Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Visualization of Hierarchical Multivariate Data

In this paper, we study the visual design of hierarchical multivariate data analysis. We focus on the extension of four hierarchical univariate concepts—the sunburst chart, the icicle plot, the circular treemap, and the bubble treemap—to the multivariate domain. Our study identifies several advantageous design variants, which we discuss with respect to previous approaches, and whose utility we evaluate with a user study and demonstrate for different analysis purposes and different types of data.
Sequence analysis i6mA-Pred: identifying DNA N 6 -methyladenine sites in the rice genome

Motivation: DNA N 6 -methyladenine (6mA) is associated with a wide range of biological processes. Since the distribution of 6mA site in the genome is non-random, accurate identiﬁcation of 6mA sites is crucial for understanding its biological functions. Although experimental methods have been proposed for this regard, they are still cost-ineffective for detecting 6mA site in genome-wide scope. Therefore, it is desirable to develop computational methods to facilitate the identiﬁcation of 6mA site. Results: In this study, a computational method called i6mA-Pred was developed to identify 6mA sites in the rice genome, in which the optimal nucleotide chemical properties obtained by the using feature selection technique were used to encode the DNA sequences. It was observed that the i6mA-Pred yielded an accuracy of 83.13% in the jackknife test. Meanwhile, the performance of i6mA-Pred was also superior to other methods. Availability and implementation: A user-friendly web-server, i6mA-Pred is freely accessible at http://lin-group.cn/server/i6mA-Pred.
Distracted Driver Monitoring with Smartphones: A Preliminary Literature Review

Distracted driving is known to be one of the leading causes of vehicle accidents. With the increase in the number of sensors available within vehicles, there exists an abundance of data for monitoring driver behaviour, which, however, have so far only been comparable across vehicle manufacturers to a limited extent due to proprietary solutions. A special role in distraction is played by the smartphone, which is repeatedly a source of distraction for drivers through calls and messages. However, the smartphone can be used for driver behaviour monitoring (like driver distraction detection) too, as current developments show. As vehicle manufacturer-independent device, which is usually equipped with adequate sensor technology, smartphones can provide significant advantages, however, an overview of such approaches is missing so far. Thus, this work carries out an author-centric literature review of 16 research papers to illustrate the opportunities in using smartphones to detect driver distraction.
Enhanced-Quality Gan (EQ-GAN) on Lung CT Scans: Toward Truth and Potential Hallucinations

Lung Computed Tomography (CT) scans are extensively used to screen lung diseases. Strategies such as large slice spacing and low-dose CT scans are often preferred to reduce radiation exposure and therefore the risk for patients’ health. The counterpart is a significant degradation of image quality and/or resolution. In this work we investigate a generative adversarial network (GAN) for lung CT image enhanced-quality (EQ). Our EQ-GAN is trained on a high-quality lung CT cohort to recover the visual quality of scans degraded by blur and noise. The capability of our trained GAN to generate EQ CT scans is further illustrated on two test cohorts. Results confirm gains in visual quality metrics, remarkable visual enhancement of vessels, airways and lung parenchyma, as well as other enhancement patterns that require further investigation. We also compared automatic lung lobe segmentation on original versus EQ scans. Average Dice scores vary between lobes, can be as low as 0.3 and EQ scans enable segmentation of some lobes missed in the original scans. This paves the way to using EQ as pre-processing for lung lobe segmentation, further research to evaluate the impact of EQ to add robustness to airway and vessel segmentation, and to investigate anatomical details revealed in EQ scans.
Arterial Blood Pressure Regulation using a Novel Fractional Order Based Sliding Mode Elephant Herd Controller

In today's world, high arterial blood pressure is a foremost issue in medical field. Moreover, a suitable regulation of drug injection value is important to hold the disease. For this reason, a control method has been developed in this field to regulate the parameters of biological system. In this paper, a novel Fractional Order based Sliding Mode Elephant Herd (FO-SMEH) control algorithm has been proposed to control the arterial blood pressure problem. The projected method has been attained finest performance in terms of variable time and optimal outcome. In this study, the stability analysis is presented for FO-SMEH controller. The implementation of this work has been carried out in MATLAB/Simulink platform. The simulation outcomes illustrates that the projected method has achieved better result in terms of less error, rise time, overshoot and so on while compared with the old control methods.
Risk Assessments of Unattended Smart Contactless Cards

Contactless smart card technology serves various applications ranging from personal identification to financial payment. A well-known brand, Mifare cards, supplies more than three-quarters of the world's smart contactless cards. When left unprotected and unattended, these cards are potentially vulnerable to external threats that enable an attacker to read the card and steal the UID (Unique Identifier) and write to the card, potentially corrupting it. This study assesses the risk of unattended Mifare Classic smart cards through a simulation of the attack. The mobile applications NFC Tools and Mifare Classic Tool which were installed on an NFC-capable smartphone were utilized as the primary card reader/writer to perform the said functions to the cards. The attack was performed against a Mifare Classic 1K card; Its contents were read and written to and its UID was duplicated/cloned using a UID-writable card and a Proxgrind Chameleon Mini Rebooted. The duplicates/clones were tested on a simulated card system scanner that was built using a CH340G Arduino board and an RC522 module that was designed to accept only the genuine Mifare Classic 1K card. The simulation of the attack presents that cards can easily be accessed, even when placed inside relatively thick layers of wrapping such as inside a wallet and a bag. Once accessed, the UID of the cards was obtained and its memory can also be read and written to. The duplication/cloning process was successfully executed as the simulated card system scanner accepted and recognized the duplicates/clones as if they were the original card.
Research and Simulation of Motion Planning for Underwater Hexapod Robot Based on Improved Artificial Potential Method

The obstacle avoidance method and its effect of underwater hexapod robot is one of the important aspects of whether the underwater hexapod robot can work smoothly. As a simple and real-time obstacle avoidance method, artificial potential method has been widely concerned. Based on the improved artificial potential method, the motion planning of underwater hexapod robot is studied in this paper. The three-dimensional information of obstacles in the working area is detected by scanning sonar with orthogonal arrangement, and the obstacles are abstracted into a circle to facilitate the construction of obstacle avoidance model. The improved physical model of artificial potential method is established by optimizing the functional equations of gravitational potential field and repulsive potential field, and the real-time crawling path of the underwater hexapod robot is obtained. On this basis, the realtime crawling path of the underwater hexapod robot is constructed for the realization of obstacle avoidance. Finally, the motion planning effect of the underwater hexapod robot is verified by simulation. The simulation results show that the motion planning of the underwater hexapod robot based on the improved artificial potential method can avoid obstacles well. The path generated is smooth and the crawling efficiency is more efficient. It can make the underwater hexapod robot maintain a safe crawling distance and avoid obstacles to reach the end point smoothly, which has good practical application value.
An Interactive System for the Study of EMG Signal ML-Processing and Prototyping of Human-Machine Interfaces

A system has been created for controlling devices using EMG signals from a small number of sensors by recognizing signal sections corresponding to the operator's gestures. The solution implements a scheme of three independent components: sensors, a user interface and a data processor, connected by a simple data transfer protocol, which allows replacing any of the parts if necessary. Software has the realization of the most popular ML-algorithms, which widely used for EMG gesture recognition and intuitive GUI. It allows to use machine learning algorithms to users without any special background.
SCREENING OF COMMONLY USED JORDANIAN SPICES FOR INHIBITORY ACTIVITY AGAINST ACETYLCHOLINESTERASE AND BUTYRYLCHOLINESTERASE IN ALZHEIMER’S DISEASE

The aim of this study was to evaluate the ability of commonly used Jordanian spices to inhibit acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) for the management of Alzheimer’s disease (AD). Both aqueous and methanolic extracts (1:1; v/v) of 30 spices were prepared and evaluated using a thin-layer chromatography (TLC) assay and Ellman’s spectrophotometric method. TLC bioautography revealed 3 active aqueous extracts and 11 active methanolic extracts were able to inhibit AChE and BuChE. The methanolic extracts of 11 spices were able to inhibit either AChE or BuChE, or both (> 60% inhibition at a concentration of 100 μg/mL). The most active extracts against both enzymes were found in turmeric, cumin, dill, lemon grass, lemon verbena, sumac, fennel, white and black pepper, cinnamon and cardamom. Both methanolic and aqueous extracts of cumin, dill and fennel were active against both enzymes. The results indicate that the intake of theses spices and phytochemicals can greatly impact the onset and progression of chronic neurodegenerative diseases such as AD. Public health efforts should be directed at increasing the consumption of spices as part of a regular diet, which should also include consumption of protective phytonutrients. Of these the most common Jordanian herbs and spices, turmeric and cumin have the greatest potential for lowering the risk of Alzheimer’s disease.
Survey of FPGA Methodology Considering High Performance Design Implementation

The most popular platform emerging for the implementation of complex engineering algorithms is Field programmable gate arrays (FPGAs) due to their parallelism of implementation and flexibility in the design architecture. However, in order to achieve great performance, the FPGA must have design methodology and optimization techniques which are effective and efficient. This paper presents FPGA based design optimization techniques and methodology that are used to obtain speed, power and area.
The Impact of Internet of Things on the Domain Name System

The rise of connected objects known as the Internet of Things (IoT) has conquered the world. It is a platform for the functioning of various smart devices on a large scale. IoT devices can communicate with each other without interaction between the user and devices. Security stakeholders across the planet have confirmed that the growth of IoT has created the right set of circumstances for malicious users to attack the Domain Name System (DNS). Illicit users may threaten the frameworks with large-scale botnets and can also compromise the services provided by organizations. This paper has highlighted the impact of IoT systems on the domain name system. Also, this research work provides a cutting-edge overview of current solutions that can help to tackle the security and policy challenges in the domain name system.
Skin Lesion Classification using Bag-of-3D-Features

Computer-aided diagnostic has become a thriving research area in recent years, namely on the identification of skin lesions such as melanoma. This work presents a novel approach to this field by exploiting the 3D characteristics of the skin lesion surface, advancing beyond common features such as, shape, colour, and texture, extracted from dermoscopic RGB images. To this end, a relevant set of features was investigated to obtain 3D skin lesion characteristics from images with depth information. These features were used to train a Bag-of-Features model to distinguish between malignant and benign lesions, also discriminating melanoma from all other lesion types. Despite the large class imbalance, often present in medical image datasets, the feature set achieved a top accuracy of 73.08%, comprising 75.00% sensitivity and 66.67% specificity when classifying between malignant and benign lesions, and 88.46% accuracy (100.00% sensitivity and 86.96% specificity) when discriminating melanoma from all other lesion images, using only depth information. The achieved experimental results indicate the existence of relevant discriminative characteristics in the 3D surface of skin lesions which allow the improvement of existing classification methods based on 2D image characteristics only.
Cardiovascular Disorder Prediction using Machine Learning

Cardio-Vascular disorder is one of the premier critical reasons for loss of life inside the present reality. The presupposition of turmoil might be a fundamental test inside the territory of medical information examination. AI (ML) has been exemplified to be viable in helping with resolving on choices and conjecture from the huge clinical data delivered by the medical services industry. Additionally, it is viewed that, the ML methods are getting utilized in ongoing improvements in a few regions of the Internet of Things (IoT). Different examinations give just a brief of apprehending heart condition with ML strategies. In this research work, an exceptional strategy targets on finishing the critical highlights by applying AI strategies by prompting and enlightening the precision inside the expectation of turmoil. The forecast model is presented with numerous combination of highlights and also several other known grouping procedures and finally an improved exhibition level is produced through the expectation replica for a cardio-vascular condition with the hybrid random forest with a linear model with a precision level of 88.7%.
Fog-Centric IoT Based Framework for Healthcare Monitoring, Management and Early Warning System

Internet of things (IoT) and machine learning based systems incorporating smart wearable technology are rapidly evolving to monitor and manage healthcare and physical activities. This paper is focused on the proposition of a fog-centric wireless, real-time, smart wearable and IoT-based framework for ubiquitous health and fitness analysis in a smart gym environment. The proposed framework aims to aid in the health and fitness industry based on body vitals, body movement and health related data. The framework is expected to assist athletes, trainers and physicians with the interpretation of multiple physical signs and raise alerts in case of any health hazard. We proposed a method to collect and analyze exercise specific data which can be used to measure exercise intensity and its benefit to athlete’s health and serve as recommendation system for upcoming athletes. We determined the validity of the proposed framework by giving a six weeks workout plan with six days a week for workout activity targeting all muscles followed by one day for recovery. We recorded the electrocardiogram, heart rate, heart rate variability, breath rate, and determined athlete’s movement using a 3D-acceleration. The collected data in the research is used in two modules. A Health zone module implemented on body vitals data which categorizes athlete’s health state into various categories. Hzone module is responsible for health hazards identification and alarming. Outstandingly, the Hzone module is able to identify an athlete’s physical state with 97% accuracy. A gym activity recognition (GAR) module is implemented to recognize workout activity in real-time using body movements and body vitals data. The purpose of the GAR module is to collect and analyze exercise specific data. The GAR module achieved an accuracy of above 89% on athlete independent model based on muscle group.
Implementation of Demand Response Management in microgrids using IoT and Machine Learning

Accounting for the increasing power demands and need to conserve natural resources, there is a necessity to develop a grid system which can allow integration of renewable power resources as well produce energy efficiently. This can be executed by using a microgrid arrangement which allows discrete management of energy and implementation of appropriate management strategies. Our paper uses the demand response management strategy to bring about a balance between the demand and generation of power. Demand response management is a system where the customer assumes an active role in the reduction of demand during peak hours in response to some financial benefits. The proposed research work has developed and simulated a demand response management system with microgrids by using potentiometers to depict the continuous variation of load and applied dynamic pricing and incentivization techniques so as to bring about active customer involvement in meeting the demand and increasing the reliability of the grid. Using IoT, all the information regarding load demand, generation and cost is continuously recorded made available for the customer readily on the web. IoT is also used for providing customers a convenient method to reduce their demand during peak hours. Machine learning algorithms are used for fruitful load forecasting for getting some insight and also making intelligent decisions in association with the system.
Lubrav: A New Framework For The Segmentation Of The Lung’s Tubular Structures

The segmentation of the bronchus tree and pulmonary arteries and veins in CT scans plays an important role in patient care for both diagnosis and treatment phases. The extraction of theses tubular structures using either manual or interactive segmentation tools is time-consuming and prone to error due to the complexity of aerial and vascular trees. In this work, we propose a fully automatic method, relying on cascaded convolutional neural networks, to tackle lungs, bronchus and pulmonary arteries and veins segmentation. The first component based on a 2DU-Net architecture is dedicated to right and left lung segmentation. The second component relies on a three-paths 2.5D fully convolutional networks along axial, coronal and sagittal slices and focuses on tubular structures. Performance is assessed on a database of 193 chest CT scans.
A medical tele-tutoring system for the Emergency Service

Currently, most of the Emergency Service interventions involve as operators on board of ambulances only nurses and volunteers, without the presence of doctors. In this work REC-VISIO 118, a new system of remote medical tutoring to the operators involved in emergency interventions, is illustrated. The system is composed by a subsystem worn by the operators and by a web application for the doctors in the Operations Center. The wearable subsystem collects the video of the operator visual perspective of the scene of action, locally stabilizes the video using Machine Learning algorithms, and sends the content using Web Real-Time Communication to the Emergency Service Operations Center through the 4G network. Here the application receives the call allowing the doctors to directly follow and support the actions of the operators, playing an important role in providing a prompt pre-diagnosis that is particularly crucial in the case of time-dependent pathologies such as heart attacks, heart failure, or stroke.
Paper Title (use style: paper title)

In order to realize a wide-range measurement current transformer that can meet the requirements of measurement accuracy under a wide range of currents, this article analyzes the influencing factors of commonly used turns compensation. And based on influencing factors, the error of current transformer under different parameters is simulated. The study found that simply using turns compensation cannot meet the error requirements of a wide-range current transformer. This paper then uses a fixed compensation resistor based on the principle of zero magnetic flux to reduce the error under a wide range of primary current. However, this kind of improvement measures adopted for economy and large-scale promotion cannot achieve true zero magnetic flux. Coupled with the low initial permeability of the silicon steel core material, the accuracy under 1% of the rated current still cannot meet the requirements of wide-range transformer errors. In order to meet the measurement error of not more than 0.2% under the rated current of 1%~200%, it is necessary to combine the two methods of zero magnetic flux technology and turns compensation. And it is necessary to further optimize the parameters of the compensation resistance and the number of turns compensation. The research results of this article provide a certain reference for the design and implementation of wide-range current transformers. Keywords—Wide range current transformer, zero magnetic flux principle, iron core material, turns compensation
Forecasting Parking Lots Availability: Analysis from a Real-World Deployment

Smart parking technologies are rapidly being deployed in cities and public/private places around the world for the sake of enabling users to know in real time the occupancy of parking lots and offer applications and services on top of that information. In this work, we detail a real-world deployment of a full-stack smart parking system based on industrial-grade components. We also propose innovative forecasting models (based on CNN-LSTM) to analyze and predict parking occupancy ahead of time. Experimental results show that our model can predict the number of available parking lots in a ±3% range with about 80% accuracy over the next 1-8 hours. Finally, we describe novel applications and services that can be developed given such forecasts and associated analysis.
Radar Waveform Design Under Communication Sum Capacity Constraint

This paper considers a joint radar and wireless communication systems where the radar transmit waveform is adaptively designed. To ensure acceptable performance of both radar and communication systems operating in the same frequency bands, the radar makes use of knowledge provided by the communication systems and vice versa. Specifically, the radar transmit waveform is designed not only to maximize radar performance such as signal-to-interference-and-noise ratio (SINR) but also to guarantee acceptable communication systems performance by ensuring a prescribed total throughput or sum capacity of communication systems. We formulate an optimization problem that maximizes the radar performance subject to the radar power constraint and the communication sum capacity constraint. Since the sum capacity constraint is not a convex constraint, the resulting optimization problem is not convex. We provide a geometric analysis of the sum capacity constraint and the solution of the SINR maximization problem in two steps: 1) obtain the optimal solution with only radar power constraints, i.e., without the sum capacity constraint, and then 2) starting from the optimal solution of the first step, find a solution satisfying the sum capacity constraint. In this process, a closed form solution to find the locally optimal point at each search is derived, which reduces computational complexity of the proposed algorithm. Numerical results are provided to verify performance of the proposed method.