Reviewerly/exHarmony huggingface dataset:
Implementation of Blockchain in Healthcare: A Systematic Review

Blockchain is an emerging technology to ensure the security and trustworthiness for data sharing in different areas such as the economic sector, supply chain management, food industry, energy sector, education, internet of things, and healthcare. In healthcare, balancing patient care with the privacy of information, ease of accessibility and comprehensiveness has been a challenge. Healthcare directly affects humanity and so is one of the first areas where blockchain is implemented because the healthcare system has various problems that should be addressed. Blockchain can solve not only the data management issues between different stakeholders but also saves millions of dollars, reduce forging, and empower the patients. It has contributed to the transparency of insurance claims, management of Electronic Health Record (EHR), and also helps in genome research and so much more. This paper presents a systematic review of the recent research done in the application and major challenges for blockchain technology in the healthcare system. We have reviewed almost 56 papers selected from various search engines. This review focuses on some key areas that are discussed in many pieces of research such as interoperability, security management, fraud prevention, and financial issues. Major challenges and opportunities in the implementation of this technology are also discussed in this review. The basic issue in the healthcare industry is extraordinary costs of the medical facility and subsequent insurance claims. Where the involvement of various parties (hospitals, doctors, insurance companies, and government) makes it very difficult to ensure transparency which casts doubts among stakeholders. It is also observed that despite the favorite area for researchers the technology is not yet implemented on vast levels. Still, a lot of work is required to identify and then resolve the major challenges and issues in the way to transform the health system. The investors need to be convinced about the benefits of blockchain in the healthcare system so that the technology can be implemented on large scale.
Secure protocols for smart contract based insurance services

Abstract Cryptocurrency‐based transactions are exposed to serious security risks, usually higher than risks related to traditional online transactions. The threats range from unintentional credential leaks, through fraudulent cryptocurrency exchanges and poorly implemented protocols, to broken private keys. Therefore, there is a business need for insurance services dedicated to cryptocurrencies that would be technologically as well as economically secure for both insuring and insured party. In this paper an architecture for cryptocurrency insurance framework along with a set of corresponding communication protocols and a smart contract template that jointly reduce various security risks are proposed. The detailed description of the solution is followed by the security analysis section. Also, the description of the presented solution is preceded by a general risk analysis related to cryptocurrency unit security and countermeasures analysis related to those risks. The authors believe that the presented technological solution can potentially enable new business models based on crypto‐insurance services.
Blind Image Restoration with Defocus Blur by Estimating Point Spread Function in Frequency Domain

A blind image restoration method is proposed to enhance the quality of defocus blurred image. Firstly, The defocused blurred image is converted to the frequency domain in order to analyze its spectral characteristics. Secondly, the defocus radius of the defocus point spread function is automatically estimated by calculating the distance between the center point and the nearest black circle. Thirdly, utilizing the estimated defocus point spread function, the image is blindly restored by the constrained least squares algorithm. Forthly, the impact of defocus point spread fuction on the restored image is tested. Experimental results show that the proposed defocus point spread function estimation algorithm has high accuracy, and the restored image achieves better visual effect and higher PSNR around the real defocus point spread function.
Cost-Benefit Analysis of Cloud Computing in Education Using the Base Cost Estimation Model

Cloud computing is one of technology&#x0027;s miracle workers that fulfill today&#x0027;s needs. Especially with the current economic crisis due to COVID-19 and the demand for higher quality education, schools and colleges are under increasing pressure to deliver more from less. To aid this issue, educational institutions can utilize cloud computing to increase their capacity without requiring heavy infrastructure investments while lowering the Total Cost of Ownership (TCO). However, despite the dependence on technology and its vast development, many institutions are still hesitant to make the right choice by going cloud. Because of this, the researcher intends to analyze the Data Center&#x0027;s expenditures at Universitas Klabat and compare them with pricing estimations made in the cloud so that the university can gain an idea of how much the data center truly costs and whether going cloud can minimize the costs. The method used by the researcher to obtain data used in measurement through interviews, financial data of the university&#x0027;s data center in three years, 2018-2020. The physical data center will be calculated by a framework derived from a previous cost-benefit analysis of the cloud computing framework by Nanath and Pillai. As for the cloud data center, the cost estimations will be made based on the servers&#x0027; specifications analyzed from the data while comparing two cloud providers, Amazon Web Services and Microsoft Azure. The calculations on the physical data center costs, the AWS cloud costs, and the Azure cloud cost are then compared to find which alternative has the least expenditures. It is found that alternative Microsoft Azure is the most affordable among others. However, interestingly, the highest cost among the three alternatives is the AWS cloud cost instead of the physical data center cost. Because of this, it is found that the cloud data center can be more affordable than the physical data center depending on the cloud provider that any company uses. In addition, cloud computing, particularly with Microsoft Azure as its provider, can be worth the shift to UNKLAB from a financial standpoint.
FakeAP Detector: An Android-Based Client-Side Application for Detecting Wi-Fi Hotspot Spoofing

Network spoofing is becoming a common attack in wireless networks. The trend is going high due to an increase in Internet users. Similarly, there is a rapid growth of numbers in mobile devices in the working environments and on most official occasions. The trends pose a huge threat to users since they become the prime target of attackers. More unfortunately, mobile devices have weak security measures due to their limited computational powers. Current approaches to detect spoofing attacks focus on personal computers and rely on the network hosts’ capacity, leaving guest users with mobile devices at risk. Some approaches on Android-based devices demand root privilege, which is highly discouraged. This paper presents an Android-based client-side solution to detect the presence of fake access points in a perimeter using details collected from probe responses. Our approach considers the difference in security information and signal level of an access point (AP). We present the detection in three networks, (i) open networks, (ii) closed networks and (iii) networks with captive portals. As a departure from existing works, our solution does not require root access for detection, and it is developed for portability and better performance. Experimental results show that our approach can detect fake access points with an accuracy of 99% and 99.7% at an average of 24.64 and 7.78 milliseconds in open and closed networks, respectively.
Computerized Tomography Images Processing Using Artificial Intelligence Techniques

The study and analysis of images have been increased with time due to the extraction of different features. This research area is leadership by Image Processing because it contributes with interesting techniques and many advances. There are several applications in image processing, such as medicine, agriculture, sports, and object recognition. In medicine, images reveal their importance to improve the processes to treat patients. It also helps the doctors to make better diagnostic. Besides, the environment of medical images is vast and includes x-ray, magnetic resonance, and computerized tomography images. However, the usage of CT images needs much medical staff to detect the organ with disease or with any problem. Thus, it is necessary to apply segmentation methods to improve diagnostics or disease detection. Methods that involve image segmentation require an in-depth study of traditional and non-traditional methods. In that sense, Artificial Intelligence (AI) methods are crucial in the studies of CT image segmentation due to those methods automated all of the processes. The majority contributions of AI are the novel methods from its subfields of Deep Learning and Neural Networks. They facilitate the processes through the usage of algorithms with learning and prediction. It is essential to mention that Image processing techniques need to work with AI techniques to solve segmentation problems and improve the precision of the results. Therefore this paper aims to present state-of-the-art image segmentation using Computed Tomography images.
Kgvideo:A Video Captioning Method Based on Object Detection and Knowledge Graph

The task of video captioning is to transform the video into the corresponding text description. The most important part of video captioning task is the objects in the video and the interaction between the objects. The previous methods usually focus on the overall scene information, and relatively little attention is paid to the objectsâ€™ interaction in the video. This kind of interactive information can actually be regarded as an additional characterization of the video. By representing the video clips with higher quality, it is naturally helpful for the model to better understand the meaning of the video. In this paper, we use knowledge graph to process the object detection information, and then further explicitly the interactive information between the objects, and then guide the generation of higher-quality video caption. Our method applies the methods and results of object detection, instance segmentation, and knowledge graph to guide generate more reasonable and interpretable video captions.
PReDIHERO – Privacy-Preserving Remote Deep Learning Inference based on Homomorphic Encryption and Reversible Obfuscation for Enhanced Client-side Overhead in Pervasive Health Monitoring

Homomorphic Encryption is one of the most promising techniques to deal with privacy concerns, which is raised by remote deep learning paradigm, and maintain high classification accuracy. However, homomorphic encryption-based solutions are characterized by high overhead in terms of both computation and communication, which limits their adoption in pervasive health monitoring applications with constrained client-side devices. In this paper, we propose PReDIHERO, an improved privacy-preserving solution for remote deep learning inferences based on homomorphic encryption. The proposed solution applies a reversible obfuscation technique that successfully protects sensitive information, and enhances the client-side overhead compared to the conventional homomorphic encryption approach. The solution tackles three main heavyweight client-side tasks, namely, encryption and transmission of private data, refreshing encrypted data, and outsourcing computation of activation functions. The efficiency of the client-side is evaluated on a healthcare dataset and compared to a conventional homomorphic encryption approach. The evaluation results show that PReDIHERO requires increasingly less time and storage in comparison to conventional solutions when inferences are requested. At two hundreds inferences, the improvement ratio could reach more than 30 times in terms of computation overhead, and more than 8 times in terms of communication overhead. The same behavior is observed in sequential data and batch inferences, as we record an improvement ratio of more than 100 times in terms of computation overhead, and more than 20 times in terms of communication overhead.
Recipe Bot: The Application of Conversational AI in Home Cooking Assistant

Conversational Artificial Intelligence (AI), which allows people to have human-like interactions with computers, has experienced a boom in recent years. A wide range of fields such as healthcare, finance, and retail have applied conversational AI in their websites to save efforts of completing easy tasks and provide voice interaction with end-users. It enables customers to find quick answers to frequently asked questions and service providers to save time to tackle more complex problems. This paper introduces the Recipe Bot, which is a conversational agent that provides matching recipes based on the information given by users. The intention of Recipe Bot is to help users get rid of unused ingredients in the fridge by providing related recipes. Recipe Bot allows users to input a specific dish name or provide a region, type, and/or ingredients of the food they would like to have, and then it returns a recipe list according to sorting and nutrients requirements given by users. The chatbot is built using Google Dialogflow platform to recognize the user’s intentions and Spoonacular API to find recipes that match the search query. This paper discusses Recipe Bot’s architecture, functionality, and inadequacies to be improved. It gives a detailed example of the interaction between the chatbot and a user, which demonstrates how the user interface will simplify the problem of finding a user-interested recipe.
A Web Based Approach for Navigation Technology using Fused Location API

The method employed in today's scenario is service-oriented web applications to turn business model methods into processes where they can be integrated. The key idea was to develop a web application that would allow friends, relatives to be monitored. The proposed application is capable of gathering information from GPS and storing the main web application that helps for specifics of the maps (Google Map API). This paper presents a new web based approach which primarily aims at improving the storage and data security since they are of prime importance when any navigation based technology is concerned.
A Blockchain-based Airplane Meteorological Data Sharing Incentive System

The airplane can collect meteorological data during climb, cruise, and descent phases of the airplane through airborne sensors. The meteorological data detected by the airplane can make up for the shortfall of the atmosphere data detected over the ocean and in the upper atmosphere by existing meteorological data detection means. However, airline companies may not actually take the initiative to share the meteorological data detected by airplanes for reasons of information security and privacy protection, resulting in a waste of data assets. In order to encourage airline companies to share the meteorological data detected by airplanes, this paper proposes an important meteorological data sharing incentive system based on the blockchain. The system can realize the sharing of meteorological data detected by airplanes among the airline companies under the premise of privacy protection, and under the action of the incentive mechanism, increases the willingness of airline companies to share important weather information. The airplane meteorological data sharing incentive system proposed in this paper can strengthen the airline companies' ability to monitor, forecast and warn important weather, and thus improve the flight safety.
Special Aspects of Organizing Teaching Activities by Simultaneous Learning

This paper analyzes the experience in organizing teaching activities using distance learning technology and e-learning. Positive and negative aspects of this technology are considered from the teachers’ and students’ points of view. Comparative analysis of classroom attendance and online studies is performed, quality of education is assessed considering the use of different techniques, and methods of synchronous, asynchronous, and combined learning are considered, as well as the related legal issues. Distance learning technology is considered, and its special features are discussed.
Extending Interaction Flow Modeling Language as a Profile for Form-making Systems

Form-making systems are used to create, store, and spread forms such as questionnaires, surveys, and online exams. In general, the constant change of requirements makes the development process of such systems more complicated and longer. In order to reduce the complexity of generating form-making systems, in this paper, we propose a UML profile based on Interaction Flow Modeling Language (IFML) standard. On top of the rich set of concepts that exists in IFML for modeling the user interface and behavior of software systems, we extend this standard to support concepts dedicated to form-making systems. As tool support, we have implemented our UML profile in Papyrus. The tool helps designers build a valid model with less effort. To assess our profile and validate its efficiency, we experiment a case study for making an exam for a course. The results show that the proposed profile can support many scenarios of using form-making systems.
Neural Transfer Learning For Vietnamese Sentiment Analysis Using Pre-trained Contextual Language Models

We propose a fine-tuning methodology and a comprehensive comparison between state-of-the-art pre-trained language models (PLM) when applying to Vietnamese Sentiment Analysis. The fine-tuning architecture includes three main components: (1) pre-processing, (2) a pre- trained language model, and (3) a multi-layer perceptron. The method exploits pre-trained contextual language models in order to represent input sentences. Pre-trained contextual language models are belong to three different kinds: multilingual, cross-lingual and monolingual. We conduct experiments to evaluate trained classifiers fine-tuned using five different contextual language models. The experimental results on two open-access datasets show that the sentiment classifiers trained using the monolingual language model outperform of which cross-lingual and monolingual language models. The results provide an additional evidence about the representation power of monolingual PhoBERT in comparison with multilingual BERT and cross-lingual XLM.
Dynamics, Adaptation and Control for Mental Models: A Cognitive Architecture

In this chapter, an overview of the wide variety of occurrences of mental modelsMental model in the literature is discussed. They are classified according to two dimensions obtaining four categories of mental models: static-dynamicDynamics and world-mental, where static refers to mental modelsMental model for static world statesWorld state or for static mental statesMental state and dynamicDynamics refers to mental modelsMental model for world processes or for mental processesMental process. In addition, distinctions are made for what can be done by mental modelsMental model: they can, for example, be (1) used for internal simulationInternal simulation, they can be (2) adapted, and these processes can be (3) controlledControlled. This leads to a global three-level cognitive architectureCognitive architecture covering these three ways of handling mental modelsMental model. It is discussed that in this cognitive architectureCognitive architecture reflection principlesReflection principle play an important role to define the interactionsInteraction between the different levels.
Comparative Study of Open Source Automation Testing Tools: Selenium, Katalon Studio &amp; Test Project

Software testing is one of the critical phases in the software development life cycle (SDLC). In testing, we adjust the actual results according to the end user's expectation by removing the unexpected errors in the functional and nonfunctional requirements. Our primary purpose is to give a comparative study on open source tools and we choose to compare the most commonly used Selenium with new tools Katalon Studio (KS) and Test project (TP). This research evaluates three tools: Selenium, Katalon Studio &#x0026; Test Project on their test tool characteristics, record and playback efficiency, scripts reusability, ease to learn, cross-platform, data-driven testing, cost, test result report, cross-browser, and team collaboration. The main goal is to help developers and software quality assurance engineers (SQAE) to choose the right tool that reduces manual effort, increasing script usability, reduces cost, and working in team collaboration to give their best to ensure the quality of software.
Malicious HTTPS Traffic Classification Algorithm Based on DCGAN_1D-CNN

Aiming at the problems of fast classification and unbalanced data classification of encrypted malicious traffic in the internet, a classification method of encrypted malicious based on DCGAN_1D-CNN model is proposed.DCGAN-IDCNN uses the idea of generating confrontation to generate a few samples in the data level to improve the lack of original training samples and sample imbalances,and use the ID-CNN to train the original sample and the generated few samples,so as to accurately classify the encrypted malicious.The experimental results on the public dataset CICAndMal2017 show that the F1 value reaches 96.55% in the binary classification experiment, which is at most higher 11.77% than that of using 1D-CNN model alone,indicating that the DCGAN_1D-CNN model has stronger ability to classify encrypted malicious traffic.
An Image Encryption Method by Sub-image Shuffling, Bit-level Permutation and Diffusion using Chaotic Maps

Transmission of images securely through a channel can be ensured using cryptography techniques.Encrypting an image with the help of chaotic maps provides security and authentication.This paper presents an image encryption method that consists of three main steps: Sub-image shuffling, bit-level permutation, and diffusion.The sub-image shuffling step is performed using a logistic map to increase the robustness of the proposed method.In the bit-level permutation process, the highest four bits of each pixel are obtained.The bitlevel row and column transformations are applied using the sequences generated by a chaotic tent map.Finally, in the diffusion step, the values of the image's pixels are altered.Two different chaotic sequences which are obtained from the logistic map and tent map are employed.Five different parameters are selected as the secret keys for the encryption.The performance of the suggested method is tested with several analysis methods.Key space analysis shows that the proposed method can withstand brute force attacks.It is also proven that the method is highly sensitive to secret keys.Histogram analysis illustrates the fact that the encrypted image's pixels are uniformly distributed.Also, the correlation between the neighboring pixels is reduced in the encrypted image compared to the plain image.The differential analysis demonstrates that the method is sensitive to the slightest changes in the plain image even though the same secret keys are used.