Miracle-dz/UniFAR-FTUs huggingface dataset:
Rumor Spreading Model Considering Iterative Spread on Social Networks

Social networks have been witnessed tremendous success and become an indispensable part of people's daily life. Rumors take this opportunity of social networks to behave like a tyrant and threat the social order and virtual assets. Therefore, it is very significant to study the mechanism of the rumor spreading and find a way to control the rumors. Considering that a person has been told a rumor repeatedly, the suspicion will be sapped and this person would be persuaded and spread the rumor at last. Based on the classical SIR model, the SIDR rumor spreading model is proposed with an additional state to describe the situation that a stifler is persuaded and become a spreader. After theoretical and experimental analysis, the performances of rumor spreading with various parameters are illustrated to study the dynamical process of rumor.
ETHICAL FRAMEWORK FOR MACHINE LEARNING

Artificial Intelligence (AI) with its core subset of Machine Learning (ML) is rapidly transforming life experiences as humans begin to grow more dependent on these ‘smart machines’ for their needs – ranging from routine mundane chores to critical personal decisions. However, these transformative technologies are at the same time proving unpredictable too as has been reported worldwide in certain cases. Therefore, several studies/reports, such as COMEST report on Robotics ethics (UNESCO, 2017) point to an obvious need for inculcating more ethical behavior in machines. The present study aims to look at the role and interplay of ML (the hard sciences) and Ethics (the soft sciences) to resolve such predicaments that are inadvertently manifested by machines not constrained or controlled by human expectations. Based on focused review of literature of both domains-ML and Ethics, the proposed paper attempts to first build on the need for introduction of an ethical algorithm in the domain of machine learning and then endeavors to provide a conceptual framework to resolve the ethical dilemmas.
OPTICAL FLOW BASED LEARNING APPROACH FOR ABNORMAL CROWD ACTIVITY DETECTION WITH MOTION DESCRIPTOR MAP

Automated abnormal crowd activity detection with faster execution time has been a major research issue in recent years. In this work, a novel method for detecting crowd abnormal activities is proposed which is based on processing of optical flow as motion parameter for machine learning. The proposed model makes use of magnitude vector which represents motion magnitude of a block in eight directions divided by a 45 degree pace angle. Further, motion characteristics are processed using Motion Descriptor Map (MDP), which takes two main parameters namely aggregate magnitude of motion flow in a block and Euclidean distance between blocks. Here, the angle of deviation between any two blocks determines which among the eight values in the magnitude vector to be considered for further processing. The algorithm is tested with two standard datasets namely UMN and UCSD Datasets. Apart from these the system is also tested with a custom dataset. On an average, an overall accuracy of 98.08% was obtained during experimentation.
Facial Expression Recognition Based on G-2DPCA Feature and Sparse Representation Classification

In this paper, we proposed an effective method of facial expression recognition based on a G-2DPCA feature and Sparse Representation-based Classification (SRC). Gabor filters with five scales and eight directions are first employed for feature extraction. To address the high dimension of Gabor features, we select one out of forty Gabor filters with an optimal parameter pair of scales and directions to filter facial images. Two-dimensional principal component analysis (2DPCA) is utilized for image representation and data dimension reduction. It retains the 2D geometric structure of an image, and the image matrix does not need to transform into a vector, which reducing the computation time greatly. Finally, Gabor plus 2DPCA (G-2PCA) features are regarded as the atoms of dictionary in SRC. Experimental results demonstrate that the proposed method has better performance than the existing algorithms.
Collaborative Filtering Algorithm Based on Trusted Similarity

In order to solve the problem of information overload, a large number of personalized recommendation algorithms merged. Data sparsity is one of the difficult problems in these algorithms. Aiming at this, a novel collaborative filtering algorithm which introduces the trust into the traditional collaborative filtering recommendation algorithm is proposed in this paper. The proposed algorithm first measures the comprehensive trust by weighting the direct trust and the indirect trust, then obtains the similarity using Pearson model, and calculates the trusted similarity for prediction at last. To verify the performance of the proposed algorithm, the Mean Absolute Error (MAE) between the proposed algorithm with adaptive coordination factor and the traditional collaborative filtering recommendation algorithm with empirical factor is compared. The result shows that the proposed algorithm has better prediction accuracy.
Nonlinear Observer Design for RC Battery Model for Estimating State of Charge & State of Health Based on State-Dependent Riccati Equation

This paper investigates a novel nonlinear observer design approach based on the State-Dependent Riccati Equation (SDRE) technique for estimation of the state of charge (SOC) and state of health (SOH) parameters of nonlinear RC battery model. Due to practical restrictions on direct measurement of SOC, we try to introduce effective and accurate observing scheme which excel estimation results. The estimation of SOC and SOH has a crucial role in applications involving rechargeable batteries. SDRE observer is an extended form of Kalman Filter (KF) estimator for nonlinear systems. In this paper, the SDRE-based observer has been proposed for nonlinear RC battery model which is widely used. The resulting observer has several advantages including a faster convergence, better accuracy, and simpler structure in comparison with most existing methods. The simulation results show the merits of SDRE filter in estimating of SOC and SOH.
Agent-Based Decentralized Optimal Charging Strategy for Plug-in Electric Vehicles

This paper presents a game-theoretic decentralized electric vehicle charging schedule for minimizing the customers’ payments, maximizing the grid efficiency, and providing the maximum potential capacity for ancillary services. Most of the available methods for electric vehicle charging assume that the customers are rational, there is a low-latency perfect two-way communication infrastructure without communication/computation limitation between the distribution company and all the customers, and they have perfect knowledge about the system parameters. To avoid these strong assumptions and preserve the customers’ privacy, we take advantage of the regret matching and the Nash Folk theorems. In the considered game, the players (customers) interact and communicate locally with only their neighbors. We propose a mechanism for this game, which results in a full Nash Folk theorem. We demonstrate and prove that the on-off charging strategy provides the maximum regulation capacity. However, our mechanism is quite general, takes into account the battery characteristics and degradation costs of the vehicles, provides a real-time dynamic pricing model, and supports the vehicle-to-grid and modulated charging protocols. Moreover, the developed mechanism is robust to the data disruptions and takes into account the long/short-term uncertainties.
Pedestrian Detection and Attribute Analysis Program Based on CNN

In recent years, deep learning object detectors including Fast/Faster R-CNN, SSD, R-FCN and Mask R-CNN have shown significant performance for general object detection except for pedestrians. The Region Proposal Network (RPN) in Faster R-CNN works well yet lacks of adaptability. Therefore, we propose an adaptive real-time pedestrian detection and attribute identification scheme based on Caffe. The first contribution is the adaptive threshold adjustment (ATA) algorithm for intelligent monitoring, utilizing the pedestrian movement information to adjust the threshold. Moreover, to overcome the time-consuming defect, we analyze the influences of the number of layers, the size of convolution kernels and the number of feature maps to reduce redundant computation while maintaining satisfactory performance. By optimizing the neutral network structure, choosing model parameters and data augmentation, a stable and well-performed model with fast detection rate and high accuracy is obtained. Besides, pedestrian information can also be identified in our program, offering better service in security monitoring, intelligent robots and other fields. Extensive experimental results demonstrate that even in complex and athletic scenarios, our method can make an improvement in quality and speed over state-of-the-art.
Smartwatch Strap Wireless Power Transfer System With Flexible PCB Coil and Shielding Material

In this paper, we designed and demonstrated a smartwatch strap wireless charging system for the first time. First, we designed a flexible printed circuit board (PCB) coil, shielding material, and receiver (Rx) circuit in a watchstrap. In the design process, we proposed a model for the flexible PCB coil with a bending radius of 40 mm and shielding materials. We used a flexible PCB coil that has 215 μm thickness with dimensions of 54.5 × 16 mm. In addition, ferrite core and sheet are applied on the transmitter (Tx) and Rx coils. We verified the proposed model through a three-dimensional (3-D) electromagnetic (EM) simulation and measurement in the frequency and time domains. The proposed flexible PCB coil inductance modeling results showed 7.5% and 3.4% errors when compared to the 3-D EM simulation and measurement results, respectively. Furthermore, we demonstrated the smartwatch strap wireless charging system using an LG Watch Urbane. A resonance frequency of 100 kHz with the series–series tuning topology is used in accordance with the Qi specifications. Finally, we achieved 30% dc–dc power transfer efficiency and exposed magnetic field of 270 mG, 1 cm away from the system through measurements.
A Robust Trust Model for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are vulnerable to various security attacks due to its broadcast nature. Trust management is one of the effective methods to defend against threats posed to WSN. The trust management system helps to mitigate many insider attacks and malicious behaviour of nodes. In this paper, we proposed a robust trust model that prevents various attacks viz; bad mouthing, self-promoting and collusion attacks. It uses the time lapses function based on forgetting curve to calculate direct trust and reputation function for indirect trust. Our proposed trust model is scalable. When the trust value is above the threshold value, then we consider a communication link as active and node is trusted, otherwise we remove the communication link and mark the node as malicious. The simulation results indicate the effectiveness of proposed method and maximizes the defence against the internal attacks.
Analysis of Different Types of Core Materials in Photonic Crystal Fiber

Photonic crystal fiber is a widely searched research area which grabs the attention of researchers towards this field. Actually since last two decade innovations and experiments have been increased in this field. Here various materials and design are used for calculation of dispersion, minimizing the loss, verifying the refractive index [13 14, 15] of the material used etc. Since Photonic crystal fibers [10]-[15] have various properties among them the above mentioned are widely used to calculate and measure in every design and structure. In this paper we tried to compare the above mentioned properties of various materials like the conventional material of PCF [10]–[11] is Silica Glass [1]–[2], borosilicate crown glass (BK7) [3], chalcogenide glass [4]–[5], Borofloat glass [6], GE 124 & NSG OZ, Vycor [7]–[8] and Soda Lime [9]. Apart from the mentioned properties we have also considered some other physical and chemical properties of these materials. To compare all these properties of these materials we used to refer various articles and research papers then finally concluded that all the three material have their own merits and demerits and they shows their properties in best manner for the selected wavelength region. We have prepared various tables with the data collected and draw graphs between them to show the variation of these parameters for various material used.
Event Triggered Adaptive Fuzzy Consensus for Interconnected Switched Multiagent Systems

This paper concentrates on the problem of event triggered adaptive leader-following control for a class of multiagent systems. The considered systems contain completely unknown interconnections and arbitrary asynchronously switching. To handle these nonlinearties, a new hierarchical barrier Lyapunov method is proposed. Based on this method, a novel adaptive fuzzy control strategy is designed to make the output tracking errors of the multiagent systems converge to a small neighborhood of origin. Stability analysis shows that the proposed method can guarantee the compact set conditions of the fuzzy logic systems during the entire design process. Meanwhile, to reduce the computational burden, novel event triggered mechanisms are presented for both the transmission between two connected agents, and the communication between the sensor and the controller in one agent. An illustrative example is presented to verify the effectiveness of the proposed controller.
Natural Image Character Recognition Using One Class-Principal Component Analysis Classifier

The character recognition from natural scene image is used in many applications as for instance in intelligent transportation systems. Indeed, many recent methods have been focused to design a better feature representation of the character. However, the performances of such system is still far from perfect since it depends also of the appropriate choice of the classifier. Hence, the present paper addresses the problem of natural character recognition and tries to investigate the performance of the One Class-Principal Component Analysis Classifier (OC-PCA). The OC-PCA classifier has the main advantage to absorb the high dimension of the feature vector with few samples. For evaluating the performance of the proposed classifier, experimental results are conducted on challenging Char74k dataset highlighting its robustness against the state-of-art.
Design Optimization of RF-MEMS Based Multiband Reconfigurable Antenna Using Response Surface Methodology

Reconfiguration of antennas, in terms of frequency response and return loss, is the most desirable feature in the world of modern wireless communication systems. This paper is dedicated to investigate the optimized characteristics of Reconfigurable Sierpinski Fractal Antenna by using Radio Frequency Micro-electromechanical Systems (RF MEMS) switches. A triangular Fractal patch antenna is designed upto fourth iteration with a 60° flare angle and self similarity with a log-periodicity of $\delta=2$. Four sets of RF-MEMS switches are separately designed that are used in the antenna to analyze the diversity in terms of output responses. Depending upon states of the switches; the antenna can operate in five different modes due to which a variation in output values can be observed. The antenna along with the switches is integrated on a substrate with permittivity $\varepsilon_{r}=2.5$. A Response Surface Methodology (RSM) is a technique used to analyze the impact of various design parameters on the output responses of the antenna including resonance frequencies and return loss. A combined desirability function is used to find the optimal values of the design parameters that result in desired output response values for the antenna. The analysis is carried out from 1 to 15 GHz using Ansys Electromagnetics suit 17.2 software. Moreover, since the Seirpinski Gasket Fractal antenna has a multiband behavior, therefore it radiates at five different frequencies, while keeping the radiation patterns constant. However, operating frequency and return losses of the final model are examined in detail to analyze the reconfigurability of antenna.
Mixed-Bandwidth Cross-Channel Speech Recognition via Joint Optimization of DNN-Based Bandwidth Expansion and Acoustic Modeling

Automatic speech recognition (ASR) systems are often built using scene related speech data due to large variations of transmission channels and sampling rates in different scenarios. In this study, we propose a general framework that establishes a unified model for diversified speech data with different sampling rates and channels. The framework is a joint optimization of deep neural network (DNN)-based bandwidth expansion and acoustic modeling to exploit a large amount of diversified training data. First, we design two novel DNN architectures to map the acoustic features from narrowband to wideband speech through direct mapping and progressive mapping. The learning targets of the direct mapping DNN (DNN-DM) are the acoustic features extracted from speech with the largest bandwidth, while the acoustic features from speech with all the other bandwidths are used as input. A progressive stacking network (PSN) gradually maps the features from the low sampling rates to the highest sampling rate through the design of intermediate target layers via multitask training. Then, in addition to these bandwidth expansion networks, we investigate several joint training strategies for DNN-based acoustic models. Our experiments conducted on three diversified large-scale Mandarin speech datasets with different recording channels and sampling rates (6, 8, and 16 kHz) show that the proposed unified model using PSN for bandwidth expansion not only is a more flexible and compact design than conventional multiple acoustic models with each bandwidth for a specific sampling rate, but also yields consistent and significant improvements over bandwidth-dependent models with an average relative word error rate reduction of 6.2%, indicating that the proposed model can fully utilize the diversified cross-channel speech data with multiple bandwidths. Moreover, the proposed methods are verified to be robust on different realistic scenes and can be effectively extended to a long short-term memory framework.
A Machine Learning Approach for Detecting DoS Attacks in SDN Switches

Software Defined Networking (SDN) breaks the vertical integration of existing Internet architecture and makes the network programmable from a logically centralized control point. Even though the centralized network control provides several advantages, attacks toward SDN framework remain as a challenge. In this paper, we propose a method based on machine learning to detect Denial of Service (DoS) attack in data plane devices, i.e., the OpenFlow switches, resulting from flow-table overflow. We created an SDN dataset using Mininet and features are extracted from switch-controller communication trace as well as flow-table snapshots of OpenFlow switches. Further, we use three algorithms, (i) Neural Network, (ii) Support Vector Machines, and (iii) Naive Bayes, to classify the network to either malicious or benign. The results show that neural network and Naive Bayes provide 100% accuracy with the extracted features.
Peak Current Control of Multichannel LED Driver With Selective Dimming

A novel selective dimming method of the output channels of a multichannel LED driver is proposed in this paper. This LED driver comprises a front-end ac–dc boost power factor correction converter followed by symmetric half-bridge isolated nonresonant dc–dc converter and symmetric voltage-multiplier rectifier with purely capacitive output filters. By controlling the duty ratios of the secondary side switches, which are strategically placed at the output voltage-multiplier-based rectifiers, the average LED current and hence the brightness of individual channels are controlled. Moreover, a unique primary side peak current control is implemented to control the peak currents of LEDs during pulsewidth modulation dimming, which prevents electromigration-related fast aging of the LEDs. This driver also has the advantages of high power (>120 W) operation capabilities, and high efficiency (>94%) with primary side switches zero voltage switching turning on and secondary side rectifier diodes zero current switching turning off. A detailed analysis and design procedure of the proposed converter is presented. To validate the analysis, an industry-standard 150 W prototype is developed, which is digitally controlled by a cost-effective non-DSP microcontroller. The experimental results are in perfect agreement with the claims. A comparison for universal ac input and isolated LED drivers with selective dimming is displayed.
MDTCP: Towards a Practical Multipath Transport Protocol for Telco Cloud Datacenters

The next-generation networks (5G) aims to support services that demand strict requirements such as low-latency, high throughput, and high availability. Telecom operators have adopted Network Functions Virtualization (NFV) to virtualize the network functions and deploy at distributed cloud datacenters. Deploying virtual network functions (VNFs) close to the end-user can reduce Internet latency. However, network congestion in telco cloud datacenters can result in increased latency, low network utilization and a drop of throughput. The existing protocols are not capable of utilizing the multiple paths offered by datacenter topologies e.g., DCTCP; require a major architectural change and face deployment challenges e.g., NDP; or increase flow completion times of short flows e.g., MPTCP. To address this, we propose a multipath transport for telco cloud datacenters called coupled multipath datacenter TCP, MDTCP. MDTCP evolves MPTCP subflows to employ ECN signals to react to congestion before queue overflow, offering both reduced latency and higher network utilization. The evaluation of MDTCP with simulated traffic indicates comparable or lower flow completion times compared with DCTCP and NDP for most of the studied traffic scenarios. The simulation results imply that MDTCP could give better throughput for telco traffic and at the same time be as fair as MPTCP in datacenters.
Feature Extraction Trends for Intelligent Facial Expression Recognition: A Survey

Human facial expression is important means of non-verbal communication and conveys a lot more information visually than vocally. In Human-machine interaction facial expression recognition plays a vital role. Still facial expression recognition through machines like computer is a difficult task. Face detection, feature extraction and expression classification are the three main stages in the process of Facial Expression Recognition (FER). This survey mainly covers the recent work on FER techniques. It especially focuses on the performance including efficiency and accuracy in face detection, feature extraction and classification methods.
Learning preference relations using Support Vector Regression

In this paper we propose a novel approach of learning preference relations using Support Vector Regression (SVR). It answers the problem of consistent ranking and improves the ability of generalization to ranking for the property of SVR method. Meanwhile, the Wilcoxon-Mann-Whitney (WMW) statistic is introduced to evaluate the result of the ranking algorithm. The experiments on an artificial dataset and some benchmark datasets show the effectiveness of the proposed algorithm. An application to ranking in web searching system based on the proposed method is also demonstrated.