Reviewerly/exHarmony huggingface dataset:
Increasing Organisations Security by Enhancing Access Control Methods

Physical security is the assurance of Personnel, Hardware, Software, systems and information from physical activities and occasions that could make genuine misfortune or harm an undertaking, organization or foundation which incorporates insurance from fire, flood, natural disasters, burglary, theft, vandalism and fear mongering.Physical security has three significant components: access control, surveillance and testing.In this paper we will discuss get to controls and the amount they are helpful and significant for the physical security so as to make sure about our systems.Some get to controls are fencing, locks, access control cards, biometric get to control, mantraps and so on. Physical access control involves who, where, and when. An entrance control framework figures out who is permitted to enter or leave, where they are permitted to exit or enter, and when they are permitted to enter or exit. Verifiably, this was in part achieved through keys and bolts. At the point when of entrance is bolted, just somebody with a key can enter through the entryway, chance upon how the lock is designed. Mechanical bolts and keys don't permit limitation of the key holder to explicit occasions or dates. Mechanical bolts
Large Scale Civilian Digital Identification Systems- A Technological Perspective

Human identity is a notion that takes into consideration the philosophical and physiological aspects. Philosophical aspects of identity narrates specifically the sentiments of humans that arises from the social status of an individual and identify individuals as belonging to a particular group. However, the physiological aspects takes into account the identification of the humans on the basis of some physical parameters such as height of an individual or the biometric traits. The Issues of national importance have steered the need for the setting-up of the formal identification system for the establishment of the physiological identity of the residents of a country, especially the digital identity. The issues of national importance include national security, border security and delivery of the government services to the residents of a country in an electronic mode etc. Before the advent of the Information and Communication Technology (ICT), documents such as passport, driving license and ration card etc. were used to establish the human identity in order to avail various services of the government and non- government organizations. ICT has enabled the government across the world to devise digital identification systems for providing the digital identity to the individuals as well as mechanisms for the authentication of the digital identity. Depending upon the national policy, some of the countries have made it compulsory and some others have made it voluntary to enroll the residents of the country into the digital identification system. The digital identification systems can take different technological shapes depending upon the variables such as the state of the art of the digital infrastructure in a country, population of a country, literacy rate in the country and the geographic dimensions of a country. The paper discusses the large scale civilian identification systems of the countries that are the pioneer in the setting up of the large scale civilian digital identification system and the technological issues involved therein.
Document Organization using Naive Bayes Related Classifier

Document organization is necessary for better utilization of documents. The major problem of organization online documents is so complex because documents should be grouped into its appropriate group during its appearance on the web. Classification is one of the best solutions to organize the documents. Naive Bayes categorization is playing a vital role in document organization. It is one of the simplest probabilistic Bayesian categorization and assumption that the effect of an attribute value on a given category is independent of the values. The document classification is the essential task of organization and necessary for efficient control of textual fact systems. The files may be classified as unconfirmed, supervised and semi supervised methods. In this paper, to review and study of various types of document organization approach using naive Bayesian classification and other related existing document organization methods.
Assessment of the Magnitude of Goiter in the Age Group of 6-12 Years in district Bandipura of Kashmir Division

Introduction: Iodine deficiency disorders (IDD) has been recognized as a public health problem in India Enlargement of thyroid gland is the common manifestation of the IDD and goiter prevalence survey is used as diagnostic tool for identifying areas of IDD.Failure to undertake early detection and intervention measure results in secondary disabling conditions.Aim: In the present study we have estimated the prevalence of goiter in the age group of 6-12 years in district Bandipura of Jammu and Kashmir state, India and have assessed type of salt consumed by the population. Material and Methods:This was a Cross-sectional study conducted in Bandipura district of J and K state and the study was conducted between July 2018 to October 2018.Sampling method was Gender stratified cluster sampling technique and the sample size was based on a goiter prevalence of 30% and 95% confidence interval.30 clusters were selected and each cluster included 90 children.Data was entered in Microsoft excel and then analyzed using appropriate statistical software.Data was interpreted using percentages, means and SD. Results:The prevalence of grade 1 and grade 2 goitre in males was 35.6% and 15.1% respectively whereas it was 30.8% and 15.1% in females and the prevalence was more in males to the extent of 52.08% and in girls it was 49.23%.Goitre was found to be highest (51.7%) in subjects 12 years of age and lowest (40.6%) in subjects aged 7 years of age.There was a significantly higher goitre prevalence in children who consumed crystalline/non-iodised salt than in children consuming iodised salt.The TGR was 61.4% and 46.9% in children consuming non-iodised and iodised salt respectively Conclusion:We conclude our study with the finding that population of Bandipura district in the north Kashmir is severely iodine deficient.We recommended a major thrust in the implementation of NIDDCP in the said district with regular and continuous monitoring of iodine status.
Designing Framework for Real Time Twitter Data Analytics using Apache Flume and Pig

In the world of technology, people prefer social media to express themselves. Record says Twitter has more than 321 million active users with 100 million users posting approximately 340 million tweets a day. Twitter is the largest source of breaking news on social issues specially election-related where people can express their views also suggest their opinion. Twitter is generating unlimited unstructured text data. Hadoop is one of the finest tools accessible for analyzing twitter data because it supports processing of distributed big data, streaming data, time stamped data, text data etc. Whereas Apache Flume is used to extract real time twitter data into HDFS. This study attempts to establish an analytical framework to derive and interpret structured as well as unstructured Twitter data. The proposed framework comprises of real time twitter data insertion, its processing, and data visualization utilizing Apache Flume and pig. In this project we fetch positive and negative tweets on election data from twitter and analyzing the party status and the probability to win the election.
Query Computation Time and Its Performance over Distributed Database Frameworks

It is essential to maintain a relevant methodology for data fragmentation to employ resources, and thus, it needs to choose an accurate and efficient fragmentation methodology to improve authority of distributed database system. This leads the challenges on data reliability, stable storage space and costs, Communication costs, and security issues. In Distributed database framework, query computation and data privacy plays a vital role over portioned distributed databases such as vertical, horizontal and hybrid models, Privacy of any information is regarded as the essential issue in nowadays hence we show an approach by that we can use privacy preservation over the two parties which are actually distributing their data horizontally or vertically. In this chapter, I present an approach by which the concept of hierarchal clustering applied over the horizontally partitioned data set. We also explain the desired algorithm like hierarchal clustering, algorithms for finding the minimum closest cluster. Furthermore, it explores the performance of Query Computation over portioned databases with the analysis of Efficiency and Privacy.
A gaussian filter-based feature learning approach using genetic programming to image classification

© Springer Nature Switzerland AG 2018. To learn image features automatically from the problems being tackled is more effective for classification. However, it is very difficult due to image variations and the high dimensionality of image data. This paper proposes a new feature learning approach based on Gaussian filters and genetic programming (GauGP) for image classification. Genetic programming (GP) is a well-known evolutionary learning technique and has been applied to many visual tasks, showing good learning ability and interpretability. In the proposed GauGP method, a new program structure, a new function set and a new terminal set are developed, which allow it to detect small regions from the input image and to learn discriminative features using Gaussian filters for image classification. The performance of GauGP is examined on six different data sets of varying difficulty and compared with four GP methods, eight traditional approaches and convolutional neural networks. The experimental results show GauGP achieves significantly better or similar performance in most cases.
International Conference on Mathematics: Pure, Applied and Computation

On behalf of the ICoMPAC 2018 organizing committee, I am honored and delighted to welcome you to the third International Conference on Mathematics; Pure, Applied and Computation (ICoMPAC 2018) at Majapahit Hotel, Surabaya. Originally, this event was planned to be held in Palu. Because of the disasters, we change the location to Surabaya. In this opportunity, I would like to express my sincere sympathy to people in Palu and Donggala for the tsunami and earthquake disasters.
Pertinent Exploration of Privacy Preserving Perturbation Methods

Digital era generates a huge amount of data in many sectors like education, medical, banking, business, marketing, etc. which can be used for research motive, analysis, prediction of trends, statistics, etc. Data mining techniques are useful in finding patterns, trends, and knowledge from such huge data. The data holders are not ready to share data because there are chances of privacy leakage. Sharing of such data immensely helps researchers to obtain knowledge from it, especially medical data. Privacy preserving data mining is one way where researchers will get mine data for gaining knowledge without breaching the privacy. In the medical sector there is a branch called the mental health section, where high confidentiality of data is maintained and is needed. Owners are not ready to share data for research motives. Mental health is nowadays a topic that is most frequently discussed when it comes to research. PPDM allows sharing data with the researcher, where the privacy of data is maintained by using perturbation techniques giving relief to doctors (owner of data). The current paper experiments and analyses different perturbation methods to preserve privacy in data mining
Local track to detect for video object detection

The existing methods for video object detection are generally achieved from searching the objects through the entire image. However, they always suffer from large computation consumption as a result of dozens of similar images needing to be operated. To relieve this problem, we propose a Local Track to Detect (LTD) framework to detect video objects by predicting the movements of objects in local areas. LTD can automatically determine key frames and non-key frames, the objects in key frames can be detected by the single frame detector, and the objects in non-key frames can be efficiently detected by the movement prediction module. LTD also has a Siamese module to predict whether objects between the key frame and the non-key frame are the same object to ensure the accuracy of the movement prediction module. Compared with other previous work, our method is more efficient and achieves state-of-the-art performance.
Fast hash-based high secure hiding technique for digital data security

In present time, data hiding emerges as one of the important issues in data embedding and security due to the increase of technology. To improve the quality of the video and the security of the data, this paper deals with data hiding technique using proposed SAJ encryption-decryption technique. The data is embedded in different video frames and it is embedded using a cryptographic hash function with a secret key to enhance the security of the data. We also compared different data hiding techniques to ensure the efficiency of algorithm such as codeword substitution, steganography, watermarking, LSB substitution and cryptography. Our focus is to enhance the security, reliability, and to keep the size of the data as low as possible. In this paper, we have proposed new data hiding technique, hash-based high secure hiding technique (HBHSHT) which is more reliable as estimated. It supports a well user-defined level of accessibility and security. As compared different techniques, it is found that the proposed algorithm is more efficient approximately 8%.
A Study on Large-scale Social Tagging Behavior of User in Online Social Network

Online social networks equally referred as Facebook, Twitter, Google+, LinkedIn and Foursquare have become majorly democratic all over the world. A social network represents a significant role in peoples daily life by building social relations among people. Nowadays people can access these social networks using both desktop PCs and new emerging mobile devices. With billions of users worldwide, Online Social Networks are a new venue of innovation with many challenging research problems. An understanding of user interactions in OSNs can provide important insights into questions of human social behavior and the pattern of social platforms and applications. In this paper, a study on user behavior in OSNs from several perspectives like connectivity and interaction among users, traffic activity, social tagging, mobile social behavior is considered. Also, this paper study malicious behaviors of OSN users and discuss several solutions to detect misbehaving users. This paper explores existing research highlights and provides various needs of significant research in these topics.
Individual Learner Support in Digital ELT Courses: Insights from Teacher Education

Individual support is crucial to help learners improve, but is complicated in times of digital teaching and physical distancing.The present paper demonstrates how individual support can nevertheless be accomplished in digital ELT (English language teaching).It reports on two teacher education courses in Germany that were conducted in a purely digital form as a response to the unprecedented coronavirus situation in 2020.The purpose of these courses was two-fold: On the one hand, the aim was to develop preservice teachers' (PTs') skills of providing individual support to their prospective EFL (English as a foreign language) learners by means of screencast feedback (SCFB).On the other hand, the teacher educator (TE) sought to offer individual support to the PTs in order to promote their feedback skills and advance their English language proficiency even further, especially with respect to academic writing.The multifaceted challenges arising from this complex objective were met through a peer SCFB approach that resorted to a purposeful combination of live webmeetings and individual consultations as well as software tutorials, instructor-generated videos and step-bystep manuals.Also, regular polls and screensharing during the webmeetings helped to keep track of the PTs' progress and understanding.In addition, online surveys gave the PTs room for regular reflection and provided the TE with opportunities for formative assessment.Finally, cognitive, affective and strategic support was offered through group work in digital breakout rooms as well as through individual consultations with the TE and the tutor.Given the novelty of the digital course design, the paper will close with a reflection on its affordances and challenges and suggest potential modifications for future teaching and research.
Debugging agent programs with "why?" Questions

© Copyright 2017, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All Rights Reserved. Debugging is hard, and debugging cognitive agent programs is particularly hard, since they involve concurrency, a dynamic environment, and a complex execution model that includes failure handling. Previous work by Ko &amp; Myers has demonstrated that providing Alice and Java programmers with software that can answer "why?" and "why not?" questions can make a dramatic difference to debugging performance. This paper considers how to adapt this approach to cognitive agent programs, specifically AgentSpeak. It develops and formalises definitions for "why?" and "why not?" questions and associated answers, and illustrates their application using a scenario.
Policy Optimization Recommendation Algorithm Based on Mapping Network for Behavior Enhancement

The algorithm of policy optimization with learning behavior enhancement based on mapping network technology was proposed, aiming to address the issues of lack and sparsity of learning behavior data and weak generalization ability of the model in AI education. Based on the basic recommendation algorithm and the framework of rein- forcement learning, and model introduces the correlation mapping network to realize the transformation of strong and weak correlation, so as to optimize the input agent policy to improve the performance model of course recommendation. Experiment on MOOC da- tasets show that the proposed algorithm model has a stable improvement compared with the baseline models, and can effectively improve the accuracy of course recommendation.
Bal Seal enhances its presence on the Net with newly-built Web-site

To enhance its presence on the Internet, and provide visitors with faster access to information, US-based Bal Seal Engineering Inc has built and recently launched its new Web-site.
Dynamic Resource Allocation Of Heterogeneous Workload In Cloud

Circumnavigated enrolling is on intrigue relationship since it offers dynamic, adaptable and traditionalist resource isolate for strong and maintained relationship in remuneration as-you-use course to the customers. In Cloud arranging diverse cloud customers will request level of cloud benefits simultaneously, along these lines there should be a procedure that each one resources square measure made offered to referencing customer in valuable manner to satisfy their need while not picking the introduction of the focal points. Current IaaS fogs plan resources concerning virtual machines (VMs) with homogenous resource structures wherever isolating sorts of points of interest in VMs have proportionate segment of the cutoff in a strikingly physical machine (PM). Regardless, most customer businesses demand particularly astonishing wholes for various resources. for example, overwhelming selecting occupations need powerfully central processor thinks at any rate immense overseeing applications need additional memory. Dynamic point of confinement provisioning has changed into a promising response for decreasing centrality use in server creates starting late. a heterogeneity-careful structure that reasonably changes the proportion of machines to strike a cordiality between imperativeness save assets and booking delay.
Learning Medical Image Segmentation via U-net Based Positive and Unlabeled Learning Method

Assessing the location, size and number of different lesion areas is critical to medical diagnosis, surgical planning, and prognosis. Recently, the convolutional neural networks (CNN) with deep encoder-decoder architecture has shown great potential in the field of medical semantic image segmentation. Due to the supervised learning strategy used by them, these methods generally demand that the positive-labeled pixels and the negative-labeled pixels are both available in the training images. However, in many cases, the clinicians may only label part of the lesion areas as positive samples, while the negative pixels are generally too diverse to be labeled, which makes the available supervised segmentation methods inapplicable for these cases. To address this issue, we propose a U-net based positive and unlabeled learning method called U-PU, where only positive samples and unlabeled samples are used for training. It circumvents the problem of the absence of negative training samples by exploring the correlation between the underlying positive and negative pixels and thereby ensuring that the posterior class probability density functions (PDFs) are far away from each other. The experiments conducted on electron microscopy (EM) images show that U-PU can obtain good classification results and high-quality segmentation when only positive-labeled and unlabeled pixels are available.
Classifying the Category of Workers using Crowd-sourced Job Seekers Data

Crowdsourcing refers to decomposing complex jobs to multiple tasks and solve those task with multiple workers through open call in distributed networking environment. The recruitment of employees for organization has undergone transformation from traditional method to digital domain. Online recruitment facilitates just-in-time hiring to requesters and enables the workers to compete in the global market. This paper proposed an Efficient Machine Learning Crowdsourced(EMLC) method for E-recruitment which uses Crowdsourcing method to collect resumes from the workers and details of work from the requesters. The data is collected from a private job agency through an online recruitment portal which consist of recruiters from companies and job seekers based on qualifications and experience related to their field. The data collected from recruitment portal is analyzed with Machine Learning Approach with decision tree algorithms like ID3, CART and C4.5 for better selection of efficient person to complete the job. Various performance metrics such as Accuracy, Error rate, Recall etc were used to the Crowdsourced Database to categorize the job seekers efficiently. The proposed method gives better result for online recruiting through Crowdsourcing.