Reviewerly/exHarmony huggingface dataset:
Fuzzy Relevant Regions Segmentation in Breast Histopathology Images using FCM

According to the International Agency for Research on Cancer (IARC), breast cancer has become the most diagnosed cancer in the world. The analysis of breast histopathology images is important. Segmentation of relevant and irrelevant regions is an important pre-processing for the analysis of breast cancer. In the conventional method, the histopathologists need to use the eyeball rolling method to find the tumor regions. The main objective of this paper is to develop an automation segmentation procedure for the relevant regions, which are referred as tumor regions, and irrelevant regions refer as non-tumor regions. The proposed procedure consists of four main stages: (1) color normalization; (2) color model conversion; (3) relevant regions segmentation using FCM, and; (4) masking processing. The proposed procedure was tested using 31 breast histopathology images. The obtained results show that the average accuracy and precision of the relevant region detection are 86.27% (±8.4129) and 84.53% (±10.6636), respectively.
Performance-Aware Orchestration and Management over 5G and Beyond Infrastructures Based on Diagnostic Mechanisms

This work introduces the advanced orchestration and diagnostic components for 5G and beyond infrastructures enabling intelligent automated management of deployed vertical services. The paper presents the progress and results produced and the actions for creating, deploying and monitoring a network service. The developed components can support both currently developing innovative 5G and 6G network services and solutions for vertical industries. These include features that enable the design phase of vertical users, intelligent orchestration mechanisms with optimal placement of services and finally offer insights and performance analysis. In the context of the performance diagnosis mechanisms we propose a fault detection and localization model based on a combination of two respective modules, one that utilizes Self-Organizing Maps (SOM) as a Machine Learning technique and one that implements an algorithm based on adjacency lists to perform Root Cause Analysis (RCA). Finally, the aforementioned aspects are supported by some preliminary results in order to show the effectiveness of such approach.
Ontology‐based data access control model supported with grid computing for improving security in healthcare data

Abstract Ontology is broadly applied in various fields for creating trust‐based data access control models. In the case of the medical field, utilizing ontology is highly recommended for securing the medical report and patient information. Here, ontology is integrated with the grid computing system for achieving better authentication and security in the healthcare system. Various ontology based data access control models are designed by different authors for achieving enhanced user authorization and security. Security is still found to be a major issue in the existing techniques. To overcome this issue, the proposed work developed a medical ontology with three tier secure access control. The proposed method is processed in three layers. In the first layer, job scheduling for user is achieved. Job scheduling is carried out by scheduler using a deadline priority based scheduling algorithm. Users are scheduled with a different job based on their priority level such as low, medium, and high. In the second layer, role based policy is developed for finding and eliminating the accessing of unauthorized users. The third layer is used for securing medical database. Medical database is secured from various through encrypting the data using XOR cryptography algorithm. The proposed architecture is validated using some of the metrics such as turnaround time, tardiness, actual time delay, and throughput. The turnaround time, tardiness, and actual‐time delay attains for the proposed architecture are 64.60, 4.90, and 1.52 seconds, respectively. Then, the throughput value reaches for 25 bits length of encryption is 0.88 Mbps. The simulation analysis reveals that the proposed architecture reduces the waiting time of users and improves medical data security.
Modelling multiple problem‐solving strategies and strategy shift in cognitive diagnosis for growth

Abstract Problem‐solving strategies, defined as actions people select intentionally to achieve desired objectives, are distinguished from skills that are implemented unintentionally. In education, strategy‐oriented instructions that guide students to form problem‐solving strategies are found to be more effective for low‐achieving students than the skill‐oriented instructions designed for enhancing their skill implementation ability. Although the existing longitudinal cognitive diagnosis models (CDMs) can model the change in students' dynamic skill mastery status over time, they are not designed to model the shift in students' problem‐solving strategies. This study proposes a longitudinal CDM that considers both between‐person multiple strategies and within‐person strategy shift. The model, separating the strategy choice process from the skill implementation process, is intended to provide diagnostic information on strategy choice as well as skill mastery status. A simulation study is conducted to evaluate the parameter recovery of the proposed model and investigate the consequences of ignoring the presence of multiple strategies or strategy shift. Further, an empirical data analysis is conducted to illustrate the use of the proposed model to measure strategy shift, growth in skill implementation ability and skill mastery status.
A TAXONOMY OF SECURITY AND RESEARCH CHALLENGES IN CLOUD COMPUTING

Cloud computing is delivered as a storage service by third party. It gains wide acceptance from various Business organizations &amp; Information Technology (IT) Industries. Cloud computing provides various services to users through the internet; those services are like Applications, computation, and storage etc. In spite of these advantages, cloud technology faces different types of privacy and security related issues. These issues become major barriers to adopt cloud technology into various organizations. This survey paper addresses the cloud architecture, various security and privacy issues, challenges and threats, attacks, and future research directions to overcome the security and privacy related problems in the cloud environment.
AidPack: A Web-Based Report Management System for Community Incident Response

The incidents and crimes in the Philippines are rapidly increasing year after year based from the reports from the PNP (Philippine National Police), BFP (Bureau of Fire Protection), and LGU. AidPack is a web application that allows civilians to report various street crime with the reports directing to barangay officials. Incident reporting addresses various problems and ensures the safety of each citizen around the community. The results from the functionality test, usability test, and user-acceptance test suggest that developing an effective incident reporting system requires the developer to consider ease-of-use, clarity, and clear proper instructions. The researchers recommend adapting other UAT models and conduct a comparative study of the system and conducting further interviews towards other agencies.
The Effect of Stock Index and Gold Price Movements on Bitcoin Price Movements

This study aims to determine the effect of the S&P 500 Stock Index Price and Gold Price movements on Bitcoin Price movements. This research uses quantitative methods and data collection used is secondary data. Secondary data sources are obtained through the website finance.yahoo.com. The data processing technique will be carried out using SPSS 23 program and the method analysis in this study using descriptive and verification analysis consisting of Multiple Linear Regression method. The results of this study show that the stock index price has a positive effect on the movement of the Bitcoin price, while the gold price has a negative effect on the movement of the Bitcoin price.
Predicting Bitcoin Prices Using Sentiment Analysis Results

Sentiment Analysis is a technique to determine the tone of a statement using computer software. It is an appliance of a linguistic and computer science intersection that could make a great impact on the business field. Lately, Sentiment Analysis has been used on social media platforms such as Twitter or Facebook to observe the tone of a statement. Examining the tone of tweets using the computer could be time-efficient and precise. SA studies could also be linked to Bitcoin. In this paper, I am using SA results of tweets on a given day to predict changes in the Bitcoin price and its returns. First, I collected the data, which included 31 data points of average sentiment scores and the corresponding 31 Bitcoin prices on the same day. The average sentiment scores were evaluated by VADER from a scale of -1 to 1 (-1 being the statements with the most negative tone, and 1 with the most positive). Then, I used linear regressions to predict Bitcoin price and returns using sentiment scores on the previous day/days. Predicting returns based on sentiments could allow me to find the relationship between Twitter users and Bitcoin and help me better understand the potential challenges. In the end, the predicted price was positively correlated to the sentiment scores the day before. Interestingly, the predicted return was negatively correlated with the sentiment scores and showed less correlation with a M coefficient of -0.86125.
The Effect of Skepticism, Big Data Analytics to Financial Fraud Detection Moderated by Forensic Accounting

The number of cases of fraud, especially fraud in the preparation and publication of financial statements is increasing. Both in the international world and also in Indonesia. The number of cases that have occurred for several years have only been uncovered, resulting in large losses for users of financial statements and stakeholders. The industrial revolution 4.0 opens up new opportunities, with the existence of big data analytics and forensic audits, which are expected to increase professional skepticism of auditors to be more observant in detecting fraudulent financial reports. Our research is a quantitative study, we tested the hypothesis between the independent variable and the moderating variable on the dependent variable. The independent variables in our research are professional skepticism, and big data analytics, then the moderating variable is forensic accounting, and the dependent variable is financial statement fraud detection. The object of our research is the auditor in a public accounting firm. We used the structural equation modeling partial least square as our data analysis. The results of our study state that professional skepticism, big data analytics have a significant impact on financial statement fraud detection. Forensic accounting moderate both professional skepticism and big data analytics.
Importance Of Blockchain Within The Big 4 CPA Firms: Cryptocurrency'S Existence

The development of technology has been growing rapidly in the current state of time. With that being said, blockchain is one of the important pillars that is currently revolutionizing the traditional way of life. It is a new system that brings not only more efficiency, but also more challenges to be faced as well to many fields, including accounting and audit as one of them. Our research is going to discuss how blockchain is going to create multiple breakthroughs in accounting and audit field, bringing not only potential benefits and efficiencies, but also new challenges that must be addressed, including how cryptocurrency is handled in terms of accounting and audit, as it is recognized as one of the most popular uses of blockchain technology worldwide. The selection of Big 4 CPA firms is based on the evidence shown by those leading accounting firms in terms of adopting blockchain technology in their way of work and how they approach the technology innovation. This is shown by their interest on the matter which is published on their own respective websites. Our research used a descriptive qualitative approach, obtaining secondary data from papers, journals, books, and relevant websites.
Incremental Attribute Reduction Algorithm for Smart City Local Area Communication Systems Based on Similarity Relation

Abstract The wide application of local area communication system brings comprehensive data information but also increases the difficulty of data mining and analysis. Therefore, the data mining preprocessing link-attribute reduction is studied. The research is divided into three parts: first, the method of distinguished matrix fast calculating is used for discerning the core attributes of a data set; second, the k-nearest neighbor algorithm is used to calculate the attribute as well as the similarity coefficient between condition attributes, and to finish at the beginning of attribute reduction; and third, the global optimization ability of particle swarm algorithm implementation attribute reduction is used again to complete local area communication system incremental attribute reduction targets. The results show that compared with the three previous reduction algorithms, the proposed algorithm has the least number of attributes and the least number of iterations, which proves the reduction degree and efficiency of the proposed method.
Implementation of knowledge management and utilizing tools in healthcare for making evidence-based decisions

Healthcare firms have understood that the valuation of “intangible resources” is a crucial driver of their profitability due to the complexity of the business environment and the intensity of competition. “Intellectual capital” is a source of “creativity and invention”, as well as one of the most important components in a company development, as it is the spark for success and growth. Technology has rapidly grown in importance around the world. As a result, the function of knowledge as a primary unit of wealth has been reliant on individuals' “creative ability, experience, and skills” to develop new information. The technological underpinning for KMS implementation is provided by information technology (IT). Since it is employed at all phases of the KM life span, it also offers a way of implementing a robust theoretical framework for KM. IT is critical during the steps of the “socialization externalization combination internalization (SECI)” paradigm. This research study mainly discusses the implementation of knowledge management and its impact on decision making in healthcare organisations. In this context, secondary method of data collection has been considered to gather relevant and factual data from different sources. Thus, keywords are used to find out topic-based information from journals, articles.
Same text, same discourse? Empirical validation of a discourse analysis methodology for cultural heritage

Abstract Most of the new knowledge about our past that is generated in cultural heritage disciplines is produced in non-structured forms, such as project reports, monographs, or research papers. This proliferation of cultural heritage knowledge in textual discourse form requires support for information structuration and extraction of the semantic relations embedded in these texts, in order to fully understand and post-process them. Previous works show that discourse analysis techniques can represent pieces of textual information in a highly structured manner, identifying the semantic relations between them and the entities referred to in the discourse according to well-established linguistic patterns. This is particularly useful for aligning the structure and information extracted with information system post-processing techniques, such as natural language processing, that are currently applied to cultural heritage. In this paper, we apply discourse models through a previously developed methodology to a real case study to represent and analyse cultural heritage textual narratives. Then, we perform an empirical study with cultural heritage experts in order to validate the models obtained, the resultant information, some usability aspects of the approach, and the degree of agreement between experts. The main objective was to obtain empirical and quantitative validation of the application of our proposed discourse analysis methodology to narratives in cultural heritage, its implications for different specialists, and its possibilities as a methodology to assist in providing structure to and capturing the semantics of freestyle documents, to automatize some discourse information extraction tasks and to facilitate their post-processing.
Improving Large-Gap Clone Detection Recall Using Multiple Features

Code clone refers to two or more identical or similar source code fragments. Research on code clone detection has lasted for decades. Investigation and evaluation of existing clone detection techniques indicate that they are resilient to function-level clone detection. Still, there may be room for further research in block-level clone detection. Particularly, type-3 clones that include large gaps, are ongoing challenges. To solve these problems, we propose a clone detection method based on multiple code features. It aims to improve the recall rate of code block clone detection and overcome large-gap and hard-to-detect type-3 clones. This method first splits the source code files based on the program’s structural features and context features to obtain code blocks. The collection of code blocks obtained in this way is complete, and the large gaps in clone pairs will also be removed. In addition, we only need to compute the similarity between code blocks with the same structural features, which can also significantly save time and resources. The similarity is obtained by calculating the proportion of the same tokens between two code blocks. Moreover, since different types of tokens have different weights in similarity calculation, we use supervised learning to obtain a classifier model between token features and code clone. We divide the tokens into 13 types and train the machine learning model with the manually confirmed clone or non-clone pair. Finally, we develop a prototype system and compare our tools with existing tools under the Mutation Framework and in several actual C projects. The experimental results also demonstrate the advancement and practicality of our prototype.
Verification and Control for Autonomous Mobile Systems

We study finite-state controllers (FSCs) for partially observable Markov decision processes (POMDPs) that are provably correct with respect to given specifications.The key insight is that computing (randomised) FSCs on POMDPs is equivalent to-and computationally as hard as-synthesis for parametric Markov chains (pMCs).This correspondence allows to use tools for synthesis in pMCs to compute correct-by-construction FSCs on POMDPs for a variety of specifications.Our experimental evaluation shows comparable performance to well-known POMDP solvers. *Supported by the DFG RTG 2236 "UnRAVeL".can infer the likelihood of the system being in a certain state.This likelihood is called the belief state.Executing an action leads to an update of the belief state according to new observations.The belief state together with an update function form a (typically uncountably infinite) MDP, referred to as the belief MDP (Shani et al., 2013).
Innovation and Integration Development of the Cultural Industry Based on Mapping Knowledge Domains

In view of the less current user data of the cultural industry, its classification and presentation form are complex. This paper constructs the MKD of the cultural industry integration through the RDF graph model and realizes the distributed vector representation of cultural project semantic information by using the fine-tuning translation distance model TransH. The semantic information of cultural industry fusion is mapped to a continuous vector space, and the distributed vector representation of cultural projects is realized. The results show that it is helpful to accurately express the correlation between different regions and cultural types and to explore the data association and implicit relationship of innovation and integration development in the cultural industry.
Secure and Efficient Semantic Extension Search over Encrypted Documents by Integrating Cloud and Fog Systems

With the explosive increase of document files, more and more data owners outsource their documents to the public cloud, which can decrease the costs of local data management systems. However, the problem of information privacy leakage in the cloud is a great challenge and it has been attracting more and more attention. In this article, we propose a secure and efficient document search scheme, named SES, based on both the cloud and fog systems. All the documents are symmetrically encrypted before being outsourced to the cloud, and an index vector is constructed based on the keywords for each document. Specifically, we integrate the position information of keywords into the TF-IDF model to generate document vectors, which are accurate and inherent summarizations about the documents. In query requests, a data user needs to provide a set of keywords, which are first extended by the Word2Vec tool and then mapped to a query vector. The extension process of keywords makes the provided keywords more comprehensive and accurate, and hence, it improves document search accuracy. To achieve the forward and backward security, both the document vectors and query vectors are appended with an ingenious vector. The relevance score between a document and a query is defined as the inner product of the document vector and the query vector. We return the most <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>k</mi> </math> relevant documents as the search results to the data users. To protect the contextual information stored in the document and query vectors, we encrypt the vectors by the secure kNN algorithm. To improve the search efficiency, a searchable index structure for the document set is constructed based on the Diffie–Hellman secret key negotiation algorithm. The analysis and simulation results illustrate that the proposed scheme performs well in terms of both security and search efficiency.
Learning Progression-based Automated Scoring of Visual Models

Visual models are defined as drawings created by students to illustrate their understanding of an observed scientific phenomenon. Along with the corresponding textual answers describing the phenomenon, these multimodal responses serve as rich vehicles of information for conducting assessment on students in their grasp of the underlying scientific concept. Though effective, manual scoring of these responses are both laborious and expensive. In our work, we apply a user interface tool for students to construct multimodal responses to scientific prompts, and propose an automated approach that relies on image processing to classify shapes in visual models, extract relevant features, and, ultimately, assign a learning progression score to each model. This effort is the first in a series of planned approaches targeted at utilizing multimodal evidence to evaluate, scaffold and guide students in their learning pathways in science education.
A combined approach for cloud tasks scheduling based on NSGA-II and harmony search

Abstract The task scheduling is to map and arrange the execution of tasks on resources to optimize one or more efficient criteria. This paper tries to provide an improved model for scheduling workflow tasks of the cloud, which simultaneously considers three aspects of optimizing: makespan time, resource utilization, and scheduling length ratio. The model combines the non-dominated sorting genetic algorithm and the harmony search approach to achieve these goals. Harmony search algorithm attempts to perform a local search around the best solutions in each repetition of the algorithm to prevent it from getting stuck in the local optimal. The results on the four Cybershake, Epigenomics, Inspiral, and Montage datasets depict that the suggested algorithm is more efficient for all three criteria.