Reviewerly/exHarmony huggingface dataset:
Oppositional Red Fox Optimization Based Task Scheduling Scheme for Cloud Environment

Owing to massive technological developments in Internet of Things (IoT) and cloud environment, cloud computing (CC) offers a highly flexible heterogeneous resource pool over the network, and clients could exploit various resources on demand.Since IoT-enabled models are restricted to resources and require crisp response, minimum latency, and maximum bandwidth, which are outside the capabilities.CC was handled as a resource-rich solution to aforementioned challenge.As high delay reduces the performance of the IoT enabled cloud platform, efficient utilization of task scheduling (TS) reduces the energy usage of the cloud infrastructure and increases the income of service provider via minimizing processing time of user job.Therefore, this article concentration on the design of an oppositional red fox optimization based task scheduling scheme (ORFO-TSS) for IoT enabled cloud environment.The presented ORFO-TSS model resolves the problem of allocating resources from the IoT based cloud platform.It achieves the makespan by performing optimum TS procedures with various aspects of incoming task.The designing of ORFO-TSS method includes the idea of oppositional based learning (OBL) as to traditional RFO approach in enhancing their efficiency.A wide-ranging experimental analysis was applied on the CloudSim platform.The experimental outcome highlighted the efficacy of the ORFO-TSS technique over existing approaches.
Analytical Review on Techniques and Challenges in Sentiment Analysis

Opinion mining and sentiment analysis help in extracting valuable information from the huge user data generated in the form of opinions and reviews from the customers and comments. Such a huge increase in data available on the electronic or social media has also increased interest in this field. Sentiment analysis helps in extraction of opinions of others (writer or speaker) from a given source (text) using different methods and techniques like NLP, text mining, and linguistic computation and classifying them into positive, negative, and neutral opinions. Decision making for both consumers as well as the seller becomes much easier with such type of classification. This chapter consists of survey report on different elements of sentiment analysis, its applications, and challenges involved. The chapter studies and compares some of the techniques used to evaluate the item's reputation using sentiment analysis. A number of tools and features for sentiment analysis are also included in this chapter that can later help to perform better sentiment analysis of data.
Framework for Blockchain-Based Smart Healthcare Systems

Healthcare is one of the basic human needs and an important aspect of connected living. With the advancement of technologies, it is gradually transitioning away from traditional hospital-centric care towards a more virtual and distributed care. Health 4.0 is new buzz word in the healthcare systems. It is artificial intelligence (AI)-based intelligent devices enabled through the internet of medical things (IoMT). It is made up of medical sensors, cloud, edge and fog-computing, and wireless communication technology. The data collected from the patients are sensitive as it is crucial for diagnosis and to make critical decisions. Hence, the cyber criminals invest significant amounts of effort, time, and resources to exploit these data. So, in order to improve the security of the system, a framework for a blockchain-based smart healthcare system is proposed. In addition to it, the prelude on the Health 4.0 components and the current research challenges, benefits, and applications are discussed. Finally, the use case along with the applications of blockchain in the healthcare sector are discussed.
COVID-19 Test Report and Vaccine Certificate Verification Through Blockchain and E-Commerce

COVID-19 was first reported in December 2019 and has spread since then. Different vaccines are under development and digital technologies are used for contact tracing, vaccine monitoring, screening of infection, etc. But the healthcare providers are dealing with many challenges, such as cases of fake documents (i.e., test reports and vaccine certificates). Blockchain can help mitigate the challenges with its features, such as data transparency, accuracy, and decentralization. Even in general day purpose, the verification of the documents is required for booking of airline ticket, hotel room, etc. E-commerce deals with online selling and buying of products, which together with blockchain can fulfill the requirement. This chapter proposes a framework for verification of COVID-19 test reports and vaccine certificates with the help of blockchain technology and e-commerce. The aim of the proposed framework is to validate the details online so that the proper treatment and medication can be provided to the patient. IPFS is used to store the data.
Manajemen Kearsipan Digital dalam Upaya Meningkatkan Mutu Sistem Penyimpanan Dokumen Kepegawaian di Kota Banda Aceh: Hambatan dan Tantangan

The quality of staffing documentation stored electronically at the Aceh Public Works and Spatial Planning Office of Banda Aceh City is unsatisfactory. This study aims to find out how the process of managing archives management improves the quality of the employee document storage system by using electronics as a storage medium. The data techniques employed were observation, interviews and documentation. The type of this research is qualitative. The results of this study can be reported that the obstacles that occur in the Aceh Public Works and Spatial Planning Office of Banda Aceh City are: (1) Lack of understanding of the importance of archives by members of the organization; (2) It has not been carried out or cultivated regarding the guidelines for borrowing archives so that every employee borrows files without clear regulations; (3) There is no planning regarding the shrinkage of archives so that the libraries are piling up and are no longer accommodated. It is hoped that government agencies will be able to improve the quality of documentation storage systems using electronic media and utilize employees who are reliable and capable of operating computers properly.
Formally Verified Scalable Look Ahead Planning For Cloud Resource Management

In this article, we propose and implement a distributed autonomic manager that maintains service level agreements (SLA) for each application scenario. The proposed autonomic manager supports SLAs by configuring the bandwidth ratios for each application scenario and uses an overlay network as an infrastructure. The most important aspect of the proposed autonomic manager is its scalability which allows us to deal with geographically distributed cloud-based applications and a large volume of computation. This can be useful in look ahead optimization and in adaptations using complex models, such as machine learning. We formally prove the safety and liveness properties of the implemented distributed algorithms. Through experiments on the Amazon AWS cloud, using two different use cases, we demonstrate the elasticity and flexibility of the autonomic manager as a measure of its applicability to different cloud applications with different types of workloads. Experiments also demonstrate that increasing the size of a look ahead window, up to a certain size, improves the accuracy of the adaptation decisions by up to 50%.
FLAG: A Feedback-aware Local and Global Model for Heterogeneous Sequential Recommendation

Heterogeneous sequential recommendation that models sequences of items associated with more than one type of feedback such as examinations and purchases is an emerging topic in the research community, which is also an important problem in many real-world applications. Though there are some methods proposed to exploit different types of feedback in item sequences such as RLBL, RIB, and BINN, they are based on RNN and may not be very competitive in capturing users’ complex and dynamic preferences. And most existing advanced sequential recommendation methods such as the CNN- and attention-based methods are often designed for making use of item sequences with one single type of feedback, which thus can not be applied to the studied problem directly. As a response, we propose a novel feedback-aware local and global (FLAG) preference learning model for heterogeneous sequential recommendation. Our FLAG contains four modules, including (i) a local preference learning module for capturing a user’s short-term interest, which adopts a novel feedback-aware self-attention block to distinguish different types of feedback; (ii) a global preference learning module for modeling a user’s global preference; (iii) a local intention learning module, which takes a user’s real feedback in the next step, i.e., the user’s intention at the current step, as the query vector in a self-attention block to figure out the items that match the user’s intention well; and (iv) a prediction module for preference integration and final prediction. We then conduct extensive experiments on three public datasets and find that our FLAG significantly outperforms 13 very competitive baselines in terms of two commonly used ranking-oriented metrics in most cases. We also include ablation studies and sensitivity analysis of our FLAG to have more in-depth insights.
Classification of microscopic cervical blood cells using inception ResNet V2 with modified activation function

Cervical cancer is the most frequent and fatal malignancy among women worldwide. If this tumor is detected and treated early enough, the complications it causes can be minimized. Deep learning demonstrated significant promise when imposed on biomedical difficulties such as medical image processing and disease prognostication. Therefore, in this paper, an automatic cervical cell classification approach named IR-PapNet is developed based on Inception-ResNet which is an optimized version of Inception. The learning model’s conventional ReLu activation is replaced with the parametric-rectified linear unit (PReLu) to overcome the nullification of negative values and dying ReLu. Finally, the model loss function is minimized with the SGD optimization model by modifying the attributes of the neural network. Furthermore, we present a simple but efficient noise removal technique called 2D-Discrete Wavelet Transform (2D-DWT) algorithm for enhancing image quality. Experimental results show that this model can achieve a top-1 average identification accuracy of 99.8% on the pap smear cervical Herlev datasets, which verifies its satisfactory performance. The restructured Inception-ResNet network model can obtain significant improvements over most of the state-of-the-art models in 2-class classification, and it achieves a high learning rate without experiencing dead nodes.
Multimodal body, multimodal mind, multimodal communication

This chapter reviews prospects for improving the scientific study of communication by developing big data infrastructure and computational, technical, and statistical tools. Such big data systems are typically free of the methodological threats to validity and underpowered datasets common in lab experiments.
Solution to Linear Programming Patterns using Machine Learning

In this research work, linear programming patterns with bounded variables are solved by Supervised Neural Network with back propagation (SNN). A decomposition of large scale optimization tasks into lower order tasks, in optimization problem, is proposed. Samples of linear programming problems (LPPs) are converted into linear programming patterns. The SNN is able to give correct solution to pattern classification. The novel approach of formulation of the linear programming pattern is important in this paradigm. The detailed experimental results are reported.
IPASS: A Novel Open-Source Intelligence Password Scoring System

Password is one of the first and simplest lines of defence that is usually implemented to protect against unauthorized access to a network, local computer, or software application. Unfortunately, all security reports warn that more than 80% of cyber incidents and security breaches are caused by a weak or stolen password. Thus, users should maintain strong passwords for all the online and offline accounts they have. However, determining what makes a strong password is no easy task, with current methods of password scoring producing various degrees of accuracy and success. This paper outlines a novel password scoring method that utilises a person’s social media accounts to improve the accuracy of password scoring by building custom word lists, comparing the password to words found in a person’s social media and searching through a large database of leaked or breached passwords. The testing results show that the proposed method is more accurate than the classic zxcvbn scoring method. We also intend to address privacy concerns that may arise as a result of the access to user personal data by using a blockchain-based data sharing solution that can ensure respect of the individual’s privacy and allow them to control the trade-offs around disclosure of their personal information.
Cultural Heritage Digitisation Using Game with a Purpose

The digitisation of cultural heritage is a priority area in the European Union. This task require a close collaboration between researchers in humanities and computer scientists. The paper presents our effort to create a a training set of recognized handwritten text. To this mean we propose an online game with a purpose (GWAP). This is a human-based computing technique in which some stages of a computational process are transmitted to humans in a form of an online game. The input of the game is a line of handwritten text and the user have to type in its transcription. We investigated how to make this in an attractive and enthralling way in order to involve more users and obtain a large set of transcribed texts.
Analysis of Noxious Comments using Machine Learning Algorithms

Noxious comments are impolite, rude, profane, or disrespectful online comments that can result in restricting people from expressing their thoughts and views, and to stop seeking others’ opinions because of the fear of harassment. Conversational toxicity is a snag that is making people uneasy and ultimately leading them to leave the discussion. This paper comprehensively examines the online abuse in text/comments and categorize the content into multi-label classification to study the toxicity as accurately as possible. Here, the impacts of Machine learning algorithms like Logistic regression, Naive Bayes, K-Nearest Neighbors (KNN), and Decision Tree have been studied and enforced to the facts to answer the troubles in textual categories, and the first-rate algorithm is chosen on the basis of metrics. Moving ahead, the work aims to achieve higher overall performance, and the levels of toxic content is studied for better accuracy to prevent its negative results which ultimately helps plenty of groups to inspect those issues.
U-shape-based architecture with Adjusted Bilateral Guided Aggregation Layer for nuclei image segmentation

Medical image segmentation with AI models has been showing its incredible improvements recently and one of the most significant building blocks is U-shape architecture. With that being said, a big issue of encoder-decoder based models is the lack of semantic information during the decoding process despite the backing up of the skip connection layer. In this paper, we address this challenge with our proposed model. Inheriting the idea of spatial path and semantic path from BiSeNetV2, we replaced the skip connection between encoder and decoder blocks with Adjusted Bilateral Guided Aggregation (ABGA) layer. In addition, we also leveraged the efficiency and elegance from EffecientNet as the model’s encoder. Besides that, a new loss function based on Kulczycki 2 coefficient is introduced.
Penerapan Algoritma Outguess dalam Penyembunyian Pesan Pada Citra Berwarna

The storage or circulation of secret messages is vulnerable to being accessed or wiretapped by parties who do not have legal authority. Therefore, to avoid this and also to secure a secret message known an encryption process called cryptography. In order for the message to be kept confidential, the message is inserted into the image object. Inserting a message in an image or more commonly known as steganography is the development of previously known cryptographic methods. The method with steganography can be said to be better than the previous cryptography. To secure secret messages from unauthorized third parties, steganography is used
Analisis Tingkat Kepuasan Pelanggan dengan Menerapkan Algoritma C4.5

Furniture is currently a secondary item that is quite needed among the community to support their daily activities. In its use, furniture becomes an item that really helps human activities in their daily lives. But now furniture marketing is not only in terms of use, but now people have their own assessment of the aesthetic value of a furniture. So this is a demand for furniture business people to make variations on each product to be marketed. Customer satisfaction is one of the most important things in assessing the level of service provided by the company to its customers. The purpose of this study was to determine the level of satisfaction of CV. Karinda customers. at the company CV. This aspect of Karinda has not been measured, so CV. Karinda finds it difficult to determine which aspects must be improved. The method used in this study is the C 4.5 algorithm, where the data source used is a questionnaire/questionnaire technique given to CV. Karinda customers. the process of testing this research using RapidMinner software to create a decision tree. From the results of the analysis is expected to improve the company's performance in providing services to customers CV. Karinda to be better
SeeShells: An Optimized Solution for Utilizing Shellbags in a Digital Forensic Investigation

The Windows registry is a treasure trove for digital forensics investigators. Shellbags, an important element in registry, can assist investigators with detailed timeline evidence. Several existing applications provide access to Shellbags, but they lack a complete and effective interface for searching and reporting event timelines. In this paper, we develop an optimized and configurable application called "SeeShells" to query Shellbags to build history of criteria-based events and efficiently display them in a rich user interface to facilitate forensic investigation. Our application provides analysis capabilities to flag suspicious events in an easy-to-view frequency map with corresponding event labels. Our frequency map, also known as a heat map, will show density plots in a range of colors to identify the intensity of activities satisfying a query. In addition, our application can export parsed timeline event information into various commonly used file formats to compliment an investigator’s digital forensic report.
Auditing a Software-Defined Cross Domain Solution Architecture

In the context of cybersecurity systems, trust is the firm belief that a system will behave as expected. Trustworthiness is the proven property of a system that is worthy of trust. Therefore, trust is ephemeral, i.e. trust can be broken; trustworthiness is perpetual, i.e. trustworthiness is verified and cannot be broken. The gap between these two concepts is one which is, alarmingly, often overlooked. In fact, the pressure to meet with the pace of operations for mission critical cross domain solution (CDS) development has resulted in a status quo of high-risk, ad hoc solutions. Trustworthiness, proven through formal verification, should be an essential property in any hardware and/or software security system. We have shown, in "vCDS: A Virtualized Cross Domain Solution Architecture", that developing a formally verified CDS is possible. virtual CDS (vCDS) additionally comes with security guarantees, i.e. confidentiality, integrity, and availability, through the use of a formally verified trusted computing base (TCB). In order for a system, defined by an architecture description language (ADL), to be considered trustworthy, the implemented security configuration, i.e. access control and data protection models, must be verified correct. In this paper we present the first and only security auditing tool which seeks to verify the security configuration of a CDS architecture defined through ADL description. This tool is useful in mitigating the risk of existing solutions by ensuring proper security enforcement. Furthermore, when coupled with the agile nature of vCDS, this tool significantly increases the pace of system delivery.
Data Volume Reduction for Deep Packet Inspection by Multi-layer Application Determination

Attack detection in enterprise networks is increasingly faced with large data volumes, in part high data bursts, and heavily fluctuating data flows that often cause arbitrary discarding of data packets in overload situations which can be used by attackers to hide attack activities. Attack detection systems usually configure a comprehensive set of signatures for known vulnerabilities in different operating systems, protocols, and applications. Many of these signatures, however, are not relevant in each context, since certain vulnerabilities have already been eliminated, or the vulnerable applications or operating system versions, respectively, are not installed on the involved systems. In this paper, we present an approach for clustering data flows to assign them to dedicated analysis units that contain only signature sets relevant for the analysis of these flows. We discuss the performance of this clustering and show how it can be used in practice to improve the efficiency of an analysis pipeline.
Using Potential Effects on Threat Events (PETE) to Assess Mitigation Effectiveness and Return on Investment (ROI)

A chronic problem in cybersecurity and cyber resilience is the definition of metrics, so that return on investment can be evaluated. This paper discusses how the Potential Effects on Threat Events (PETE) framework for describing the potential effects of technologies, operational practices, and engineering decisions on threats, which can be used to define metrics and measures of effectiveness.
Analisis Pola Penjualan Produk Makanan dan Minuman Menggunakan Algoritma Apriori

Sales are seen as a unit of business parameters that are very vital and valuable for business people to manage the business they are running, especially in the cafe business. Cafe Aksara as one of the business actors in the culinary world has a lot of demand in the sale and supply of goods on certain days due to the dynamic nature of visitor patterns and makes business people have to be wiser in setting strategies for the combination pattern of sales of food and beverage products found in Indonesia. Aksara cafe to attract customers. In a process of determining the sales pattern strategy at Cafe Aksara, customers or buyers greatly influence the sales transaction data recorded by the Aksara Cafe business person. However, from the results of these recordings, sales transaction data tend not to be used in a process of determining sales pattern strategies to attract customers and are only used as useless archives for Aksara Cafe business people. From these problems, there are many branches of computer science that can be used as reference points in overcoming sales problems, one of which is the branch of computer science Data Mining. Data Mining is a method that is quite categorical to be recommended in solving problems with the sale of food and beverage products, especially the Apriori Algorithm. The output of the Apriori Algorithm can help Cafe Aksara business people determine sales patterns on the sales data of their products