Miracle-dz/UniFAR-FTUs huggingface dataset:
An Extensive Survey of Prominent Researches in Face Recognition Under Different Conditions

Face recognition has witnessed a lot of attention because of its numerous applications in many fields like computer vision, security, pattern recognition and computer graphics, but still is a challenging and active research area. In the paper, we have discussed all the recent assessment of the major researches in recognition of faces. We initially present the basics of Face recognition process. Then, the various challenges in face recognition like pose, lighting, occlusion, expression, aging and the research papers with these factors is discussed. Also we have projected a crucial evaluation of the present researches on the combined approaches concerned with the recognition of face techniques. Also, the paper have elaborated a wide and an up-to-date review of significant researches on face recognition accompanied by the approaches concerned with the various aspects that influence the face recognition.
Control of Electrical Conductivity for NFT Hydroponic Systems using Fuzzy Logic and Android Environment

Nutrient Film Technique (NFT) is a method of hydroponic systems that utilizes circulating water containing nutrients as a medium for plant growth. One important parameter in NFT that can affect plant growth is the density of the nutrient solution based on the level of conductivity. Conductivity of the solution is expressed by Electrical Conductivity (EC) which is a measure of the amount of salt dissolved in the solution. Control of EC parameters is intended to maintain EC values in ideal conditions depending on the type of plant. In this study, we applied fuzzy logic methods to control the condition of EC values in NFT hydroponic plants. By using an Android-based application connected to the internet, we can control and monitor all parameters remotely. The experiment was conducted on a proportional small scale cultivation model consisting of two reservoirs (ABmix and water) and one mixing tank with pump flow that can be adjusted using fuzzy logic. EC parameter values are maintained in the ideal range according to the setpoint input provided. The ideal EC parameter values on the plants tested are 1.0 mS/cm to 1.5 mS/cm. The system takes around 101 seconds to restore the lowest EC value to be ideal and around 89 seconds to restore the highest EC value to an ideal condition.
EruditeX: A Comprehension Based Question Answering System

This paper introduces a modular approach to the task of factoid Question Answering based on a given context. This involves breaking down the task into subtasks and using traditional NLP techniques along with Deep Neural Networks to solve each subtask independently. Many disadvantages of using end-to-end neural networks for the complex task of Question Answering are overcome with the use of a modular approach. The paper also introduces a new Neural Network based approach for the Answer Extraction subtask, called the VDT Node Ranker. The results obtained using the system with limited amount of training fall below expectations, although that of the VDT Node Ranker alone are quite impressive.
Privacy Preserving Data Aggregation on Secure Cloud

Secure Cloud is a cloud based data storage and computation service that works on Encrypted data without inferring any knowledge out of it. However on such secure cloud in order to perform Privacy Preserving analysis it is necessary to decrypt that data and then perform Privacy Preserving transformation. The proposed solution provides a way to perform Privacy Preserving transform on secure cloud without decrypting it. This is done by applying appropriate Differential Privacy techniques along with Homomorphic transform. The experimentation results done over the encrypted data are consistent with the utility-privacy trade-off in Differential Privacy approach on plaintext which states that the utility of data decreases as the privacy increases.
The Smaller the Better: Fine-Grained Image Classification with Compressed Networks

Fine-grained image classification is a challenging problem, due to the small inter-class variance caused by highly similar subordinate categories and large intra-class variance in poses, viewpoints and rotations. In this paper, we propose a novel end-to-end model for fine-grained image classification(FGIC). The proposed model consists of two sub-networks: detection sub-network and classification sub-network. The detection sub-network is constructed on the basis of R-FCN, and the classification sub-network contains a two-steam CNN for feature extraction and three fully connected layers for object classification. In addition, the network compression technology is adopted in both of the sub-networks to improve efficiency and reduce storage space. Experimental results on the CUB-200-2011 shows that the accuracy of our method is close to state-of-the-art with higher efficiency and lower storage requirement than the other compared methods (10 frames/sec during inference on TitanX). The proposed high-efficiency framework is believed to be effective in some of the practical applications, especially in the applications of mobile terminals.
A Fault-Tolerant Strategy for Three-Phase Dual Active Bridge Converter

Due to several advantages, three-phase Dual Active Bridge (DAB) converter is widely used in numerous applications nowadays. On the other hand, this converter is very vulnerable to Transistor Open-Circuit Fault (TOCF). Therefore, a fault-tolerant (FT) scheme has been proposed in this paper to solve the problem. First, normal and faulty conditions are investigated, and according to the results, a fault-diagnosis (FD) approach is introduced. Using the outcomes of FD unit, a new post-fault strategy is proposed for the converter. The FD method is based on the DC component of transformer phase currents, and the basis of FT technique is shedding the faulty phase. Some benefits of the proposed scheme are preventing the shut-down of the system, no overvoltage or overcurrent, no additional power components, and insensitivity to operational conditions. Simulation results are also provided to confirm the analyses, and evaluate the performance of the proposed scheme.
Novel Variational Approach for Generalized Signal Dependent Noise Removal

Image denoising is fundamental in image processing and computer vision field, where the assumption of a signal independent additive white Gaussian noise model dominates the most state-of-the-art denoising algorithms. In this paper, we propose a novel variational approach that addresses more challenging generalized signal-dependent noise model, which is more flexible and practical for real imaging scenarios. The generalized noise model is decomposed into two independent procedures by introducing an intermediate latent variable, with which the variational model is thus constructed. Moreover, the popular second-order total generalized variation regularizer is introduced into our variational model. To effectively solve the variational problem, we first simply the objective functional by substituting one of the corresponding Euler-Lagrange equations and thus eliminating the intermediate variable. A semi-explicit iterative scheme is present in the proximity operation so as to overcome the difficulty of the high nonlinearity involved in the data term when applying primal and dual hybrid gradient algorithm. Experimental results validate the effectiveness and efficiency of our method.
Automatic Hysteresis Feature Recognition of Vehicle Dampers Using Duhem Model and Clustering

Dampers of vehicle suspension systems usually show unsymmetrical nonlinear hysteresis characteristics, which affects the control accuracy of suspension system. Automatic recognition hysteresis features of unknown vehicle dampers can reduce the burden for developing efficient control strategies for suspension systems. According to the Duhem hysteresis model, a model-based unsupervised classification approach is proposed in this paper for recognizing hysteresis features of vehicle dampers. Through constructing Duhem models for different force-displacement hysteresis loops of the same sample, significant hysteresis features are extracted for multivariate time series of vehicle dampers. In terms of model parameters of samples, the clustering method is used to group dampers into different classes. Clustering results of 700 samples show that model-based features can efficiently describe the hysteresis features of vehicle dampers. K-means and Fuzzy-C Mean (FCM) are more appropriate for clustering the damper model feature data than hierarchical agglomerative clustering (HAC).
Comparison of Three-Point P&O and Hill Climbing Methods for Maximum Power Point Tracking in PV Systems

Solar energy and photovoltaic (PV) systems will play an important role in the future production market. One of the challenges in optimal operation of solar PV systems is the low efficiency of these systems compared to fossil fuel production units. The reason is the sudden changes in climate factors, including the temperature and intensity of radiation in the environment. One of the solutions used to solve the low power efficiency problem in PV systems is the MPPT techniques which have been widely investigated by many researchers. In this paper, a new form of conventional Perturb and Observe (P&O) method named three-point P&O is introduced and compared with Hill Climbing (HC) method under constant and variable atmospheric conditions. The DC-DC converter is Boost which has an acceptable performance in solar applications. The simulation results in MATLAB and experimental results indicate a better performance of three-point P&O method in terms of output power fluctuations and maximum power point tracking capability.
Approximation resistance from pairwise independent subgroups

We show optimal (up to constant factor) NP-hardness for Max-k-CSP over any domain, whenever k is larger than the domain size. This follows from our main result concerning predicates over abelian groups. We show that a predicate is approximation resistant if it contains a subgroup that is balanced pairwise independent. This gives an unconditional analogue of Austrin--Mossel hardness result, bypassing the Unique-Games Conjecture for predicates with an abelian subgroup structure. Our main ingredient is a new gap-amplification technique inspired by XOR-lemmas. Using this technique, we also improve the NP-hardness of approximating Independent-Set on bounded-degree graphs, Almost-Coloring, Two-Prover-One-Round-Game, and various other problems.
Lane Datasets for Lane Detection

Challenges are loved by the researchers and there are many lane dataset challenges which are motivating the researchers to implement the algorithms for the complex lane datasets. Lane detection and departure is a broad research area. This paper covers the information related to some lane detection and departure datasets. The straight lines, curved lines, faint lines etc are the part of the dataset as well as the different environmental conditions and time such as day and night time scenes are also covered by some datasets. A comprehensive survey of different lane dataset and their comparison is given in this paper.
Reducing Variation of Switching Frequency in Finite-State Predictive Torque of three-Phase Induction Motor

In this research, a new technique is applied to the predictive direct torque control of the induction motor in order to reduce the variation of the switching frequency. The method is a modification of the finite set model predictive control (FS-MPC). In this method, the switching options are examined in a cost function and the best option is selected based on the minimum cost function. Since the switching technique in FS-MPC is direct switching without using a modulator, the switching frequency will be variable. In the proposed method, the periodic selection of the active and zero voltage vector is used to reduce the variation of the switching period. Thus, the zero voltage is mandatorily exerted after an active voltage and vice versa. With this method, the cost function is only examined for the active voltage vectors and the zero voltage is selected for the next control interval. This manner is repeated in the whole algorithm. The proposed method is evaluated by simulation via MATLAB/Simulink. The results showed that the proposed method reduced the variation of the switching frequency and it was stable for the different operating point.
Resilient Reorder Buffer Design for Network-on-Chip

Functionally safe control logic design without full duplication is difficult due to the complexity of random control logic. The Reorder buffer (ROB) is a control logic function commonly used in high performance computing systems. In this study, we focus on a safe ROB design used in an industry quality Network-on-Chip (NoC) Advanced eXtensible Interface (AXI) Network Interface (NI) block. We developed and applied area efficient safe design techniques including partial duplication, Error Detection Code (EDC) and invariance checking with formal proofs and showed that we can achieve a desired safe Diagnostic Coverage (DC) requirement with small area and power overheads and no performance degradation.
STHT Based Speech Signal Processing Using Phase Vocoder

Approximately 5% of the global population is affected by hearing loss, causing significant disability. There is number of hearing impaired persons with mild to moderate or severe hearing loss. The Fourier transformation technique is used in speech enhancement. The speech enhancement in hearing aid is designed and developed by Fourier transformation technique. The conventional hearing aids have a number of functional limitations such as fixed frequency, lack of flexibility, etc. The purpose of this work is to develop the Hartley Fourier transformation (STHT) based speech processing technique for hearing impairment. The STHT is used in phase vocoder for the speech processing. The proposed algorithm is implemented in MATLAB and verified with VCV (vowel consonant vowel) data sets. The results are obtained in terms of the pitch and power of VCV data sets. The change in pitch for the modified signal is observed and the proposed technique can be used to solve the hearing loss problems of sensorineural hearing impaired.
Trust Measurement Model of Services Computing Systems: A Case Study on E-commerce Systems

Information technology has developed rapidly. The rapid development of IT is stimulated by users who want a fast-paced service. Many IT-based services are circulating in the community, all with the aim to be more effective and efficient in the service process. An example of a service is e-commerce, where e-commerce includes several integrated business services including sales services, payment services, and shipping services. All of these services are wrapped in computing so this is what is called a service computing system. This e-commerce service is very dependent on the customer-sourced profile, so the customer’s trust in shopping on e-commerce is needed. For that e-commerce services must provide a high sense of trust that ultimately customers can survive and can provide recommendations to other customers. Challenges related to trust are the extent to which trust can be measured, which shows that the trust created can be seen qualitatively. This paper provides an overview of how studies include trust in service computing systems, how to measure trust in service computing systems. Measurement testing through data simulation. The results of the trial obtained results that show that trust can be measured in e-commerce case studies.
A Proposed Technique for Conversion of Unstructured Agro-Data to Semi-Structured or Structured Data

With the increase in the use of social media, agriculture data is growing rapidly thereby producing extensive amounts and vivid types of data each day. The same data can be accessed to be further examined and managed to deliver the useful information to address the needs. According to estimation of IDC(International Data Corporation) 90% of the data is unstructured which is the fastest growing data whereas the remaining is unstructured data and structured data which does not possess a predefined data type or may be the data that does not fit into traditional database for data access. The sources for this data may be IOT, sensors, satellites, emails etc. There are many techniques available for conversion of agriculture data from unstructured to semi-structured or structured data but there are some loop holes present in the existing approaches like they will work particularly for single text, and some work only with the cluster setup etc. This paper presents an approach to convert unstructured data into semi-structured or structured data using the NoSQL tool Couchbase. The Couchbase is the document database with distributed architecture which provides scalability, performance and availability.
Comparative Analysis of Hadoop Tools and Spark Technology

Data continuously goes on increasing day by day due to rapidly increasing population, use of sensors, use of social media, use of Internet of things etc. Data generated from these various sources may be small or it may be large, also it can be structured or unstructured. We can easily process and analyze small amount of data in a single system. But nowadays data generating through various sources is big in size and mostly in unstructured form. So, in order to analyze such big amount of unstructured data we need some mechanism which can handle this amount of data and process it. Hadoop and Spark are the technologies which can handle any kind of data and can process it. For storing, processing and analyzing the data, different technologies and tools are used under hadoop and spark ecosystem. In this paper, we specifically focus on use of Hadoop tools and technologies like Map-Reduce, Apache flume, Apache Pig and Apache Spark technology and their comparative analysis.
Reducing the Overhead of Multicast Using Social Features in Mobile Opportunistic Networks

Through utilizing user mobility and short-range device-to-device communication techniques, mobile opportunistic networks (MONs) enable end-to-end message delivery without the dependence on reliable network infrastructures. Examples of these networks include mobile social networks and vehicular networks. Multicast in MONs is used to disseminate information to a group of mobile nodes, which have attracted considerable attention due to high resource utilization. In this paper, we focus on reducing the overhead of multicast in MONs without compromising the delivery performance, through utilizing static social features of nodes and time-varying social behaviors. We first conduct a trace data analysis using the information entropy theory to identify the most important and representative social features in a popular trace, the Infocom06 trace. Based on these static social features, we propose a Social Profile-based Multicast (SPM) routing scheme, that supports efficient multicast message dissemination with a small maintenance overhead, i.e., little cost on maintaining the historical records. Furthermore, by exploring the time-varying social behaviors during daytime and nighttime, we verify that a small number of forwardings during that daytime is sufficient to achieve a desirable delivery ratio. We thus propose an improved overhead-reducing scheme Social Profile-based Multicast-Overhead Reducing (SPMOR) that restricts the number of forwardings during the daytime. The extensive trace-driven simulations show that SPM achieves desirable delivery performance with small maintenance and transmission cost, and SPMOR can further reduce the transmission overhead under diurnal user behaviors. At last, we conduct a similar study on a campus-based MON trace, SocialBlueConn. We find that the main conclusions and performance results on SocialBlueConn are consistent with the Infocom06 trace, which verifies our methodology is scalable.
A Multi-Hop Link Prediction Approach Based on Reinforcement Learning in Knowledge Graphs

Knowledge Graphs (KGs), both manually and automatically constructed, are far from complete. Recently, Knowledge Graph Completion (KGC) has been receiving a great deal of attention. Link prediction is a key task in KGC. In this work, we research multi-hop link prediction based on reinforcement learning (RL) in KGs. More specifically, we propose a novel RL framework for learning more accurate link prediction models: we frame link prediction problem in KGs as an inference problem in probabilistic graphical model (PGM) and use maximum entropy RL to maximize the expected return. Our experimental evaluations show that our proposed method performs well on FB15K-237 and NELL-995 datasets.
Micro-Expression Spotting Using Facial Landmarks

Micro-expressions (MEs) are brief involuntary facial expressions and it usually happens when people try to mask their emotions in high-stake situations. As a result, it is hard to detect the occurrence of a spontaneous MEs due to the limitation of human vision in spotting the brief and subtle change of facial expression. A MEs recognition system can be categorized into two major tasks which are the spotting of the fleeting change of facial expression and the classification of the emotion behind the spotted MEs. In fact, most of the spotting of MEs proposed in the earlier work by other researchers have low accuracy and complex system model. As a result, this paper proposed a MEs spotting system that requires no training and it spots the spontaneous MEs from the videos by detecting the changes in the ratio of the Euclidean distances of facial landmark in three facial regions (left eyebrow, right eyebrow and mouth) . The proposed method was evaluated on CASME II dataset with an average accuracy of 64.77 % and the highest accuracy was 82.30 %.
Context-Aware 3D Visualization of the Dynamic Environment

We present a graphical interface that provides context-dependent information about possible interactions with a complex cluttered environment, which can be used as a video overlay system in Head Mounted Devices (HMDs) in Augmented Reality applications. The system identifies task relevant objects in a cluttered scene, learns the typical interaction patterns with them in a teaching phase, and supports the user with information about the object functionality and handling in the interaction phase. The underlying systen is capable of identification of objects in cluttered environment, providing information about possible task-dependent grasps, tracking object motion with high frame-rate and prediction of possible actions from a learned action graph that represents possible handling variants for a specific object. The system can be used to train a novice in an unknown environment providing expert knowledge. We present our implementation of the system in a service scenario of a kitchen environment, where the system supports the user while performing the actions.
Motion Trajectory based Spatial-Temporal Degradation Measurement for Video Quality Assessment

With the rapid growth of digital video through the Internet, a reliable video quality assessment (VQA) technology is greatly demanded for video management. Motion information plays a dominant role for video perception, however it is too difficult to be accurately analyzed for VQA. The human visual system (HVS) is highly adaptive to track moving objects with pursuit eye movement. Inspired by the motion process in the HVS, we suggest to analyze the degradation along attended motion trajectories for VQA. As a convenient representation of motion, optical flow is calculated for motion trajectory searching. Next, the degradation on the motion velocity along each trajectory is analyzed with the optical flow for temporal quality measurement. Meanwhile, visual information along each trajectory is extracted for joint spatial-temporal quality measurement. Finally, considering the spatial quality degradation from each frame, a novel VQA model is introduced. Experimental results on the public available VQA databases demonstrate that the proposed VQA model performs highly consistency with the subjective perception.