Miracle-dz/UniFAR-FTUs huggingface dataset:
A Microscopic Hyperspectral-Based Centroid Wavelength Method for Measuring Two-Dimensional Junction Temperature Distribution of LEDs

In this letter, we propose an experimental method for determining two-dimensional (2D) junction temperature distribution of light-emitting diodes (LEDs) using microscopic hyperspectral-based centroid wavelength. The 2D centroid wavelength matrix is extracted from the hyperspectral data cube as temperature sensitive optical parameters (TSOP), which is linearly calibrated by short pulse voltage signal in order to eliminate the influence of self-heating effect. The 2D junction-temperature distributions of LEDs under working conditions are derived by TSOP matrix. The comparative experiments by micro-thermocouple ( $\mu $ -TC) and IR imaging reveal good coincidence in both averaged temperature and temperature distribution trends.
Topological Interference Management for MIMO Interference Broadcast Channels

In this paper, we investigate the topological interference management (TIM) for MIMO interference broadcast channels (IBC) under the fixed topology, where only topology information is needed to cancel the inter-cell interference (ICI). For the single user case, where the MIMO IBC network is reduced to the MIMO interference channel (IC) networks, we propose one novel TIM scheme by exploiting the solution to the corresponding SISO IC TIM based on topology information. And also, we present one theorem to characterize the achievable degrees of freedom (DoFs). For the multiple user case, assuming that each BS not only knows topology information but also knows the channel state information at transmitters (CSIT) within its own cell, we propose two novel TIM schemes, i.e., the virtual user compression-based scheme and the virtual base station extension-based scheme. For each scheme, we design transceivers to cancel both the ICI only based on the topology information by exploiting the solution to the corresponding SISO IC TIM and the multiple user interference (MUI) within its own cell based on the CSIT. Furthermore, we present one theorem to characterize the achievable DoF, respectively. Moreover, we prove that the DoF achieved by the virtual base station extension-based scheme is higher than or equal to that achieved by the virtual user compression-based scheme. Hence, the achievable DoF of MIMO IBC TIM is lower bounded by the optimal DoF attainable for the base station extension scheme. Furthermore, the numerical results show that our schemes can achieve significant performance gain against the traditional interference avoidance scheme.
Applying Neural Network Approach to Homomorphic Encrypted Data

Nowadays, a new era is beginning, the Data Era, which comes with a multitude of challenges and opportunities. Due to technology advancement and increased demand for powerful computing resources, many organizations outsource their storage and computing services to providers of such services. The benefits of cloud computing are numerous, but the security of cloud stored data remains an important issue. Cyber-attacks can be hard to detect and the culprits hard to identify, which encourages the cyber attackers to further pursue their malicious purposes. Big Data combined with analytical methods can produce the best defensive techniques against intrusions in the cyber security domain. Neural networks are not yet feasible to work over encrypted data, but with a well-designed architecture for such an analytical method they can contribute to the security of a system. Our paper aims to present a solution for the protection of personal data in insecure environments, which can also use the same data to perform analytical tasks without affecting confidentiality and security of individuals.
Words Diffusion an Analysis of across Facebook Pages in Thailand

Facebook Pages in Thailand have become prominent and very well-known in online media, business, and news broadcasting. Facebook pages seem to have become importantly technological tools which can provide methods and frameworks for identifying trending words and analyzing their trends and flows. The results demonstrated that incorporating reaction factor when weighting importance of words found in messages posted in different period of time and domains can help identify flows of trendy words.
FSK-Based Simultaneous Wireless Information and Power Transfer in Inductively Coupled Resonant Circuits Exploiting Frequency Splitting

Inductively coupled resonant circuits are affected by the so-called frequency splitting phenomenon at short distances. In the area of power electronics, tracking one of the peak frequencies is state-of-the-art. In the data transmission community, however, the frequency splitting effect is often ignored. Particularly, modulation schemes have not yet been adapted to the bifurcation phenomenon. We argue that binary frequency shift keying (2-ary FSK) is a low-cost modulation scheme which well matches the double-peak voltage transfer function <inline-formula> <tex-math notation="LaTeX">$H(s)$ </tex-math></inline-formula>, particularly when the quality factor <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> is large, whereas most other modulation schemes suffer from the small bandwidths of the peaks. Additionally, we show that a rectified version of 2-ary FSK coined rectified FSK (RFSK), is even more attractive from output power and implementation points of view. The analytical and numerical contributions include the efficiency factor, the impulse response, and the bit error performance. A low-cost noncoherent receiver is proposed. The theoretical examinations are supported by an experimental prototype.
Risk Prediction of Ischemic Heart Disease Using Artificial Neural Network

The fatty plaque deposits narrow artery walls leading to the heart Ischemic Heart Disease (IHD). For that, the flowing of blood is reduced. Hyperpiesia polygenic disorder, drug addiction, and lipidemia are the initiators of tachycardia. From the healthcare unit, 835 IHD patients data with 14 features like Age, Sex, Diabetes Mellitus (DM), Electrocardiograph (ECG), Exercise Tolerance Test (ETT) etc. have been collected. By implementing artificial neural network (ANN) techniques in our collected dataset the risk prediction is determined around 84.47%. The precision, sensitivity, specificity, and fl-score, for the ANN, are 79.97, 82.69, 72.63, and 85.17 respectively. The significant contingency of tachycardia is anticipated.
Learning Local Structured Correlation Filters for Visual Tracking via Spatial Joint Regularization

Robust visual tracking is a fundamental problem in the field of computer vision and has a wide range of practical applications. Recent progress in developing robust tracking methods are mainly made upon discriminative correlation filters (DCF). However, most DCF-based methods develop their trackers under the assumption of a holistic appearance model, ignoring the underlying spatial local structural information. In this paper, we introduce the tree-structured group sparsity regularization into the DCF-based formula. The correlation filter to be learned is divided into hierarchical local groups. The relationship between the response and the circularly shifted target appearance is regularized by applying the $l_{1}$ -norm across the $l_{2}$ -norm of the hierarchical local filter groups. Moreover, a local response consistency term is incorporated together with the structured sparsity to make each local filter group contributes equally to the final response. The accelerated proximal gradient method is employed to optimize this non-smooth composite regularization problem. Benefiting from the properties of circulant matrices, several key steps in the optimization process can be efficiently solved in the frequency domain. The experiments are conducted on four publicly available visual tracking benchmarks. Both quantitative and qualitative evaluations demonstrate that the proposed tracking method performs favorably against a number of state-of-the-art tracking methods.
The impact of summarisation on textual entailment - a case study on global warming arguments

The challenging task of recognizing textual entailment aims to check whether the meaning of a smaller text - the hypothesis h - can be inferred from another text T. Current methods interleave natural language processing, machine learning, search and lexical resources. All these instruments pose computational challenges that make textual entailment unfeasible for large texts. Hence, we investigate how textual entailment is affected by text summarization. By summarising the text $T$ we expect a decrease of accuracy, but an increase of computation speed. We aim to assess the expected decrease in accuracy caused by summarisation against time benefits due to smaller text given to entailment machinery. Our results show that the time needed for computing entailment is decreased four times, while the accuracy decreases with two percentages.
Brain fMRI Image Classification and Statistical Representation of Visual Objects

The application of the given paper work is to estimate what image a human brain is visually perceiving based on the neuroimaging information observed from the ventral temporal cortex (VT) portion. In the process, we used the nilearn library from python repository along with the haxby dataset which includes a set of functional MRI from 6 subjects viewing images that contains a grid of black and white pictures of some certain objects. Firstly, the haxby dataset was collected and few pre-processing steps such as masking, scaling and smoothing was done in order to reduce the complexity, noise and to standardize the data. Furthermore, the entire dataset was splitted into 80% of training example and 20% of test example. After that, the training examples were passed through a set of machine learning frameworks which consist of ‘Nearest Neighbors', ‘Linear SVM’, ‘RBF SVM’, ‘Gaussian Process', ‘Decision Tree’, ‘Random Forest’, ‘Neural Net’, ‘Ada-Boost’, ‘Naive Bayes' and ‘QDA’ algorithms. Completing the training, the accuracy of the frameworks were tested and on an average the most accuracy of 95% was found with Neural Network and Support Vector Machine (SVM) across all the subjects.
Situation-Based Dynamic Frame-Rate Control for on-Line Object Tracking

On-line object tracking is an essential technology in computer vision. Object tracking systems need to reduce their energy consumption because the technology is increasingly being utilized for battery-operated systems, e.g., driving assist systems, smartphones, drones and so on. To tackle this problem, dynamic frame-rate optimization has been proposed. This approach optimizes the frame-rate on the basis of target object speed by taking into account the energy trade-off between the image capturing and tracking processes. In order to improve tracking accuracy, the approach selects a frame-rate based on a specific fixed value. However, the required parameters are different depending on the scene and content of the input video. In this paper, we propose a method to adaptively select parameters. Simulation results show the energy consumption is reduced by up to about 65.0%, and 45.0 % on average without critical tracking accuracy degradation.
MCD: Mutually Connected Community Detection using clustering coefficient approach in social networks

The recognition of communities from social networks is a remarkable and purposeful thing to be determined. Several past studies agonized from insufficient methods for the detection mechanism of communities in the given network due to the different variables influencing social networks and a large amount of the complexity to be manipulated, caused by the high scaling structures of these networks. Therefore, the analysis of social networks is enhancing the linkage between social spaces. Community detection is a vital problem to be solved in analyzing the social network. consumers or simply the users in a social network ordinarily consists of some regular social communication or interaction in simple, with their own friends or connected peoples residing in a particular community due to their common interests or almost similar profiles. In this paper, an efficient process, MCD(Mutual Community Detection) is proposed. The proposed approach is then applied to datasets of U.S Airline companies and Zachary Karate Club using the clustering coefficient approach. This approach detects the community of strongly mutually connected components having different values of the relation residing in the network. Experimental results demonstrated that components having a higher value of clustering coefficient are strongly connected and form a community of mutual connectivity. In addition, density and average degree communities were also investigated. Experimentation conducted on the two, human social and real data-sets reveal the effectiveness and the efficiency of our proposed method.
3D particle‐in‐cell simulation of positive streamer initiation in highly pressurized gaseous, liquid and supercritical CO
 2
 with field ionization

A 3D particle ‐ in ‐ cell/Monte Carlo collision model is used to investigate the streamer discharge inception in CO 2 at elevated pressures including gaseous, liquid and supercritical phases. Generation of free electrons is a prerequisite for initiation and development of positive streamers. Field ionization from impurity molecules of low density and low ionization energy is assumed as the source of primary ionization in the model. The field dependent generation rate of electrons is calculated by Zener's model. Tree ‐ like streamer with filamentary shape presents at the needle tip and propagates towards the plane electrode in all three phases of CO 2 , with differing in length and propagation speed. Furthermore, the influence of different parameters such as applied voltage, electrode tip size, and ionizable density and ionization potential of impurity molecules on the evolution of streamer discharge in supercritical CO 2 are investigated. Simulation results obtained from the 3D model agree well with experimental observa-tions in the literature, which show that field ionization might be one of possible ionization sources in pressurized CO 2 in the presence of high electric field.
Linearity and Efficiency in 5G Transmitters: New Techniques for Analyzing Efficiency, Linearity, and Linearization in a 5G Active Antenna Transmitter Context

Every new generation of mobile systems should provide higher capacity, serve more users, be more energy efficient, and have lower cost. Radio-access hardware constitutes a major bottleneck for reaching these goals, which is a particularly noticeable issue now that 5G communication systems are being developed. A combination of breakthroughs in communication theory-as in massive multiple input/multiple output (MIMO) [1], highly integrated semiconductor and packaging technologies, and extended spectrum allocations- has enabled a paradigm shift in the way radio hardware will be realized. Today's high-power, fewantenna, sectorized systems will soon be replaced with highly integrated active antenna systems having up to hundreds of individually driven low-power radios operating with very wideband signals.
Effect of the electron blocking layer in dual-wavelength emission of InGaN/GaN MQW light-emitting diodes

‘III-nitride’ semiconductor based light emitting diodes (LEDs) have completely transformed the lighting industry due to its unique optical and electrical properties, which leads to higher efficiency and brightness. However, electron leakages, poor hole injection, and efficiency droop significantly affect the electrical and optical properties of InGaN/GaN LEDs that leads to the poor power conversion efficiency. These problems can be effectively minimized by introducing an Electron Blocking Layer (EBL) in conventional LEDs. The objective of this work is to investigate the effect of EBL layer (i.e., AlxGal-xN) on dual wavelength InGaN/GaN LEDs with different Al compositions (x). Different compositions of AlxGal-xN have been used to study the electrical and optical properties of LEDs and find out their optimal design parameters by using APSYS simulation program. The simulation results suggested that the composition of Al of the EBL and properties of quantum well has significant effects on the optical and electrical properties of InGaN/GaN LEDs. The gradually increasing composition (from $x$ = 0 to 0.13) of the EBL shows large output power from the LED as it significantly reduces the electrons leakages of the device.
Frequency Domain Approach in CSP based Feature Extraction for EEG Signal Classification

EEG signal in the time domain with high sampled rate faces difficulties for their noise sensitive properties that lead to erroneous feature extraction. Since the feature extraction is one of the most significant steps in EEG signal classification, common spatial pattern (CSP) is a widely used approach for feature extraction. Conventional CSP in the time domain may often fail to maintain the discriminative features between the classes. Therefore, a frequency domain CSP (FCSP) is proposed by the work to overcome the limitations of the conventional CSP. We have applied the conventional and FCSP method on the motor imagery data for feature extraction. The average classification accuracies of the conventional and FCSP method were found 74% and 84%, respectively. Eventually, the proposed scheme outperforms the conventional method by increasing the classification accuracy up to 10%.
Classification on BDHS data analysis: Hybrid approach for predicting pregnancy termination

Pregnancy termination is a trivial anomaly for third world countries like Bangladesh. The greater aspiration of this research is to downturn the rate of pregnancy termination. This research finds out the attributes that contribute to pregnancy termination and leads to propose a hybrid of supervised machine learning approach for predicting “Pregnancy Termination” in Bangladesh. The Bangladesh Demographic and Health Survey (BDHS), 2014 dataset has been used to perform analysis containing two or more variables. This dataset is further reduced by analyzing attributes that exhibit information of interest to explore the current reasons for pregnancy termination. After extracting out the features of interest with the help of Weka provided feature ranking attribute evaluator, hybridization of supervised machine learning classifiers are done concerning the negatively biasedness of the dataset with respect to pregnancy termination. On this investigation, we've developed a hybrid approach with 67.2% accuracy considering the biasedness of the dataset which is relatively better than other classifiers in terms of performance metrics.
The Relationship between Spiritual Well-Being and Happiness: An Investigation on Teachers

This study aims to examine the relationship between teachers’ spiritual well-being and happiness levels. Being quantitative in nature, this study employs a relationship survey model. A total of 390 teachers employed in various grades of different types of schools (i.e., primary schools, middle schools, Imam-Khatib middle schools, Anatolian-science high schools, Imam-Khatib high schools, and vocational high schools) in Istanbul’s Küçükçekmece district volunteered to participate in this study. The Personal Information Form, the Spiritual Well-Being Scale, and the Oxford Happiness Questionnaire were employed to collect data, which were then analyzed using Pearson’s correlation coefficient and a regression analysis. The results of these analyses reveal that happiness is significantly and positively correlated with transcendence and harmony with nature whereas happiness is significantly and negatively correlated with anomie. While transcendence positively affects teachers’ happiness, anomie negatively affects them. It has been found that this effect size is medium. Another important finding obtained in the study is that harmony with nature, one of the sub-dimensions of spiritual well-being, does not affect teachers’ happiness.
Associations among sleep problems, executive dysfunctions, and attention-deficit/hyperactivity disorder symptom domains in college students

Abstract Objective: The present study examined whether executive functions (EFs) and sleep problems operate together to predict four attention-deficit/hyperactivity disorder (ADHD) symptom domains. Methods: A sample of 306 college students completed ratings of sleep quality, EFs, and ADHD symptoms from January to December 2014. Simultaneous multiple regressions were used to examine (a) the unique contribution of EFs and sleep problems to ADHD symptoms, (b) the relations among EFs, sleep problems, and GPA, and (c) the relations between specific EF facets (i.e., time management, emotion regulation, problem solving, self-restraint, and self-motivation) and ADHD symptoms. Results: While approximately 52.8% of participants were categorized as poor-quality sleepers, 10.6% of the sample reported an ADHD diagnosis. EF ratings were associated uniquely with ADHD symptoms after controlling for sleep quality. All EF facets were related to ADHD symptoms. Conclusion: ADHD symptoms are associated with all EF facets even after controlling for sleep quality.
Head Pose Estimation using Transfer Learning

Head Pose Estimation is one of the most sought after problems in Computer Vision, as it is used in many realtime applications such as Advanced Driver Assistance System, Augmented Reality and many other Artificial Intelligence applications. But most of the existing systems are trained for controlled environment applications. They are not robust enough to apply them in real-time critical systems. We present a robust Head Pose Estimation System using convolution neural network augmented by transfer learning algorithm, which enables the system to adjust with the wild environment. Transfer learning approach also avoids the system from over-fitting and it also enables the model to be retrained for its respective application, in a fraction of time as compared to the time required to train an existing CNN model.
A Benchmark for Edge-Preserving Image Smoothing

Edge-preserving image smoothing is an important step for many low-level vision problems. Though many algorithms have been proposed, there are several difficulties hindering its further development. First, most existing algorithms cannot perform well on a wide range of image contents using a single parameter setting. Second, the performance evaluation of edge-preserving image smoothing remains subjective, and there is a lack of widely accepted datasets to objectively compare the different algorithms. To address these issues and further advance the state of the art, in this paper, we propose a benchmark for edge-preserving image smoothing. This benchmark includes an image dataset with ground truth image smoothing results as well as baseline algorithms that can generate competitive edge-preserving smoothing results for a wide range of image contents. The established dataset contains 500 training and testing images with a number of representative visual object categories, while the baseline methods in our benchmark are built upon representative deep convolutional network architectures, on top of which we design novel loss functions well suited for the edge-preserving image smoothing. The trained deep networks run faster than most of the state-of-the-art smoothing algorithms with leading smoothing results both qualitatively and quantitatively. The benchmark will be made publicly accessible.