Reviewerly/exHarmony huggingface dataset:
Crime Analysis and Intelligence System Model Design using Big Data

The advent of new trend in information and communication technology specifically data science, machine learning and artificial intelligence unleashed various opportunities and offers solution to distinct level of problems in various domains.Globally, many countries have adopted the use of data driven technologies and crime analysis to predict and handle crime patterns and logics.However, most countries under-use this technology by using conventional or traditional techniques in crime analysis.A large amount of data is rapidly generated by various agencies of the government and independent organizations especially in Nigeria; agencies share common objectives or mandates.Other contributing factor is enforcement personnel ratio to the total population density.Thus, technology is required to complement the lack of adequate personnel.As a result, this research is aimed at designing a crime analysis and intelligence model using big data.Document Analysis, Questionnaire, and Interview is used to collect data from various law enforcement agents.Microsoft excel is used to generate accurate result and visualized the result in form of a pie chart, while UML models are used to depict logical and physical schema of the proposed model.The results analysis supports the hypothesis of the research by revealing the manual and traditional techniques of policing and crime analysis.Most of the agencies operate and keep track of records manually, while technology is applied to track cellphones.Moreover, a high majority of the respondents lack basic computer literacy and modern crime analysis techniques and big data.Hence, most of the respondent wish to adopt the use of big data analytics.
THE ART OF ENHANCING VOCABULARY THROUGH TECHNOLOGY

Art is a learning strategy or tool used by the teacher currently. Integrating art in learning can stimulate students’ brain and makes learning activity become more effective and interesting. This study used technology-Quizlet as the art of enhancing vocabulary. Vocabulary is the most important tool in learning English to communicate, either orally or written. This was a quantitative research with a comparative design, using 32 students in the experimental group and 32 students in the control group. The pre and post-test designs were used. Results showed that both groups enhanced in vocabulary. Further, there was a significant difference between the two groups. It can be concluded that Quizlet Application is an effective strategy to enhance students’ vocabulary mastery.
Application of Correlation-Regression Method to Correlate the Statistical Relationship between the Parameters of Some Indexed Journals

This paper proposes an application of correlation-regression method to establish the statistical relationship between acceptance rate &amp; first decision time of some indexed journals. We collect the data from two web sources: Elsevier journal finder, and Springer journal suggester. In this problem, we concentrate on the data of the acceptance rate and first decision time. To examine the relationship between these measures, we apply a statistical approach, which is based on correlation and regression analysis. We determine the relative error of the data collected. We plot the scatter diagram between the two measures. Correlation coefficient value is also calculated, that checks the significant connection between the two variables. The correlation analysis and regression analysis demonstrate that there is a moderate positive correlation between acceptance rate &amp; first decision time.
Symptoms to Disease Mapping and Doctor Recommendation System

To find an appropriate doctor who is specialized to treat a certain disease while only symptoms are known is not easy job for the patients. In this paper, we describe a recommended framework to find the best doctors in accordance with patients' requirements. In the proposed system, first it considers only those doctors whose profile match with patients' requirements. Second, the best doctors will be recommended out of previously obtained doctors based on the parameter patients' feedback i.e., patients' review. Our proposal will suggest a doctor recommendation system that uses review mining technique, which can be used in those countries that have huge uneven distribution of medical resources. In our model we have used the decision tree for symptoms to disease mapping and Naive Bayes classifier for sentiment analysis which are connected to each other using a bridge of python logic and the required output is top doctors based on the users input
EbolApp – A digital tool for smart contact tracing in infectious diseases (HCID) outbreaks

Background: Outbreaks due to infectious diseases like Ebola virus disease require quick and proper epidemiological responses to implement effective countermeasures. Traditional epidemiological tools are often not easy to apply. PC-based digital tools can be cumbersome in field work. As smart phone adoption is very high in many parts of the world, smartphone-based tools could be an alternative. An app was developed for this purpose and tested in a proof-of-principle study. Methods & materials: EbolApp is an Android-based application that assigns identifiers to smartphones after installation and stores GSM/GPS tracking data on the mobile. With activated Bluetooth function any further device within a defined distance is recorded. If a user contracts a disease, ID-based user profiles are synchronized on a server by an alarm function, which informs any detected contact person and recommends a doctor's visit. Server data will be completely deleted afterwards. Individual movement profiles can be read out by authorized persons. A proof-of-principle analysis regarding compatibility and functionality was carried out by health care workers from a clinic for infectious diseases and tropical medicine. Results: On a total of 16 smartphones with Android version 6, 7 and 8, the app (Version 0.9) was installed without any problems. The complete app functionality was given for 13/16 smartphones (81.3%), no tracking was recorded in two, in one the app crashed while reading profiles. Battery consumption with continuously activated Bluetooth was significantly increased in 50% of users. Thirteen users were able to use the secure alarm function receiving corresponding contact warnings. Graphic mapping and temporal assignment of the motion profiles confirmed the correctness of the alarms. Conclusion: EbolApp is promising digital tool for contact tracing in infectious diseases. It allows fast track detection of contacts and can provide information as well as instructions to those which are at risk. This also may allow timely use of post-expositional vaccinations or medical therapies. The prerequisite is the adequate distribution of appropriate telephones within risk populations and compliance with corresponding data protection laws. In the updated Version 2.0, compatibility of the app has been improved again. A field trial in the Republic of Liberia is on the way.
Hybrid SVR-ACNN model: Proposed Video Super-Resolution method for video enhancement

Video super-resolution techniques are the need of the hour for the high-resolution display devices as the current high-resolution videos are the basic question.Even though there are a large number of techniques employed for the video super-resolution, all these existing techniques face a hectic challenge at various conditions.Thus, this paper proposes an effective video resolution strategy using the hybrid Support vector regression-Actor Critic Neural Network (SVR-ACNN) model for video enhancement.The super-resolution images formed using the individual SVR model and Actor Critic Neural Network are integrated using the weighted average concept.The Actor Critic Neural Network is tuned optimally using the proposed Fractional-based Sine Cosine algorithm (F-SCA) that is responsible for the global optimal convergence.The experimentation of the proposed method utilizes three videos taken from the Cambridgedriving Labeled Video Database (CamVid), and the results are analyzed for three scaling factors.The outcome of the analysis proves that the proposed method offers a better super-resolution image with a better PSNR, SSIM, and SDME of 33.6447dB, 0.9398, and 45.2779, respectively.
HDL Implementation of Five Moduli Residue Number System

The demand for residue number system (RNS) is increasing day by day because of its high speed and fault tolerant characteristics. RNS encodes a large number into group of small numbers, which consequently increases the overall data processing rate. This paper presents an analysis of the forward converter designed using ripple carry adder (RCA), carry save adder (CSA), and half adder-like (HAL), for the figure of merits area, delay, and power for five moduli set: 2n-1, 2n, 2n+1, 2n+1-1, and 2n-1-1 with the standard cells at 90 nm technology. The designing of different blocks has been done in Verilog-HDL. The area, delay, and power of the implemented circuits are obtained using the Synopsys Design Compiler at 90 nm technology node, while VCS is used for verification. It is observed that the area of the architecture using CSA is less, whereas power utilization and timing behavior are better in HAL.
Matherpiece: An Introduction to Math E-Learning PC Game Application for Grade 1 Pupils

The purpose of this project is to help and encourage Grade 1 students with the ages ranging 6-7 to improve their math skills and make mathematical concepts more interactive. It is built with the use of a Kinect device with a motion sensor that enables the users to control the game application through hand gestures. The project covers topics like counting, addition, subtraction, patterns, and comparing numbers. The application has lessons with learning activities and quiz as for the assessment. The application can run on Windows OS 10 with a screen display of 1366 x 768. It was developed using Microsoft Visual Studio 2015, C# for the programming language, Kinect SDK for references, and Adobe Photoshop CS6, Moovly and Sony Vegas Pro 14 for the design, videos and audios. The researchers successfully conducted test cases such as the functionality of the overall features, the stress test for stability of the Kinect device and compatibility testing for the software and hardware requirements. The evaluation was participated by ten (10) IT experts and thirty-five (35) end-users comprised of Grade 1 pupils with their parents and Grade 1 teachers. The user’s acceptability of the project was based on the criteria of PC Game application such as functionality, engagement, and aesthetics. The overall evaluation results garnered an average mean of “3.68” and a standard deviation of “0.10” with an interpretation of “Highly Acceptable”. Hence, participants strongly agreed that the project can be an educational tool in learning basic mathematics for children.
Mobile Application for Breast Cancer Diagnosis Using Morphological Associative Memories Implemented on a Cloud Platform

The astounding advances that have been observed in mobile device technologies and their underlying algorithms have prompted a worldwide surge in attention to their capabilities and potential for improving different human activities. The present work is framed by the academic cooperation process between Mexico and Saudi Arabia; it consists of the description of the design and development of a mobile information system aimed at performing diagnosis and verification of breast cancer using an application for mobile devices. The problem to be solved is represented as a binary classification problem between healthy patients and people that have been confirmed as control cases. The classification algorithm is a hybrid model, consisting of Morphological Associative Memories and the k-Nearest Neighbor classifier. The hybrid model improves upon the performance of its components. The proposed model was implemented on a cloud computing platform in order to optimize the response time for the diagnosis. A comparative study of our proposal and the state of the art shows that the proposed mobile information system has a high classification performance as well as a low false positive rate.
Big data Performance Evalution of Map-Reduce Pig and Hive

Big data is nothing but unstructured and structured data which is not possible to process by our traditional system its not only have the volume of data also velocity and verity of data, Processing means ( store and analyze for knowledge information to take decision), Every living, non living and each and every device generates tremendous amount of data every fraction of seconds, Hadoop is a software frame work to process big data to get knowledge out of stored data and enhance the business and solve the societal problems, Hadoop basically have two important components HDFS and Map Reduce HDFS for store and mapreduce to process. HDFS includes name node and data nodes for storage, Map-Reduce includes frame works of Job tracker and Task tracker. Whenever client request Hadoop to store name node responds with available free memory data nodes then client will write data to respective data nodes then replication factor of hadoop copies the blocks of data with other data nodes to overcome fault tolerance Name node stores the meta of data nodes. Replication is for back-up as hadoop HDFS uses commodity hardware for storage, also name node have back-up secondary name node as only point of failure the hadoop. Whenever clients want to process the data, client request the name node Job tracker then Name node communicate to Task tracker for task done. All the above components of hadoop are frame works on-top of OS for efficient utilization and manage the system recourses for big data processing. Big data processing performance is measured with bench marks programs in our research work we compared the processing i.e. execution time of bench mark program word count with Hadoop Map-Reduce python Jar code, PIG script and Hive query with same input file big.txt. and we can say that Hive is much faster than PIG and Map-reduce Python jar code Map-reduce execution time is 1m, 29sec Pig Execution time is 57 sec Hive execution time is 31 sec.
A visual approach for user-guided feature fusion

Dimensionality Reduction transforms data from high-dimensional space into visual space preserving the existing relationships. This abstract representation of complex data enables exploration of data similarities, but brings challenges about the analysis and interpretation for users on mismatching between their expectations and the visual representation. A possible way to model these understandings is via different feature extractors, because each feature has its own way to encode characteristics. Since there is no perfect feature extractor, the combination of multiple sets of features has been explored through a process called feature fusion. Feature fusion can be readily performed when machine learning or data mining algorithms have a cost function. However, when such a function does not exist, user support needs to be provided otherwise the process is impractical. In this project, we present a novel feature fusion approach that employs data samples and visualization to allow users to not only effortlessly control the combination of different feature sets but also to understand the attained results. The effectiveness of our approach is confirmed by a comprehensive set of qualitative and quantitative experiments, opening up different possibilities for user-guided analytical scenarios. The ability of our approach to provide real-time feedback for feature fusion is exploited in the context of unsupervised clustering techniques, where users can perform an exploratory process to discover the best combination of features that reflects their individual perceptions about similarity. A traditional way to visualize data similarities is via scatter plots, however, they suffer from overlap issues. Overlapping hides data distributions and makes the relationship among data instances difficult to observe, which hampers data exploration. To tackle this issue, we developed a technique called Distance-preserving Grid (DGrid). DGrid employs a binary space partitioning process in combination with Dimensionality Reduction output to create orthogonal regular grid layouts. DGrid ensures non-overlapping instances because each data instance is assigned only to one grid cell. Our results show that DGrid outperforms the existing state-of-the-art techniques, whereas requiring only a fraction of the running time and computational resources rendering DGrid as a very attractive method for large datasets.
Industrializing Your Web Application Development Project

Abstract In any software development course, a good project is key. It challenges students and drives decisions about other course material. Keeping it fresh and up-to-date can be a challenge, especially in fast-changing areas like web or mobile development. This paper presents two distinct experiences and insights (one of us is a university professor, and one is an industry professional) on "industrializing" a full-semester project in a web application development course that we co-teach. By industrializing, we mean evaluating the project with a focus on whether it is consistent with current industry practices, and then developing the project as any good company would – using best practices from software engineering and industry, such as iterative development, continuous integration, refactoring, and regression testing. Over the course of only three years, we went through not one, but two major revisions of a semester-long project, and we anticipate more revisions in the future. We began with a project that was effective from a teaching standpoint but was in need of a significant update to its design and implementation. We first discuss the environment in which the course is taught. It is part of a graduate, online degree program that caters to working professionals from a variety of backgrounds. We then introduce the project - a mini e-commerce web site that is presented to students over the course of the semester in 10-12 separate assignments. We detail why and how the project implementation was modified, despite the fact that the functional requirements remained relatively stable. We then summarize the lessons learned from these revisions and talk about how we plan to manage changes in the future. Finally, we offer suggestions on how others might go about industrializing their own software development projects.
Multi-Modal Biometrics Systems: Concepts, Strengths, Challenges and Solutions

Authentication is a very important aspect of computer security. Most systems employ strategies such as Password-based authentication, Multi-factor authentication, Certificate based authentication which are accompanied with a lot ofchallenges. To address this issue, most security systems have introduced the use of biometrics for authentication. Unimodal biometrics systems have many limitations regarding performance and accuracy. The use of Multimodal biometrics systems for authentication is recently attracting the attention of researchers due to its capacity to overcome most of the drawbacks of Unimodal biometric systems. This paper focuses on the use of biometric technology for authentication. The strengths of multimodal biometric systems, together with the challenges of multimodal biometric systems are presented. The paper also suggests solutions to the challenges of multimodal biometric systems.
Face Recognition System for Attendance Marking

The executives of the participation can be a diligent work on the instructors/representatives in the event that it is finished by hand. To take care of this issue, keen and auto participation the executives framework is being utilized. This paper shows a model of a mechanized participation framework to lighten the manual exertion of chronicle information taking out the odds of fraudulency which spotlight on face acknowledgment with credentials recognize the approved understudies/workers and includes as they enter and go out from the class-room. Savvy Attendance System Maintains and keeps the true record of each enrolled understudy and workers. In addition, this savvy framework keeps the information of each understudy/representatives enrolled for a specific course/work in the participation log and gives vital data as indicated by the need. By perceiving the substance of the individual and checked by face acknowledgment calculation all the while in our task, the constraints in the current manual participation framework are for the most part dispensed with. Another additional element to the venture is that we are sparing vitality by actualizing a framework utilizing IR modules where the room's hardware possibly turn on when there are individuals inside.
Equivalence, (Crypto) Morphism and Other Theoretical Tools for the Study of Information

The meaning of information can be understood as a relationship between information systems. This study presents a brief outline of theoretical tools for the analysis of this relationship. Considering the informational character of reality, it is natural to extend the relationships between signs to include the concept of meaning as another instance of a relation between the informational entities of a sign and its denotation. However, this approach to the semantics of information does not require any specific ontological commitment, as the intention is always directed towards the object presented to us as a structural manifestation of information. Whether there is something that differs from this informational structure and is beyond our capacity to comprehend directly, or whether there are objects that are the result of our own active engagement in their formation, is a matter of ontological position, with respect to which our approach is neutral. The experience of logic tells us about the dangers of self-reference and the problem of the non-definability of the truth, demonstrated by Tarski. To avoid similar problems, we need precise theoretical tools to analyze relationships between information systems and between instances of information involved in semantics. These tools are also necessary for the definition and analysis of levels of informational abstraction that extend beyond the traditional linguistic and logical context.
WhatsApp Encryption- A Research

In the recent years, battle between national security and a person’s privacy has become prime conflict. Governments and secret services are asking encrypted messaging services such as WhatsApp to allow them access to user’s data. After the March attack at Westminster(2017), UK home secretary, stated that accessibility of WhatsApp's data is mandatory to authorize the legitimate user since the government couldn’t smell the encrypted messages of suspected terrorists. But in turn such access to messages will allow authorities to thwart future terror attacks. In this paper, novel enhanced End to end encryption technique is proposed to identify the breaches and mitigate the terror attacks. Also different encryption techniques are compared with respect to their performance and the best encryption technique is revealed
Comprehensive Analysis of Machine Learning Algorithms for Face Detection

Face detection is the most common application used in security system, cameras, fun face filter apps, etc. many techniques and algorithms are introduced by developers for face detection in real time but all techniques or algorithms does not give best results while applying on all ranges of processors. In this, three machine learning algorithms i.e. Histogram of Oriented Gradient, Haar cascade classifier and deep neural networks implemented on different processors for verifying processing speed of each algorithm on the different processor.