Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature Extraction Using Histogram of Oriented Gradient and Hu Invariant Moment for Face Recognition

Face recognition was one of many popular fields in Image Processing and Computer Vision. Face recognition had many problems like the pose variation, Illumination, Image Quality, etc. Many methods were developed to find the solution to this problem. Some method uses Approach that uses the whole face as features, while other method uses Features-Based Approach that uses Local Feature like Eyes, Nose, and Mouth as features. The proposed method to combine two methods: HOG and Hu Invariant moment. Two methods as feature extraction has been implemented with tested on 3 database: Markus's, ORL/AT&T and our database. This database was tested with three scenarios testing. Finally, the proposed method result on Recognition Rate average is 79.82 %, then the best Recognition Rate result is 97.22 %.
Comparing and Combining MLPs and CNNs for Image Denoising

Deep Learning has shown great success in image processing tasks such as image denoising. The superiority of those learning based methods is obvious that there is no need for hand-crafted priors. In fact, two of main neural network models Multi-Layer Perceptron (MLPs) and Convolutional Neural Network (CNNs) have been explored for image noise removing. In this work, we concentrate on comparing and combining those two models for image denoising. We identify the MLPs and CNNs have different preference for different image patches, and reveal that the property is related to image patch Signal-to-Noise-Ratio [1]. Furthermore, a new approach based on MLPs and CNNs are proposed for image noise reducing. Experiments on realistic noisy images prove the effectiveness of the new method.
Comparative Study of Artificial Intelligence Based Intrusion Detection for Software-Defined Wireless Sensor Networks

Nowadays, Wireless Sensor Networks (WSNs) are intensively used in highly sensitive environments such as water treatment plants, airports and hospitals. For this reason, the security of communications in WSNs is a very critical problem that must be tackled accordingly. A Software-defined network (SDN) is an architecture aimed at making networks more agile and flexible. A Software-Defined Wireless Sensor Network (SDWSN) is realized by infusing a Software Defined Network (SDN) model in a WSN. In this paper, three Artificial Intelligence (AI) approaches (decision tree, naïve Bayes and deep artificial neural network) used as intrusion detection systems (IDSs) in SDWSNs are analyzed and the results show that the decision tree approach is the best approach for implementing IDSs in classical SDWSNs given its performances.
DeepCEP: Deep Complex Event Processing Using Distributed Multimodal Information

Deep learning models typically make inferences over transient features of the latent space, i.e., they learn data representations to make decisions based on the current state of the inputs over short periods of time. Such models would struggle with state-based events, or complex events, that are composed of simple events with complex spatial and temporal dependencies. In this paper, we propose DeepCEP, a framework that integrates the concepts of deep learning models with complex event processing engines to make inferences across distributed, multimodal information streams with complex spatial and temporal dependencies. DeepCEP utilizes deep learning to detect primitive events. A user can define a complex event to be detected as a particular sequence or pattern of primitive events as well as any other logical predicates that constrain the definition of such an event. The integration of human logic not only increases robustness and interpretability, but also greatly reduces the amount of training data required. Further, we demonstrate how the uncertainty of a model can be propagated throughout the complex event detection pipeline. Finally, we enumerate the future directions of research enabled by DeepCEP. In particular, we detail how an end-to-end training model for complex event processing with deep learning may be realized.
Memory-Compact Membership Lookup for Multiple Data Sets by a Single Bloom Filter

Bloom filter is a memory-compact data structure to encode a set of data items, which can address the set membership query with no false negative and a configurable false positive rate. It is a fundamental tool with a wide range of applications in multiple disciplines, such as data science, networking, computer architecture, and distributed computing. However, Bloom filter faces a challenge of memory allocation: How much memory should be given to its data structure when its encoded data set is dynamically formed and has no prior-known set size. As a result, when more set elements continuously arrives, its data structure will become more crowded, causing its false positive rate of addressing membership query to increase. This problem becomes even more challenging, when there are multiple data sets to represent and each data set is independently formed in a streaming fashion. The traditional way to support the set membership checking for multiple data sets is to allocate each data set a separate Bloom filter. Instead, this paper takes a dramatically different approach: We encode all data sets in a single large filter and yet supports membership lookup for all of them, with a false positive rate bound that is independently configurable for each set. We analyze the properties of the filter and, in particular, the formulas for its feasible region where the false positive rate requirements are met for all data sets.
Word Relevancy Evaluation Based on GBDT Regression Model

Word relevancy evaluation is an important part of natural language processing. This paper will take the word embedding as research object, use machine learning technology to conduct quantitative analysis of Chinese word relevancy. Word embedding is a way to express words by vector of real numbers. In addition to its advantages of low dimension and denseness, word embedding itself also carries the semantic information of corresponding words in context. For example, the cosine value between two word embeddings can reflect the semantic relevance of corresponding words to a certain extent. However, this paper finds that the mapping from cosine value to relevancy is not accurate enough through analyzing actual data. Therefore, this paper adopts a machine learning algorithm as Gradient Boosting Decision Tree (GBDT). Firstly, extract training samples from word embedding library, then build a training set by relevancy scoring to the target word. Secondly, build GBDT model based on the training set to conduct regression analysis of mapping from word embedding to relevancy score. Finally, fit the whole word embedding library with the resulting model. Experimental results show that comparing with the cosine value of word embedding, the GBDT regression model significantly improves the fitting values of the words closely related to the meaning of the target word, and the fitting values of words weakly related to the target word is effectively suppressed, which proves that the GBDT model performs better in accuracy.
An Effective Cluster Heads Selection Method for Wireless Sensor Networks

An effective cluster heads (CHs) selection scheme based on the energy and density is proposed for the wireless sensor networks. In the proposed method, the residual energy and the dynamic density of nodes are jointed to determine the CHs to balance the energy consumption, where the nodes with more neighbors and more residual energy will be preferred as CHs. Moreover, the dynamic weight is used to trade off the effect of the residual energy and node density in the selection of CHs. As the residual energy of the node decreases, the weight value will make the residual energy have a higher priority, so that the nodes with high density and low energy can avoid death of being selected as a CHs. Simulation results show that the proposed algorithm can balance energy consumption and greatly prolong the network lifetime compared with the exiting schemes.
Stable Consensus for Heterogeneous Nonlinear Multi-agent Systems with Non-zero Inputs

This paper presents a result which outsides of existing revisions that cannot achieve the fully cooperative be-haviors of heterogeneous nonlinear systems only by the general static consensus protocol using the relative states of neighboring agents. In the present framework, we propose a novel distributed algorithm which removes the assumption that the leader must have a path to every follower to pursue the complete consensus behaviors. By introducing some mild assumptions, we develop some sufficient conditions for control of heterogeneous nonlinear multi-agent systems with an unknown but bounded leader, by proposing a class of integrated leader-follower stable consensus protocols. The proposed protocols can friendly synthesize the limited state information among neighbours, the appropriate feedback gains, the effective compensation controllers between the equilibrium point of the systems, and distributed estimate of the unknown leader input.
A Novel Zero Cross-correlation Diagonal Identity Matrix Code

Optical Code Division Multiple Access system (OCDMA) is an imminent technology to expunge the limitations of ever increasing data traffic and multiple user access in optical networks. However Multiple Access Interference (MAI) and cross correlation among different codes are prominent issues of SAC OCDMA systems. In order to eliminate cross correlation and to suppress MAI, in this work, we proposed novel Zero cross-correlation (ZCC) Diagonal Identity Matrices codes (DIM). Further, comparison of DIM code is carried out with Enhanced Double weight Codes (EDW) in similar environment in terms of Bit error rate, extinction ratio, Q factor, signal to noise ratio, eye height etc. Codes are investigated at the speed on 30 Gbps and total simultaneous users considered are seven. Mapping free technique is used for both OCDMA SAC-codes which permits service provider to design codes for large no. of users. Proposed novel SAC-OCDMA diagonal identity matrix codes have advantage that it can be designed for any number of users. It is perceived that due to zero cross correlation DIM codes exhibits better performance than EDW codes.
DNN-VolVis: Interactive Volume Visualization Supported by Deep Neural Network

In this work, we propose a novel approach of volume visualization without explicit traditional rendering pipeline. In our proposed method, volumetric images can be interactively ‘reversed’ given the volumetric data and a static volume rendered image under the desired rendering effect. Our pipeline enables 3D-navigation on it for exploring the given volumetric data without explicit transfer function. In our approach, deep neural networks, combined usage of Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNN) are employed to synthesize high-resolution and perceptually authentic images directly, inheriting the desired transfer function and viewing parameter implicitly given by the input images respectively.
FSM Stator Tooth to Rotor Pole MEC Modelling and Full Machine Simulation

This paper presents the magnetic equivalent circuit modelling (MEC) and simulation of flux switching machines (FSMs) that can be applied to a FSM with any number of rotor poles and stator teeth. The paper focuses on modelling the air-gap permeance between one rotor pole and one stator tooth and its variation with rotor position. This modular magnetic equivalent circuit (MEC) model is then used to implement a full FSM model iterating it for all the combinations of rotor poles and stator teeth in an FSM. The full MEC model is simulated and the results are compared with finite element simulations.
Assisted Labeling for Spam Account Detection on Twitter

Online Social Networks (OSNs) have become increasingly popular both because of their ease of use and their availability through almost any smart device. Unfortunately, these characteristics make OSNs also target of users interested in performing malicious activities, such as spreading malware and performing phishing attacks. In this paper we address the problem of spam detection on Twitter providing a novel method to support the creation of large-scale annotated datasets. More specifically, URL inspection and tweet clustering are performed in order to detect some common behaviors of spammers and legitimate users. Finally, the manual annotation effort is further reduced by grouping similar users according to some characteristics. Experimental results show the effectiveness of the proposed approach.
Text Sentiment Analysis: A Review

Sentiment analysis is a big branch in the field of natural language processing. The development of text sentiment analysis not only has a great influence in the field of natural language processing, but also has a profound influence in the fields of politics, economy and social sciences which are influenced by people's subjective view. As an interdisciplinary research field, text sentiment analysis has been paid more and more attention by scholars both at home and abroad. The research process and progress of sentiment analysis on the basis of investigation and analysis are summarized in this paper. It is also introduced that the common sentiment lexicon and corpus, the sentiment analysis based on dictionary, the sentiment analysis based on the conventional machine learning algorithm and the sentiment analysis based on the deep learning algorithm. The sentiment analysis of the Chinese and some special texts are discussed. Finally, the difficulties of text sentiment analysis and the future development direction are summarized.
Dimensionality Reduction Applied to Time Response of Linear Systems Using Autoencoders

An Autoencoder is a multilayer neural network that is used as a powerful tool to perform dimensionality reduction. Due to its structure, is possible to find low-dimensional representations of high-dimensional data in its most hidden layer. In this paper, a deep autoencoder is used to perform a compact representation of the time response of linear systems. The parameters of deep autoencoder are trained using gradient descent and backpropagation. The proposed method is validated with five different first-order systems and six of second order. Results show that is possible to use a deep autoencoder to capture the dynamical behavior of a dynamical system in its latent layer.
Non-Rigid Registration Under Articulated Deformation for Incomplete Point Clouds Targeting 3D Human Body Reconstruction with Occlusion

This paper presents a simulation of non-rigid registration under articulated deformation between a full-body template and an incomplete point cloud with different poses. Our method tackles two issues. First, it solves a non-rigid registration problem between the full and partial point clouds. Second, it deals with model completion which fills missing target surfaces while performing template deformation. Our framework is subdivided into global and local registrations. The global term performs the template deformation mainly to align rigidly articulated parts with an existing blending technique. However, the blending technique is also applied to areas in between articulated parts, non-rigidly. The local term further enhances natural shape deformation based on a graph-structure of non-rigid deformation. In this experiment, we conducted trials on multiple human poses. The results are shown in our table and graphics; error values are determined from indexed correspondences.
Position Estimation on Image-Based Heat Map Input using Particle Filters in Cartesian Space

This paper presents an approach for using an image-based heat map as measurement input of a particle filter. Pixels of the heat map are transformed into Cartesian space relative to the robot and regarded as single measurements. The approach uses a novel observation model to weight the particles accordingly to the heat map pixels. While this paper focuses on handling FCNN output, the method is also applicable to other feature recognition methods like saliency approaches. The proposed method shows similar performance in standard cases and huge improvements on erroneous input compared to the conventional approach.
Crack Detection Using Fast Spectral Clustering Considering Graph Connectivity

Cracks on pavement roads cause various traffic problems. Therefore we should repair them properly. Nowadays a variety of crack detection methods in computer vision have been proposed. Spectral clustering is one of them and an effective method, but suffers from processing time due to the large amount of calculation. Among them, calculating of Laplacian-matrix and eigenvalues especially affect processing time. Therefore we propose two methods to improve the efficiency of algorithm. One applies sparse process considering graph connectivity for Laplacian-matrix, and the other considers amount of pixel of crack of pavement road images.
Dynamic Spectrum Allocation for Heterogeneous Cognitive Radio Networks With Multiple Channels

The rapid growth of wireless communication technology has resulted in the increasing demand on spectrum resources. However, according to a recent study, most of the allocated frequency experiences significant underutilization. One important issue associated with spectrum management in heterogeneous cognitive radio networks is: How to appropriately allocate the spectrum to secondary sender–destination (S–D) pair for sensing and utilization. In this paper, the authors investigate the spectrum allocation problem under a more practical scenario where the heterogeneous characteristics of both the secondary S–D and primary channels are taken into consideration. With the objective to maximize the achievable throughput for secondary S–D, we formulate the spectrum allocation problem as a linear integer optimization problem under spectrum availability constraint, spectrum span constraint, and interference free constraint. This problem is proven to be Non-deterministic Polynomial (NP)-complete, and a recent result in theoretical computer science called randomized rounding algorithm with polynomial computational complexity is developed to find the $\rho$-approximation solution. Evaluation results show that our proposed algorithm can achieve a close-to-optimal solution at a low level of computation complexity.
Maximizing Minimum Phase Difference Based Hybrid Beamforming for Multiuser mmWave Massive MIMO systems

In this paper, we propose a hybrid beamforming algorithm based on maximizing the minimum phase difference for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) multiuser systems. The proposed algorithm uses the channel gain threshold and the correlation characteristic of the array response vectors to select vector sets that provide higher signal power from analog beamforming vector codebook. Then, inter-user interference is measured by the phase difference between the analog beamforming vector and the main propagation path array response vector. Additionally, the criterion of maximizing the minimum phase difference is proposed to suppress the strongest inter-user interference. Analysis and simulation results show that the proposed algorithm can provide higher system sum rate with lower computational complexity than traditional approaches.
MPCC-based Path Following Control for a Quadrotor with Collision Avoidance Guaranteed in Constrained Environments

This paper describes an autonomous mission on safe traversal of consecutive gates for quadrotors. Considering that the effective sensing range of onboard sensors is practically short, we integrate the path planning into model predictive path following control (MPFC) with sigmoid-based path template which can be easily revised when the new obstacles information is within the detection range. We leverage the framework of model predictive contouring control (MPCC) to suit high speed path following control applications. The MPFC structure allows for input and state constraints for the specific practical scene. The trade-off between contouring accuracy and traversal time is addressed using the MPFC cost function, allowing the system to deviate from the desired path in order to make the path be traversed faster and whereas sacrificing path speed (traversal time) in order to increase safety. The weights in the cost function determine the relative importance of the competing control objectives. In case of traversal, we build a virtual safety corridor in the horizon for quadrotors, which facilitates the control scheme to be extended to incorporate obstacle avoidance by adjusting the constraints and thus re-plan its traversal path. The control performance is investigated experimentally using a realistic dynamics model of DJI M100 platform. The simulation results demonstrate the effectiveness of the proposed control algorithm.
Denoising sEMG Signals Using the Combination of Complementary EEMD with Adaptive Noise and Improved Threshold

Surface electromyography (sEMG) signals have to be denoised to remove baseline noise, motion artifact and power line interference to extract effective information. The purpose of this study is to present a denoising method of sEMG signals, combination of complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved threshold. The signals are separated into several intrinsic mode functions (IMFs) by CEEMDAN method. The noise-dominant IMFs and the signal-dominant IMFs are analyzed based on energy entropy. Then, the denoised sEMG signal is obtained by reconstructing partial IMFs processed using improved threshold. The performances of method is evaluated using synthetic signals and real signals by calculating indicators such as SNR and MSE.
Spectral Analysis of Sounds by Acoustic Hearing Analyzer

It is shown that the transient evoked otoacoustic emission in response to a short (broadband) sound impulse is an impulse function of the auditory canal, middle ear, and cochlea of the inner ear. Its form corresponds to the basic functions form of wavelet representations of the "convexity" type, which are characterized by a shift (delay) in time, position and shift of position. It is established that the division into frequency groups, which is the main property of hearing, does not occur in the central nervous system, as previously thought, but in an acoustic auditory analyzer. It is a correlation filter with many inputs - transverse fibers and exits of the outer hair cells to the nerve fibers. The acoustic system of the acoustic analyzer is a unique correlation filter, i.e. a kind of patent of nature.
Online Clustering for Estimating Occupancy in an Office Setting

The research presented in this paper is a first investigation of the application of online unsupervised learning techniques in the case of smart buildings. An online learning approach is proposed to estimate the number of people (within a range) in a room based on data collected from standard sensors. This estimated range is changing and depends on the highest occupancy faced in a training dataset for instance. Occupant behavior is modeled using an online learning approach. The resulting algorithm makes use of recorded sensor data for motion detection, power consumption, and door position as well as acoustic pressure from a microphone. This paper presents an online algorithm for a mixture model-based clustering which has been applied to estimate the occupancy in an office context, with an excellent estimation accuracy.