous Authentication so as to predict unauthorized users with the consideration of the classifier called Novel Fuzzy Kernel Support Vector Machine (NFKSVM). The experimental results provide the enhanced performance by utilizing the formulated UPCKA in correlation with the NFKSVM classifier when compared with SVM and Iterative Keystroke Continuous Authentication (IKCA) techniques.
Machine Learning Based Supervised Feature Selection Algorithm for Data Mining

Data Scientists focus on high dimensional data to predict and reveal some interesting patterns as well as most useful information to the modern world. Feature Selection is a preprocessing technique which improves the accuracy and efficiency of mining algorithms. There exist a numerous feature selection algorithms. Most of the algorithms failed to give better mining results as the scale increases. In this paper, feature selection for supervised algorithms in data mining are considered and given an overview of existing machine learning algorithm for supervised feature selection. This paper introduces an enhanced supervised feature selection algorithm which selects the best feature subset by eliminating irrelevant features using distance correlation and redundant features using symmetric uncertainty. The experimental results show that the proposed algorithm provides better classification accuracy and selects minimum number of features.
A Wavelet Based Unsharp Masking Algorithm for Enhancing Lung Images

Low contrast and poor quality are the often cited problems in the generation of medical images. In this paper the lung images are sharpened using a technique called Unsharp Masking for contrast enhancement. A simple algorithm using wavelet based unsharp masking is developed in order to improve the quality of the visual data and provide insights to the physician for better and faster diagnosis of diseases. The proposed algorithm and the sharpened images with increasing level of intensity are illustrated. Experimental results shows that in addition to enhancing the details of the image, the proposed algorithm also preserves the edge features of the lung images effectively.
A Higher Order Fuzzy Logic Model with Genetic Algorithm Used to Predict the Rice Production in India

Forecasting paddy production is considered as a difficult problem in the real world due to in deterministic behavior of the nature. Specifically, rice production is forecasted for a leading year for overall planning of the crop, utilization of the agricultural resources and the rice production management. Likewise, the key challenge of the forecasting rice production is to create a realistic model that can able to handle the critical time series data and forecast with minor error. Prognostication of the Future data is highly correlated with the time series data set. If the accuracy of your prediction is more appropriate, then the value of the forecast will improve as well. This paper represents a new technique depends on Higher Order Fuzzy Logical Relationship. Here, Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) are used to estimate the errors of predicted data. historical data relating to the rice production of 1981 to 2003 is used as secondary data and the error of the predicted data is further reduced using different soft computing technique.
Context-sensitive contrastive feature-based opinion summarisation of online reviews

Contrastive opinion summarisation (COS) systems produce summary by selecting and aligning contrastive sentences from a set of positive and negative opinionated sentences. Most of the existing COS methods do not consider the implicit opinion present in a sentence while producing summary. Implicit opinion can be identified based on context terms present in a sentence. Therefore, a new COS approach called context-sensitive contrastive opinion summarisation is proposed. Initially linguistic rules are framed based on dependency relation to extract context-feature-opinion phrases. To automatically cluster the extracted context-feature-opinion phrases into contrastive arguments, a clustering algorithm is proposed. Context sensitive weight is calculated for each phrase based on their probability of occurrence in the concepts of ConceptNet. Clustering algorithm integrates context sensitivity with contrastive similarity for producing better arguments summary. Experimental conducted on car and product review datasets demonstrate that the context-sensitive clusters achieved good coverage and precision when compared to state-of-art approaches.
Cryptographic algorithms

In this chapter, different types of cryptographic algorithms applicable for security in IoT, cloud computing, and Big Data are presented. IoT, cloud computing, and Big Data are based on a highly interconnected network of devices, servers, and massive volumes of data where all kinds of communications seem to be possible. Therefore, the security requirement for such technologies becomes critical, which should be provided by cryptographic algorithms. Two main categories in cryptography are symmetric key cryptography and asymmetric or PKC. The applicable methods for PKC are rather slow compared to symmetric key cryptographic algorithms. Therefore, PKC is used as a complement to symmetric key cryptography for providing security in IoT, cloud computing, and Big Data.
A Security Framework for A Sustainable Smart Ecosystem using Permissioned Blockchain: Performance Evaluation

This world always revolves around technology. There is always some disruptive technology that topples down any technology that people normally consider as a de-facto standard or norm. The Internet was one of these disruptive technologies which turned the whole way of how people were communicating with each other. It has revolutionized computing and communication. After a few decades of disruption, today people have accustomed to make their jobs easier with automation, remote control over the Internet, unsupervised actuation etc., This is all feasible with the advent of Internet of Things (IoT). IoT is one of the disruptive technologies of this era. Consumers of IoT are of different sorts ranging from, normal home automation to space engineering. These consumers share important details in these IoT networks which are susceptible to attacks from intruders who abuse the flaws of the IoT network for their own benefit. IoT security is one of the major concerns among all the issues of implementing an IoT based solution. Blockchain another disruptive technology is amalgamated to form a robust secure Smart Home Ecosystem. An Ecosystem is designed to incorporate IoT and Blockchain seamlessly. Where services could be automated and operated in a secure manner. This paper proposes the performance results of a Security framework for a Sustainable Smart Ecosystem using permissioned Blockchain.
Design and Built an Expert System Application Fir Diagnosing Human Eye Diseases by using Forward Cahining Metodh Web-Based

The eye is one of the five senses that has a very important role in human life, namely as the organ of vision. If there is interference with the eye or there is a disease, it is very influential on human vision. Its mean that we can conclude that eye is very important in our lie and must be keep for our body. The purpose of this research to planning and develop the diagnose of expert system about diagnosing of diseases in the eye which include disease information about symptoms or the solution and have role to replace and imitate the process of the reason from an expert in solve the specification problem. the method used for reasoning is forward chaining method. The result of this research is expert system of diagnosing eyes diseases in humans which computerized who can be used for giving information and have benefit in diagnosing disease
Valeant Pharm. Int'l, Inc. v. Mylan Pharm. Inc. United States Court of Appeals of the Federal Circuit, 2020 955 F.3d 25

Biotechnology Law ReportVol. 39, No. 3 As presented by Chris HolmanValeant Pharm. Int'l, Inc. v. Mylan Pharm. Inc. United States Court of Appeals of the Federal Circuit, 2020 955 F.3d 25Edited for Biotechnology Law Report by CHRISTOPHER M. HOLMAN*Published Online:3 Jun 2020https://doi.org/10.1089/blr.2020.29175.cmhAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 39Issue 3Jun 2020 InformationCopyright 2020, Mary Ann Liebert, Inc., publishersTo cite this article:Valeant Pharm. Int'l, Inc. v. Mylan Pharm. Inc. United States Court of Appeals of the Federal Circuit, 2020 955 F.3d 25.Biotechnology Law Report.Jun 2020.256-261.http://doi.org/10.1089/blr.2020.29175.cmhPublished in Volume: 39 Issue 3: June 3, 2020PDF download
Machine Learning Algorithms for Human Activity Recognition

It becomes essential to monitor the Activity of Daily Living(ADL) of elderly people living alone by keeping track of their day to day activities &amp; helping those having strong health issues. In this paper various machine learning algorithms for human activity recognition is analyzed. Along with this, an extensive study is carried out to learn about the current technologies used in activity recognition. Activity recognition is generally done in the form of signals generated through sensors. The signals are then preprocessed, segmented, features are extracted and activity is recognized. The main objective of Human Activity Recognition System is to explore the limitations of self-dependent old age persons and suggest ways of overcoming it. By using the different wearable and non-wearable sensors, one can easily monitor the human activity and evaluate the data generated through it.