Miracle-dz/UniFAR-FTUs huggingface dataset:
An Energy Efficient Management in Various Fog Platforms

Cloud computing is alludes to programming as a service, infrastructure as an administration and as a stage that gives those administrations. It can likewise be alluded to an administration that encourages us to utilize registering assets like preparing elements, stockpiling and applications as on-request benefits over the web. Generally, with respect to cloud computing, “Cloud” is defined as service. It may be a software or platform or infrastructure that is implements the features of Cloud Computing. In this paper, a hybrid method with the top goal of decrease of vitality utilization in cloud systems is projected. Initial, a correct arrangement for position of virtual machines is found and subsequently perceptive the uncommon highlights of virtual things for coming up with and adjusting the dynamic masses with the help of live relocation strategy. The outcomes demonstrate that the projected technique diminishes the employment of intensity once contrasted with different existing calculations.
Beam-Steering in Metamaterials Enhancing Gain of Patch Array Antenna Using Phase Shifters for 5G Application

This article investigates how beam steering makes the antenna intelligent by increasing channel capacity, range, and reducing interference to enhance the gain of a rectangular patch array antenna for the 5G application using a novel double negative metamaterial (DNG). Beam-steering antennas are the ideal solution for a variety of system applications; it is mostly achieved using phased arrays, where phase shifters are used to control the main-beam. In this paper, the pin-diode phase shifters were considered. ”ON” and ”OFF” states provide phase shift due to different current paths. The main beam direction can be switched between the angles of 25 degrees with a 3dB beamwidth of 23 degrees. The metamaterial structure is composed of 8x8 mm on Rogers RT/duroid 5880 with 0. 254mm thickness and dielectric constant of 2.2. The proposed phase array antenna has four microstrip patch with a thickness of 0. 0175mm printed on the same Rogers RT-duroid substrate with a dielectric constant of 2.2 with dimensions of 5.40 mm x3.94 mm. The phased array antenna without metamaterial structure presented a directivity of 14. 8dBi.Gain increase to 16. 6dBi upon placing the metamaterial structure at a distance 24. 3mm.
A comparison of machine leaming methods using a two player board game

The board games are usually performed by game theory algorithms: minimax and minimax with alpha-beta pruning. Tic-tac-toe (X-O) is the best-known two-player board game. The game tic-tac-toe, based on machine learning algorithms, has been shown in this research. The neural network has been developed and trained to play the game utilizing three implemented agents: an agent based on deep Q-learning, an agent based on policy gradient method and a random agent. The agent can play the game perfectly in the 10-minute training interval, on an average graphics processing unit.
Evaluation of Judicial Imprisonment Term Prediction Model Based on Text Mutation

In recent years, artificial intelligence has witnessed great advancement, and its application in the legal field has experienced more than 60 years. The use of "machine learning" technology to aid the decision-making of legal intelligence systems is no longer far away. However, no comprehensive evaluation methods for predictive models of judicial cases can be found. The performance of the machine learning prediction model not only related to the accuracy but also should be measured in many different aspects. Mutation is a common means of traditional software testing, which can be borrowed in the evaluating of prediction models. This paper introduces the text mutation method, to evaluate the robustness of the judicial case prediction model. The following three evaluation methods are adopted: Classification preference test, Word order variation test and Noise variation test. This paper applies the proposed evaluation method to the judicial imprisonment term prediction model. We use the fastText, TextCNN, and Multi-layer LSTM models. Using the proposed evaluation method to test the above prediction model, and evaluate the robustness of the judicial case prediction model in different aspects.
Low Rank Self-calibrated Brain Network Estimation and Autoweighted Centralized Multi-Task Learning for Early Mild Cognitive Impairment Diagnosis

Detection of mild cognitive impairment (MCI) is important, and appropriate interventions can be taken to delay or prevent its progression to Alzheimer's disease (AD). The construction of brain networks based on brain image data to depict the interaction of brain functions or structures at the level of brain connections has been widely used to identify individuals with MCI/AD from the normal control (NC). Exploring the structural and functional connections and interactions between brain regions is beneficial to detect MCI. For this reason, we propose a new model for automatic MCI diagnosis based on this information. Firstly, a new functional brain network estimation method is proposed. Self-calibration is introduced using quality indicators, and functional brain network estimation is performed at the same time. Then we integrate the functional and structural connected neuroimaging patterns into our multitask learning model to select informative feature. By identifying synergies and differences between different tasks, the most discriminative features are determined. Finally, the most relevant features are sent to the support vector machine classifier for diagnosis and identification of MCI. The experimental results based on the public Alzheimer’s disease neuroimaging (ADNI) show that our method can effectively diagnose different stages of MCI and assist the physician to improve the MCI diagnostic accuracy. At the same time, compared with the existing classification methods, the proposed method achieves relatively high classification accuracy. In addition, it can identify the most discriminative brain regions. These findings suggest that our approach not only improves classification performance, but also successfully identifies important biomarkers associated with disease.
Automatic Segmentation of the Paranasal Sinus from Computer Tomography Images Using a Probabilistic Atlas and a Fully Convolutional Network

In this paper, we present an automatic approach to paranasal sinus segmentation in computed tomography (CT) images. The proposed method combines a probabilistic atlas and a fully convolutional network (FCN). The probabilistic atlas was used to automatically localize the paranasal sinus and determine its bounding box. The FCN was then used to automatically segment the paranasal sinus in the bounding box. Comparing our proposed method with the conventional FCN (without probabilistic atlas) and the state-of-the-art method using active contour with group similarity, the proposed method demonstrated an improvement in the paranasal sinus segmentation. The segmentation accuracy (Dice coefficient) was about 0.83 even for the case with unclear boundary.
Adaptive learning in the detection of Movement Related Cortical Potentials improves usability of associative Brain-Computer Interfaces

Brain-computer interfaces have increasingly found applications in motor function recovery in stroke patients. In this context, it has been demonstrated that associative-BCI protocols, implemented by means the movement related cortical potentials (MRCPs), induce significant cortical plasticity. To date, no methods have been proposed to deal with brain signal (i.e. MRCP feature) non-stationarity. This study introduces adaptive learning methods in MRCP detection and aims at comparing a no-adaptive approach based on the Locality Sensitive Discriminant Analysis (LSDA) with three LSDA-based adaptive approaches. As a proof of concept, EEG and force data were collected from six healthy subjects while performing isometric ankle dorsiflexion. Results revealed that adaptive algorithms increase the number of true detections and decrease the number of false positives per minute. Moreover, the markedly reduction of BCI system calibration time suggests that these methods have the potential to improve the usability of associative-BCI in post-stroke motor recovery.
Identifying Optimal Features from Heart Rate Variability for Early Detection of Sepsis in Pediatric Intensive Care

Sepsis as bacterial infection is the most common and costly causes of mortality in critically ill patients. The early diagnosis of sepsis is significantly important for effective treatment. In this study, over a period of two years, the electrocardiogram of nearly 500 pediatric and neonate patients with heart diseases were collected in 24 hours before diagnosis. The collected data of 22 patients were studied including 11 sepsis patients with positive blood cultures and 11 non-sepsis patients. After extracting the HRV (Heart Rate Variability) signal, 28 linear and nonlinear features according to previous research were extracted. By using the relative entropy method as a feature selection technique, the extracted features were evaluated for their ability to discriminate the data in sepsis and non-sepsis groups, and the best features were entered into the classification process. Using the four classification models of SVM, LDA, KNN and Decision Tree, the accuracy of 86.36% was obtained with Decision Tree for discrimination of sepsis patients from other patients.
Constructing workflow models of alarm responses via trace labeling and dependency analysis

Human factors are critical to industrial alarm monitoring. Learning how operators cope with alarms and manage abnormal situations would be helpful to prevent the repetition of human errors and provide decision supports for realtime alarm responses. The events of operator actions and alarm notifications are recorded in Alarm & Event (A&E) logs, making it available to capture the experience of skilled operators from historical data. This paper presents a data driven method to construct workflow models of operator actions in response to alarms, either univariate or multivariate, from historical A&E logs. The discovery of operator responses is formulated as a causal net model construction problem and solved via a process discovery method comprised by two steps, namely, the trace labeling and the dependency analysis. The effectiveness of the proposed method is demonstrated based on A&E data from a real industrial facility.
EEG-Based Emotion Recognition with Similarity Learning Network

Emotion recognition is an important field of research in Affective Computing (AC), and the EEG signal is one of useful signals in detecting and evaluating emotion. With the development of the deep learning, the neural network is widely used in constructing the EEG-based emotion recognition model. In this paper, we propose an effective similarity learning network, on the basis of a bidirectional long short term memory (BLSTM) network. The pairwise constrain loss will help to learn a more discriminative embedding feature space, combined with the traditional supervised classification loss function. The experiment result demonstrates that the pairwise constrain loss can significantly improve the emotion classification performance. In addition, our method outperforms the state-of-the- art emotion classification approaches in the benchmark EEG emotion dataset-SEED dataset, which get a mean accuracy of 94.62%.
Deep Learning Based Dosimetry Evaluation at Organs-at-Risk in Esophageal Radiation Treatment Planning

Rapid esophageal radiation treatment planning is often obstructed by manually adjusting optimization parameters. The adjustment process is commonly guided by the dose-volume histogram (DVH), which evaluates dosimetry at planning target volume (PTV) and organs at risk (OARs). DVH is highly correlated with the geometrical relationship between PTV and OARs, which motivates us to explore deep learning techniques to model such correlation and predict DVHs of different OARs. Distance to target histogram (DTH) is chosen to measure the geometrical relationship between PTV and OARs. DTH and DVH features are then undergone dimension reduction by autoencoder. The reduced feature vectors are finally imported into deep belief network to model the correlation between DTH and DVH. This correlation can be used to predict DVH of the corresponding OAR for new patients. Validation results revealed that the relative dose difference of the predicted and clinical DVHs on four different OARs were less than 3%. These promising results suggested that the predicted DVH could provide near-optimal parameters to significantly reduce the planning time.
Research on OMR Recognition Based on Convolutional Neural Network Tensorflow Platform

Aiming at optical mark reader (OMR) recognization problems and the complexity of virtual operating that has resulted in much increased human labor, the model architecture based on convolution neural network(CNN) and TensorFlow platform is proposed. In order to improve OMR recognition technology, we uses deep learning tools in TensorFlow to construct the neural network model. For the sake of training the model we uses TensorFlow to preprocess the OMR input data, obtains the TFRecord file, and then train model to reach the requisite OMR recognition level in test paper image and obtain the standard parameters. The view tool TensorBoard is used to display the calculated flow graph of the TensorFlow model.
A Mobile Application for Driver's Drowsiness Monitoring based on PERCLOS Estimation

Vehicular accidents caused by driver drowsiness involve about 7,000 people/year in Brazil, only on federal highways, and cause psychic damage and traumatic stresses for both the victims involved and their families. Drowsiness is characterized by reduced level of vigilance and concentration, which are essential during driving activity. Due to this adversity, many applications of drowsiness detection had been continuously developed through electrical body signals to alert the driver at the time when sleepiness is identified, such as heart rate variability (HRV) and electroencephalogram (EEG). Although these methods work, the use of electrodes in the driver’s body is highly invasive. Therefore, we propose a drowsiness detection system based on driver’s real time video capture, by estimating the percentage of eyelid closure over a period, without any contact device. Since the use of smartphones has been growing in the last decade, the system has been implemented in a mobile phone even with memory and processing limitations. Processing reduction procedures were developed to improve the application performance, such as the reduction of the region of interest and the limitation of the search window, which increased by 93.09% the number of frames per second and allowed the application to operate smoothly.
Analysis and Classification for Single-Trial EEG Induced by Sequential Finger Movements

In recent years, motor imagery-based BCIs (MI-BCIs) controlled various external devices successfully, which have great potential in neurological rehabilitation. In this paper, we designed a paradigm of sequential finger movements and utilized spatial filters for feature extraction to classify single-trial electroencephalography (EEG) induced by finger movements of left and right hand. Ten healthy subjects participated the experiment. The analysis of EEG patterns showed significant contralateral dominance. We investigated how data length affected the classification accuracy. The classification accuracy was improved with the increase of the keystrokes in one trial, and the results were 87.42%, 91.21%, 93.08% and 93.59% corresponding to single keystroke, two keystrokes, three keystrokes and four keystrokes. This study would be helpful to improve the decoding efficiency and optimize the encoding method of motor-related EEG information.
An SSVEP-BCI in Augmented Reality

Steady-State Visual Evoked Potentials (SSVEP) based Brain-Computer Interface (BCI) has achieved very high information transmission rate (ITR), but its portability and fundamental interactions with the surrounding environment were limited. The combination of Augmented Reality (AR) and BCI is expected to solve these problems. In this paper, we combined AR with the SSVEP-BCI to build a more portable and natural BCI system in Microsoft HoloLens. We designed the AR-BCI system and studied the influence of different algorithms on the system performance. The analysis of SSVEP signals collected in AR environment shows that the extended filter bank canonical correlation analysis was better than task-related component analysis. The average recognition accuracy and ITR obtained by using Electroencephalography (EEG) data of 1s, 1.5s, and 2s length were 87.7%、95.4%、 97.6% and 64.6 bit/min 、 62.9 bit/min 、 55.6 bit/min, respectively. Compared with the existing AR-BCI studies, the ITR has been greatly improved in this study.
Cross-Channel Phase-Amplitude Transfer Entropy Conceptualize Long-Range Transmission in sleep: a case study

A causal algorithmic framework quantifying cross-channel phase-amplitude transfer entropy was proposed to measure long-range transmission dynamics between frontal and occipital brain areas during sleep. To this end, a noise-assisted multivariate empirical mode decomposition method was used to guarantee the consistent scales across multivariate signals. On the other side, transfer entropy was applied to measure information transfers from a low-frequency phase to a high-frequency amplitude across different brain regions. Our results showed δ phase may modulate either θ or α amplitude. The frontal cortex transferred information to the occipital brain area more than its inverse direction during Awake and N3 sleep stages, whereas N1 was more likely of serving as a transition state. On the other side, the information flow transferred from the occipital area to the frontal cortex surpassed its inverse flow in the N2 sleep stage. The proposed causal algorithmic framework facilitated identifying information flow and driving force across brain regions in sleep.
Detection and Classification of Chronic Total Occlusion lesions using Deep Learning

Cardiovascular disease (CVD) is one of the diseases with the highest mortality rate in modern society, while chronic total occlusion (CTO) is the initial factor that influences the success rate of percutaneous coronary intervention (PCI), which is one of the most common treatments for CVD. In this work, novel deep convolutional neural networks (CNNs) are proposed to detect the entry point of CTO and classify its morphology according to the coronary angiography automatically. Specifically, feature pyramid networks (FPN) module and model fusion technique are applied to the detection network, and data augmentation and attentive regularization loss by reciprocative learning algorithm are used in classification network. Extracted from contrary angiography, the dataset consists of 2059 cases annotated by professional cardiologists. Experiment results show that the recall of CTO detection can reach up to 89.3%, and the sensitivity and specificity of CTO classification can reach up to 94.5% and 89.1% respectively.
A License Plate Recognition System for Severe Tilt Angles Using Mask R-CNN

In the past few years, license plate recognition systems have been widely used in parking lots. In order to identify license plates easily, traditional license plate recognition systems used in the parking lot have a fixed light source and a shooting angle. For particularly tilting angles, such as license plate images taken with super wide-angle lenses or fisheye lenses, the deformation of the license plate can be particularly severe, resulting in poor recognition of traditional license plate recognition systems. In this paper, we propose a three-stage license plate recognition system based on Mask R-CNN that can be used for various shooting angles and more oblique images. Experimental results show that the proposed architecture can identify license plates with bevel angles over 0∼60 degrees and achieve mAP rates of up to 91%. Compared with the approach using YOLOv2 model, the proposed method with Mask R-CNN has made significant progress in identifying characters that are inclined above 45 degrees. The experimental results also show that the proposed method is superior to other methods in the open Taiwan license plate data set (called AOLP data set).
Physical activity estimation from accelerometry

Objective physical activity (PA) quantification is traditionally achieved using lightweight accelerometers accounting for activity frequency, intensity and duration. The accelerometer data are usually converted into activity counts and these counts can be used on their own to quantify the intensity and duration of a PA period or they can serve as features for energy expenditure computation or activity classification. This paper investigates the way how Actigraph counts are computed. Several points are discussed regarding bandpass filtering and amplitude non-linearities that may hamper some analysis. Experimental data were used 1) to assess reconstructed filter performances to replicate ActiGraph counts during an urban-circuit involving 20 subjects wearing an ActiGraph GT3X+ and 2) explain filter limitations (e.g. plateauphenomenon) thanks to a treadmill test with incremental speed (n=4). This study reproduces well ActiLife filter and reveals the impact of band-pass filtering on ActiLife count conversion. These results provide some keys to interpret knowingly ActiLife count based studies.
A Hybrid Scheme Using PCA and ICA Based Statistical Feature for Epileptic Seizure Recognition from EEG Signal

Epilepsy is commonly regarded as a neurological disorder which can be characterized by repetitive unprovoked seizures. Electroencephalogram (EEG) is the neuro-physiological measurement of the brain's electrical activity recorded by electrodes placed in the cerebral cortex. The EEG signals play an essential role in the diagnosis of epilepsy. This paper proposes an approach for classifying the epileptic seizure patterns that carry significant indications regarding chronic neurological disorders. In this regard, a dimensionality reduction scheme, hybrid in nature, utilizing Independent and Principal Component Analysis (ICA and PCA) is developed followed by the extraction of statistical features for epileptic seizure identification. At first, a particular number of sub-frames is extracted from the given EEG signal. The extracted sub-frames are considered as the input of PCA and ICA. After that, the significant components of ICA and PCA are utilized for statistical feature extraction. Lastly, the supervised support vector machine (SVM) classifier is employed for the seizure classification purpose. To evaluate the raised method, the publicly available EPILEPTIC dataset is used. According to the experiments and result analysis, the proposed scheme achieves convincing performance in terms of accuracy when the first components of ICA and PCA algorithms are used for feature extraction.
Identification of association rules in orders of distribution companies’ clients

The identification of association rules is the problem of finding associations between different items in the same transactions. In this paper, performance comparison of different variants of Apriori, FP-Growth and ECLAT algorithms was performed over the real transactional data set of the distribution company by using R programming language and its appropriate packages, and the results obtained are later on explained. Then, the identification and visualization of the association rules of the said real data set was performed.