Reviewerly/exHarmony huggingface dataset:
A New Track Initiation Algorithm Based on Hierarchical Clustering And Correlation Coefficient

This paper proposes a novel track initiation algorithm based on agglomerated hierarchical clustering and correlation coefficients. The algorithm firstly using the agglomerative hierarchical clustering algorithm to classify the targets and clutter points. Then selects the candidate tracks according to the logic-based algorithm. Finally, to eliminate false tracks, the correlation coefficient method is used to determine the track initiation results. Simulation results show that the algorithm has a lower false track probability compared to the conventional logic-based method in heavy clutter environment.
Automatic Event Detection in User-Generated Video Content: A Survey

The aim of event detection is to identify interested events in a user-generated content using multiple modalities automatically. However, it is a challenging task particularly when videos are captured in a restricted environment by nonprofessionals. Such videos suffer from poor quality, deprived lighting, blurring, complex camera motion chaotic background clutter, and obstructions. However, with the rise of social media, there is rising popularity of user-generated videos on the Web day-by-day. Each minute, 300 hours of user-generated video are uploaded on you tube due to which people find difficult to search the appropriate content among a large number of videos. Therefore, solutions to this problem are in great demands. In this paper, we study existing technologies for event detection in user-generated videos using multiple modalities. This paper provides key points about feature representations across different modalities, classification techniques.
A Study on Recognition Algorithm of Ultrasonic Sensing Gesture Based on Improved Hidden Markov Model

Gesture is a convenient means of humancomputer interaction. After ultrasonic sensing and extraction of gesture feature sequence, Hidden Markov Model is commonly used to recognize gesture categories. Aiming at the problem that the accuracy of gesture recognition algorithm based on conventional Hidden Markov Model is unsatisfactory, a recognition algorithm of ultrasonic sensing gesture based on improved Hidden Markov Model was proposed in this paper. In this algorithm, state transition probability matrix was improved by Support Vector Machine, and the output probabilities of hidden states in the state sequence were processed by Sigmoid function to optimize the classification performance, so as to improve the accuracy of gesture recognition. In this paper, eight gesture recognition experiments were carried out and the test results showed that the improved algorithm based on Hidden Markov Model optimized by Support Vector Machine could accurately recognize gesture, and the average recognition rate is 94.625 percent, which is 10.35 percent higher than that of conventional Hidden Markov Model. And for each gesture, the recognition rate of the proposed method in this paper was higher than that of based on conventional Hidden Markov Model.
An Automated Approach to Detect and Label Abandoned Objects from Videos using Generalized ROIs

In this paper, abandoned object detection has been performed and represented through named labels using generalized ROIs. In the surveillance system, various activities are monitored for security purposes, among which Abandoned Object Detection is one. The proposed method is performed on various existing benchmark datasets such as CAVIAR, ABODA, and TCD for the verification and validation of work. Initially, the model is trained using the generalized ROIs labels to create ground truth data. The eigenvalue distance method is used for identifying the change in Point-Tracker with generalized ROI to generate the results. The experimental results show that the generalized ROI gives 50-95% improvement in terms of accuracy in the detection of Abandoned Object in the various samples of the Datasets as compared to the existing techniques.
Implementation of An Automatic Image Enhancement Algorithm for Contrast Stretching on FPGA

In the current era of mobile Internet and big data, as an important part of the network information source of video images and its applications throughout various fields. Video image acquisition process is susceptible to the changing external environment, resulting in low image contrast, grayscale darkness and gray dynamic range contraction and other issues, resulting in image quality degradation, the need for image enhancement processing. Image enhancement, while maintaining the original semantics, is targeted to enhance processing, improve image quality and facilitate subsequent analysis and processing, based on the full study of image enhancement algorithms, an improved adaptive scene transformation image enhancement algorithm is proposed. Starting from CMOS image sensor technology, OV8825 CMOS chip is selected as the front-end chip for video capture and combined with SCCB bus control technology to realize drive control.
A Method for Constructing Supervised Time Topic Model Based on Variational Autoencoder

Topic modeling is a probabilistic generation model to find the representative topic of a document and has been successfully applied to various document-related tasks in recent years. Especially in the supervised topic model and time topic model, many methods have achieved some success. The supervised topic model can learn topics from documents annotated with multiple labels and the time topic model can learn topics that evolve over time in a sequentially organized corpus. However, there are some documents with multiple labels and time-stamped in reality, which need to construct a supervised time topic model to achieve document-related tasks. There are few research papers on the supervised time topic model. To solve this problem, we propose a method for constructing a supervised time topic model. By analysing the generative process of the supervised topic model and time topic model, respectively, we introduce the construction process of the supervised time topic model based on variational autoencoder in detail and conduct preliminary experiments. Experimental results demonstrate that the supervised time topic model outperforms several state-of-the-art topic models.
Global Contrastive Person Re-identification

Abstract Solving the problem of pedestrians being occluded by objects is extremely challenging. Using part-level features to describe pedestrian images can provide fine-grained information. However, only paying attention to the local features of body will lack global pedestrian information. And the network consumes time and memory. To solve these problems, we propose a new person re-identification network. The network uses a global contrastivemodule to obtain the features of pedestrians. Through effective use of the pedestrian’s global features, as well as the pedestrian’s personal information and global contrastive information, the pedestrian can be found in the object occlusion to provide a reliable feature embedding. Our model is tested on Market1501, Duke MTMC-reID, CUHK03 and MSMT17 datasets. The experimental results show that our method is effective in occluded person re-identification
Correlation analysis for illegal tampering image evidence detection

Recently, forged pictures, pirated image products, and tampered photos become more and more rampant in our society, which may endanger the rights of owners to earn illegal profits or change the key evidence in law case. It is urgent to accurately trace the real source of images by using the digital image forensics technology. This paper adopts correlation coefficient according to the image content for digital image tampering detection. The image to be detected is masked by a sliding window which moves from left to right or up to bottom. The area in the window is matched with the fingerprint, which is related to the used camera, to determine the tampered area in the image. The fingerprint is determined by the photo‐response non‐uniformity (PRNU) sensor. It is estimated by the residue noise and a group of images from the camera. The correlation coefficient based method can detect the tampered areas from the same image or several different images. The experiments on synthetic tampered images demonstrate most of the tampered areas can be detected.
Remote Sensing Image Registration Using Equivariance Features

We propose a deep learning-based framework for remote sensing image registration using equivariance features. Unlike conventional methods, the networks in the framework are trained on not invariance features but equivariance features since keeping invariancy in the areas of registration of remote sensing images can reduce the accuracy of matching results. Our framework is tested on four sets of KOMPSAT-3 remote sensing images and compared with the conventional machine learning based and the invariant feature based deep learning methods. The experimental results show that the proposed approach outperforms all the comparing methods.
Video stabilization algorithm based on background feature point matching

Global motion estimation between two successive frames is important to the process of video stabilization. In the proposed approach, the estimation of global motion was based on the background feature points (BFPS). First, feature points were collected from the input video by FAST operator; second, feature point's descriptor and matching were based on FREAK operator.The MSAC is used to classify the BFPS. Last, the six parameters of the affine transform model to calculate the interframe motion estimation vector. The experiment results show that he proposed method can stabilize inter-frame jitter, in the meanwhile, it improve the video quality effectively.
An Efficient Probabilistic Model for Anomaly Prediction in Aerial Ad Hoc Networks

The increasing use of Unmanned Aerial Vehicles (UAVs) in various applications of Aerial Ad Hoc Networks, such as precision agriculture and aerial remote sensing, is fast contributing to their adoption in many civilian applications. Generally equipped with multiple sensors, such as cameras and movement detectors, UAVs are also deployed in hostile environments such as disaster zones or military fields. Therefore, and in order to ensure the success of any deployment of Aerial Ad hoc Networks, detecting and isolating failures is of great importance to allow a high level of security and reliability. In this research, we first introduce a model using a Bayesian network that copes with this type of issues and tries to detect any faulty UAV. Second, we develop a probabilistic predictive scheme to avoid the unexpected failure of a UAV. The proposed approach is validated using realistic synthetic datasets provided by the UAV laboratory at the University of Minnesota.
A Phase-wise Analysis of Machine Learning based Human Activity Recognition using Inertial Sensors

Advancements in sensor technology, expanded analytical skills, and advancement in the field of Machine Learning (ML) and Deep Learning (DL), have all resulted in a substantial increase in popularity and wearable sensor performance for Human Activity Recognition (HAR). The real challenge is to spot the events in an unsupervised environment. In this study, we have tried to build a user-friendly, as well as an effective HAR system using an inertial sensor for eight everyday activities performed. The data was collected by our research team, using a single inertial sensor in a fully unsupervised setup. The eight tasks include: standing, sitting, sleeping, running, walking, cycling, upstairs and downstairs. This paper aims to present a detailed analysis and comparison for three primary aspects of a general HAR which contributes to the overall system performance. This involves analyzing the effects of pre-processing, comparing several extraction and selection methods for generating features from time-series data, and finally building and validating the performance of various classification methods to obtain the best combination of the three. The classification methods included in this study are Logistic Regression, K-Nearest Neighbors, Support Vector Machines and Artificial Neural Networks. After choosing the best parameters and techniques, we achieve a remarkable performance for recognizing the eight activities with an overall accuracy of 93.6%.
Methods and Techniques for Privacy Preserving in Blockchain

The rapid developments in blockchain technology in various field mostly concerns about decentralization, transparency and security. The publishing nodes and subscribers nodes in the networks ensures the transaction is recorded in the ledger by giving some transaction fees to the miners. The security is achieved by the various consensus algorithms used in the network. The blockchain technique is widely used in various applications like healthcare, education, supply chain, auction system, E-voting, etc. All these application contains valuable information that can be inferred by attackers through various learning analytic methods while sharing in the public ledgers. So privacy is at risk for all these types of data that are outsourced. The paper presents a study on the various types of techniques that are carried out in the field of privacy preserving for blockchain technology.
Multi-target GM-PHD trackers based on strong tracking cubature Kalman filter

Multi-target tracking technology based on probability hypothesis density (PHD) filter has become a hot research topic due to the feature that it does not require measurement-to-track association. Aiming at the problem that the Gaussian mixture probability hypothesis density (GM-PHD) cannot update the mean and covariance in a nonlinear system, a cubature integration method is used to numerically compute multivariate moment integrals and a suboptimal fading factor of strong tracking filter is introduced to enhance the filter performance in this paper. The proposed algorithm is referred as STCKF-GM-PHD, which combines strong tracking cubature Kalman filter with GM-PHD and realizes the application of GM-PHD in a nonlinear situation. Simulation results support that the proposed approach STCKF-GM-PHD has obvious performance improvement over EKF-GM-PHD and UKF-GM-PHD in numerical stability and filtering accuracy.
Augmented reality Application based Navigating the A Famosa Fortress Site

Our proposal focuses on an augmented reality (AR) mobile application that will assist in navigating the old fortress site, A Famosa in Melaka, Malaysia. During the past few decades, the fortress was greatly damaged. The only remnant is a gate known as Porta de Santiago. In 2011, a 3D model of the fortress is developed, and no efforts were taken to promote this 3D model for other purposes. Due to the fact that it is a UNESCO World Heritage site, and found that the model is significant and provides visitors with an assisted tour guide. The current approach to promote this historical site is not very engaging. It lacks interactivity. For this proposal, an advantage of the advancement in augmented reality technology is based on affordable computer graphics hardware, and the availability of mobile devices such as smartphones and tablets. This application incorporates the history of the fortress, architectural information, cultural values, alongside audio and 3D elements. The aim of this proposal is to allow the visitors to obtain real time information, virtually explore, and access historical facts prior to their visit or whilst on location. This research theoretically demonstrates how existing, available technology, combined with factual data are gathered to disseminate knowledge to the public. Finally, our proof of concept demonstrates the ability to navigate the reconstructed 3D model of the fortress, understanding the background history and promote its features; architectural information and cultural values. The following is a summary of stages adopted to create the application followed by suggestions from our own experience. From the study, one can say that AR is an effective method to promote cultural heritage in more interesting and engaging approach. In general, the experience provided a new method to promote a favorable and renewed image of the monument.
Contrast Enhancement of Dark Images using Weighted Blending of Bright Channel Prior and Robust Retinex Method

The image took in the dark light has low contrast, which affects the clarity of details in it. This results in the loss of information and details in poorly illuminated images. Such images are not suitable for computer vision analysis and observations. In many places, images taken in the dark light like CCTV images at night, military, satellite images, medical images, etc. Several methods proposed for contrast enhancement of low light (darker) images like histogram equalization, bright channel prior, camera response model, and robust retinex model. The contrast enhancement gone using existing methods have some limitations like getting blurring effect, getting over the brightening of details. To overcome these disadvantages, the paper proposes the contrast enhancement of darker images with the weighted blending of bright channel prior (BCR) and robust retinex model (RRM) with different assigned weights. For the performance evaluation of the variations of the proposed method, the image entropy value is computed. From the experimentation done on images from the ExDark dataset, it observed that the proposed weighted blending based contrast enhancement method gives better performance over existing BCR and RRM.
On the Impossibility of NIZKs for Disjunctive Languages From Commit-and-Prove NIZKs

This paper considers the problem of expanding a language class that can be proven by a non-interactive zero-knowledge proof system (NIZK) in a black-box manner in the common reference string model. Namely, given NIZKs for two languages, <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , can we construct an NIZK for <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> v <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> in a black-box manner? NIZKs for disjunctive languages have a large number of applications, such as electronic voting. Therefore, such a black-box construction may enable the efficient constructions of such applications. However, Abe et al. (PKC 2020) showed that this is impossible if the two given NIZKs are simulation-sound. In this paper, we prove that it is also impossible if the two given NIZKs are constructed by the commit-and-prove methodology that is typically used in many cryptographic protocols, including NIZKs. This result suggests that if we want to augment the capability of NIZKs in terms of the languages they can prove, we should rely on certain properties or structures of the underlying NIZKs, such as algebraic structures.
Analysis of Web Components in Website Design

The main objective of research article is to measure the download performance of website. In the methodology various elements are taken into consideration viz., HTML tags, Objects, Images, CSS, Scripting, Website size. The download time of website is measured in ranking grades viz. A, B, C, D, E, F etc. The website developer needs to observe the performance of various elements and optimum download time into order to improve the performance of website design.
Research on Adaptive Sliding Mode Robust Control Algorithm of Manipulator Based on RBF Neural Network

This paper proposes a new algorithm for manipulator system-an adaptive sliding mode robust control algorithm based on RBF neural network. Based on the traditional sliding mode control method, the RBF neural network is used to approximate the manipulator model information and external interference. We established the system model of the six-degree-of-freedom manipulator XIOPM developed by our research group. In order to verify the effectiveness and superiority of the algorithm in the simplest possible case, we took the models of the first two joints and performed it through MTALAB. The simulation results are consistent with our expectations. Compared with the movement of the manipulator under traditional sliding mode control, our method can not only make the actual output trajectory of the manipulator system converge to the desired trajectory at a relatively faster speed, but also reduce chattering to a large extent. The control algorithm reduce the disadvantages of traditional sliding mode control. Its good tracking performance and tracking accuracy make this manipulator system well controlled.
Doctraining mobile

With significant advances in mobile technologies in recent years, educational institutions are providing new opportunities for teaching and learning and, as an example, the popularization of serious games as an educational tool. Based on this context, a data survey was conducted with medical professors from a federal university in northeastern Brazil, to investigate new technological needs aimed to teaching and learning. After analyzing the data, it was decided to design and develop a serious smartphone game focused on training medical students in clinical cases. The development process was divided into two phases. The first one involves the design and development of the gaming activities, and the second one includes activities of data collection and analysis for the validation process. This paper presents results obtained from the requirements gathering with the professors up to the implementation of the serious game.
Error Bounds of Projection Models in Weakly Supervised 3D Human Pose Estimation

The current state-of-the-art in monocular 3D human pose estimation is heavily influenced by weakly supervised methods. These allow 2D labels to be used to learn effective 3D human pose recovery either directly from images or via 2D-to-3D pose uplifting. In this paper we present a detailed analysis of the most commonly used simplified projection models, which relate the estimated 3D pose representation to 2D labels: normalized perspective and weak perspective projections. Specifically, we derive theoretical lower bound errors for those projection models under the commonly used mean per-joint position error (MPJPE). Additionally, we show how the normalized perspective projection can be replaced to avoid this guaranteed minimal error. We evaluate the derived lower bounds on the most commonly used 3D human pose estimation benchmark datasets. Our results show that both projection models lead to an inherent minimal error between 19.3mm and 54.7mm, even after alignment in position and scale. This is a considerable share when comparing with recent state-of-the-art results. Our paper thus establishes a theoretical baseline that shows the importance of suitable projection models in weakly supervised 3D human pose estimation.