Miracle-dz/UniFAR-FTUs huggingface dataset:
Advertisement System Based on Facial Expression Recognition and Convolutional Neural Network

Advertisements play a crucial role in the mass spreading and success of a product, a service or an entire business. For gaining effectiveness, they should be judged thoroughly by means of direct and indirect rating, portfolio tests, transactions and interactions. Instead of those, human emotions can be a powerful source of reliable feedback. This information is valuable for company to not only re-evaluate the efficiency of their ads but also make them more interactive to customers. To build a good facial expression recognition program, deep learning is applied in the form of Convolutional Neural Network, increasing the accuracy of classifying 7 basic emotions to 90% on CK+ dataset. Moreover, we have designed 2 systems, one for storing and updating the commercials and one for extracting emotion data and attracting customers.
A Neutral-Point Potential Balancing Method for a Three-Level Neutral-Point-Clamped Back-to-Back Converter

In this paper, a neutral-point (NP) potential balancing method based on zero-sequence voltage injection is presented for the traditional three-level neutral-point-clamped (NPC) back-to-back converter. In the control algorithm, make the NP current on the rectifier side follow the reference NP current to adjust the NP potential balancing. The relationship between NP average current and zero-sequence voltage is analyzed in theory. On this basis, choosing several key zero-sequence voltages can reduce the complexity of calculation, and one of the three phases switches will keep unchanged during each switching period, thus the switching loss is reduced, and the NP potential balancing method is optimized. The simulation results show that the optimized zero-sequence voltage injection method can suppress the NP potential fluctuation well, and has little influence on the input current harmonics.
Iris Authentication Utilizing Co-occurrence Matrices and Textile Features

Biometric features have attracted great attention for numerous security applications. Border control, forensics, access control, banking are a few examples where biometrics authentication is applicable. Iris constitutes one of the best suited human physical traits for such applications due to its uniqueness and permanence over time. In this paper, a new iris authentication method is proposed based on gray level co-occurrence matrices of the normalized iris images. The co-occurrence matrices are calculated for four different directions and are utilized for the extraction of textural Haralick features such as energy, contrast, variance, entropy, homogeneity and correlation. This procedure is applied both to the whole image and to segments of it to evaluate the contribution of more localized information to the methodology. For the evaluation of the iris, the aforementioned features are utilized by a Random Forest classifier that judges the authenticity of the user. In order to further enhance the presented method’s performance, both left and right eye irises are evaluated by the classifier and their authentication probabilities are fused forming a robust algorithm.
Planning of OLTC Transformers in LV Systems under Conservation Voltage Reduction Strategy

The wide voltage control capacity of on-load tap changers (OLTCs) forces them into the list of reinforcement alternatives in the expansion planning of distribution systems. Their high investment and operation cost, however, is a downside. The voltage dependent nature of loads can be deemed as an opportunity to reduce the system demand and operation cost by managing the voltage at substation level (referred to as conservation voltage reduction strategy). Here, it is shown that consideration of this strategy justifies the investment on OLTCs against the other options even for LV systems. Both short-term and long-term variations of the system parameters are taken into account, since as it will be shown, the rate of system variations affects the decision on replacing the available transformers with OLTC transformers. The proposed method embodies a prognosis of long-term future by modeling the operating conditions in a set of daily scenarios with hourly time resolution to include the cost of tap changing operations in the objective function. The oltcs' control strategy in this daily scenarios matches the industry common practice.
Fully Proximal Splitting Algorithms In Image Recovery

Structured convex optimization problems in image recovery typically involve a mix of smooth and nonsmooth functions. The common practice is to activate the smooth functions via their gradient and the nonsmooth ones via their proximity operator. We show that, although intuitively natural, this approach is not necessarily the most efficient numerically and that, in particular, activating all the functions proximally may be advantageous. To make this viewpoint viable computationally, we derive a number of new examples of proximity operators of smooth convex functions arising in applications.
Measurement-Error Controlled Iterative Least-Squares Solutions of Inverse Field Transformation Problems

The inverse equivalent source problem related to near-field antenna measurements is typically ill-posed, i.e., the forward operator suffers from non-trivial null spaces. This issue is commonly tackled by pursuing a least-squares solution of the reconstructed near fields. We propose to find a solution of the normal error system of equations which minimizes the ℓ2-norm of the source-coefficients reconstruction deviation. In the scope of near-field to far-field transformations (NFFFTs), advantages are found in a slightly better iterative solver convergence, a reduced number of unknowns, and—most importantly—a more convenient control of the stopping criterion of the iterative solution process. Since the residual of the normal-error solution equals the reconstruction deviation, the proposed formulation includes a meaningful stopping criterion based on the measurement error. All these claims are corroborated by NFFFTs of synthetic and real-world measurement data.
Determination of Surface States Energy Density Distributions and Relaxation Times for a Metal-Polymer-Semiconductor Structure

The capacity/conductivity-voltage (C/G-V) data of the PVP interlayered metal-semiconductor structure were examined at ±4V biases and 1-500 kHz frequency interval at room temperature. The surface states (<inline-formula><tex-math notation="LaTeX">$N_{ss}$</tex-math></inline-formula>) in the insulation layer and the metal-semiconductor interface are seen as the main causes of high capacitance and conductivity values at low frequencies. AC signals, which result in an increase in capacitance and conductivity values, are easily monitored by <inline-formula><tex-math notation="LaTeX">$N_{ss}$</tex-math></inline-formula> at low frequencies. The graph extracted against the conductivity and frequency logarithm (<inline-formula><tex-math notation="LaTeX">$G_{p}$</tex-math></inline-formula>/ω-log(f)) indicates a peak appearance as a result of the <inline-formula><tex-math notation="LaTeX">$N_{ss}$</tex-math></inline-formula> effect. In the energy range of (<inline-formula><tex-math notation="LaTeX">$E_{c}$</tex-math></inline-formula>−0.423) − (<inline-formula><tex-math notation="LaTeX">$E_{c}$</tex-math></inline-formula>−0.604), surface states (<inline-formula><tex-math notation="LaTeX">$N_{ss}$</tex-math></inline-formula>) and relaxation times (τ) values alter from 8.71 × 10<sup>11</sup> to 5.84 × 10<sup>11</sup> eV<sup>−1</sup> cm<sup>−2</sup> and 6.54 × 10<sup>−6</sup> s to 1.10 × 10<sup>−4</sup> s, respectively.
Comprehensive Review of Literature on the Statistical Behaviors of Electric Vehicle Aggregators (EVAs): Modelling the Pdf of Power Production of EVA Using Classic CLT

In this paper, first a comprehensive review of literature on the stochastic modeling of Electric Vehicles (EVs) and EV Aggregators (EVAs) is provided. This review is divided into two sections: studies dealing with the modeling of Probability Density Function (pdf) of EVs parameters, indicating various types of Pdfs and non-identical distribution proposed for modeling the uncertainties of EVs and studies about the modeling of pdf of EVA. In bulk of research, Normal Distribution has been used for the uncertain parameters of EVA according to Central Limit Theorem (CLT). In these studies, however, the condition of identical random distribution is not satisfied. In the second step, it is proved that the classic-CLT, which substitutes the condition of identical distribution of variables in CLT with Lévy conditions, can model the pdf of power production of EVAs using Normal Distribution. Finally, the power injection by EVA, which manages a maximum of 10,000 EVs with various Pdf of power production, is simulated. The results revealed that the pdf of power production of EVA displays a tendency for Normal Distribution when numbers of EVs is increased. Therefore, the simulation results confirm the analytical results obtained from the classic CLT.
Investigation of Spiking Neural Networks for Modulation Recognition using Spike-Timing-Dependent Plasticity

Spiking neural networks (SNNs) operating on neuromorphic hardware can enable cognitive functionality with relatively low power consumption as compared to other artificial neural network implementations, making it ideally suited for resource-constrained space platforms such as CubeSats. The objective of this study is to investigate the implementation of a modulation recognition capability using SNNs, which may eventually be applied to neuromorphic hardware for implementation. This preliminary analysis uses a software simulation approach with an unsupervised learning algorithm based on spike-timing-dependent plasticity for classification of digital modulation constellation patterns. This modulation recognition capability can provide enhanced situational awareness for a space platform and facilitate additional high-level cognitive functionality that can be investigated in future studies.
MMW Imaging using polarimetric measurements

In this paper we present a complete process that goes from measurements to image extraction with the objective to help radar designers to set their system specifications. We focus on objects of interest for security scanners and therefore choose to perform the tests on a ceramic knife and a fake gun made of aluminum. We highlight polarization issues. We show that polarization has a dramatic influence on the image contrast when objects are concealed due to the creases of the clothes. This can be overcome with the use of a dual polarized system and segmentation algorithms exploiting polarizations results.
The Spring of Artificial Intelligence in Its Global Winter

Against the dominant narrative of the history of artificial intelligence (AI), this paper shows how, during the period considered to be second global winter (1987–1993), the AI field experienced spring in South Korea. Putting the Korean language processing problem at the center, researchers began to lay the intellectual and social foundation of AI in South Korea in the late 1980s.
Prototype of a Security Model Applied to IoT with Big Data for Tourist Management in the Cities of Ecuador

We analyzed a part of the global ecosystem and massive data known as Internet of Things and Big Data respectively, with theoretical or applied security models. The problem is the introduction of solutions in public security, user privacy and cybernetic coercion, to deliver trusted services for data generated from public networks. The objective is to define a prototype of security model applied to Internet of Things with Big Data for tourism management in cities of Ecuador. Were applied the deductive method and the exploration to examine the information of the referenced researches. It turned out the following: Architecture prototype for the tourism management, Security model applied to architecture, Flowchart for the management of tourism information and Formula of lost packages. It was concluded that a data security strategy is necessary through a correct routing, reliable and high availability that allows to increase its level of reliability; also have a captive portal by sectors, which allows us to generate confidentiality agreements to guarantee the privacy of data; the model should be considered at the country level for a standard management of tourist information.
Formulation and Evaluation of dispersible tablet (ampicillin and cloxacillin)

Present research work planned to develop a simple economical and effective technology which yield better product in termed of improved oral absorption, faster onset of action minimized first pass effect improved bioavailability, improved compliance, contains necessarily the generally regarded as safe excipients. The drug was ampicillin and cloxacillin used to the treatment of Gonorrhea. The drug ampicillin and cloxacillin were tested for the bacterial infections of staphylococcus. New formulation of dispersible tablet of new combination of ampicillin and cloxacillin and evaluated to various physical parameters of tablet. T
A Hybrid Approach for Intrusion Detection Based on Machine Learning

Due to the advancement of Internet over last decade, network security has turned out to be one of the important areas of research. Therefore, Intrusion Detection (ID) is widely studied to achieve overall network security success. Aim of Intrusion Detection System (IDS) is to put checks on attacks and provide desirable support for defense management along with information about the intrusion. The several intrusion detection approaches are proposed so far to predict malicious traffic from the network. In this paper, existing techniques of intrusion detection are reviewed and a hybrid approach based on SVM and KNN for the network traffic classification is proposed where KNN will be used for feature extraction and SVM will classify the data in different classes based on extracted features.
Application of spectral reflectance for increasing plant discrimination speed in precision agriculture

Increasing the speed of weed/crop discrimination sensor engines is an increasingly challenging research area in precision agriculture (PA). Data collection, modelling, and real-time operation are currently the major challenges for accurate plant classification and effective weed control. In the current study, a new low-resolution spectral reflectance sensing is proposed for data collection and applied in conjunction with state-of-art convolutional neural network (CNN) algorithm for real-time weed detection. Experimental results demonstrate that the speed of the algorithm is ten times faster than typical spatial imaging based counterparts, while its discrimination accuracy is almost the same.
Sum-Throughput Maximization in Dual-Hop Wireless Powered DF-Based Relay Networks

In this paper, we consider a dual-hop wireless powered communication network (WPCN) consisting of a hybrid access point (H-AP), multiple users, and multiple energy-constrained relays. While relay-assisted WPCNs have been investigated to mitigate the doubly near-problem in conventional WPCNs, they still suffer from this problem depending on the relay location. To address the issue, we modify the conventional harvest-then-transmit protocol to combine wireless power transfer (WPT) in the downlink and simultaneous wireless information and power transfer (SWIPT) with power splitting (PS) in the uplink. Specifically, we formulate an achievable sum-throughput maximization problem, and provide analytical solutions to optimal time allocation and power splitting coefficients. As a special case when the transmission time of the first hop is identical to that of the second hop in the uplink, we provide a more compact iterative algorithm with closed-form solutions. Simulation results show that the proposed algorithms achieve higher performance gain than the conventional scheme of optimizing either time allocation or power splitting coefficients, and reveal that the doubly near-far problem can be alleviated even when relay(s) is located far from the H-AP.
Robust Neighbor Discovery Through SideLink Demodulation Reference Signal for LTE ProSe Network

By introducing the Proximity Services (ProSe) functionalities in its 12th release, the 3rd Generation Partnership Project (3GPP) made possible the ability to perform device-to-device (D2D) communication in Long Term Evolution-Advanced (LTE-A) cellular network. Direct communication between nearby User Equipments (UEs) is then enabled through the newly introduced sidelink. This paper investigates neighbor discovery for D2D communication exploiting Demodulation Reference Signals (DMRS), which are associated with physical sidelink channels for coherent demodulation. By simply listening to its sidelink signal, a ProSe-enabled UE can identify its peers and initiate a direct link without any additional overhead. The study examines the properties of DMRS sequences and proposes a robust neighbor discovery scheme tailored to DMRS structure based on a power normalized correlation process. A reduction in the computational load required to establish a link between two neighbors is proposed and evaluated. Simulation results show that nearby users can be discovered with a reasonable complexity while achieving a high discovery accuracy.
FWFS: Selecting Robust Features Towards Reliable and Stable Traffic Classifier in SDN

Real-time Internet traffic flow classification is important in managing network resources in accordance to Quality of Service (QoS) requirements. The centralized network’s control in Software Defined Networking (SDN) provides a platform for Internet Service Provider (ISP) to perform specific actions on the classified flows through routing and scheduling. Though machine learning (ML) can be the alternative to Deep Packet Inspection (DPI) in classifying SDN traffic flows, several problems, such as classifier’s accuracy, computational complexity, multi-class imbalanced data, and concept drift, need to be addressed in order to have a reliable solution. Therefore, this work has proposed a hybrid filter-wrapper feature selection (FS) algorithm, named Filter-Wrapper Feature Selection (FWFS). The algorithm selects robust features that represent minority classes and resistant to concept drift and is also computationally inexpensive by discarding irrelevant features before further processing with wrapper function. Based on the performance evaluation, the feature selection process of FWFS is computationally inexpensive; i.e. 59.6s, which produces a classifier with an overall accuracy of 98.9%. The result is better than state-of-the-art FS algorithm, Efficient Feature Optimization Approach (EFOA) which requires >400s to select features which can produced a classifier with 97.7% accuracy. In addition to the high overall accuracy, the classifier trained with features selected by FWFS has better F-measure values for each classes including minority classes; i.e. >0.8 in MULTIMEDIA and INTERACTIVE which consist only 0.15% and 0.03% instances, respectively, of the total 377,526 instances in the dataset. Furthermore, the classifier is stable and reliable for classifying new data; i.e. 98.7% accuracy for classifying new data and F-measure of more than 0.8 in every class. The classifier model will be embedded in the SDN-ISP traffic classification solution which provides insights for resource allocations and traffic scheduling in the network.
Data Management Challenges for Deep Learning

Deep learning is one of the most exciting and fast-growing techniques in Artificial Intelligence. The unique capacity of deep learning models to automatically learn patterns from the data differentiates it from other machine learning techniques. Deep learning is responsible for a significant number of recent breakthroughs in AI. However, deep learning models are highly dependent on the underlying data. So, consistency, accuracy, and completeness of data is essential for a deep learning model. Thus, data management principles and practices need to be adopted throughout the development process of deep learning models. The objective of this study is to identify and categorise data management challenges faced by practitioners in different stages of end-to-end development. In this paper, a case study approach is employed to explore the data management issues faced by practitioners across various domains when they use real-world data for training and deploying deep learning models. Our case study is intended to provide valuable insights to the deep learning community as well as for data scientists to guide discussion and future research in applied deep learning with real-world data.
Pattern reconfigurable antennas for passive motion detection: WiFi test-bed and first studies

The paper considers the adoption of pattern reconfigurable antenna devices as a new opportunity for human-scale passive radio sensing using ambient (or stray) WiFi signals designed for wireless communications. Pattern reconfigurable antennas allow to dynamically channelize the antenna radiation pattern to pre-defined directions of interest. These antennas are essential RF components both in forthcoming 5G devices and next generation WiFi radios. We discuss signal modeling, beam-steering technology integration and adaptation for passive motion detection. First case studies and performance analysis inside a smart workspace are also considered.
Serial Concatenated Convolution Codes for Coded OFDM in Digital Audio Broadcasting Environment

In this paper, we are simulating the Bit Error Rate (BER) for computerized Digital Audio Broadcasting (DAB) framework that utilizes Co-OFDM system by parallel and serial coding schemes. Serial convolution coded data appears to be an Additive White Gaussian Noise (AWGN) Channel that is dependent on various imperative constant dimensions and code originator polynomial values obtained by convolution coding. Bit error rate simulations are done by applying sound signal and then estimating their performance with BER execution for the convolution coded. Serial convolution with various processor speeds 1/2 and 1/3 in AWGN is increasingly dependent on the perceptual coding. It is estimated, measured and plotted for developing a convolution coded DAB system environment.
The effect of twelve week aerobic exercise programme on health related physical fitness components and blood lipids in obese girls.

The aim of this study was to investigate the effects of 12 week aerobic exercise program on health related fitness components and blood lipids in obese girls. In this study, a total of 40 girls were recruited as exercise group (n = 20) and control group (n = 19). Participants joined sessions for 60 min per day, 3 days per week for 12-week. There were significant differences in weight, body mass index (BMI), flexibility, sit-ups, hand grip for both hands, VO2max, skin fold measurements (thigh, triceps, biceps, abdomen, suprailiac, subscapula, chest, body fat percent, heart rate, high density lipoproteins (HDL), low density lipoproteins (LDL),, total cholesterol, and triglyceride between pre-test and post test scores in the exercise group (p<0.05). It was concluded that regular aerobic exercise may affect health related fitness components and blood lipids positively in girls. Furthermore, it may result in decreasing obesity in girls. Key words: Blood lipids, flexibility, muscle strength, body mass index (BMI), max. VO2,aerobic exercise.