Reviewerly/exHarmony huggingface dataset:
Implementation of MediaLab in Hematology Courses

<h3>ABSTRACT</h3> Reaching students in a technology-savvy society can be difficult, but the implementation of software helps to extend an institution’s reach. Hematology slides, which show students a variety of diseases, can be difficult to obtain and evaluate once stains begin to fade. It may also be difficult to give feedback to a class full of students with questions about the identity of cells on a peripheral smear. Using PowerPoint to show cells gives a limited view of what the cellular picture looks like, and the determination of the actual size in comparison with other cells can be difficult. The white-blood-cell– and red-blood-cell–differentials simulator provided online, through MediaLab Inc. Case Simulator, has helped to move Indian Hills Community College’s Advanced Hematology and Coagulation course to a hybrid format. Students can see cells they may not otherwise get to see in a classroom laboratory. Cases are presented with patient history and laboratory values to aid with the differential that needs to be completed. This helps students learn critical-thinking skills through the identification of cells and interpretation of laboratory values presented in different disease states. Feedback for our students is immediate per cell during the differential, and—once the differential is completed—the student can watch a review video that explains each cell. A review of each cell the student had to identify, an explanation of what the student identified, and what experts identified can be viewed. Students’ results are easily graded by the instructor and can be compared with the class of students. These statistics can be exported to Excel and used in an assessment of the students’ understanding of each type of cell. These statistics have helped the department assess student-learning outcomes and identify individual needs based on specifically identified areas that need work per class or per student basis. When looking at 4 years’ worth of data, those students who consistently completed assignments and scored a 78% or better on their differentials passed their practical examinations when given in PowerPoint form. Of the students that passed the course, 100% of them passed their board examination and 88% of those same students passed the hematology section of the board examination. MediaLab Inc. has helped students understand cell identification, and students have given great feedback on the software. Students like that they can complete work on their own time and get immediate feedback on the cells that were identified incorrectly.
Initial strategy of an assistive technology for rehabilitation and monitoring of elderly patients

This article presents an initial assistive technology strategy for the rehabilitation and monitoring of elderly patients, focused on lower limb muscle recovery, from a condition of low gait stability to recovery without the use of crutches. The project is in progress, and its first module, the recovery one, measures the flexion and extension of the lower limbs in degrees and sends the data to a database for analysis by the physiotherapist. The second module consists of the design of support for a telephone integrated to the crutch, to facilitate the integration with existing applications, also containing an embedded system for integration with the first module. When the patient is already on crutches, the cell phone and an embedded system coupled to the patient are used, which send signals in case the patient falls. The results are still partial and from isolated parts of the project, so that, after the completion of the modules, it will be possible to integrate and quantitatively analyze the results. The voltage source showed a current output that was maintained despite the load and without a voltage drop. The movement monitoring system through the camera recognized the knee flexion movement. The initial architecture of the system managed to include a system involving computer graphics, and screens that concern the human-computer interface. The crutch and the telephone support underwent usability tests that proved basic mechanical resistance to not present elementary difficulties in sustaining a person and a cellular device.
Demystifying Random Number in Ethereum Smart Contract: Taxonomy, Vulnerability Identification, and Attack Detection

Recent years have witnessed explosive growth in blockchain smart contract applications. As smart contracts become increasingly popular and carry trillion dollars worth of digital assets, they become more of an appealing target for attackers, who have exploited vulnerabilities in smart contracts to cause catastrophic economic losses. Notwithstanding a proliferation of work that has been developed to detect an impressive list of vulnerabilities, the bad randomness vulnerability is overlooked by many existing tools. In this paper, we make the first attempt to provide a systematic analysis of random numbers in Ethereum smart contracts, by investigating the principles behind pseudo-random number generation and organizing them into a taxonomy. We also lucubrate various attacks against bad random numbers and group them into four categories. Furthermore, we present RNVulDet - a tool that incorporates taint analysis techniques to automatically identify bad randomness vulnerabilities and detect corresponding attack transactions. To extensively verify the effectiveness of RNVulDet, we construct three new datasets: i) 34 well-known contracts that are reported to possess bad randomness vulnerabilities, ii) 214 popular contracts that have been rigorously audited before launch and are regarded as free of bad randomness vulnerabilities, and iii) a dataset consisting of 47,668 smart contracts and 49,951 suspicious transactions. We compare RNVulDet with three state-of-the-art smart contract vulnerability detectors, and our tool significantly outperforms them. Meanwhile, RNVulDet spends 2.98s per contract on average, in most cases orders-of-magnitude faster than other tools. RNVulDet successfully reveals 44,264 attack transactions. Our implementation and datasets are released, hoping to inspire others.
SONG RECOMMENDATIONS SYSTEM

A music recommendation system was developed that can learn users' preferences.The system can classify a wide range of stored music using automatic music content analyses.Users can opt for music according to their mood, using such words as "bright", "exciting", "quiet", and "sad".Building a music recommendation system is one of the information retrieval tasks.This research is devoted to a content-based music recommender system.The main peculiarity of our work is that the developed recommender system is based on the acoustic similarity of musical compositions.Two approaches to building a content-based music recommender system are considered in this paper.The first is a quite common approach that uses acoustic features analysis.The second approach includes deep learning and computer vision methods applications aimed at improving the results of the recommender system.
SURVEY ON INNOVATIVE VIRTUAL HEALTHCARE ASSISTANT WITH BMI CALCULATOR USING MACHINE LEARNING TECHNIQUE

Technologies like AI ,machine learning, data science are becoming upgraded.The advancement in available, portable, low cost handheld devices like cell phones and availability of network connection has resulted within the user's mobility at an unprecedented level.We've studied different methodologies like Smart goal annotation, state phase annotation, collection process, agreement results also as annotation skills for achieving the health goals.The user has got to type their health regarding the query based on that assistant giving the acceptable answer.The facilities like report generation and scheduling assignment are provided.It'll increase the interaction between humans and machines with the assistance of different technologies, vast dialogue ,conversational knowledge based, public knowledge based.The system uses different algorithms for disease recognition, behavior abnormality detection, prediction etc. Experimental results show that: Compared with traditional methods,the proposed method is more accurate and faster and patients can get service anywhere and anytime.
ENSEMBLE LEARNING FOR BREAST CANCER DIAGNOSIS: RULE CONVERSION AND FEATURE SELECTION APPROACHES WITH MULTIPLE CLASSIFIERS

Now-a-days, there are more types of cancers found across all over the world.Somehow, Breast cancer is one of the most common forms of cancer in women worldwide.For a successful treatment of Breast cancer, we need to detect at the earliest stage.There is a challenge in diagnosing breast cancer accurately.Often there is a need for more effective methods to detect and diagnosis the disease.The main objective of this project is to generate rules from the Breast Cancer Wisconsin dataset using a combination of data exploration, feature reduction, and machine learning algorithms.The first step is to explore, pre-process and understand the dataset, followed by rule conversion using random forest algorithm.After the rule conversion, Feature reduction is performed using Extra Tree Classifier, Recursive Feature Elimination (RFE), and correlation between features, and the top 10 features are selected.Then, we used SelectFromModel method for further reduction of reduced features to 5. Again, Rule conversion is performed using random forest algorithm on the selected features.Finally, the generated rules are predicted with the original dataset using several machine learning algorithms such as SVM, MLP, Gradient Booster, Ada Booster, CNN, Extra Tree, and Logistic Regression.By identifying the most important features for predicting breast cancer, we aim to provide clinicians and researchers with valuable insights and tools for more accurate diagnosis and treatment of the disease.
The Relationship between Manager’s Communication Style and Employees’ Job Satisfaction

Communication can be described as giving and receiving messages between two or more employees in an organization.This study applied the four components of a manager's communication style: telling, selling, joining and consulting within an organizational context and analyzed its effect on job satisfaction.The communication variables were strongly related to employees' communication styles.The focal purpose of this study is to determine the relationship between a manager's communication style and employees' job satisfaction at Malaysian Palm Oil Board (MPOB) Bangi, Selangor.A total of 291 respondents among the Malaysia Palm Oil Board (MPOB) Head Quarters Bandar Baru Bangi workers responded to a survey.Data were analysed by using SPSS to see the relationship between managers' communication styles and employees' job satisfaction at this organisation.Results showed a significant relationship between a manager's communication style and employee job satisfaction at MPOB Bangi, Selangor, where most of the employees preferred the managers to use consulting to communicate with others.This shows that these organisations have an atmosphere of trust, honesty and open communication.
Multilevel Inverse Patchmatch Network with Local and Global Refinement for Underwater Stereo Matching

Vision-based underwater autonomous systems play a significant role in marine exploration. Stereo matching is one of the most popular applications for vision-based underwater autonomous systems, which recovers the geometric information of underwater scenes via stereo disparity estimation. While stereo matching in the air has achieved great progress with the development of neural networks, it generalizes poorly to the underwater scenario due to the challenging underwater degradation. In this paper, we propose a novel Multilevel Inverse Patchmatch Network (MIPNet) to iteratively model pair-wise correlations under underwater degradation and estimate stereo disparity with both local and global refinements. Specifically, we first utilized the inverse Patchmatch module in a novel multilevel pyramid structure to recover the detailed stereo disparity from the input stereo images. Secondly, we introduced a powerful Attentional Feature Fusion module to model pair-wise correlations with global context, ensuring high-quality stereo disparity estimation for both in-air and underwater scenarios. We evaluate the proposed method on the popular real-world ETH3D benchmark, and the highly competitive performance against the popular baselines demonstrates the effectiveness of the proposed method. Moreover, with its superior performance on our real-world underwater dataset, e.g., our method outperforms the popular baseline RAFT-Stereo by 27.1%, we show the good generalization ability of our method to underwater scenarios. We finally discuss the potential challenges for underwater stereo matching via our experiments on the impact of water.
How Can Hospitality Industry Improve Customer Satisfaction by Determining the Relevant Degree of Robot Staff Implementation?

This study tackled the issues with service robots in hospitality industry and how to leverage robotic technology effectively. The implementation of robots in the hospitality industry was pushed to the boundary; however, customer satisfaction did not improve much as people still complain about the lengthy check-in process, the confusing communication, and other common complaints; there are more associated issues, such as underwhelming welcome, robots do not understand a specific accent, robots run out of battery, robots struggle to understand guests’ requests. Another issue was that some guests were uncomfortable with being served by service robots. The study aimed to determine the relevant degree of robotic staff implementation in the hospitality industry to improve customer satisfaction. For the empirical analysis, the data are coming from the given-out survey, including people who work at the hotel and people who do not work at the hotel, to gather comprehensive opinions from people with diversified occupancy. The study conducted a multiple-choice survey and a short answer question to leave them blank for people who want to express their feelings for service robots.
Junction Temperature Prediction Model for GaAs HBT Devices Based on ASO-ELM

In this study, an accurate temperature prediction model is proposed for GaAs HBT, which considers both the bias voltage and current rather than power consumption only. The increase in temperature is closely related to the heat source property, which leads to a complex interaction between the lattice vibration and the uneven distribution of the electric field and current density. To improve the accuracy and stability of the temperature prediction model, a machine learning method of Extreme Learning Machine (ELM) optimized with an Atomic Search Algorithm (ASO) is introduced. The validity of the model is verified by comparing it with experimental observations by the QFI InfraScope TM temperature mapping system. The predicted temperatures for an 8 × 8 HBT power cell fabricated with 2 μm GaAs technology show good agreement with the measurement results, with a ±2 °C error and a relative error deviation below 3%. This demonstrates the superior performance of the proposed model in accurately predicting the temperature of GaAs HBT.
Towards Automated and Optimized Security Orchestration in Cloud SLA

In cloud computing, providers pool their resources and make them available to customers. Next-generation computer scientists are flocking to the cutting-edge field of cloud computing for their research and exploration of uncharted territory. There are still several barriers that cloud service providers must overcome in order to provide cloud services in accordance with service level agreements. Each cloud service provider aspires to achieve maximum performance as per Service Level Agreements (SLAs), and this is especially true when it comes to the delivery of services. A cloud service level agreement (SLA) guarantees that cloud service providers will satisfy the needs of large businesses and offer their clients with a specified list of services. The authors offer a web service level agreement–inspired approach for cloud service agreements. We adopt patterns and antipatterns to symbolize the best and worst practices of OCCI (Open Cloud Computing Interface Standard), REST (Representational State Transfer), and TOSCA (Topology and Orchestration Specification for Cloud Applications) with DevOps solutions, all of which API developers should bear in mind when designing APIs. When using this method, everything pertaining to the cloud service, from creation to deployment to measurement to evaluation to management to termination, may be handled mechanically. When distributing resources to cloud apps, our system takes into account the likelihood of SLA breaches and responds by providing more resources if necessary. We say that for optimal performance, our suggested solution should be used in a private cloud computing setting. As more and more people rely on cloud computing for their day-to-day workloads, there has been a corresponding rise in the need for efficient orchestration and management strategies that foster interoperability.
Segmentation of Medical Images with Adaptable Multifunctional Discretization Bayesian Neural Networks and Gaussian Operation

Bayesian statistics is incorporated into a neural network to create a Bayesian neural network (BNN) that adds posterior inference aims at preventing overfitting. BNNs are frequently used in medical image segmentation because they provide a stochastic viewpoint of segmentation approaches by producing a posterior probability with conventional limitations and allowing the depiction of uncertainty over following distributions. However, the actual efficacy of BNNs is constrained by the difficulty in selecting expressive discretization and accepting suitable following disseminations in a higher-order domain. Functional discretization BNN using Gaussian processes (GPs) that analyze medical image segmentation is proposed in this paper. Here, a discretization inference has been assumed in the functional domain by considering the former and dynamic consequent distributions to be GPs. An upsampling operator that utilizes a content-based feature extraction has been proposed. This is an adaptive method for extracting features after feature mapping is used in conjunction with the functional evidence lower bound and weights. This results in a loss-aware segmentation network that achieves an F1-score of 91.54%, accuracy of 90.24%, specificity of 88.54%, and precision of 80.24%.
Multi-Resolution Feature Embedded Level Set Model for Crosshatched Texture Segmentation

In image processing applications, texture is the most important element utilized by human visual systems for distinguishing dissimilar objects in a scene. In this research article, a variational model based on the level set is implemented for crosshatched texture segmentation. In this study, the proposed model’s performance is validated on the Brodatz texture dataset. The cross-hatched texture segmentation in the lower resolution texture images is difficult, due to the computational and memory requirements. The aforementioned issue has been resolved by implementing a variational model based on the level set that enables efficient segmentation in both low and high-resolution images with automatic selection of the filter size. In the proposed model, the multi-resolution feature obtained from the frequency domain filters enhances the dissimilarity between the regions of crosshatched textures that have low-intensity variations. Then, the resultant images are integrated with a level set-based active contour model that addresses the segmentation of crosshatched texture images. The noise added during the segmentation process is eliminated by morphological processing. The experiments conducted on the Brodatz texture dataset demonstrated the effectiveness of the proposed model, and the obtained results are validated in terms of Intersection over the Union (IoU) index, accuracy, precision, f1-score and recall. The extensive experimental investigation shows that the proposed model effectively segments the region of interest in close correspondence with the original image. The proposed segmentation model with a multi-support vector machine has achieved a classification accuracy of 99.82%, which is superior to the comparative model (modified convolutional neural network with whale optimization algorithm). The proposed model almost showed a 0.11% improvement in classification accuracy related to the existing model.
Experimental Study based on the Implementation of a Regulatory Framework for the Improvement of Cyber Resilience in SMEs

Currently, applying regulations oriented to cybersecurity, cyber resilience is relevant to face the high rates of cyberattacks, which have caused an interruption in the operational processes of organizations, generating an economic loss, and affecting the continuity of their business processes on the web. In this scenario, small and medium-sized enterprises (SMEs) are the most affected due to their weak technological infrastructure. Given this, this experimental study was developed to implement a regulatory framework for the improvement of cyber resilience; the criteria anticipate, resist, recover and evolve presented significant statistical values of improvement after the application of the experiment. This research contributes to counteract the refusal to use information technologies for business development; Improvement actions were carried out to face threats and computer vulnerabilities to which organizations are exposed when carrying out operations in cyberspace.
Risk Assessment of Fire Hazards in Semarang City Residential Areas

Based on data from the Semarang City Fire Department in 2013-2016, 1028 fire incidents took place in Semarang City. This incidence reached its highest in 2015 with 399 cases of fire which caused material losses of up to IDR 398.3 billion. Based on these figures, Semarang City possesses the highest incidence rate of fire cases in Central Java Province. However, studies on the risk and mitigation of fire hazard disasters in Semarang City still need to be carried out. This study aims to identify fire risks, to mitigate the disaster’s hazards in Semarang City, and to provide recommendations for arranging relevant buildings and the environment. The method used in this study is a qualitative descriptive method, in which interviews are conducted with key persons, with the utilization of GIS analysis to assess the risk of fire. The results of this study exhibit that in Semarang City, 14 villages have a high level of fire hazard risk, one of which is Rejosari Village which has the highest level of fire risk. This occurred mainly because historically, there have been many fire incidents in this area and even claiming several lives. The density of buildings and the difficulty of access further increase vulnerability in the case of fire. Nevertheless, the inhabitants still lack an understanding of this hazard. Accordingly, the fundamental mitigation to be carried out in Rejosari Village is to increase community comprehension and to consider the addition of fire protection and safety facilities, including hydrants, in densely populated buildings and areas, in order to reduce the risk of fire hazards.
Mythology and Folklore with Effect on Facts of Culture Intelligence

Mythology and Folklore with Effect on Facts of Culture Intelligence