Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Genetic Mutations in Genes CACNA 1 C , CACNB 2 , SCN 1 B , KCNE 3 , KCND 3 , SCN 10 A , HEY 2 , SCN 5 A , GPD 1 L in Brugada

Brugada syndrome is a genetic disorder that causes the heart rhythm to become abnormal. In fact, this syndrome can lead to an irregular heartbeat in the left ventricle, also known as ventricular arrhythmia. Signs and symptoms of ventricular arrhythmias, including sudden death due to cardiac arrest, can occur from birth to late puberty. Sudden death in people with Brugada syndrome usually occurs around the age of 40. The genetic form of Brugada syndrome is most often caused by a defect in the SCN5A gene but other genes can be involved, too. It can be inherited from just one parent. However, some people develop a new defect of the gene and don’t inherit it from a parent. This genetic defect causes heart muscles cells to handle sodium abnormally, which can then lead to abnormal heart rhythms. Sometimes you may have Brugada syndrome but it’s dormant and doesn’t cause any problems. However, some medicines such as antidepressants and antipsychotics, illicit drugs, conditions that cause fever, and electrolyte problems can unmask the syndrome. Sometimes people may appear to have Brugada syndrome based on an electrocardiogram but don’t have the disease itself. This is called a Brugada ECG pattern and may not pose a risk if the condition is temporary and doesn’t cause symptoms or dangerous heart rhythms.
Implementing wearable sensor technology for the determination of a biomarker profile for cancer-related fatigue

Cancer Related Fatigue (CRF) is a well-recognised symptom of malignant breast disease and may affect up to 70% of those undergoing therapy or deemed to be in remission. The condition is frequently subject to unpredictable recurrence that can result in unavoidable and unforeseen detriment to quality of life. Moreover, management of the condition can place significant financial burden on health and social care facilities. CRF is distinct from normal tiredness which may be resolved by periods of sleep or rest. Customers' extensive use of wearable technologies has contributed to the evolution of clinical trial procedures and, as a result, health data can also be obtained using wearables [1]. New technologies have the potential to improve data accuracy and timeliness, improve efficiency and increasing patient engagement in the clinical trial process Medical quality tracking devices are already supporting patient care in several clinical areas [1]. The main aim of this study is to define an accurate fatigue baseline for individuals diagnosed with breast cancer to determine potential relationships between possible fatigue markers, measurable daily activity and individual perceptions of fatigue.
Composite Power System Reliability Assessment Considering Transmission Line Flexibility

Penetration of intermittent renewable generation, power market effects and dynamic conditions are driving the power system to an operation with higher flexibility. The concept of dynamic line rating is envisioned as a solution to enhance transmission system flexibility. This paper proposes to include the dynamic conductor thermal modeling on the composite power system reliability assessment using a sequential Monte Carlo simulation. An hourly capacity series for each transmission line is generated regarding the type of conductor, voltage level and weather conditions, respecting the maximum conductor temperature. The IEEE-RTS 79 is utilized to illustrate the proposed methodology.
RangeKV: An Efficient Key-Value Store Based on Hybrid DRAM-NVM-SSD Storage Structure

Persistent key-value (KV) stores are an integral part of storage infrastructure in data centers. Emerging non-volatile memory (NVM) technologies are potential alternatives for future memory architecture design. In this study, we use NVM to optimize the KV store and propose RangeKV, an LSM-tree based persistent KV store built on a heterogeneous storage architecture. RangeKV uses RangeTab in NVM to manage L0 data and increases L0 capacity to reduce the number of LSM tree levels and system compactions. RangeKV pre-constructs the hash index of RangeTab data to reduce NVM access times and adopts a double-buffer structure to reduce LSM-tree write amplification due to compactions. We implement RangeKV based on RocksDB and conduct a comparative test and performance evaluation with RocksDB and NoveLSM. The test results show that the overall random write throughput is improved by <inline-formula> <tex-math notation="LaTeX">$4.5\times $ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$5.7\times $ </tex-math></inline-formula> compared to RocksDB. In addition, RangeKV has a significant performance advantage over NoveLSM.
Effect of Data Parameters and Seeding on k-Means and k-Medoids

k-means and k-medoids are arguably the two most popular clustering methods. This paper reports an empirical study of the relative (de)merits of these two methods. We compare their performances under varying data conditions. We also assess the impact of replacing random selection of initial cluster centers, with a systematic approach. The technique employed in our study, for initial centroid selection, is the state-of-art $\pmb{D}^{2}$ -weighing technique used in k- means ++, a variant of k-means. We use an extensive set of well-known and recommended benchmark datasets. These datasets allow for the study of performance under varying data size; varying cluster count, imbalance and overlap; and varying dimensionality. We establish that although k-means is much less accurate than k-medoids (which is grossly inefficient and less scalable), the accuracy gap is reduced significantly by seeding k-means. On the other hand, seeding does not improve k-medoids' accuracy but significantly worsens its efficiency (typically doubles its running time). Increased dimensionality tends to improve the accuracy of both algorithms, while increased overlap tends to worsen them.
Deep Human Activity Recognition With Localisation of Wearable Sensors

Automatic recognition of human activities using wearable sensors remains a challenging problem due to high variability in inter-person gait and movements. Moreover, finding the best on-body location for a wearable sensor is also critical though it provides valuable context information that can be used for accurate recognition. This article addresses the problem of classifying motion signals generated by multiple wearable sensors for the recognition of human activity and localisation of the wearable sensors. Unlike existing methods that used the raw accelerometer and gyroscope signals for extracting time and frequency-based features for activity inference, we propose to create frequency images for the raw signals and show this representation to be more robust. The frequency image sequences are generated from the accelerometer and gyroscope signals from seven different body parts. These frequency images serve as the input to our proposed two-stream Convolutional Neural Networks (CNN) for predicting the human activity and the location of the sensor generating the activity signal. We show that the complementary information collected by both accelerometer and gyroscope sensors can be leveraged to develop an effective classifier that can accurately predict the performed human activity. We evaluate the performance of the proposed method using the cross-subjects approach and show that it achieves an impressive F1-score of 0.90 on a publicly available real-world human activity dataset. This performance is superior to that reported by another state-of-the-art method on the same dataset. Moreover, we also experimented with the datasets from different body locations to predict the best position for the underlying task. We show that shin and waist are the best places on the body for placing sensors and this could help other researchers to collect higher quality activity data. We plan to publicly release the generated frequency images from all sensor positions and activities and our implementation code with the publication.
Control Design Method for Obtaining Special Performances in Discrete-Time Linear Switched Systems with Time-Varying Delay Based on the Average Dwell-Time Approach

This paper devotes to propose control design methods imposing general quadratic and bounded peak to peak gain performances on discrete-time linear switched positive systems with time-varying delay. General quadratic constraints are limitations that are modeled as a quadratic matrix to make a relationship between exogenous input and output signals. Moreover, the problem of bounding outputs peak amplitude for bounded disturbance inputs is named bounded peak to peak gain performance. Sufficient conditions are derived for the existence of a set of state feedback controllers guaranteeing the closed-loop switched system with time-varying delay in states not only is positive and globally uniform exponential stable but also has two stated performances for switching signals with an average dwell-time, which is greater than a positive certain constant. By using the Lyapunov-Krasovskii functional theorem, these conditions are formulated in terms of linear matrix inequalities. The quadruple tank system model is employed to illustrate the effectiveness of the proposed method.
Adaptation and Integration of GPU-Driven Physics for a Biology Research RIS

Developmental biology studies biophysical processes that lead to the development of tissues, organs, and organisms. Like other complex scientific domains, developmental biology can greatly benefit from real-time interactive systems (RIS). In addition to utilizing various innovative RIS technologies, highly efficient domain models have to be provided as well. In this paper, we present a prototypical RIS for developmental biology research that achieves this goal by adapting an existing, GPU-driven, position-based physics engine called FleX to support the required biological interaction mechanisms. The adapted cell model is further augmented by a GPU-based substance diffusion system that simulates biochemical signals that allow cells to communicate and react to their environment. We present model specifics with an emphasis on their efficient integration in an existing game engine, and we elaborate on future improvements.
Design and Fabrication of Two Stage 2 KVA Inverter using Push-Pull Topology

This paper presents a single phase two stage inverter which is based on combination of push pull converter topology and fully controlled H-bridge topology. In the first stage, 12V dc voltage is converted into 324V dc using ferrite core transformer at a high switching frequency that is 125 KHz, push pull converter topology is used in this conversion stage. Design of high frequency trans-former which is an essential part of this stage of conversion is presented. In the second stage, 324V dc is converted into 230V, 50Hz ac. The second stage is based on the fully controlled H-bridge inverter. Nonetheless the use of line frequency transformer reduces the stages of conversion but this topology gives the com-pact design, saving of copper and low-cost solution. Experimental results of implemented model and its control technique are presented at different loads to authenticate the implemented topology.
Detection of spinal fracture lesions based on Improved Yolov3

Yolo[1]algorithm has a good detection effect in target detection. Because of its high detection accuracy and fast detection speed, it is widely used in practice. Because of the problem that the complexity of spine CT images, the irregular shape of vertebral boundary, which needs doctors’ prior knowledge and clinical experience to determine lesions location in CT images, so it can not meet the clinical real-time needs. In this paper, We use deep learning to process the CT images of spine, and to detect and locate lesion of (cervical fracture, cfracture), (thoracic fracture, tfracture), (lumbar fracture, lfracture) by the improved YOLOv3. Through using lesions bounding box dimensional cluster, multiscale transformation of input CT images, and change NMS to MAX value to improve detection efficiency and accuracy. The experiment shows the results are more accurate, and mAP (mean average precision) of detection algorithm is 73.63%, detection rate is 0.027 seconds per detection, and IOU((Intersection-over-Union) is 75.9, which can basically meet the clinical real-time needs.
Multi-Model Fake News Detection based on Concatenation of Visual Latent Features

Online Social media for news consumption is a double-edged sword. If we ponder on the positives outcomes for this, it includes easy access, negligible cost, smart categorization and out reach to the very customer in seconds. But, as every coin has two sides and when we flip side of this, a series of issues come up which need immediate attention and most important among them is spreading of fake news. This has become a serious threat for the governments of countries to keep their harmony intact, keep faith of public in democracy and justice and sustenance of public trust. Therefore fake news detection, especially in social media platform has become an emerging research topic that is attracting tremendous attention. Current set of detection algorithms are specially showing their inability to learn the shared representation of texts and visuals combined (popularly known as multimodal) information. Therefore, we present a variational auto encoder based framework, which consists of three major components encoder, decoder and fake news detector. It utilize the concatenation of visual latent features from three popular CNN architecture (VGG19, ResNet50, InceptionV3) combined with textual information to detect fake news with the help of binary classifier. We conducted the experiment on publically available Twitter dataset. The experimental result shows that out model improves state of the art method by the margin of $\sim$2% in accuracy and $\sim$3% in F1-score.
Circuit Design for Electro-migration Testing

In power electronics, electro-migration is an important challenge and it is the main aspect to raise current concentration. On the other hand, in power electronics wrapping it is considered as advanced electrical current and voltages. That's why the result of electro-migration is a significant matter in claims which includes high current concentrations that were used in microelectronics and correlated constructions. EM failure caused owing to the dimension of integrated circuits (ICs) reduction and other practical significance. The effects of EM in power devices under high temperature is analysed in this research work.
Large-area reduced graphene oxide photodetectors for low-light intensity and low-driving voltage operation

We report the fabrication and characterization of large-area reduced graphene oxide (rGO) photodetectors by a cost-effective solution process. To meet the requirement of high-sensitivity and low-power consumption, the photodetectors are operated under low-light intensity and low-driving voltage conditions. The photodetectors show low dark current and great light current under 0.41W cm−2 light illumination at the bias of 0.5V. Our study provides a useful method to construct rGO-based samples through a simple way and has a potential for the future applications of green devices operated under a low-bias voltage.
A Review on Automatic Image Captioning Techniques

Ongoing advancement on Automatic Image Caption ing (AIC) has demonstrated that it is conceivable to depict the most remarkable data passed on by pictures with exact and correct sentences. Image captioning means creation of textual depiction of an image automatically. Captioning of images goes for portray a picture utilizing characteristic language. The important step in image captioning is identifying different objects in an image, find their relationships and classify them and combine the words that may not use proper language modelling. Captioning with proper sentences requires computer vision and Natural Language processing(NLP) for obtaining accurate sentences. Classified objects are then passed to language model to create captions. Semantic knowledge about an object in a picture need to obtain by capturing characteristics of an image globally and locally. There are various methods used for captioning an image but supervised learning provides better experience. This paper deals with some of the methods for image captioning.
Semantic and Context Information Fusion Network for View-Based 3D Model Classification and Retrieval

In recent years, with the rapid development of 3D technology, view-based methods have shown excellent performance in both 3D model classification and retrieval tasks. In view-based methods, how to aggregate multi-view features is a key issue. There are two commonly used solutions in the existing methods: 1) Use pooling strategy to merge multi-view features, but it ignores the context information contained in the continuous view sequence. 2) Leverage grouping strategy or long short term memory networks (LSTM) to select representative views of the 3D model, however, it easily neglects the semantic information of individual views. In this paper, we propose a novel Semantic and Context information Fusion Network (SCFN) to compensate for these drawbacks. First, we render views from multiple perspectives of the 3D model and extract the raw feature of the individual view by 2D convolutional neural networks (CNN). Then we design the channel attention mechanism (CAM) to exploit the view-wise semantic information. By modeling the correlation among view feature channels, we can assign higher weights to useful feature attributes, while suppressing the useless. Next, we propose a context information fusion module (CFM) to fuse multiple view features to obtain a compact 3D representation. Extensive experiments are conducted on three popular datasets, i.e., ModelNet10, ModelNet40, and ShapeNetCore55, which can demonstrate the superiority of the proposed method comparing to the state-of-the-arts on both 3D classification and retrieval tasks.
Detection of similar objects and localizing on each using Depth Camera

This paper introduces the problem of detection of similar color multiple objects and localizing on each object. The similar color multiple objects in an image are segmented using CIELAB color space. The reason for the use of CIELAB space is that the nonlinear relations of L*, a*, and b* mimic the nonlinear response of the human eye. This results in increased accuracy in color segmentation. Further localization on each of these objects is done from the information of depth image. In this work, the 3D sensor is used, Intel® RealSense™ depth camera D435i which captures depth & RGB images. The novelty in this work is in use of depth image from Intel 3D sensor in localization of each object. The advantage of this 3D sensor is, this does not require computation in image alignment of RGB image and depth image. The results of segmenting similar objects and localizing on each are presented, in three different scenes of laboratory/indoor environment.
On the Lifetime Analytics of IoT Networks

The Internet of Things (IoT) is an evolving technology for improving human’s quality of life. Wireless Sensor Network (WSN) is one of the key enablers for IoT infinite list of applications. However, minimizing energy consumption is a challenging task that directly affects the performance and lifetime of IoT networks due to limited energy available in these battery-powered devices. In order to improve the energy efficiency of such IoT sensor nodes, several approaches have been proposed in the literature with many trade-offs. In this paper, we study the problem of network lifetime analytics utilizing various routing protocols and parameters for IoT networks. We evaluate energy consumption and network lifetime through extensive simulation based on Sensoria simulator. Our simulation results confirm that the selected routing protocol and network connectivity play a significant role in reducing dissipated energy and prolonging network useful lifetime.
A New Model for Emotion Prediction in Music

Music based sentiment analysis has various applications in the form of music recommendation system, sales and advertisement etc. Various studies have dealt with lyrics and used Natural Language Processing to perform sentiment analysis. Others have directed their focus on the audio features to find relevant answers. But the biggest challenge faced while predicting music emotions is that no music depicts only a single emotion. Therefore, in this study, Russell’s scale is used to predict arousal and valence, rather than emotion. Audio feature selection via Multi-linear Regression is performed and comparative study is done between Linear Support Vector Machine, Decision Tree, Kernel SVM, K nearest neighbours (K-NN), Naive Bayes, Logistic Regression and Random Forest on the audio features. Moreover, a hybrid model based on Multi-Layer Perceptron is proposed to enhance the precision of the predictions. The data set of this research has been taken from PMEmo 2019 data.
System Decomposition to Optimize Functionality Distribution in Microservices with Rule Based Approach

The microservice architecture is an architecture in which a single system is divided into small independently deployed services that are orchestrated together with the use of a lightweight mechanism. Each microservice does not rely much on other microservices (low coupling), but rather on its own resources to perform its task (high cohesion). This paper proposes a novel methodology which decomposes a monolith or other system into microservices in such a way that each microservice will function independently of other microservices while preserving some other key features, with the functionality distribution across each microservice being optimized with regards to usage of the functionality. This methodology makes use of dynamic analysis to identify resources that play a role in enabling the microservice to fulfill its functionality. We establish a set of rules which allows optimized distribution of the functionality. We evaluate the approach by applying it to real systems.
Improving the Classification of Landsat-8 OLI Images using Neighborhood Median Pixel Values

Image classification in remote sensing is defined by categorizing image pixels or raw data sensed by satellites into a distinct set of labels. In this paper, an improved technique for classifying pixels from satellite images is proposed. The technique makes use of the median value of the pixels in the rectangular neighborhood centered at the given pixel to be classified. A scoring system was developed that compares this median value in relation to the expected median values for each of the different classes. The proposed method was tested on Landsat-8 Operational Land Imager (OLI) bands 1 to 7 images and three index images—Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Water Index (NDWI). The experimental results showed an overall accuracy of 94%, a remarkable improvement from the 84% accuracy of the previous work that uses a distance-based classifier. The obtained results indicate that the proposed method can be a better alternative way to classify images in remote sensing.
A Review on Antenna Design for 5G Applications

This paper describes the trends that are being followed in 5G antenna design. The antenna design techniques that are used in Sub-6 GHz range to achieve better isolation, efficiency and channel capacity will be discussed. Increasing the number of antenna in 5G MIMO antenna array can increase the efficiency up to 80% and capacity more than 50b/s/Hz in sub-6GHz range. Apart from this, designs of antenna in millimeter range are also investigated. Comparison of various antennas based on their performance is done.