Miracle-dz/UniFAR-FTUs huggingface dataset:
SDTR: Soft Decision Tree Regressor for Tabular Data

Deep neural networks have been proved a success in multiple fields. However, researchers still favor traditional approaches to obtain more interpretable models, such as Bayesian methods and decision trees when processing heterogeneous tabular data. Such models are hard to differentiate, thus inconvenient to be integrated into end-to-end settings. On the other hand, traditional neural networks are differentiable but perform poorly on tabular data. We propose a hierarchical differentiable neural regression model, Soft Decision Tree Regressor (SDTR). SDTR imitates a binary decision tree by a differentiable neural network and is plausible for ensemble schemes like bagging and boosting. The SDTR method was evaluated on multiple tabular-based regression tasks (YearPredictionMSD, MSLR-Web10K, Yahoo LETOR, SARCOS and Wine quality). Its performance is comparable with non-differentiable models (gradient boosting decision trees) and better than uninterpretable models (regular FCNN). On top of that, it can produce fair results with a restricted number of parameters, only using a small forest or even a single tree. We also propose an “average entropy” metric to evaluate the level of interpretability of a trained, soft decision tree neural network. This metric also helps to select proper structure and hyperparameters for such networks.
Liquid Metal-Filled 3D Structure Wearable Sensor for Finger Bending Monitoring

Wearable sensors have many potential applications in health and tactile touch monitoring. In particular, Liquid Metal (LM) based wearable sensors have attracted more and more attentions as they can measure large deformations without failure. However, most of the current LM-based wearable sensors are 2D flaky structure, which making them position unstable while wearing, and induce measurement errors. In this report, a novel finger bending angle monitoring sensor with 3D hollow design based on embedded LM microchannels is presented. Because of the hollow design, this sensor can be easily worn on the finger joints and can accurately distinguish the finger bending angle. And real-time monitoring can be achieved by measuring the inductance value. The results suggest that the studied wearable sensor has excellent stability and reliability. And this sensor has better practical applications in finger action health detection.
E2Beam: Energy Efficient Beam Allocation in 5G HetNet using Cooperative Game

Beamforming technology is a key element of 5G mobile networks. In this article, a HetNet is proposed using massive Multiple Input Multiple Output (MIMO) and small cell base stations (SBSs) where SBSs allocate beams to mobile devices and they exchange data with mobile devices through beamforming communication. A cooperative game theory is used among the adjacent SBSs, they allocate frequencies in a cooperative way which reduces the amount of network interference. We have proposed a utility function, through which SBS select low power consumption beam from all beams and allocate this specific beam to the mobile device. In E2Beam, we have calculated the power consumption, signal to interference plus noise ratio (SINR) and spectral efficiency of the proposed approach and compared it with the previous networks. The power consumption of E2Beam HetNet reduces by ~ 30% and 18%, and enhances the SINR and spectral efficiency by ~ 6% and 5%, and ~ 4% and 3% respectively than the existing approaches, which has lead the proposed E2Beam to an energy efficient network.
Location-Based Vertical Handovers in Wi-Fi Networks With IEEE 802.11ah

IEEE 802.11ah is a new sub-GHz Wi-Fi technology that provides several advantages over traditional Wi-Fi such as a higher communication range, enhanced scalability, and lower energy consumption, however at the cost of substantially lower throughput. With the aim of simultaneously benefiting from multiple Wi-Fi technologies, recent proposals suggest combining a number of these technologies into a single device. This, however, compromises the energy efficiency of the device, as it implies concurrent utilization of different radio access interfaces. To mitigate this issue, the device should utilize the interface of a certain technology only when there is a high probability of establishing communication over that technology. Traditional vertical handover algorithms are not designed for this purpose as they rely on continuous beacon listening or active probing, even if the device is not in the range of a given technology. To address this issue, vertical handover algorithms based on the combination of devices’ physical locations and either Radio Environmental Maps (REM) or propagation modeling have been proposed. Moreover, their suitability and encouraging performance have been demonstrated for a number of the established Low-Power Wide-Area Network (LPWAN) technologies. However, their appropriateness for Wi-Fi-based networks with IEEE 802.11ah is currently unknown, which provides the main motivation for this work. Specifically, we carry out an extensive experimental performance evaluation of two location-based vertical handover algorithms in the context of Wi-Fi-based networks with IEEE 802.11ah. Our results demonstrate the feasibility of location-based handovers in this context. We base our findings on the fact that location-based algorithms can maintain comparable data communication quality as the beacon listening-based baseline, while simultaneously reducing the utilization of the IEEE 802.11ah and IEEE 802.11n radio access interfaces by a factor of 2 and 10, respectively.
Primary Stage of Diabetes Prediction using Machine Learning Approaches

As per the report of the World Health Organization (WHO), diabetes has become one of the rapidly expanding chronic diseases that has affected the life of 422 million people all over the world. The number of deaths in Bangladesh due to diabetes has reached 28,065, which is 3.61% of the total deaths of Bangladesh, according to the latest data published by the WHO in 2018. So we need to be concerned about the risks of diabetes disease. If we cannot take proper steps to diagnose diabetes at an early stage, eventually we have to face serious health issues. In this paper, we have shown the relation of different symptoms and diseases that cause diabetes so that we can help a person to diagnose diabetes at an early stage. Nowadays, machine learning classification approaches are well accepted by researchers for developing disease risk prediction models. Therefore eleven machine learning classification algorithms such as Logistic Regression (LR), Gaussian Process (GP), Adaptive Boosting (AdaBoost), Decision Tree (DT), K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Bernoulli Naive Bayes (BNB), Bagging Classifier (BC), Random Forest (RF), and Quadratic Discriminant Analysis (QDA) have been used in this study. Among all these machine learning classifiers, Random Forest (RF) classifier has showed the best accuracy of 98%. And its Area Under Curve(AUC) is also the highest.
A drug information embedding method based on graph convolution neural network

New drug development is an extremely time-consuming and high-risk process. [1]It has been widely valued by the biomedical industry to fully explore the new uses of existing drugs and reorientate them. [2]How to find drug disease with potential therapeutic relationship from a large number of unproven relationship pairs is the research focus of drug reorientation. With the help of machine learning model, we can improve the enrichment degree of potential drug disease relationship pairs, and reduce the false positive rate of prediction. In the past few years, a series of graph based convolutional network models have been developed to calculate the information latent feature representation of nodes and links. Researchers at home and abroad have done a lot of research on network embedding technology based on biomedical data, and have achieved a series of important research results. Among them, the research methods used can be divided into two categories: one is the traditional machine learning algorithm based on artificial feature extraction, the other is the method based on deep learning. For example, kipf and welling [3]proposed a new graph convolution network (GCN) with parts of existing models, DeepDR [4] and DTINet [5] based on node characteristics and their connections, which can be used for node classification. Aiming at the problem of imbalance of drug information data samples, the invention provides a drug relocation method based on deep learning multi-source heterogeneous network. In order to avoid the limitations of traditional feature extraction methods, such as highly dependent on the experience and knowledge of medical staff, strong subjectivity, consuming a lot of time and energy to complete, and extracting high-quality features with distinguishing features often exists In this paper, with the help of graph convolution encoder model and variational auto encoder neural network, we can automatically learn the characteristics of multi-source and heterogeneous drug low-dimensional network, and complete the drug relocation of drug disease association prediction.
Early detection of Alzheimer’s disease using ensemble of pre-trained models

Alzheimer’s disease is a neurological state in which the damage of neurons causes memory disabilities. Automatic early detection of Alzheimer’s disease (AD) is always demanding the competence of a machine to distinguish different dementia stages. The different dementia stages related to the human cognitive state are cognitively normal (CN), mild cognitive impairment convertible (MCIc), mild cognitive impairment nonconvertible (MCInc), and Alzheimer’s disease (AD). Both the stages MCIc and MCInc show the similar symptoms of memory disorders. The stage MCInc is quite normal in all aged people. The memory problems are due to normal aging. But the stage MCIc will lead to AD in few years. The stage MCIc is the early stage of AD. Proper meditation can reduce the rate of memory loss if the disease is identified in the early stage MCIc. The two dementia stages included in this study are cognitively normal and mild cognitive impairment convertible. In this work, an ensemble model is used to improve the classification accuracy between CN and MCIc. The ensemble model consists of two pre-trained network models Xception and MobileNet. The performance of pre-trained and ensemble models is tested using Alzheimer’s disease neuroimaging initiative (ADNI) dataset. The classification between CN and MCIc is crucial for the early detection of AD. The classification accuracy between MCIc vs CN is only included in this study. The accuracy obtained using Xception and MobileNet models are 89.23% and 89.89%. The proposed model provides a classification accuracy of 91.3%. The results show that the proposed ensemble model is providing good discrimination capability of the stages MCIc and CN.
Shortest-Path-Based Two-Phase Design Model for Hydraulically Efficient Water Distribution Network: Preparing for Extreme Changes in Water Availability

Environmental issues can cause changes in source water availability in water distribution networks (WDNs). Thus, an efficient connection between the source and consumers is important for securing water serviceability, which can generally be achieved by minimizing energy losses. In this study, a novel two-phase design (TPD) model is proposed to design an energy-efficient WDN by maximizing a hydraulic geodesic index (HGI), which is the weighted shortest path from the source to the demand node. Before applying the TPD model for WDN design, a correlation analysis between the system HGI, hydraulic performance, and graph theory indices is conducted using 33 J-City networks to verify the proposed HGI. Next, the TPD model is used to determine the optimal layout of the grid network (Phase I). Based on this layout, the optimal diameter set is identified in Phase II. The TPD is thereafter compared with the traditional single-phase design (SPD) model, which determines the optimal layout and diameter simultaneously, and a least-cost model for each phase in the grid network layout and pipe-sizing problem. The correlation analysis clearly indicates that the system HGI with the weighted graph theory successfully determines the hydraulic performance without any hydraulic analysis. Furthermore, TPD is advantageous for designing energy-efficient, hydraulically and structurally sustainable, and resilient networks, as compared to SPD and the least-cost model. The TPD model is expected to provide a better opportunity to prepare for extreme water availability changes by enhancing the hydraulic performance and efficiency through a better connection between the source and nodes.
Graduate Admission Chance Prediction Using Deep Neural Network

Every year many students apply for graduate admission to different universities. To select an applicant, each university has different selection criteria such as GRE score, CGPA, research background, statement of purpose, letter of recommendation, university rating etc. There are some web applications as well as some consultancy services for suggesting the appropriate university based on students’ portfolio. These help to give an idea which universities should be applied for admission. But they have limitations because humans are incapable of considering all the conditions and universities. Moreover, web applications have accuracy problems. In this study, we have proposed a deep neural network (DNN) to predict the chance of getting admitted to a university according to the students portfolio. All the selection criteria are considered here to predict the chance of admission. The DNN model has been compared with existing methods in terms of different performance metrics including mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), R-squared score. It has shown the most promising result that includes R-squared score of 0.8538 and MSE of 0.0031. The proposed method has also outperformed all the existing methods in each benchmark.
Analysis of Energy Consumption at Slow Charging Infrastructure for Electric Vehicles

Here, we develop a data-centric approach to analyse which activities, functions, and characteristics of the environment surrounding the slow charging infrastructure impact the distribution of the electricity consumed at slow charging infrastructure. We analysed the probability distribution of energy consumption and its relation to indicators characterising charging events to gain basic insights. The energy consumption can be satisfactorily modelled by a transformed beta distribution and the number of charging transactions is the driving factor among the characteristics constituting the energy consumption. We collected geospatial datasets and prepared a large number of candidate features modelling the spatial context in which the charging infrastructure operates. Using statistical methods, we identified and interpreted a relatively small subset of the most influential features correlated with energy consumption. The majority of these features are related to the economic prosperity of residents. Residents and businesses with high (low) income, situated nearby charging infrastructure, are linked to a positive (negative) impact on energy consumption. Similarly, charging infrastructure located close to expensive newly built housing shows higher energy consumption. The largest adverse impact has the high concentration of residents receiving social assistance. By applying the methodology to a specific charging infrastructure class, e.g. determined by the used rollout strategy, we differentiated the selected features. Business types, working sector of residents and public venues in the proximity are linked to higher consumption of energy at charging infrastructure deployed strategically. Characteristics linked with the age structure of the population are linked to the energy consumption at charging infrastructure placed based on the demand. Data collection and data processing are among the most time-consuming activities. The paper provides valuable insights into which data to collect and use as features when developing prediction models to inform charging infrastructure deployment and planning of power grids.
Modeling of SJ-MOSFET for High-Voltage Applications with Inclusion of Carrier Dynamics during Switching

Demands for higher-voltage MOSFET application are increasing, for which a Super-Junction MOSFET, sustaining the voltages in the range of 500V, has been developed based on the trench-type structure. Due to the huge bias applied, a new leakage-current type is induced during switching, which causes a switching-power-loss increase. Creating a compact model for circuit design, which includes this additional leakage current, is the purpose of the present development. The model describes the depletion-width variation, caused during the switching-on of the device, with the use of the internal node potential, determined accurately by iteration. It is verified, that the new compact model can accurately predict the device performances for different device structures. This capability can be used for device optimization to realize low-power circuitry.
Tomato Plant Diseases Classification Using Deep Learning Based Classifier From Leaves Images

In most agricultural countries, farmers face a great loss every year due to diseases in crops. So, early detection of tomato plant diseases has achieved a great concern of the researchers. In this paper a deep convolutional neural network model is used to recognize unhealthy plants from the healthy plants and to classify the tomato plant diseases. We have used VGG16 deep cnn classifier to recognize unhealthy plants and their diseases from the images of tomato plants. We have used Plant Village dataset which contains ten different classes of tomato leaf images including healthy plants. Using transfer learning method in a pre-trained VGG16 model, this dataset shows a satisfying classification performance which about 95.5%. Top 2 accuracy of this model reaches to 99% to recognize tomato plant diseases. Without using any segmentation or preprocessing of leaves images our trained model shows a performance of approximately 100% to differentiate unhealthy plants from healthy plants.
Boosting Inertial-Based Human Activity Recognition With Transformers

Activity recognition problems such as human activity recognition and smartphone location recognition can improve the accuracy of different navigation or healthcare tasks, which rely solely on inertial sensors. Current learning-based approaches for activity recognition from inertial data employ convolutional neural networks or long short term memory architectures. Recently, Transformers were shown to outperform these architectures for sequence analysis tasks. This work presents an activity recognition model based on Transformers which offers an improved and general framework for learning activity recognition tasks. For evaluation purposes, several datasets, with more than 27 hours of inertial data recordings collected by 91 users, are employed. Those datasets represent different user activity scenarios with varying difficulty. The proposed approach consistently achieves better accuracy and generalizes better across all examined datasets and scenarios. A codebase implementing the described framework is available at: https://github.com/yolish/har-with-imu-transformer.
An Optimization of Computational Resources Allocation for Multi-MEC and Cloud Networks

Mobile edge computing (MEC) technology has been studied for several years [1] and is still attracting more attentions. The optimization of MEC with cloud computing targets on improving efficiency of resources. In this work we consider a practical MEC application case in mobile video surveillance and analytics. By investigating some real application systems and using a simple model to analyze an optimization issue, we obtained some numerical results using MATLAB. The results shows that it is possible to improve the system performance in terms of energy saving and efficiency improving by balancing the workload between MEC and the cloud.
Design, Simulation and Analysis of Swastika Shape Uniplanar ACS Fed Antenna for Multiple Frequency Band Operation

A novel printed swastika shape ACS fed monopole antenna well suited to 2.4/5.2/5.8 GHz WLAN and 2.5/3.5/5.5 GHz WiMAX applications for tri-band operation is presented in this research. The proposed swastika shaped antenna encompasses of mirrored C shape strip, L shape strip, and meander shape strip with partial ground plane (alongside 50 Ω rectangular ACS feedline). Through the legitimate positioning of the appropriate length strips, desired multiple impedance bandwidths excited over 2.4/3.6/5.5 GHz centered frequency with a less than -10 dB standardized value of input impedance frequency response can be obtained. The prototype of swastika oriented configuration has been fabricated and experimentally validated its parameters with an admissible agreement perceived with simulated values. The simulated results of antenna covers frequency bands are 2.37 GHz to 2.57 GHz, 3.4 GHz to 3.94 GHz and from 5.0 GHz to 5.9 GHz for (S11< -10 dB). Also, the 2D radiation characteristics performance of designed structure at different frequencies are also observed and discussed.
Hybrid Topology of Graph Convolution and Autoencoder Deep Network For Multiple Sclerosis Lesion Segmentation

The notion of deep learning is being revolutionised, e.g., the advent of graph convolution network (GCN) in the application of medical image classification. Though GCN is gaining significant popularity, the potential of GCN is not exploited to its maximum. This work proposes a new hybrid network based on convolution neural network (CNN) autoencoder and GCN. The proposed method has been applied to graph datasets which are designed by considering 3D medical resonance image (MRI) voxel as a node. CNN autoencoder is used to extract the imaging grid information and GCN learns these features in graph connectivity space. This hybrid network has been applied for the application of segmentation of lesions in multiple sclerosis (MS) diseases. This framework has been used for training on 30 MS patients of white matter lesions captured at the university medical center Ljubljana (UMCL) and validated on 20 longitudinal MS patient dataset of UMCL at one time instant. This novel framework improves the performance of dice similarity coefficient 85.5 % score in segmentation for three neighbors in graph data.
End-to-End Image-to-Speech Generation for Untranscribed Unknown Languages

Describing orally what we are seeing is a simple task we do in our daily life. However, in the natural language processing field, this simple task needs to be bridged by a textual modality that helps the system to generalize various objects in the image and various pronunciations in speech utterances. In this study, we propose an end-to-end Image2Speech system that does not need any textual information in its training. We use a vector-quantized variational autoencoder (VQ-VAE) model to learn the discrete representation of a speech caption in an unsupervised manner, where discrete labels are used by an image-captioning model. This self-supervised speech representation enables the Image2Speech model to be trained with the minimum amount of paired image-speech data while still maintaining the quality of the speech caption. Our experimental results with a multi-speaker natural speech dataset demonstrate our proposed text-free Image2Speech system’s performance close to the one with textual information. Furthermore, our approach also successfully outperforms the most recent existing frameworks with phoneme-based and grounding-based Image2Speech systems.