Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-Ensemble Learning using Neural Network for Classifying Traffic Condition

The growth of technology aims to help human’s activity. One of human’s activity which could use technology is in the transportation area by implementing machine learning. This paper discusses the semi-ensemble method for classifying traffic condition, which could be used to classify the traffic condition for shorten travel time in the road. Semi-ensemble that applied is using voting system which consists of several neural networks. The proposed method in this paper gives better performance result than single neural network Even though the performance result is not increased significantly, enhancement in semi-ensemble with voting system which comes from best-5 performance neural networks also give better result than voting system using 10 neural networks. The performance increased from 82.58% to 82.81% for its accuracy and the rests of performance value increased from 65.09% to 65.62%.
DBNS: A Distributed Blockchain-Enabled Network Slicing Framework for 5G Networks

5G technology is expected to enable many innovative applications in different verticals. These applications have heterogeneous performance requirements (e.g., high data rate, low latency, high reliability, and high availability). In order to meet these requirements, 5G networks endorse network flexibility through the deployment of new emerging technologies, mainly network slicing and mobile edge computing. This article introduces a distributed blockchain-enabled network slicing (DBNS) framework that enables service and resource providers to dynamically lease resources to ensure high performance for their end-to-end services. The key component of our framework is the global service provisioning (GSP), which provides admission control for incoming service requests along with dynamic resource assignment by means of a blockchain-based bidding system. The goal is to improve users' experience with diverse services and reduce providers' capital and operational expenditures.
Influence of Artificial Intelligence on Personalized Medical Predictions, Interventions and Quality of Life Issues

The main intention of this paper is to give a brief discussion on modern approaches in gathering, processing, and using huge amounts of data in medicine and healthcare influenced by powerful techniques and approaches of Artificial Intelligence. Attention is put on several possibilities to improve patients’ health status and quality of life issues. Characteristic examples and scenarios are presented inspired by contemporary activities within several big projects in medical domains and authors' long-lasting experiences in using Artificial Intelligence, Machine Learning, Agent Technologies in numerous applications including several medical areas.
Key-Point Matching Guided Coronary Artery Extraction from CT Coronary Angiography Sequence

Extracting coronary arteries is important in clinical applications of cardiac Computed Tomography Angiography (CTA). However, obtaining an accurate segmentation of coronary artery is challenging because of complex anatomy, variation in image quality, as well as motion induced by heart beating and respiration. This paper presented a semi-automatic framework dedicated to coronary artery segmentation in 3D+t Computed Tomography Angiography (CTA) sequence. The proposed segmentation scheme consists of two parts: vessel centerline extraction and vessel shape recovering. For the starting phase, spherical flux based minimal path method was employed to extract the main centerlines of coronary artery tree. To implement similar centerline extraction scheme automatically in subsequent phases, a novel method key-points detecting method was proposed and an endpoints searching procedure was taken. In this process, the key-points on distal vessels are easily got mismatched. A centerline tracking algorithm constrained by patient-specific vessel length was used to complete the extraction. The the overlap (OV) results of proposed framework are 93.56% (LAD), 90.96% (LCX) and 93.76% (RCA). We also achieved accuracy (AC) result: 0.6508mm (LAD) 0.6141mm (LCX) and 0.6297mm (RCA) which are less than the data voxel size (0.68mm).
Estimation of indoor position and motion direction for smartphones using DNN to BLE beacon signal strength

We investigated a method to simultaneously detect the location and motion direction of a pedestrian walking indoors. Multiple time instances of Received Signal Strength Indicator (RSSI) readings from multiple Bluetooth Low Energy (BLE) beacons on a smartphone held by a pedestrian, traveling in one of 9 directions, were fed to a trained Deep Neural Network (DNN), and the location of this smartphone, as well as the direction of its motion, was simultaneously estimated. Previous experiments in a $4m\times 7m$ area showed estimated location accuracy of 0.91m, and average estimation accuracy of 83.5% in 5 directions. In this paper, we significantly increased the quality of the RSSI training data, as well as the number of motion direction to nine from five. The estimated location accuracy of 0.439m, and average estimated direction accuracy of 81.2% in 9 directions was shown.
Comparative Analysis of Multivariable Deep Learning Models for Forecasting in Smart Grids

Clean-energy generation in smart grids is limited by the availability of the energy to be transformed and advanced energy management strategies requires solid and anticipated information about its dynamic behavior. This includes multivariable prediction of meteorological and user consumption data simultaneously in time series. The selection of a predicting model, from long short-term memory (LSTM), convolutional neural networks (CNN), gated recurrent units (GRU), or their hybrid models merging CNN with LSTM and GRU, is a very complex task. In this paper, a mean absolute error, absolute percentage error (MAPE), and root mean square error (RMSE) comparative analysis, for prediction of energy consumption, and solar and onshore wind generation, is presented. A three-day prediction-horizon is used, with four-year hourly training data from Madrid. The combination of the best GRU and CNN models found, subject to the given hyperparameters grid, has a better prediction performance, including if they predict separated. Relevant information about training and coding appreciations is also presented.
Similarity Analysis of Actual Fake Fingerprints and Generated Fake Fingerprints by DCGAN

This paper proposes a veriﬁcation method whether fake ﬁngerprints generated by DCGAN are similar to actual fake ﬁngerprints in order to augment fake ﬁngerprint data. The ﬁrst method to verify is to compare the distributions of the mean and standard deviation of fake ﬁngerprints generated by deep convolutional generative adversarial network (DCGAN) with those of actual fake ﬁngerprints. In the second method, the mean Hamming distance, which is a method of evaluating the similarity of images, is used for measuring the similarity between the generated fake ﬁngerprints and the actual fake ﬁngerprints. The third method is to obtain the histograms of the generated fake ﬁngerprints and actual fake ﬁngerprints and measure the similarity by calculating Pearson correlation of the histograms. The fourth method is to calculate intersection of union, which is a method of evaluating the shape similarity of images, between generated fake ﬁngerprints and actual fake ﬁngerprints. From extensive experiments it was conﬁrmed that fake ﬁngerprints generated by DCGAN could be used to augment fake ﬁngerprint data because generated fake ﬁngerprints are similar to actual fake ﬁngerprints in terms of four similarity measures.
Healthcare costs of stroke and major bleeding in patients with atrial fibrillation treated with non-vitamin K antagonist oral anticoagulants

Abstract Objective: To assess long-term healthcare costs related to ischemic stroke and systemic embolism (stroke/SE) and major bleeding (MB) events in patients with non-valvular atrial fibrillation (NVAF) treated with non-vitamin K antagonist oral anticoagulants (NOACs). Materials and methods: Optum’s Clinformatics Data Mart database from 1/2009–12/2016 was analyzed. Adult patients with ≥1 stroke/SE hospitalization (index date) were matched 1:1 to patients without stroke/SE (random index date), based on propensity scores. Patients with an MB event were matched to patients without MB. All patients had an NOAC dispensing overlapping index date, ≥12 months of eligibility pre-index date, and ≥1 NVAF diagnosis. The observation period spanned from the index date until the earliest date of death, switch to warfarin, end of insurance coverage, or end of data availability. Mean costs were evaluated: (1) per-patient-per-year (PPPY) and (2) at 1, 2, 3, and 4 years using Lin's method. Results: The cost differences were, respectively, $48,807 and $28,298 PPPY for NOAC users with stroke/SE (n = 1,340) and those with MB (n = 3,774) events compared to controls. Cost differences of patients with vs without stroke/SE were $49,876, $51,627, $57,822, and $60,691 at 1, 2, 3, and 4 years post-index, respectively (p < 0.001). These cost differences were $31,292, $35,658, $44,069, and $47,022 for patients with vs without MB after 1, 2, 3, and 4 years post-index, respectively (p < 0.001). Limitations: Limitations include unobserved confounders, coding and/or billing inaccuracies, limited sample sizes over longer follow-up, and the under-reporting of mortality for deaths occurring after 2011. Conclusions: The incremental healthcare costs incurred by patients with vs without stroke/SE was nearly twice as high as those of patients with vs without MB. Moreover, each additional year up to 4 years after the first event was associated with an incremental cost for patients with a stroke/SE or MB event compared to those without an event.
First Principles Based Compact Model for 2D-Channel MOSFETs

We propose a generalized compact model for any two-dimensional material channel-based metal-oxide-semiconductor field-effect transistors. Unlike existing ones, the proposed model is first principles based and thus has ability to predict the circuit performance only using the crystallographic information of the channel material. It is ‘core’ in nature and developed following the industry-standard drift-diffusion formalism based ‘top-down’ hierarchy employing the Fermi-Dirac statistics. We also implement the model in professional circuit simulator and good convergence is observed in 15-stage ring oscillator simulation.
Improved Air Spacer for Highly Scaled CMOS Technology

We report an improved air spacer (AS) integration scheme to overcome problems with the conventional AS process. The new scheme is fully compatible with other emerging CMOS technology elements such as self-aligned contact (SAC) and contact over active gate (COAG). Using a fan-out3 (FO3) ring oscillator (RO) on a 10-nm FinFET platform, we experimentally demonstrate that the new AS provides 15% reduction in the effective capacitance (<inline-formula> <tex-math notation="LaTeX">${C}_{{\text {eff}}}{)}$ </tex-math></inline-formula>. Such a <inline-formula> <tex-math notation="LaTeX">${C}_{{\text {eff}}}$ </tex-math></inline-formula> reduction translates to 21% performance gain at the constant power (iso-power) or 36% power reduction at the constant performance (iso-speed). The benefits provided by AS exceed the benefits of a full CMOS node scaling from 7 to 5 nm. Clearly, AS is a viable technological element for continuing CMOS scaling.
Weighted Cross-Entropy for Unbalanced Data with Application on COVID X-ray images

Since December 2019 the world is infected by COVID-19 or Coronavirus disease, which spreads very quickly, out of control. The high number of precautions for laboratory access, which need to be taken to contain the virus, together with the difficulties in running the gold standard test for COVID-19, result in a practical incapability to make early diagnosis. Recent advances in deep learning algorithms allow efficient implementation of computer-aided diagnosis. This paper investigates on the performance of a very well known residual network, ResNet50, and a lightweight Atrous CNN (ACNN) network using a Weighted Cross-entropy (WCE) loss function, to alleviate imbalance on COVID datasets. As a result, ResNet50 model initialized with pre-trained weights fine-tuned by ImageNet dataset and exploiting WCE achieved the state-of-the-art performance on COVIDXRay-5K test set, with a top balanced accuracy of 99.87%.
A Local Trust Inferring Algorithm based on Reinforcement Learning DoubleDQN in Online Social Networks

The development of online social network has greatly promoted the interaction between users. Trust plays an important role in social activities of social network, which can effectively avoid the risk from unreliable users. In fact, most users have no direct interaction, i.e., there is no direct connection in the social network, so the indirect trust of the target user can only be evaluated by the friends who contact indirectly. The propagation and aggregation methods of trust affect the results of trust evaluation to a great extent. The existing aggregation methods generally have the problem of low prediction accuracy. In addition, how to find a reliable path to propagate trust is also a major challenge. This paper proposes a DoubleDQNTrust(DDQNTrust) algorithm based on reinforcement learning DoubleDQN to find reliable trust paths. Secondly, based on standard collaborative filtering and considering the similarity between users, a new aggregation method is proposed. The experimental results on Filmtrust online social network data set show that DDQNTrust algorithm can effectively find reliable trust paths, and can evaluate trust with high prediction accuracy.
Survival Rate Prediction Model of Cardio Vascular Disease Patients by Quantifying the Risk Profile using SVM

This research paper provides a clinical analysis of heart patient data to predict the survival rate of patients suffering from cardio-vascular diseases (CVD). The proposed solution will facilitate medical specialists in terms of providing quality health services to patients including the intensive treatments. Our model determines the chances of survival of patients suffering from any cardiovascular disease by analyzing the risks associated with them and other factors of their life style, physical activity, smoking habit, etc. The proposed survival rate prediction model is efficient and economical solution to facilitate the medical specialists in terms of following the most appropriate medical procedures for given symptoms. This paper presents an improved Stochastic Gradient Descent (iSGD) approach along with Hinge Loss Function of Support Vector Machine (SVM). Experimental results illustrate the effectiveness of the proposed prediction model in terms of predicting the survival rate of CVD patients.
Experts Evaluation of a Pedagogical Game Design Document (Ped-GDD)

The application of gamification concepts is becoming a popular research area in many subjects. The application of gamification on a pedagogical aspect has shown a positive effect on motivation and learners’ skills in current studies. This paper suggests a pedagogical gamification design lifecycle that involves teachers and game developers in a collaborative approach. This paper presents an example of a pedagogical gamification Design Document (Ped-GDD) which is used to structure the game requirements for a given lesson. The paper also, discusses the process of evaluating a pedagogical gamification Design Document (Ped-GDD) using elements of the Technology Acceptance model (TAM).
Personalizing Activity Recognition With a Clustering Based Semi-Population Approach

Smartphone-based approaches for Human Activity Recognition have become prevalent in recent years. Despite the amount of research undertaken in the field, issues such as cross-subject variability are still posing an obstacle to the deployment of solutions in large scale, free-living settings. Personalized methods (i.e. aiming to adapt a generic classifier to a specific target user) attempt to solve this problem. The lack of labeled data for training purposes, however, represents a major barrier. This is especially the case when taking into consideration that personalization generally requires labeled data to be user-specific. This paper presents a novel personalization method combining a semi-population based approach with user adaptation. Personalization is achieved through the following. Firstly, the proposed method identifies a subset of users from the available population as best candidates for initializing the classifier to the target user. Subsequently, a semi-population Neural Network classifier is trained using data from this subset of users. The classifier’s network weights are then updated using a small amount of labeled data from the target user subsequently implementing personalization. This approach was validated on a large publicly available dataset collected in a free-living scenario. The personalized approach using the proposed method has shown to improve the overall F-score to 74.4% compared to 70.9% when using a generic non-personalized approach. Results obtained, with statistical significance being confirmed on a set of 57 users, indicate that model initialization using the semi-population approach can reduce the amount of labeled data required for personalization. As such, the proposed method for model initialization could facilitate the real-world deployment of systems implementing personalization by reducing the amount of data needed for personalization.
Data Acquisition System of the CLOUD Experiment at CERN

The Cosmics Leaving OUtdoor Droplets (CLOUD) experiment at the European Organization for Nuclear Research (CERN) is investigating the nucleation and growth of aerosol particles under atmospheric conditions and their activation into cloud droplets. The experiment comprises an ultraclean 26 m3 chamber and its associated systems (the CLOUD facility) together with a suite of around 50 advanced instruments attached to the chamber via sampling probes to analyze its contents. The set of instruments changes for each experimental campaign according to the scientific goals. The central function of the CLOUD DAQ (data acquisition) system is to combine the data from these autonomous and inhomogeneous instruments into a single, integrated CLOUD experiment database. The DAQ system needs to be highly adaptable to allow a fast setup over a single installation week at the start of each campaign when the instruments are brought to CERN and installed at the CLOUD chamber. Each campaign requires high flexibility and fast response to changes in instrument configuration or experimental parameters. The experiments require online monitoring of the physical and chemical measurements with delays of only a few seconds. In addition, the raw data, the monitoring databases, and the processed data must be archived and provided to the international collaboration for both real-time and later analyses. We will describe the various components of the CLOUD DAQ and computing infrastructure, together with the reasons for the chosen solutions.
Predictive Control of PMSM in Stator Frame under Passivity Constrain

In this paper we discuss a torque control strategy in stator frame for a Permanent Magnet Synchronous Machine (PMSM) based on a non-linear model predictive controller. We introduce a passivity constraint to ensure closed-loop passivity of the control system. The mathematical model of the PMSM is deduced from the Euler-Lagrange (EL) equation by using the expressions of the kinetical and potential energies of the PMSM. We improve the capability of rejecting parameter deviations by including a simple observer in the electrical subsystem. The algorithm is simulated numerically in MATLAB-Simulink.
Vessels Segmentation Base on Mixed Filter for Retinal Image

The retinal vessels segmentation by computer usually assist doctor to detect diabetic retinopathy. Due to the characteristic of retinal vessels structure is complex and changeable, the automatic vessels segmentation still is a challenging task. In this paper, the Mixed filter method is proposed for the retinal vessels segmentation, which utilizes matched filter (MF) combining B-COSFIRE filter to extract the vessels network. Firstly, the CLAFLE algorithm is used to processe with the green channel of retinal image for enhancing the contrast between blood vessel and background. Then, in the matched filter channel, morphological top-hat and bottom-hat are used to further enhance the contrast and Gaussian kernel is used to to extract the thin vessels tree. At the same time, B-COSFIRE filter is make use of filtering the thick vessels tree for green channel of retinal image. The corresponding results of dual channel filtering are segmented and fused to achieve retinal vessels segmentation map. Experimental results show that the proposed algorithm can effectively improve the performance of retinal vessels segmentation compared with single filtering method.
Bayesian Accuracy Analysis of Stochastic Circuits

Understanding accuracy and the tradeoffs it entails is key to evaluating the growing list of stochastic computing (SC) circuit designs. Due to shortcomings of current SC error theory, simulation has become the standard way to estimate a circuit's accuracy. However, simulation can demand large computational resources and lead to uncertain, misleading, or unexplainable results. A soundly based analytic approach is therefore preferable to simulation. In this work, we first show the input value distribution's large influence on circuit accuracy. Then we develop a Bayesian error analysis methodology which uses the input value distribution as a prior to inform better accuracy estimates. This error formulation introduces concepts new to SC such as estimator dominance and points to ways of improving simulation-based accuracy estimates. Orthogonal to the Bayesian ideas, we also show how to use bias-variance decomposition to simplify and aggregate the effects of SC's many error sources. We present techniques that use the beta distribution to model the stochastic number value distribution. Finally, we demonstrate the use of these ideas to improve the accuracy and analysis of an SC-based neural network.
SWIPE: Enhancing Robustness of ReRAM Crossbars for In-memory Computing

Crossbar-based in-memory architectures have emerged as an attractive platform for energy-efficient realization of deep neural networks (DNNs). A key challenge in such architectures is achieving accurate and efficient writes due to the presence of bitcell conductance variations. In this paper, we propose the Single-Write In-memory Program-vErify (SWIPE) method that achieves high accuracy writes for crossbar-based in-memory architectures at 5×-to-10× lower cost than standard program-verify methods. SWIPE leverages the bit-sliced attribute of crossbar-based in-memory architectures and the statistics of conductance variations to compensate for device non-idealities. Using SWIPE to write into ReRAM crossbar allows for a 2× (CIFAR-10) and 3× (MNIST) increase in storage density with < 1% loss in DNN accuracy. In particular, SWIPE compensates for 4.8×-to-7.7× higher conductance variations. Furthermore, SWIPE can be augmented with injection-based training methods in order to achieve even greater enhancements in robustness.
A Metal-Insulator-Semiconductor Non-Volatile Programmable Capacitor Based on a HfAlOₓ Ferroelectric Film

In this letter, we proposed and experimentally demonstrated a metal-insulator-semiconductor (MIS) ferroelectric capacitor with non-volatile programmable capacitance. By switching the polarization of the Al-doped HfO2 ferroelectric insulator with the external electric field, the depletion width of the semiconductor can be modulated, leading to the continuously adjustable capacitance of the device. Due to the non-volatility of ferroelectricity, the programmed capacitance is stable without the constant DC bias. We also proposed some potential applications of the capacitor in the data storage and circuit design.