Reviewerly/exHarmony huggingface dataset:
Role Playing Next Generation 9-1-1: Sensemaking with Social Media in Public-Safety Answering Points

For over a decade, research has suggested that social media can enhance the situational awareness of emergency responders during a crisis.Rarely, however, do studies examine the sensemaking processes of emergency responders by which situational awareness is achieved.We examine sensemaking in a Public-Safety Answering Point (PSAP) through role plays with 9-1-1 telecommunicators that imagine how social media analysts can contribute to sensemaking processes among 9-1-1 call takers, dispatchers, and emergency responders.These role plays suggest social media can address information gaps that emerge when 9-1-1 callers fail to provide critical information and vice versa, suggesting social media enhances situational awareness only when integrated into sensemaking processes that synthesize information across multiple, incomplete, but complementary data sources.This synthesis, however, requires cooperative information gathering and sharing among call takers, dispatchers, and social media analysts that PSAPs can coordinate using common interpretive frameworks and common information spaces.
A Survey on Load Balancing Algorithms for Cloud Environment

Cloud computing is the cutting edge technology in the information field to provide services to the users over the internet through web–based tools and applications. One of the major aspects of cloud computing is load balancing. Challenges like Quality of service (QoS) metrics and resource utilization can be improved by balancing the load in cloud environment. Specific scheduling criteria can be applied using load balancing for users prioritization. This paper surveys different load balancing algorithms. The approaches that are existing are discussed and analyzed to provide fair load balancing and also a comparative analysis was presented for the performance of the existing different load balancing schemes.
A Comparative Study of Machine Learning Approaches for Handwriter Identification

During the past few years, writer identification has attracted significant interest due to its real-life applications including document analysis, forensics etc. Machine learning algorithms have played an important role in the development of writer identification systems demonstrating very effective performance results. Recently, the emergence of deep learning has led to various system in computer vision and pattern recognition applications. Therefore, this work aims to assess and compare the performance between one of the deep learning algorithms, AlexNet model, with two of the most effective machine learning classification approaches: Support Vector Machine (SVM) and K-Nearest-Neighbour (KNN). The evaluation has been conducted using both IAM dataset for English handwriting and ICFHR 2012 dataset for Arabic handwriting.
Comparative Analysis of Classification Method for Wart Treatment Method

Wart disease is a benign tumor that grows in all parts of the body with varying amounts. There have been many methods of treatment of wart diseases that are found, but not much research leads to proving which treatments are best at treating wart disease. As a result, Patients sometimes need extra costs and time to cure wart disease because they have to be treated with more than one method. The purpose of this study was to compare the method of data mining classification to predict the best treatment of warts for patients between cryotherapy treatment which is the treatment of warts in general and the treatment of immunotherapy which is the latest treatment. Decision tree, random forest and k-nearest neighbor will be used as a classifier in predicting wart treatment in this study. From the research, the results obtained from the comparison of these three algorithms are amazing and promising with the best prediction accuracy on cryotherapy treatment achieved by the k-nearest neighbor algorithm of 95.66% while the best accuracy for immunotherapy treatment is achieved by random forest algorithm with an accuracy of 88.89%. We also predicted by combining the two datasets into 1 part, the result of the comparison for the three algorithms with the combined dataset it was found that the k-nearest neighbor algorithm was the best classifier to predict the wart treatment method with an accuracy of 88.03%.
Information Flow Complexity Analysis for Design Flaws Detection in Object-Oriented Software

In order to achieve desirable levels of software availability and extendibility it is first necessary to be able to detect design flaws effectively. Flaw detection must be considered a key element of the software development process and also a part of the maintenance process. When design flaws are discovered and addressed early in the development process, the cost and life cycle of the software can be improved, and to accomplish this aim there have been attempts made to develop an automated method for fixing flaws. This paper seeks to create an improved technique to offer better threshold determination which can be used in a metric-based approach to flaw detection employing information flow complexity. The metrics applied are fan-in and fan-out, along with the calling mechanism in an object-oriented system since these can emphasize the improved information flow complexity estimation method, which allows assessment of the software design static measure and enables limitation of the current work. The findings reveal that the approach established in this study permits the determination of thresholds for a metric-based technique to detect design flaws.
Data visualisation of vehicle crash using interactive map and data dashboard

Vehicle crash contributes to a significant number of deaths and injuries in human races around the world. The government and the transportation expert work together around the clock to lessen the number of injuries and fatalities cause from vehicle crash issues. To come out with an effective solution whether from improving the trafficking system including road conditions or increasing awareness among drivers, the experts must analyse the data of vehicle crash from the past decades to find the major causes and come out with a decision. The lack of suitable techniques and tools to analyse a large amount of data is a deterrent in analysing vehicle crash datasets, and the process costs a lot of time. Data visualisation is a technique that allows people to display data in a more infographic form via data visualisation models. Therefore, this study aims to promote and elaborate more on data visualisation techniques using the interactive map and data dashboard to display vehicle crash data. Interactive map and data dashboard can help the government and transportation experts to describe vehicle crash data for them to make right decision to lessen car accident issues. The interactive map focuses on displaying vehicle crash data in map presentation while the data dashboard shows some charts and graph that describe the vehicle crash data in the statistical form. This study can be a reference model of creating interactive map and data dashboard or enhancing the capabilities in managing vehicle crash.
Warning of Affected Users About an Identity Leak

Identity theft is a typical consequence of successful cyber-attacks, which usually comprise the stealing of employee and customer data. Criminals heist identity data in order to either (mis)use the data themselves or sell collections of such data to fraudsters. The warning of the victims of identity theft is crucial to avoid or limit damage caused by identity misuse. A number of services that allow identity owners to check the status of used identities already exist. However in order to provide proactive timely warnings to victims the leaked identity data has to be on hand. In this paper we present a system for a proactive warning of victims of identity leaks.
Enabling Dynamic SLA Compensation Using Blockchain-based Smart Contracts

Service Level Agreements (SLA) are documents that specify what Service Providers (SP) are delivering to customers. They contain information about the service, such as target performance level or monthly availability, and penalties for the violations of the SLA. The information about the penalties is essential because if the SP does not deliver what is defined, the customer must be compensated accordingly. However, the current compensation process is cumbersome and complex because of the amount of involved manual effort. To address this issue, it is proposed in this paper an approach based on blockchain and Smart Contracts (SC) to automate the compensation process while enabling dynamic payments during the SLA lifetime. The proposed approach was evaluated in an use case that simulates the management of a Quality of Service SLA between an SP and a customer. Based on the performed evaluation, parts of the SLA management process were successfully automated using a decentralized solution, and the payment of the compensation occurred without the intervention of a third party.
Sentiment Analysis on Tweets for Trains Using Machine Learning

Sentiment analysis is a popular theme in the natural language processing (NLP) domain. People at present share their stay experience in restaurants, shopping malls, hotels and their travel experience in taxis, buses, trains and airplanes. Online social media provide a platform for the people to share their experiences of stay and travel in the form of text, images and videos. Twitter is one of the popular and well known social media platforms across the world. In this study, we are using tweets data in respect to comfort services in Indian long route superfast trains. This tweet data is used to analyze the hidden sentiments using machine learning techniques such as support vector machines (SVM), Random forest (RF) and back propagation neural networks (BPNN). The results show that BPNN provides high accuracy with more training on the data. The results achieved from SVM and RF was also satisfactory but BPANN won the race with more training on the data.
Performance Evaluation of Manhattan and Euclidean Distance Measures For Clustering Based Automatic Text Summarization

In the past few years, there has been an explosion in the amount of text data from a variety of sources. This volume of text is a valuable source of information and knowledge which needs to be effectively summarized to be useful. In this paper, automatic text summarization with K-means clustering techniques is presented by employing two different distance measurement methods (Euclidean and Manhattan). The dataset extracted from African prose was preprocessed using stopwords removal and tokenization. The preprocessed document is converted into vector representation using tf-idf technique and k-means clustering is applied using Euclidean and Manhattan distance measures to generate summary. There are different distance measures for k-means which has been used in several works. However, there is dearth of work on the performance evaluation of these distance measures in text summarization. The experimental analysis was performed on Waikato Environment for Knowledge Analysis (WEKA). The results obtained showed that the Euclidean variation produced an extractive summary of sentences amounting to 72% from three different clusters while the Manhattan variation produced an extractive summary of sentences that made up 94% of the total document all in one cluster using compression ratio as the performance metric. Keywords— Text summarization, Euclidean distance, k-means clustering, Manhattan distance
An Enhanced Affinity Graph for Image Segmentation

This paper proposes an enhanced affinity graph (EA-graph) for image segmentation. Firstly, the original image is over-segmented to obtain several sets of superpixels with different scales, and the color and texture features of the superpixels are extracted. Then, the similarity relationship between neighborhood superpixels is used to construct the local affinity graph. Meanwhile, the global affinity graph is obtained by sparse reconstruction among all superpixels. The local affinity graph and global affinity graph are superimposed to obtain an enhanced affinity graph for eliminating the influences of noise and isolated regions in the image. Finally, a bipartite graph is introduced to express the affiliation between pixels and superpixels, and segmentation is performed using a spectral clustering algorithm. Experimental results on the Berkeley segmentation database demonstrate that our method achieves significantly better performance compared to state-of-the-art algorithms.
A Study to Renovate Image Data Using Data Analytics Methodologies

In the present scenario storing voluminous data becomes a challenging issue. This research work is an initiative to convert the already existing image analysis data of a pipeline study for future use. Image data is obtained by the process of digitized data concept with the application of k-means cluster algorithm and morphological operator. Data obtained through this process is voluminous and hence a new technique should be adapted to store and retrieve the data for any further use and reference. As data analytics is emerging as one of the methods to analyse data and store, this study helps to deal with the same. Data collected from oil pipeline image processing with pattern recognition which used the technique of unsupervised learning is to be converted under the process of Machine learning. The key step for this process is to first determine the data set for evaluation and then to later convert these date to the required base set, this helps to save the data in large volumes for future use. To perform this task a key study on Data Analytics and Machine Learning are required. Data analytics refers to the quality and quantity of data extracted and categorized from various resources, these data are collected to analyze the structural behaviour and pattern, which differs accordingly to the need of the organizations. The process of machine learning starts with the collection and scrutiny of data that can be obtained through direct or indirect evaluation of data, which will give a pattern for the defined image for further process. The proposed machine learning algorithm has to be fixed to study the data that is been used in the pipeline image conversion task. Structural element concept based on mathematical morphological operator helps to define the size of the data to be stored. Algorithms based on structuring element paves way for redefining the concepts to shrink and save the data for further use. This study will enhance the users to save the data from pattern recognition to Machine learning using data analytics techniques. The overall concept is to save the existing image data in data analytics techniques for future use and references. This research paper spotlights a way for an energy firm to congregate, identify and sway the distinct statistics obtained through image analysis and processing. It also helps to build a strong and confident efficient productivity thus giving an opportunity for best practices of data management breaking down the question of data security and enhancement. This helps to avoid data theft by securing the data obtained through the image analysis and processing concepts.
An Intelligent Question-Answer System over Natural-Language Texts

The success of automated reasoning techniques over large natural-language texts heavily relies on a fine-grained formal analysis of these texts. While there is common agreement that the analysis should be hyperintensional, most automatic reasoning systems are still based on intensional logic. In this paper, we introduce a hyperintensional system of reasoning and answering. We apply Tichy’s Transparent Intensional Logic (TIL) which comes with a procedural (as opposed to truth-conditional) semantics. Our goal is to analyse empirical questions that come attached with a presupposition.
AT THE JUNCTION OF MATHEMATICS AND PSYCHOLOGY: COGNITIVE ORIENTATION OF THE AHP/ANP AND NEW PERSPECTIVES OF STRUCTURING COMPLEXITY

This paper aims to draw attention to the interdisciplinary research of the AHP/ANP methodology by emphasizing how it can be studied from a cognitive perspective. We provide an overview of the main cognitive approaches in decision-making, and consider different heuristics that lie at the basis of pairwise comparisons. We emphasize that the AHP/ANP must be considered at the junction of mathematics and psychology, and for further development of the methodology, we should examine the AHP/ANP from the cognitive point of view. We review the recent experimental studies of the AHP/ANP that test human behavior in real decision problems. We also discuss the future applicability of the AHP/ANP methodology in the Experience Age - the age of not only digital information and knowledge, but also behavior. This article is just a small step on the way to discovering the cognitive aspects and future extensions of decision making with the AHP/ANP.
Public key encryption with temporary and fuzzy keyword search

Public Key Encryption with Keyword Search (PEKS) is a desirable technique to provide searchable functionality over encrypted data in public key settings, which allows a user to delegate a third party server to perform the search operation on encrypted data by means of keyword search trapdoor without learning about the data. However, the existing PEKS schemes cannot be directly applied to practice due to keyword guessing attack or the absence of a mechanism to limit the lifetime of a trapdoor. By addressing these issues at the same time, this paper presents a Public Key Encryption Scheme with Temporary and Fuzzy Keyword Search (PETFKS) by using a fuzzy function and an encryption tree. The proposed PETFKS scheme is proven adaptively secure concerning keyword confidentiality and backward and forward secrecy in the random oracle model under the Bilinear Di e-Hellman assumption. Moreover, it is also proven selectively secure with regard to the resistance of keyword guessing attack. Furthermore, the security and e ciency analyses of the proposed scheme are provided by comparing to the related works. The analyses indicate that the proposed scheme makes a threefold contribution to the practical application of public key encryption with keyword search, namely o ering secure search operation, limiting the lifetime of a trapdoor and enabling secure time-dependent data retrieval.
Unsupervised Person Re-ID in Surveillance Feed Using Re-ranking

With the increase of video feeds from the network of surveillance cameras and available sophisticated detection and bounding box techniques we have seen a jump in the use of deep learning models in the past years. These Deep Neural Network models are Supervised, in the sense that they require large labeled data samples. This hunger of more and more labeled data can be removed by moving on to Unsupervised learning. Despite of significant progress, very less attention is paid to unsupervised techniques. In our approach we tries to improve the accuracy of the Unsupervised Neural Network model by using re-ranking. K-mean algorithm is used to obtain the initial cluster as the samples are unlabeled initially and k-reciprocal nearest neighbor method is used to re-rank the output to remove false matches. Experiments are performed on DUKE and CUHK01 datasets.
Towards structure–agency integrative theories for information access disparity

Purpose Based on the assumption that information access disparity is a highly complex phenomenon demanding integrative explications that heed both structure and agency, the purpose of this paper is to outline the theoretical background against which endeavours to develop such explanations can be planned. Design/methodology/approach The study is based on a close reading of: existing explanations of information access disparity; research of other library and information science (LIS) issues that have demonstrated conscious attempts to bridge structure and agency; and cross-disciplinary integrative theories that have served as foundations for LIS research. Explanatory power of the first and applicability of the latter two are critically assessed; lessons for future research are drawn. Findings The examination shows that efforts to develop integrative theories for information access disparity are emerging but remain indistinct; integrative frameworks for other LIS phenomena exist but are developed primarily by adopting concepts from cross-disciplinary theories and are, therefore, both enabled and constrained by them. It also shows that cross-disciplinary integrative theories contribute to LIS by exporting the general integrative theorising approach and a range of specific concepts but, owing to their limitations in dealing with information-specific issues, their adequacy for explaining information access disparity cannot be assumed. Originality/value The study demonstrates that a promising way forward for developing integrative theories of information access disparity is to follow the general integrative approach, but to ground related concepts and propositions in empirical data alone, i.e., to begin the journey of integrative theorising theory-free.
An Approach to Estimate the Size of Web Application Using IFML User Interface Model

IFML is a modeling language used to develop the web and mobile application automatically, through various models. Many researchers have already applied IFML to develop the software aiding these models. In this paper, an attempt is done at analysis level, to develop user interface model for a given case study and adapt the function point analysis for estimating the size of web application. It has five components that are mapped to the user interface model and the complexity for measurement is estimated. The FP estimation was applied to our standard convention factor followed in software engineering laboratory and development effort was estimated. Thus the development effort estimation obtained signifies the reduction in development effort by applying the IFML code transformation for developing web application.
Analyzing Awareness on Data Privacy

The transition away from the old way of handling and storing information on the internet has completely shifted the entire world. Unfortunately, the new method of internet storage has created a lot of issues that concern individuals on a daily basis. We often underestimate how our data is being stored and shared on the internet. Within the last decades, there have been data breaches that have brought light to the issues of data privacy. In order to ensure data privacy, individuals need to be aware of the situation. In this paper, I have proposed a method that analyzes the awareness on data privacy. To address the issues of data privacy, individuals will be asked to complete a questionnaire. The questionnaire will measure individuals' competence level regarding data privacy.