Reviewerly/exHarmony huggingface dataset:
Error-corrected fringe discrimination using nonary-encoded patterns for phase-shifting projected fringe profilometry

Most 3D profile measurements based on phase-shifting techniques require additional projected patterns to identify fringe orders for the task of phase unwrapping. The total number of projections is usually more than that used for phase extraction. For surfaces with low signal-to-noise ratios, these approaches might further encounter challenges in phase unwrapping. In this study, an error-corrected scheme for fringe discrimination using nonary-encoded patterns is presented. There is no need to take additional projections for phase unwrapping. The fringe patterns used for phase extraction can be analyzed to obtain the absolute phase directly. Any two adjacent fringes on the inspected surface can be discerned. To identify fringes for surfaces which are projected by only two fringes is available. Even though one of the projected fringes is decoded incorrectly, the error-corrected scheme can detect such a mistake and recover the fringe order.
A New Disparity Selection Method Based on Extreme-Point Extraction

Disparity selection is an important step in stereo matching and the Winner Takes All (WTA) strategy is commonly used to compute the disparity map from the aggregated cost volume. However, this strategy is lack of robustness, a contradiction between the accuracy of the result and the complexity of the method would be caused and the application of the local method is limited severely. In this paper, the defect of the WTA strategy is elucidated by introducing the concept of the cost curve, firstly. And then, we present a new disparity selection method including two steps. The first step, some initial points are extracted from the cost curve. The second step, the matching point would be found by a discriminator in the initial points. Finally, a complete local matching framework is established and the standard image data sets are used to verify the effectiveness of our method. The experimental results show that the proposed method performs much better compared with the WTA strategy and produces good results, even though a simple cost aggregation is used.
TOR Anonymous Traffic Fingerprint Extraction and Recognition Based on Neural Network

Abstract The TOR anonymous communication system is an important means to protect network communication security and user privacy, but there are still criminals trying to destroy the confidentiality of the anonymous communication system through some special methods. Aiming at the abuse of the TOR anonymous communication system, this paper proposes a neural network-based anonymous traffic identification method, which uses a one-dimensional convolutional neural network for feature extraction, prediction and classification, and finally integration. In the experiment, nearly 100 websites were selected for the flow feature extraction and recognition based on one-dimensional convolutional neural network. The recognition accuracy rate is 87.5%, indicating that this method can effectively fingerprint TOR anonymous communication.
Multi-target Tracking with EmbedMask and LSTM Model Fusion

The problem of occlusion occurs during multi-target tracking may result in loss of characteristics of tracking target and thus lose the tracking targets. This paper proposes a multi-target vehicle tracking algorithm based on fusion of Embedding Coupling for One-stage Instance Segmentation (EmbedMask) and Long Short-Term Memory (LSTM) model. Firstly, the obtained real-time video data is input into EmbedMask target detection model by frame for target detection. The targets are separated from background, and traditional rectangular box detection is replaced by instance segmentation. Secondly, the maximum feature data of targets is generated by the resent convolution network, which is input into the LSTM model. The continuous data of targets is obtained by calculating and estimating the motion attitude of the tracking target. Finally, the motion and detection data of targets is input into new LSTM model layer, and the fusion calculation is used to reduce the tracking loss caused by overlapping, which can ensure the accuracy of target tracking. Experimental results on standard MOT data sets show that the proposed algorithm is robust and can be used to accurately track occluded overlapping targets.
FPGA based image readout from Camera using Ethernet protocol and Python

This paper presents the design and development of a prototype model of an FPGA based pixel data readout from a camera to a host system (PC). An Artix-7 series FPGA on the NEXYS4-DDR board acts as the main controlling unit between the image sensor and the PC. Ethernet mode of communication is used for transferring the image data from the FPGA to the host system. A Python application is developed for reading and visualizing the image from the Ethernet data received in the host system. Besides facilitating the data transfer between the image sensor and the host PC, the FPGA includes various blocks needed for testing and debugging. Using these blocks, the image readout system is designed in a lab-access limited environment. The image sensor operates at 25 MHz with a spatial resolution of 640 x 480 and a temporal resolution of 30 fps.
Reversible Data Hiding and Coupled Chaotic Logistic Map Using Image Encryption

Data hiding and encryption are the two successful techniques to protect the information in public domain while transmission. Conceal information in a cover image and reconstruction of the same cover image after extracting the hidden information without any alteration is recognized as reversible data hiding (RDH) technology. Reversible data hiding is mainly found application in military and medical field applications. Many such types of schemes are technologically advanced to ensure the security, genuineness, and integrity of the images on transmission through Internet or through any other medium. The cover image used here is a gray image and it is preprocessed for prediction error detection. After the detection of possible prediction error pixels, the position of the predicted error pixel is stored in a binary location map. For prediction error detection the median edge detector (MED) is used. By using information in the binary location map, the error position information is inserted to the encrypted image by most significant bit (MSB) substitution method. Chaotic generator used in this work for encryption process is two chaotic logistic map, which is used to produce pseudo-random binary sequence. The encryption algorithm used here is Coupled Chaotic Logistic Map, to generate pseudo-random binary sequence to encrypt the image pixel by pixel. The secret message is embedded into the prediction error highlighted encrypted image by MSB substitution method. MSB prediction method is used in the decoding phase as it is essential for the successful reconstruction of cover image. The embedded image can be productively extracted and the original cover image can absolutely recovered and its PSNR and SSIM value are infinity and 1.00, respectively. By using the coupled logistic chaotic map, high visual security, better NPCR, UACI, and correlation values are achieved.
PEA: Predicting Expert Agents approach

In multi-agent recommender system, the knowledge degree of an agent and its trust degree are two main criteria in the decision-making phase. These criteria are used to recommend the expert agent. Therefore, how to model agent and evaluate its trust is becoming a challenging issue. This problem can affect the whole prediction of expert agents. In this paper, we propose a Predicting Expert Agents approach (PEA). We applied a clustering method, Fuzzy Formal Concepts Analysis, to model agent and evaluate its trust and a probabilistic method, Theory of Belief Functions, to predict the expert agent.
Generating new nonclassical state by repeatedly operating number operator on coherent state

In this paper, using the principle of quantum state superposition, we report a nonclassical quantum state which is constructed by repeatedly operating the number operator on the coherent state. Nonclassical effects of this state are discussed by photon-number distribution, sub-Poissonian statistics, anti-bunching and negativity of Wigner function and squeezing effect. Our work provides an important nonclassical resource, which may be used in quantum communication and quantum optics.
Activity Modeling of Individuals in Domestic Households Using Fuzzy Logic

A model which predicts the activities of each individual in a household is developed. This model is used to simulate the activities of 25,000 households in a town in Kerala (a state in southern region of India). A fuzzy-logic based approach is used to estimate the probabilities of an individual to be in a particular state/activity. Then an optimization problem is formulated to compute the activity transitions. Further, the activity transitions of the individuals within a house are tied in an appropriate way. These activity transitions are then used to simulate a Markov chain of the activities for a sample set of households in a town in Kerala.
Wide Range and Dynamic Video Image Stabilization Applied in UAV Aerial Photographing

In the process of aerial photographing, due to the wide range of complex scene conditions such as air flow changes and the influence of light and atmosphere, which lead to the large dynamic instability phenomenon such as mechanical vibration and even violent shaking when the camera carrier moves, this paper proposes a new video stabilization algorithm for the wide range and large dynamic state. Firstly, the feature points of reference frame are extracted by GFTT algorithm, and the motion vector between frames is obtained by using sparse optical flow method to find matching feature points in the current frame of continuous video image. Secondly, on the basis of traditional Kalman filter, a hybrid filter composed of Kalman filter and Gauss discrete filter is used to smooth the motion of each frame image. Finally, a stable video sequence is obtained by motion compensation. The experimental results show that the MSE parameters of this algorithm are 21.4% lower than those of the traditional Kalman filter based image stabilization algorithm, which can meet the requirements of wide range and dynamic video image stabilization.
Confidence Score based Mini-batch Skipping for CNN Training on Mini-batch Training Environment

As the convolutional neural network becomes complex and datasets become huge, a lot of time is spent training the network. In this paper, we propose to mitigate this phenomenon with a mini-batch skipping strategy based on an arithmetic mean of confidence score of images. By skipping the unimportant mini-batch on the training phase, the mini-batch skipping provides saving a lot of time on backpropagation and weight update. We empirically demonstrate the effectiveness of our method with Resnet-18, Resnet-50, and mobilenet-v2 on Cifar-10 and Cifar-100. For Res-net-50, mini-batch skipping gives a 1.39x speedup in training operation without significant accuracy drop.
Unsupervised Monocular Depth and Pose Estimation Using Multiple Masks Based on Photometric and Geometric Consistency

With the rapid development of CNN-based deep learning methods, unsupervised monocular depth estimation and camera ego-motion estimation in consecutive frames have attracted much attention in recent years. In the process of photometric consistency-based image reconstruction, the pixel mismatching problem brings serious interference to model training. Compared with correctly matched pixels, mismatched pixels lead to severe influence on training loss. In this paper, to tackle the pixel mismatching problem, two novel binary photometric and geometric consistency-based auto-masks are presented to exclude abnormal errors in training loss. More significantly, the small loss in the forward and backward reconstructions is shoes to further reduce the interference of abnormal pixels. The combined evaluation results show that the proposed depth estimator achieves the state-of-the-art performance on the KITTI benchmark, and the results of visual odometry accuracy is competitive with the models that using optical flow or loop detection in traditional methods.
Future Bachelors Self-Organization as a Pedagogical Problem

The quality of bachelors’ professional training depends on many factors; however, nowadays the ability of students to self-organization is one of the most important, since self-organization is aimed at creating conditions for the effective use of their capabilities. This article offers a variety of interpretations and views on the concept of «self-organization» in pedagogical science.
Support for the personalization of master’s studies based on a fuzzy competency model (on the example of data analysis disciplines)

Abstract Competence models for evaluating important teaching tools for personalizing educational content. This problem is of particular importance for studying in the master’s program, the teacher of each discipline needs the diversity of the educational trajectories of the master’s students and create an adaptive educational environment for both those who deepen their profiling, continuing their education in the previously chosen direction of training, and for those who have changed the vector of professional development. The aim is to demonstrate a fuzzy model for assessing competence using the example of this organization of training in data analysis. It is developed on the basis of the integration of cognitive modeling and fuzzy logic and is distinguished by the ability to analyze the mutual influence of the concepts of the considered area of knowledge of the target level of learning and is the basis for determining the area of acceptable educational trajectories. The implementation of the proposed model will provide high-quality personalization of semi-structured educational content. The author analyzed the modern skill sets of specialists in the field of data science and analytics, as well as methods and tools for competence assessment in the context of learning adaptation. The requirements for the developed concept of soft learning in the context of personalization of learning in the master’s program are determined, the structure of a fuzzy cognitive map is proposed, which allows one to assess the semi-structured competencies of various groups and determine the priority of mastering the concept of the proposed educational content. The proposed approach will make it possible to systematize the process of identifying permissible educational trajectories at the discipline level, depending on the available knowledge and accumulated professional experience, as well as on the student’s career aspirations, thereby expanding the range of possible educational strategies and increasing the level of organization of the educational process on the part of the teacher and the level of self-organization on the part student.
Low Illumination Color Image Enhancement Based on Improved Retinex Theory

In view of the defects of the classical Retinex image enhancement algorithm, such as poor texture detail retention, halo and over enhancement, and abrupt tone, this paper proposes a low illumination color image enhancement algorithm based on Gabor filter and retinex theory. Firstly, the brightness I component of the image is extracted in the HSI color space, and then the image with the brightness component is enhanced by MSRCR (the color-recovered retinex algorithm). At the same time, the image is enhanced by SSR (the single-scale retinex algorithm) based on Gabor filter in RGB color space, and the image with better texture and edge details is obtained. Finally, the final enhanced image is obtained by weighted fusion. The algorithm is compared with SSR, MSR (the multi-scale retinex algorithm) and MSRCR. The experimental results show that the image information processed by this algorithm is richer in color, hue and the color is closer to the original image, and it effectively reduces the occurrence of halo and over-enhancement. This algorithm can enhance the image of some low illumination color images, and the visual effect of the enhanced image is relatively peaceful.
Online Configuration Selection for Redundant Arrays of Inertial Sensors: Application to Robotic Systems Covered with a Multimodal Artificial Skin

Multiple approaches to the estimation of high-order motion derivatives for innovative control applications now rely on the data collected by redundant arrays of inertial sensors mounted on robots, with promising results. However, most of these works suffer scalability issues induced by the considerable amount of data generated by such large-scale distributed sensor systems. In this article, we propose a new adaptive sensor-selection algorithm, for distributed inertial measurements. Our approach consists in using the data of a subset of sensors, selected among a larger collection of inertial sensing elements covering a rigid robot link. The sensor selection process is formulated as an optimization problem, and solved using a projected gradient heuristics. The proposed method can run online on a robot and be used to recalculate the selected sensor arrangement on the fly when physical interaction or potential sensor failure is detected. The tests performed on a simulated UR5 industrial manipulator covered with a multimodal artificial skin, demonstrate the consistency and performance of the proposed sensor-selection algorithm.
An Approach to Indoor Occupant Counting Method Based on Zenithal Video Analysis

For many applications in buildings, such as heating, ventilation and air conditioning (HVAC), lighting control under normal conditions and fast evacuation under emergency conditions, the real-time and reliable knowledge of the number of occupants in each area plays an important role. This paper works on a naive and ideal occupant estimation model by detecting the enter and leave occupants of boundary detection area, and the zenithal video analysis is utilized to reduce the cumulative error of the naive estimation model. Specifically, the Kalman Filter is utilized to track the targets that through the border of each area, and two key tunable parameters that are the matching distance threshold and scaling factor are designed and tested elaborately to reduce the estimation error. Experimental results show that the proposed zenithal video analysis based indoor occupant counting approach with the careful tuning of matching distance threshold and the scaling factor,can effectively reduce the discriminant error of occupants entering and exiting, achieving the estimation of the indoor occupant number with above 92% accuracy, the occupied or unoccupied state estimation for an area with more than 99% accuracy, and the staying time of indoor occupant with more than 99% accuracy.
Preprocessing HTTP Requests and Dimension Reduction Technique for SQLI Detection

The rapid development of web applications leads to the security problems related to the web attacks. The detection of these attacks is a critical task specially SQL injection which is the top most web vulnerability. The existing signature based detection approaches lacks the functionality to cope up with the new signatures. Machine Learning (ML) becomes an alternative concept to existing solutions. However readymade labeled dataset or corpus with SQLI patterns is unavailable and also the current existing models have not yet used the feature reduction technique on most dangerous vulnerability logs. These are the well known issues in SQL Injection research. This paper contains an approach in which SQLI logs will be collected & further processed based on ‘TFIDF-Ngram with Singular value decomposition’ approach for dataset preparation and feature engineering. The paper explores the generation and classification of feature reduced SQLI data set from HTTP traffic logs like CSIC-2010 [1] and ECML/PKDD-2007 [2, 3]. The experiments carried out on supervised ML techniques with observed evaluations presented in Confusion Matrix (CM).
Enhancing African Research Visibility Through Use of Repositories

Sub-Saharan Africa [is said to] account [s] for 13.5% of the global population but less than 1% of global research output.Part of the problem is that there is low visibility of its research output.Open access repositories have created a paradigm shift in enhancing African research output.Where are these repositories and how can we take advantage of them?How should African researchers make use of these repositories to increase their visibility?Watch the recording of the panel discussion: 0:00
AF2S: An Anchor-Free Two-Stage Tracker Based on a Strong SiamFC Baseline

Siamese network based trackers have become a mainstream in visual object tracking. Recently, several high-performance multi-stage trackers have been proposed and some of them adopt SiamRPN for the first-stage region proposal. We argue that an anchor-based region proposal network is not necessary for the tracking task, as a tracker has a strong prior about the location and size of the target. In this paper, we propose a two-stage visual tracker which uses SiamFC for region proposal. SiamFC defines a bounding box by its center, which is a typical anchor-free (AF) network, so we dub our tracker AF2S. As the model size of SiamFC is only about 1/10 that of SiamRPN, AF2S results in a significantly lighter model than its SiamRPN-based counterparts. In the design of AF2S, we first build a strong AlexNet-based SiamFC baseline which improves the AUC on OTB-100 from 0.582 to 0.665. Further, we propose a position-sensitive convolutional layer which can be stacked after SiamFC backbone to increase the robustness of proposals without losing localization precision. Finally, a relation network is used for box refinement. Experimental results show that AF2S achieves the best performance on OTB-100 and VOT-18 among the state-of-the-art trackers which use AlexNet as backbone. On LaSOT-test, AF2S achieves an AUC of 0.480, which is among the first-tier performance even when trackers with more powerful backbone and much larger model size are considered.
Two Dimensional Moving Average Filter for Performance Enhancement of Noisy Phase Shifted Interferograms

In optical metrology, phase shifting is one of the most prominent strategies to retrieve the parameters (like object shape, deformations, thickness, refractive index, focal length, etc.) from acquired interferograms. However, traditional phase shifting techniques is overly sensitive towards noise (both internal and external), which limits their applicability in accurate interferometric applications. To circumvent this drawback, in this paper, a two dimensional moving average filter is combined with phase shifting technique to analyze and extract the phase information from noisy interferograms. This novel algorithm acquires the advantages of two existing strategies in terms of accuracy and speed. Proposed technique has a remarkable ability to accurately and automatically extract full-field phase distribution from four phase shifted interferograms even in the presence of high-density additive and multiplicative noise. The performance estimation and comparison of the technique is demonstrated using controlled computer simulation.