Reviewerly/exHarmony huggingface dataset:
Development and clinical evaluation of a contactless operating interface for three-dimensional image-guided navigation for endovascular neurosurgery

In endovascular neurosurgery, the operator often acquires three-dimensional (3D) images of the cerebral vessels. Although workstation reoperation is required in some situations during treatment, it leads to time loss because a sterile condition cannot be maintained and treatment must be temporarily interrupted. Therefore, a workstation reoperating system is required while maintaining the desired sterility.A contactless operating interface using Kinect to control 3D images was developed via gesture recognition for endovascular neurosurgery and was applied to a 3D volume rendering technique (VRT) image reconstructed at the workstation. The left-hand movement determines the assigned functions, whereas the right-hand movement is used like a computer mouse to pan and zoom in/out. In addition to the interface, voice commands were used and assigned to digital operations, such as image view changes and mode signal changes.This system was used for the actual endovascular treatment of cerebral aneurysms and cerebral arteriovenous malformations. The operator and gesture were recognized without any problems. Using voice operation, it was possible to expeditiously set the VRT image back to the reference angle. Furthermore, it was possible to finely adjust gesture operations, including mouse operation, and treatment was completed while maintaining sterile conditions.A contactless operating interface was developed by combining the existing workstation system with Kinect and voice recognition software, allowing surgeons to perform a series of operations, which are normally performed in a console room, while maintaining sterile conditions.
CEOs’ understanding of blockchain technology and its adoption in export-oriented companies in West Sweden: a survey

Purpose The purpose of this paper is to study current practices in adopting blockchain technology amongst export companies in West Sweden and to capture their CEOs’ knowledge of and attitudes towards blockchains. Design/methodology/approach Factors enabling or hindering the adoption of blockchains were identified from a comprehensive literature review and a survey of 72 chief executive officers (CEOs) of export-oriented firms in West Sweden, all with turnovers exceeding €2m, regarding their knowledge of and attitudes towards blockchains. Findings Blockchain technology is not currently perceived to provide benefits that would outweigh the costs of introducing it into West Sweden’s export firms. Nevertheless, the findings suggest that such technology, though currently too immature to meet today’s industrial requirements, could experience more widespread use if certain key factors (i.e. lower cost, traceability, improved security or trustworthiness and new blockchain-enabled business models) are prioritised. Research limitations/implications Answered by 72 CEOs, the survey achieved a response rate of 6%, meaning that the findings are only exploratory. Even so, they offer new insights into CEOs’ attitudes towards blockchain technology. Practical implications The CEOs reported comparatively limited knowledge of and experience with implementing blockchains, the lack of which has hampered their large-scale implementation in multi-actor supply chains. Social implications Negative sentiment amongst CEOs towards blockchain technology may lower on-the-job satisfaction amongst tech personnel aspiring to develop and implement blockchain applications in their firms. Originality/value Knowledge of and attitudes towards blockchain technology amongst top-level managers, as well as about factors enabling or hindering its adoption, guide managers in crafting strategies for implementing blockchains in their organisations and maximising the benefits therein. Unlike past studies focussing on technological aspects or views of experts and middle-management, the study was designed to capture the views of CEOs.
Fully automated estimation of the mean linear intercept in histopathology images of mouse lung tissue

Purpose: The mean linear intercept (MLI) score is a common metric for quantification of injury in lung histopathology images. The automated estimation of the MLI score is a challenging task because it requires accurate segmentation of different biological components of the lung tissue. Therefore, the most widely used approaches for MLI quantification are based on manual/semi-automated assessment of lung histopathology images, which can be expensive and time-consuming. We describe a fully automated pipeline for MLI estimation, which is capable of producing results comparable to human raters. Approach: We use a convolutional neural network based on U-Net architecture to segment the diagnostically relevant tissue segments in the whole slide images (WSI) of the mouse lung tissue. The proposed method extracts multiple field-of-view (FOV) images from the tissue segments and screen the FOV images, rejecting images based on presence of certain biological structures (i.e., blood vessels and bronchi). We used color slicing and region growing for segmentation of different biological structures in each FOV image. Results: The proposed method was tested on ten WSIs from mice and compared against the scores provided by three human raters. In segmenting the relevant tissue segments, our method obtained a mean accuracy, Dice coefficient, and Hausdorff distance of 98.34%, 98.22%, and 109.68 μm , respectively. Our proposed method yields a mean precision, recall, and F1 -score of 93.37%, 83.47%, and 87.87%, respectively, in screening of FOV images. There was substantial agreement found between the proposed method and the manual scores (Fleiss Kappa score of 0.76). The mean difference between the calculated MLI score between the automated method and average rater's score was 2.33±4.13 ( 4.25%±5.67% ). Conclusion: The proposed pipeline for automated calculation of the MLI score demonstrates high consistency and accuracy with human raters and can be a potential replacement for manual/semi-automated approaches in the field.
STUDI TINGKAT LITERASI MEMBACA MAHASISWA SELAMA PEMBELAJARAN DARING

Online learning activities are carried out to reduce the number of covid-19 spread and are considered quite effective to be applied by utilizing various technological media as a learning process and resource. This study aims to describe the level of reading literacy skills of students during online learning. The method used in this research is descriptive quantitative. The data collection technique is done by using a questionnaire via google form and conducting interviews to strengthen the research data. The data analysis was carried out through quantitative descriptive techniques. The data obtained from the questionnaire results are presented in the form of a percentage. Furthermore, the data is described to determine the level of students' reading literacy skills during online learning. The results showed that the reading literacy level of students during online learning was quite effective as seen from the average time spent by students in reading around 1-2 hours a day by reading subject references, using social media as a learning resource, and reading online news, because it has easy access to information via a smartphone.
Biorthogonal Wavelet on a Logarithm Curve <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>ℂ</mi> </math>

According to the length-preserving projection and Euler discretization method, biorthogonal wavelet function on a smooth curve <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi mathvariant="script">C</mi> </math> is constructed in this paper, such as a logarithm curve. The properties of biorthogonal wavelet filters on a smooth curve <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi mathvariant="script">C</mi> </math> are discussed, such as induced refinable equation and symmetry. Moreover, an example is given for discussing the biorthogonal scaling function and its dual on a logarithm curve <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi mathvariant="script">C</mi> </math> . Finally, a numerical application is given for dealing with financial data.
Research on Communication APP for Deaf and Mute People Based on Face Emotion Recognition Technology

Deaf people cause inconvenience to their daily communication due to different degrees of hearing loss, and their hearing loss may also affect their ability to recognize facial emotions, thereby further affecting their interpersonal communication. Therefore, the use of emerging information technology to solve the hearing impairments of deaf and dumb people, language barriers of deaf and dumb people, regional language barriers, and emotional barriers of deaf and dumb people contributes to the smooth communication between deaf and dumb people and hearing people. And improving the education system for all has important practical significance. Based on this research background, the paper uses eye movement and event-related potential analysis methods to try to study the characteristics of deaf people's potential cognitive abilities in face emotion recognition and sign language recognition tasks from different experimental paradigms, extract the target area by means of image processing, and Use appropriate feature description, sign language video recognition technology, emotional speech synthesis technology, speech recognition technology and machine learning methods to make further accurate judgments, and finally perform sign language and emotion recognition. Finally, the APP software was tested for running. Through experiments on 630 gesture images, the recognition rate reached 94.22% and the speed reached 0. 29s/frame. The results show that the proposed algorithm effectively improves the recognition rate and can meet the realtime performance of dumb language communication.
Political institutions and economic performance in Ethiopia: an auto regressive distributed lag bound approach to co-integration

Purpose The main objective of this study is to examine how political institutions affect economic performance in Ethiopia over the 1980–2019 time periods. Design/methodology/approach Mainly, the impact of political institution indicators including, level of democracy, political violence, democratic accountability and regime durability have been examined using auto regressive distributed lag (ARDL) bound test approach to co-integration and the error correction model. Findings This study confirms that level of democracy and democratic accountability has an adverse long effect on the economic performance of Ethiopia. On the other hand, political violence has a negative short-run causal effect on economic performance in Ethiopia. The study concluded that the deterioration of political institutions harmfully affected economic performance in Ethiopia. Practical implications Government policymakers should primarily pay attention to promoting and changing those political institutions that harm economic performance. Additionally, better management of political violence has important implications for fostering the economic performance of Ethiopia. Originality/value This study provides some valuable evidence on the nexuses between political institutions and economic performance in Ethiopia. Likely, this is the first investigation on the subject under the consideration to use time analysis and will vigorously contribute to the literature as well by employing the ADRL bound test. Previous studies have examined the impact of the institution on economic growth on a cross-country basis. Further analysis is required to understand the effects of institutions such as level of democracy, political violence and democratic accountability on economic development.
WITHDRAWN: Optimized Adaptive load balancing algorithm in cloud computing

As we know that Internet speed creation in cloud computing clearly positions the scheduling algorithm in today's lifestyles very critically. In the cloud world, the right load balancing technology is essential to execute the system and manage resources. In a dispensed system, sources with varying configurations and capabilities are very difficult to obtain. In order to optimise certain final outcomes, the load balance machine will pose the obstacle to the resource that, based solely on certain specific goals, takes on essential targets. The history of cloud activities simplifies and demonstrates the scheduling system, optimization problems focused mostly on QoS in cloud architecture. At Level-1, the “planning algorithm” based on the Service Level Agreement (SLA) contains full information about the assignments and distribution to the cluster. In Level 2, the monitoring strategy for idle servers balance the demand on separate servers in each cluster. Most significantly, the current load balancing strategies will be remembered and an improved load balancing algorithm will be increased that will mainly support the cloud world. This text delineates a comparative look at the cloud computing world to classify these scheduling algorithms.
The progress of digitization and openness of local history materials published in local municipalities - A case of all cities in Kanagawa Prefecture, Japan -

近年,地域・自治体資料のデジタル化が進展しているが,どの程度デジタル化・オープン化されているかの把握は難しい.神奈川県の全市を対象に，国立国会図書館のデジタルコレクションやインターネット資料収集保存事業，および各市ホームページや図書館蔵書検索システムを使用してメタデータおよびデジタル資料を調査し，デジタル化とオープン化の進展を推測し，国立国会図書館と公共図書館との連携のあり方など今後の課題について考察した.
The Integration of the MDA Approach in Document-Oriented NoSQL Databases, the case of Mongo DB

Today with the growth of the internet, the use of social networks, mobile telephony, connected and communicating objects. The data has become so big, hence the need to exploit that data has become primordial. In practice, a very large number of companies specializing in the health sector, the banking and financial sector, insurance, manufacturing industry, etc… are based on traditional databases which are often well organized of customer data, machine data, etc ... but in most cases, very large volumes of data from these databases, and the speed with which they must be analyzed to meet the business needs of the company are real challenges. This article aims to respond to a problem of generating NoSQL MongoDB databases by applying an approach based on model-driven engineering (Model Driven Architecture Approach). We provide Model to Model (using the QVT model transformation language), and Model to Code transformations (using the code generator, Acceleo). We also propose vertical and horizontal transformations to demonstrate the validity of our approach on NoSQL MongoDB databases. We have studied in this article the PSM transformations towards the implementation. PIM to PSM transformations are the subject of another work.
Mining Social Media Data of Philippine Higher Education Institutions Using Naive Bayes Classifier Algorithm

Philippine Higher Education Institutions (HEIs) integrate social media (SM) platform like Facebook to digitally market their position and brand, post announcements, news and other pertinent information about their institution. Though SM is used in education, it is noted that its primary purpose is for users’ online social engagement. SM users react to posts, comment and give their sentiments on a particular topic to discuss and share their daily life in an informal and casual manner. Students even create unofficial Facebook pages associated to their institution and post their experiences and sentiments. These digital footprints of SM users could be a good source to use Data Mining (DM) techniques to understand HEI stakeholders’ experiences and form decision-making interventions to improve quality education. The main objective of this study is mining social media data to support Philippine HEIs identify their stakeholders’ SM behaviors and make proactive and knowledge-driven decisions. Specifically, the objectives of this study are: to develop a workflow of analyzing social media data; to use Naïve Bayes Classifier in identifying the SM behaviors of Philippine HEI stakeholders; and to interpret the results of DM and propose intervention measures to improve quality education. Using Agile Methodology CRISP-DM model, this study centers on mining users’ post and comments in Philippine HEI Facebook pages.
An efficient low bit rate image watermarking and tamper detection for image authentication

Abstract This study presents the vulnerability of digital documents and its effective way to protect the ownership and detection of unauthorized modification of multimedia data. Watermarking is an effective way to protect vulnerable data in a digital environment. In this paper, a watermarking algorithm has been proposed based on a lossy compression algorithm to ensure authentication and detection of forgery. In this proposed method, the CDF9/7 biorthogonal wavelet is used to transform the watermark image and encoded the wavelet coefficients using Set Partition in Hierarchical Tree algorithm. Then, the encoded bits are encrypted by shuffling and encrypting using symmetric keys. After that the encrypted bits are inserted into the Least Significant Bit position of the cover image. In addition, two tamper detection bits are generated based on texture information and pixel location and inserted in the watermarked image. The proposed algorithm reconstructs the watermark and the tampering region more efficiently and achieved 56.5463 dB PSNR for STARE database. Experimental result shows that the proposed algorithm is effectively prevented different attacks and ensure the integrity of watermark bits within the watermarked image. Also finds the tampered region more efficiently compared with the existing state of art algorithms.
A Neural Network Aided Real-Time Hospital Recommendation System

Over the past few years, there have been an overwhelming number of healthcare providers, hospitals and clinics. In such a situation, finding the right hospital for the right ailment can be a considerable challenge. Inspired by this challenge, this work attempts to build a model that can automatically recommend hospitals based on user requirements. In the past there have been important works in physician recommendation. Our proposed work aims to be more inclusive and provide an automated hospital recommendation system to patients based on neural networks driven classification. We suggest a model that considers several unique parameters, including geographical location. To optimize its usefulness, we design a system that recommends hospitals for general consultation, specialty hospitals, and in view of the pandemic, hospitals recommended for treatment of COVID-19. In this work, we adopt Neural Networks and undertake a comparative analysis between several different available supervised algorithms to identify one best suited neural architecture that can work best in the applied fields. Based on our results from the analysis, we train the selected neural network with context relevant data. In the image of the recommendation system, we develop a website that uses the trained neural network on its backend and displays the recommendation results in a manner interpretable by the end user. We highlight the process of choosing the right neural model for the backend of the website. To facilitate the working of the website in real-time, we use real time databases hosted on Google Firebase and edge devices on hospital ends. Additionally, we suggest two hospital side data updation tools. These tools would ensure that hospitals can update the parameters which change quickly in the real world to their latest values so as to maintain the precision of the system. We test the website with test data and find that the website recommends hospitals with sufficient precision in the specified format. The model has been designed with the limited amount of data available in this field, but its performance and utility can be easily improved with better quality and more abundant data.
Comparative Study and Analysis of Recent Computer Aided Diagnosis Systems for Masses Detection in Mammograms

Nowadays, breast cancer is considered one of the most threatening and common cancers for women due to the high rate of deaths that occurred yearly that reaches about 25% in all cancers. One of the most keys to decrease the mortal rate caused by breast cancer is its early detection. So, the research on developing computer-aided diagnosis systems (CADs) has been widely increased to improve the accuracy of breast cancer localization and classification. Generally, a proposed CAD is developed through four stages: data preparation and preprocessing, cancer detection, followed by its pathology classification. In this paper, the most recent proposed approaches to detect lesions in the breast mammograms and classify them are discussed with a comparative analysis to list the advantages and the disadvantages of most approaches. The main objective of this paper is to group the CADs with a performance evaluation and detailed analysis in order to furtherly develop others by avoiding the main weak points in the existing systems and to achieve high detection accuracy and classification performance at the same time.
Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications

There has been a growing interest in model-agnostic methods that can make deep learning models more transparent and explainable to a user. Some researchers recently argued that for a machine to achieve a certain degree of human-level explainability, this machine needs to provide human causally understandable explanations, also known as causability. A specific class of algorithms that have the potential to provide causability are counterfactuals. This paper presents an in-depth systematic review of the diverse existing body of literature on counterfactuals and causability for explainable artificial intelligence. We performed an LDA topic modelling analysis under a PRISMA framework to find the most relevant literature articles. This analysis resulted in a novel taxonomy that considers the grounding theories of the surveyed algorithms, together with their underlying properties and applications in real-world data. This research suggests that current model-agnostic counterfactual algorithms for explainable AI are not grounded on a causal theoretical formalism and, consequently, cannot promote causability to a human decision-maker. Our findings suggest that the explanations derived from major algorithms in the literature provide spurious correlations rather than cause/effects relationships, leading to sub-optimal, erroneous or even biased explanations. This paper also advances the literature with new directions and challenges on promoting causability in model-agnostic approaches for explainable artificial intelligence.
Credit Card Fraud Transaction Detection System Using Neural Network-Based Sequence Classification Technique

The movement towards digital era introduces centralization of information, web services, applications, and devices. The fraudster keeps an eye over ongoing transaction and forges data by using different techniques as traffic monitoring, session hijacking, phishing, and network bottleneck. In this study, the authors design a framework using deep learning algorithm to suspect the fraudulence transaction and evaluate the performance of the proposed system by updating data repositories. The neural network-based sequence classification technique is used for fraud detection of credit card transactions by including threshold value to measure the deviation of transaction. The reconstruction error (MSE) and predefined threshold value of 4.9 is used for determination of fraudulent transactions.