Miracle-dz/UniFAR-FTUs huggingface dataset:
Gender recognition from face images using trainable shape and color features

Gender recognition from face images is an important application and it is still an open computer vision problem, even though it is something trivial from the human visual system. Variations in pose, lighting, and expression are few of the problems that make such an application challenging for a computer system. Neurophysiological studies demonstrate that the human brain is able to distinguish men and women also in absence of external cues, by analyzing the shape of specific parts of the face. In this paper, we describe an automatic procedure that combines trainable shape and color features for gender classification. In particular the proposed method fuses edge-based and color-blob-based features by means of trainable COSFIRE filters. The former types of feature are able to extract information about the shape of a face whereas the latter extract information about shades of colors in different parts of the face. We use these two sets of features to create a stacked classification SVM model and demonstrate its effectiveness on the GENDER-COLOR-FERET dataset, where we achieve an accuracy of 96.4%.
Reception of Signals with Code Division in Hydroacoustic Channels

The possibility of using noise-like signals with a zero correlation zone (ZCZ - Zero Correlation Zone) in multipath (multi-beam) hydroacoustic communication channels is investigated. The report synthesizes adaptive algorithms for receiving noise-like signals in hydroacoustic channels with multipath (multi-beam) signal propagation.
A survey on Separation of Blood Vessels for detecting Retinal Vascular Disorders

Separating vascular from the retinal image is essential for detecting many retinal vascular disorders. Diseases which are all affecting the blood vessels of the eye are known as Retinal vascular disorders. Vessel separation of retinal image is a fundamental step in finding the affected area of vascular. Retinal Blood Vessel Separation (RBVS) is necessary for identifying diseases and changes in the retina. For segmenting the blood vessel and early detection of these disorders many different approaches and algorithms have been developed; few of them were discussed in this paper. In this paper, various existing techniques, methodologies, and algorithms for separating the vessels from the retinal image were analyzed. This paper also provides the summary of various existing techniques based on Segmentation methodology, performance matrices, and databases used.
Graphene-based Tunable Terahertz Metamaterial Absorber with High Absorptivity

In this paper, we present a graphene-based metamaterial absorber, which is comprised of three layers with a meta-dielectric-graphene structure. Numerical simulations demonstrate that two high absorptivity of 99.9% at 6.62THz and 98.9 % at 9.36THz are produced, respectively in terahertz band, and the resonance amplitude and frequency of the absorber can be controlled flexibly by changing the Fermi level of graphene, and when the relaxation time of graphene changes, the absorption intensity of the metamaterial absorber can be individually controlled. In addition, we show the thickness of the intermediate dielectric layer of the proposed absorber affects the absorptivity, which is conducive to determine the initial processing parameters of the absorber. The study demonstrates that the graphene-based metamaterial absorber proposed in this paper has a simple structure and is easy to fabricate, moreover, it is very convenient to achieve the flexible tuning of metamaterial absorbers by biased voltages or chemical dopings, and it provides an important guide for designing dual-band absorbers with high absorptivity.
Using Simulation Techniques within the Design of a Reconfigurable Architecture for Fail-Operational Real-Time Automotive Embedded Systems

In the automotive industry one clear goal is to reach the fourth SAE level of driving automation. Instead of having a redundant E/E-architecture we propose a dynamically reconfigurable one. With this paper we introduce two new simulation techniques on how to extract two important values for reconfiguration. The approach is based on an example for the loss of steering control functionality. The first value is the overall system reconfiguration time that is recognized by the human driver in case of a loss of steering control functionality. The second value is the maximum reconfiguration time possible to still perform a given maneuver. The results of both values are compared to a previous study. This leads to end-to-end reconfiguration times for future development of fail-operational self-reconfigurable real-time automotive embedded systems. A method for simulating loss of functionality and deriving specification relevant data is given to assist the automotive industry reaching fail-operational systems. It will also help in introducing dynamical reconfiguration for a fail-operational automotive E/E-architecture as the next step.
Towards Personalized Autism Diagnosis: Promising Results

The ultimate goal of this paper is to develop a novel personalized comprehensive computer aided diagnostic (CAD) system for precise diagnosis of autism spectrum disorder (ASD) based on the 3D shape analysis of the cerebral cortex (Cx), To achieve the main goal of the proposed system, we used structural MRI modality (sMRI) to be able to extract the shape features of the brain cortex. After segmenting the brain cortex from sMRI, we used a spherical harmonics analysis to measure the surface complexity, in addition to studying surface curvatures. Finally, a multi-stage deep network based on several autoencoders and softmax classifiers is constructed to provide the final global diagnosis. The presented CAD system was tested on several datasets, achieving an average accuracy of 92.15%. In addition to its global diagnostic accuracy, the local diagnostic accuracies of the most significant areas also demonstrated the ability of the proposed system to construct very promising local maps of ASD-related brain abnormalities, which can be considered an important step towards personalized medicine for autistic individuals.
Using the User’s Recent Browsing History for Personalized Query Suggestions

In this paper we use the user’s recent web browsing history in order to provide better query suggestions in an information retrieval system. We have built a Chrome browser plugin that collects each web page visited by a user and submits it to our query suggestion server for indexing, thus building a personal history profile for each user. The contribution of this paper is a method of using this personal history profile for reordering the query suggestions offered by Google when the user searches information on Google, moving query suggestions more relevant to the user’s information need to the front positions in the Google provided query suggestions list. We have collected browsing history log data for over 4 months from several users who installed our Chrome plugin on their local computers and then we performed an offline evaluation test that shows that our personalized query suggestion system improves the MRR (i.e. Mean Reciprocal Rank) score of Google query suggestions by approximately 0.04 (i.e. improves Google’s MRR score by 12 percents).
Effective Promotional Strategies Selection in Social Media: A Data-Driven Approach

Nowdays, many companies, organizations and individuals are using the function of sharing or retweeting information to promote their products, policies, and ideas on social media. While a growing body of research has focused on identifying the promoters from millions of users, the promoters themselves are seeking to know which strategy can improve promotional effectiveness, which is rarely studied in the literature. In this work, we investigate an open problem of effective promotional strategy selection via causal analysis which is challenging in identifying and quantifying promotional strategies as well as the selection bias when estimating the causal effect of promotional strategies from observational data. We study the promotional strategies not only on the content level (what to promote) but also on the context level (when and how to promote). To alleviate the issue of selection bias in observational studies, we propose a data-driven approach that is a Propensity Score Matching (PSM) based method, which helps to evaluate the causal effect of each promotional strategy and discover the set of effective strategies to predict the promotional effectiveness (i.e., the number of users infected by the promotion). We evaluate our proposed method on a real social dataset including 194 million users and 5 million promoted messages. Experimental results show that (1) the top-ranked strategies by our PSM based method significantly and consistently outperform the correlation based feature selection methods in predicting promotional effectiveness; (2) we conclude our observations from the real data with three interpretable and practical ideas for steering social media promotion.
Bacterial Epibiotic Communities of Ubiquitous and Abundant Marine Diatoms Are Distinct in Short- and Long-Term Associations

Interactions between phytoplankton and bacteria play a central role in mediating biogeochemical cycling and food web structure in the ocean. The cosmopolitan diatoms Thalassiosira and Chaetoceros often dominate phytoplankton communities in marine systems. Past studies of diatom-bacterial associations have employed community-level methods and culture-based or natural diatom populations. Although bacterial assemblages attached to individual diatoms represents tight associations little is known on their makeup or interactions. Here, we examined the epibiotic bacteria of 436 Thalassiosira and 329 Chaetoceros single cells isolated from natural samples and collection cultures, regarded here as short- and long-term associations, respectively. Epibiotic microbiota of single diatom hosts was analyzed by cultivation and by cloning-sequencing of 16S rRNA genes obtained from whole-genome amplification products. The prevalence of epibiotic bacteria was higher in cultures and dependent of the host species. Culture approaches demonstrated that both diatoms carry distinct bacterial communities in short- and long-term associations. Bacterial epibonts, commonly associated with phytoplankton, were repeatedly isolated from cells of diatom collection cultures but were not recovered from environmental cells. Our results suggest that in controlled laboratory culture conditions bacterial–diatom and bacterial–bacterial interactions select for a simplified, but specific, epibiotic microbiota shaped and adapted for long-term associations.
Estimation of Fundamental Frequencies in Stereophonic Music Mixtures

In this paper, a method for multi-pitch estimation of stereophonic mixtures of harmonic signals, e.g., instrument recordings, is presented. The proposed method is based on a signal model that includes the panning parameters of the sources in a stereophonic mixture, such as those applied artificially in a recording studio. If the sources in a mixture have different panning parameters, this diversity can be used to simplify the pitch estimation problem. The mixing parameters of the sources might be shared, resulting in a multi-pitch estimation problem, which is solved using an approach based on an expectation–maximization algorithm for Gaussian sources, where the fundamental frequencies and model orders are estimated jointly. The fundamental frequencies may be related, resulting in overlapping harmonics, complicating the estimation of the parameters. A codebook of harmonic amplitude vectors is trained on recordings of instruments playing single notes, and used when estimating the amplitudes of the mixture components. The proposed method is evaluated using stereophonic mixtures of instrument recordings and is compared to state-of-the-art transcription and multi-pitch estimation methods. Experiments show an increase in performance when knowledge about the panning parameters is taken into account. The proposed method provides a full parameterization of the components of the observed signal. Possible applications include instrument tuning, audio editing tools, modification of harmonic mixture components, and audio effects.
New Induction Heating Power Supply for Forging Applications Using IGBT Current-Source PWM Rectifier and Inverter

Induction heating can convert electrical energy to thermal energy with high conversion efficiency and quick heating. Currently, a current source rectifier/inverter-fed parallel resonant circuit is widely used as an induction heating power supply for forging applications. However, the conventional induction heating power supplies composed of phase-controlled rectifier and SCR inverter have low efficiency and low power factor at input side, and require additional starting circuitry. So this paper proposes new induction heating power supply topologies for forging applications which have high power factor, high efficiency, and large output power, and compares their characteristics and performances.
Joint Modulation Classification and OSNR Estimation Enabled by Support Vector Machine

By adopting the cumulative distribution function of the received signal’s amplitude as feature, a support vector machine-based algorithm is proposed to jointly classify the modulation format and estimate the optical signal-to-noise ratio (OSNR) in coherent optical communication systems. Three commonly-used quadrature-amplitude modulation (QAM) formats are considered. Numerical simulations have been carried out in the OSNR ranges from 5 to 30 dB, and results show that the proposed algorithm achieves a very good modulation classification (MC) performance, as well as high OSNR estimation accuracy with a maximum estimation error of 0.8 dB. Optical back-to-back experiments are also conducted in OSNR ranges of interest. A 99% average correct MC rate is observed, and mean OSNR estimation errors of 0.38, 0.68, and 0.62 dB are noticed for 4-QAM, 16-QAM, and 64-QAM, respectively. Furthermore, compared with the neural networks-based joint estimation algorithm, the proposed algorithm attains better performance with comparable complexity.
FPGA-Based Multi-Parametric Non-Linear Model Predictive Control for a Micro-Grid in Small Islands

This paper studies the concept of Non-linear Predictive Control (NMPC) to micro-grid in remote small islands in Fiji or the Pacific region. A multi-parametric model with a suitable cost function is considered for a microgrid supplied by renewable energy sources and with a flywheel energy storage system The proposed control strategy has been designed for implementation on an Field-Programmable gate array (FPGA) board, for its the great advantages of reducing the overall system cost, size, and realtime operation, making it suitable for easy and cheap application in small islands or villages.
A Survey of Attacks on the Bitcoin System

In the era of digitalization, crypto-currency has become the talk of the town and Bitcoin is one of it. Bitcoin System is not controlled by any central authority or governed by any management division. Bitcoin is the first digital decentralized currency and its economic growth has encompassed to billions of dollars within a few years. In this paper we present detailed Introduction to the digital currency including the crypto-currency, then we present Bitcoin system in depth with the process of transactions, mining, miners’ pooling.As we know that the popularity of Bitcoin is increasing day by day so are the attacks. We present an analysis of major attacks in the Bitcoin system such as eclipse attack, Sybil attack, >51% attack, selfish mining. More importantly, we present how these attacks play an important role for the attackers in order to receive unfair incentives or fraud the honest users. The current researchers are focusing on creating new protocols as defensive measures for the attacks; we also analyze the security of these protocols.
Real-time Complexity Control for High Efficiency Video Coding

The current video coding standard, High Efficiency Video Coding (HEVC), provides quad-tree structures of the coding unit (CU) to achieve high coding efficiency. Compared with previous standards, the HEVC encoder increases much computational complexity to levels inappropriate for applications of power-constrained devices. This work thus proposes a real-time complexity control scheme to control each frame complexity if the complexity of encoded frames is counted and its accumulated value is over the threshold. To further improve the coding efficiency, a fast CU depth decision algorithm is proposed. Experimental results show that a two-level of complexity control scheme was designed. In addition, the loss of the average BD-PSNR was about 0.23 dB and 0.27 dB as the target complexity was set to 80% and 60% of the unconstrained complexity, respectively.
Affective State Recognition using Image Cues

Affect is the experience of emotion. Understanding human affect play a vital role in Man-Machine Interaction. In this work, a method to recognize the primary affective states viz. anger, disgust, fear, sad, happy and surprise of an individual from the facial expressions using eNTERFACE database with automatic peak frame selection is presented. Radon transform features extracted from the facial images and K-Nearest Neighbour (KNN) classifier are proposed for the task. Performance of the system is evaluated using the performance metrics accuracy, precision, F-measure and ROC area with five-fold cross validation. An improvement of 4.22% accuracy obtained compared to the existing work reported on the same database adds novelty to the work.
Near-Field Focus Radiation of Multibeam Phased Array of Antennas Realized by Using Modified Rotman Lens Beamformer

This paper presents the design of 2-D phased array of antennas to radiate collinear multiple beams properly distributed for near-field (NF) focus (NFF) applications. In the design, the 2-D array is first decomposed into single-column subarrays, where each of the subarrays is identically excited to radiate a vertically NFF beam with the focal points located at a designated distance along the boresight direction of each subarray aperture. Afterward, the NFF column subarrays become the elements of a 1-D horizontal collinearly located array to radiate horizontally NFF multiple beams. The horizontal NFF beamforming network is implemented by extending the basic concept of conventional Rotman lens beamformer for directional NFF multibeam applications. Thus, three feeding points are selected to determine the lens’s profile such that the produced phases via the modified Rotman lens can be used to feed a planar array to radiate three NFF beams when the lens is excited at the three selected points, respectively. This antenna set is realized at 2.45 GHz for radio frequency identification applications to demonstrate the implementation concept. Both the simulation and experimental measurement results are shown to validate the feasibility.
An Online Kernel Selection Wrapper via Multi-Armed Bandit Model

Online kernel selection is critical to online kernel learning, but most of the existing online kernel learning methods ignore the online kernel selection process, and instead they empirically preset and fix a kernel or adjust kernel parameters by gradient descent, which is sensitive to the initial setting and has no theoretical guarantee. In this work, we propose an online kernel selection wrapper via the multi-armed bandit model, which can select a kernel at each round from a set of candidate kernels with theoretical guarantee and can be applied to any online kernel learning model. Specifically, the wrapper consists of two layers. In the outer layer, the wrapper corresponds each candidate kernel to an arm of the multi-armed bandit model, and chooses an arm according to the probability distribution maintained by the model at each round. In the inner layer, the wrapper updates the probability distribution according the loss of the selected arm, which is incurred by the prediction of the online kernel learning algorithm. We propose a new online kernel selection regret to measure the performance of the proposed wrapper, and prove that the proposed wrapper enjoys a sub-linear expected online kernel selection regret with respect to the cumulative loss of the optimal kernel among the candidates kernels. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed wrapper.
Discriminative Fuzzy C-Means as a Large Margin Unsupervised Metric Learning Algorithm

In this paper, a new unsupervised metric learning algorithm with real-world application in clustering is proposed. To have a desirable clustering, the separability among different classes of data needs to be improved. A common manner in accomplishing this objective is to utilize the advantages of metric learning in clustering and vice versa. Clustering provides an estimation of class labels and metric learning maximizes the separability among these different estimated classes of data. This procedure is performed in an iterative fashion, alternating between clustering and metric learning. Here, a new method is proposed, called discriminative fuzzy c-means (Dis-FCM), in which FCM and metric learning are integrated into one joint formulation. Unlike traditional approaches, which simply alternate between clustering and metric learning, Dis-FCM applies both simultaneously. Here, FCM provides an estimation of class labels. This can avoid the problem of fast convergence, which is common in previous algorithm. Moreover, Dis-FCM is able to handle not only numerical data, but also categorical data, which are not found in traditional methods. The experimental results indicate its superiority over other state-of-the-art algorithms in terms of extrinsic and intrinsic clustering measures.
Comparison of different Acoustic Models for Kannada language using Kaldi Toolkit

This paper describes a speech recognition system for the South Indian language, Kannada using Kaldi toolkit. KALDI is a open source toolkit based on Finite State Transducers (FST's). Two speech data sets has been collected from 10 different speakers (5 male and 5 female). The first data set consists of a digit corpora in Kannada where each speaker has spoken a number ten times and the second data set consists of simple Kannada phrases. The noise to a large extent has been filtered manually and the data has been segmented using the software application Audacity(v2.2.2). The main objective is to compare the word error rate (WER) of the two data sets using different acoustic models in Gaussian Mixture Models(GMM) and Sub-Gaussian Mixture Model(SGMM). The WER for Gaussian Mixture Model and Subspace Gaussian Mixture Model for the first data set is 4.54% and 4.27% respectively and for the second data set the WER for GMM and SGMM is 12.27% and 13%.
Evaluating Throughput and Delay in Aodv Algorithm using Network Simulator-2

Socially-attentive interacting is an evolving erratically associated systems be made up of mobile manipulators with societal associations and features. It is also used to enhance the individual person usage of the application in a particular area. It considers direct social relationships with individuals. The objective of this work is used to calculate the delay and throughput for a particular area by sending packets to nearest places. The developed technique is used to relate with two algorithm. These are compared initially and deliver the packets to the nodes with protocol connection to find the network parameterssuch as delay and throughput. By comparing these two algorithms, It gives the high throughputswith low delay. AODV can support both unicast and multicast packet transmission in constant movement. It is adaptable to highly dynamic network and also loop free does not require any cartelized system to handle.