Reviewerly/exHarmony huggingface dataset:
Multi-Trend Structure Descriptor At Micro-Level For Histological Image Retrieval

Since hospitals are generating and using image data extensively, medical image databases and its size are rising rapidly. This led to difficulties in browsing and managing the huge databases. Therefore, the necessity for the development of efficient content-based medical image retrieval (CBMIR) system arises and is more challenging problem for researchers. In this paper, to alleviate the unbalanced distribution of image representation using multi-trend structure descriptor (MTSD), MTSD is computed at micro level i.e., image is divided into number of sub-images and for each sub-image MTSD is exploited. In similarity measurement, we compared the MTSDs of corresponding sub-images in query and target images than the liner ordered collection of smallest similarity values between the sub-images are considered for retrieval. Experiments revels that computation of proposed feature at micro level retains the localized representation and considering the liner ordered collection of smallest similarity values between the sub-images provides consistency under illumination changes and noise and thus proposed CBMIR achieves better results.
Implementation of K Means Technique in Data Mining to Cluster Researchers Google Scholar Profile

A university usually has many Lecturers that have an important role in improving the quality of Higher Education. The Lecturers should produce scientific publications at least 1 publication on each semester. The achievements of a Lecturer in research and publication become the main indicator that describes the professionalism of lecturers as scientists. Monitoring the improvement of publication trends is very important to do as an evaluation for organizational management in choosing the best strategy to strengthen the quality of publication, and one of the common tools used for analyzing the publication data is the Google Scholar system. This paper attempts to analyze the Google scholar data using Data Mining techniques (Text Mining) by R language, to collect Lecturer’s profile and list of publications in a real-time, the aim of this research is to allow the Management for identifying the Cluster from total 1039 Lecturers on University of Lampung. The results of this research shown there were 5 Cluster of scholar profile data, with member details C0=102, C1=924, C2=1, C3=1, C4=11, total 88.93% of Lecturers are on cluster C1 with, centroid data was h_index=1.942, total_cites=20.89, i10_index=0.417.
Image Spam Filtering using Machine Learning Techniques

Unsolicited visual data is undesirable in any form. The art of hiding malicious content in images and adding them as attachments to electronic mails has become a popular nuisance. In recent years, attackers have developed various new techniques to evade traditional spam classification systems. Text-based spam classification has been in focus for a long time and, researchers have successfully created a prodigal system for identifying spam text in electronic mails using Optical Character Recognition technology. In the last decade, extensive work has been performed to tackle image spam but with unsatisfactory results. Various algorithms and data augmentation techniques are used today to develop an optimal model for image spam recognition. Many of these proposed systems come close to the ideal system but do not provide 100 percent accuracy. This paper highlights the role of three popular techniques in image spam filtering. We discuss the importance and application of Optical Character Recognition, Support Vector Machines and, Artificial Neural Networks in unsolicited visual data filtering. This paper sheds light on the algorithms of these techniques. We provide a comparison of their accuracy, which helps us draw useful insights for developing a robust unsolicited visual data classification system. This paper aims to bring clarity regarding the feasibility of using these techniques to develop an unsolicited visual data filtering system. This paper records that the most favourable results are obtained using Artificial Neural Networks.
“We Dissect Stupidity and Respond to It”: Response Videos and Networked Harassment on YouTube

Over the last decade YouTube “response videos” in which a user offers counterarguments to a video uploaded by another user have become popular among political creators. While creators often frame response videos as debates, those targeted assert that they function as vehicles for harassment from the creator and their networked audience. Platform policies, which base moderation decisions on individual pieces of content rather than the relationship between videos and audience behavior, may therefore fail to address networked harassment. We analyze the relationship between amplification and harassment through qualitative content analysis of 15 response videos. We argue that response videos often provide a blueprint for harassment that shows both why the target is wrong and why harassment would be justified. Creators use argumentative tactics to portray themselves as defenders of enlightened public discourse and their targets as irrational and immoral. This positioning is misleading, given that creators interpellate the viewer as part of a networked audience with shared moral values that the target violates. Our analysis also finds that networked audiences act on that blueprint through the social affordances of YouTube, which we frame as harassment affordances. We argue that YouTube’s current policies are insufficient for addressing harassment that relies on amplification and networked audiences.
Approaches to Organizational Leadership in the Digital Age

Scientific and technological development is an evolutionary process, which is characterized by growth and acceleration trends.Moreover, the results of scientific and technological development are becoming more complex and are changing exponentially.The topic of technological singularity and its impact on the economy, business models, entrepreneurial activities and leadership of organizations are increasingly being discussed.Acceleration of scientific and technological progress is changing the approaches to the leadership of organizations and causing a transformation of managerial thinking.In our opinion, for success in a singular environment, organizations need to rethink the traditional view of leadership based on hierarchy, power, control and submission in favor of such organizational skills: collaboration and teamwork, flexibility, creativity, involvement and inspiration.
Facial Expression Recognition using Compressed Images

Facial expression plays an integral part in expressing ones emotions. These expressions can be conveyed in various forms such as happiness, sadness, anger, surprise, fear, disgust, and neutral. So we propose a system which recognizes and classifies these expressions. We have used an image compression method (FMM) to compress the images collected. Then we detect human face in the compressed image using Viola Jones Haar-like object detector. Using the detected face, we extract the facial features that changes with the changing expressions using LBP. Finally we classify the expression using the extraction using k-NN. Presently, FER is applied in a wide variety of environments including robotics, mobile application, digital signs, as a psychiatric tool for verifying the observations made by the psychologist, etc. The existing system use grayscale/ RGB images which consumes a lot of space and requires a lot of computational time. We present a new approach of using compressed images to reduce the space and time required.
An Ontology Approach to Data Integration using Mapping Method

Text mining is a technique to discover meaningful patterns from the available text documents. The pattern sighting from the text and document association of document is a well-known problem in data mining. Analysis of text content and categorization of the documents is a composite task of data mining. Some of them are supervised and some of them unsupervised manner of document compilation. The term “Federated Databases” refers to the in sequence integration of distributed, autonomous and heterogeneous databases. Nevertheless, a federation can also include information systems, not only databases. At integrating data, more than a few issues must be addressed. Here, we focus on the trouble of heterogeneity, more specifically on semantic heterogeneity – that is, problems correlated to semantically equivalent concepts or semantically related/unrelated concepts. In categorize to address this problem; we apply the idea of ontologies as a tool for data integration. In this paper, we make clear this concept and we briefly explain a technique for constructing ontology by using a hybrid ontology approach.
Detecting Human and Classification of Gender using Facial Images MSIFT Features Based GSVM

Classification of gender using face recognition system is an essential concept for different types of applications in human-computer interaction and computer-aided related applications. It defines a wide range of features from human images to detect male, female and others using real-time data. There are different machine learning approaches were implemented to classify gender and also detects other images during the classification phase, which are not humans based on features extracted from human images datasets. All these existing techniques mostly depend on controlled conditions like features and other representations of the human image. Because of significant and uncertain variations of a particular image, it may be a challenging task in gender classification for real-time image processing application, whether it is male, female and others. So that in this document, we propose a Human detection and Face based gender Recognition System (HDFGR); to investigate male or female classification on real life faces using real world face databases. Our proposed approach consists Multi-Scale Invariant Feature Transform (MSIFT) to describes faces and Gaussian distance-based support vector machine (GSVM) classifier is used to classify gender and objects, i.e. male, female and other from features extracted human image datasets. We obtain an experimental performance of 98.7% by applying DSVM with boosted MSIFT features. Our proposed approach gives better classification accuracy and other performance parameters compared to different existing approaches with benchmark and evaluation of possible databases.
Forensic Feature-Comparison Expertise: Statistical Learning Facilitates Visual Comparison Performance

Forensic feature-comparison examiners in select disciplines are more accurate than novices when comparing visual evidence samples. This paper examines a key cognitive mechanism that may contribute to this superior visual comparison performance: the ability to learn how often stimuli occur in the environment (distributional statistical learning). We examined the relation-ship between distributional learning and visual comparison performance, and the impact of training about the diagnosticity of distributional information in visual comparison tasks. We compared performance between novices given no training (uninformed novices; n = 32), accu-rate training (informed novices; n = 32) or inaccurate training (misinformed novices; n = 32) in Experiment 1; and between forensic examiners (n = 26), informed novices (n = 29) and unin-formed novices (n = 27) in Experiment 2. Across both experiments, forensic examiners and nov-ices performed significantly above chance in a visual comparison task where distributional learning was required for high performance. However, informed novices outperformed all par-ticipants and only their visual comparison performance was significantly associated with their distributional learning. It is likely that forensic examiners’ expertise is domain-specific and doesn’t generalise to novel visual comparison tasks. Nevertheless, diagnosticity training could be critical to the relationship between distributional learning and visual comparison performance.
Transfer Learning or Self-supervised Learning? A Tale of Two Pretraining Paradigms

Pretraining has become a standard technique in computer vision and natural language processing, which usually helps to improve performance substantially. Previously, the most dominant pretraining method is transfer learning (TL), which uses labeled data to learn a good representation network. Recently, a new pretraining approach -- self-supervised learning (SSL) -- has demonstrated promising results on a wide range of applications. SSL does not require annotated labels. It is purely conducted on input data by solving auxiliary tasks defined on the input data examples. The current reported results show that in certain applications, SSL outperforms TL and the other way around in other applications. There has not been a clear understanding on what properties of data and tasks render one approach outperforms the other. Without an informed guideline, ML researchers have to try both methods to find out which one is better empirically. It is usually time-consuming to do so. In this work, we aim to address this problem. We perform a comprehensive comparative study between SSL and TL regarding which one works better under different properties of data and tasks, including domain difference between source and target tasks, the amount of pretraining data, class imbalance in source data, and usage of target data for additional pretraining, etc. The insights distilled from our comparative studies can help ML researchers decide which method to use based on the properties of their applications.
The Cybersecurity Awareness Training Model (CATRAM)

This chapter presents the outcome of one empirical research study that assess the implementation and validation of the cybersecurity awareness training model (CATRAM), designed as a multiple-case study in a Canadian higher education institution. Information security awareness programs have become unsuccessful to change people's attitudes in recognizing, stopping, or reporting cyberthreats within their corporate environment. Therefore, human errors and actions continue to demonstrate that we as humans are the weakest links in cybersecurity. The chapter studies the most recent cybersecurity awareness programs and its attributes. Furthermore, the authors compiled recent awareness methodologies, frameworks, and approaches. The cybersecurity awareness training model (CATRAM) has been created to deliver training to different corporate audiences, each of these organizational units with peculiar content and detached objectives. They concluded their study by addressing the necessity of future research to target new approaches to keep cybersecurity awareness focused on the everchanging cyberthreat landscape.
Designing Data Governance Mechanisms for Data Marketplace Metaplatforms

Data Marketplace Meta-platforms (DMMPs) federate the fragmented set of data marketplaces and are expected to become a pivotal instrument to realize a single European Data Market in 2030. However, one critical hindrance to foster the adoption of business data sharing via DMMPs is data providers' risk of losing control over data. Generally, the literature on interorganizational data sharing has highlighted that data governance mechanisms can help data providers to retain control over their data. Nevertheless, data governance mechanisms in the DMMP context are yet to be explored. Therefore, this research aims to design data governance mechanisms for business data sharing in DMMPs by employing the Design Science Research (DSR) approach. This study contributes to the literature by identifying root causes and consequences of losing control over data and defining prescriptive knowledge regarding design requirements, design principles, and a framework for designing data governance mechanisms within the novel setting of metaplatforms.
Influence of Social Moderators in the Internet of Things of Srilankan Healthcare Services

The globalization of healthcare enables us with the lens to look at the future through the economical globalization of the country and as the infrastructure for a better mankind. Healthcare in modern era is more of a system that connects health technologies, services and application provided to public health. The smart rehabilitation based on Internet of Things (IoT) was introduced very recently mainly to overcome issues of scarce resource caused by growing aging population. On the other hand, shortage of doctors is a much critical issue where the crowded hospitals and long waiting list to seek doctors as well as less time spend for consultation are because of mal distribution of doctors. This study attempted to evaluate the impact of the technological acceptance determinants on adoption towards IoT in the healthcare system and evaluate the moderating effect of provincial area as well as demographic factors in adoption towards IoT in the Srilankan hospitals. The primitive purpose of this study is to provide Healthcare departments and agencies including; hospitals, pharmacies, pharmaceutical companies to develop a balanced idea of the expectations of the users of IoT. The framework of the study was adopted by combining the Unified Theory of Acceptance and Use of Technology (UTAUT) and UTAUT2 models. The structural models of the research were generated using SPSS V26 and AMOS V26. Hypotheses were tested through the proposed final model of the study. The research revealed that provincial area moderate Social influence, Facilitating condition and perceived creditability in adoption towards IoT in the healthcare system. The finding of this study discovered that younger physicians are more likely to adopt IoT products. Hence IoT marketers might consider targeting young physicians who like to explore new technologies. This study also implicated that physicians who have sufficient resources and knowledge toward IoT at hospital have a higher intention to adopt. the research. However, the IoT technology is still in its early premature stage of development and requires an intense evangelization. To a certain extent, this research had identified the critical factors that impact adoption towards IoT in healthcare industry in the Sri Lankan context and suggests that health care organizations should create awareness of IoT products to increase physician’s adoption towards IoT.
Exploring Critical Success Factors in E-Learning Implementation

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Lightweight Security Framework for Data Outsourcing and Storage in Mobile Cloud Computing

Mobile Cloud computing is define as the new paradigm, and in this new paradigm computational power is on demand service and accessible through mobiles and tablets. When user decides to go towards cloud computing, there is a risk of losing control on his own data. So providing security of the data during transmission and when saved on the cloud storage is a major issue. Any application relying upon an emerging technology should consider the different possible threats. The principal reason why people are afraid of using Cloud technology is that data security and integrity among all other issues involved in Cloud technology constitute a great challenge. As data security issues, access control as well as key management are also a reason why Cloud technology is not widely adopted. In mobile cloud computing, the security related to data involving data integrity, confidentiality, availability and traceability is the most critical concern of cloud users. As now mobile cloud is changing how users used to work over the network, by helping them in terms of cost and reduction of tasks complexity. Hence, it is very important for the user to be ensured that the transmission of the data is secure, that the consistency as well as the storage of the data on the cloud provider side is secured. Hence the necessity for developing trust security model is demanding.
The Security Paradigm That Strikes a Balance Between a Holistic Security Mechanism and The WSN's Resource Constraints

There is a need for comprehensive holistic security methods that can provide a long-term response to the challenge of security in Wireless Sensor Networks (WSNs). Due to the widespread use of WSN around the world, particularly the Internet of Things (IoTs), quantum security is predicted to aid in the comprehensive security strategy for WSN environments. These quantum securities can be used in conjunction with WSN to provide data security anywhere and at any time. The goal of this study is to examine the security constraints of WSNs and provide a holistic strategy across all network tiers that will serve as a balanced security mechanism solution, as well as a solution to protect nodes from vital data attack, eavesdropping, disruption, destruction as well as alteration and other threats. WSNs are utilized for monitoring, tracking, and controlling applications in a variety of operations, including military, ecological, and health. However, the nature of their resource constraints causes difficulties. Low compute capabilities, tiny memory, limited energy resources, and unreliable communications are just a few of the factors that make data and node security problematic when employing WSN. As a result, as the need for WSNs grows, so does the requirement for improved security methods.
Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision

Contrastive pre-training on distant supervision has shown remarkable effectiveness in improving supervised relation extraction tasks. However, the existing methods ignore the intrinsic noise of distant supervision during the pre-training stage. In this paper, we propose a weighted contrastive learning method by leveraging the supervised data to estimate the reliability of pre-training instances and explicitly reduce the effect of noise. Experimental results on three supervised datasets demonstrate the advantages of our proposed weighted contrastive learning approach compared to two state-of-the-art non-weighted baselines.Our code and models are available at: https://github.com/YukinoWan/WCL