Reviewerly/exHarmony huggingface dataset:
Design and Implementation of a Consensus Algorithm to build Zero Trust Model

Zero Trust Model ensures each node is responsible for the approval of the transaction before it gets committed. The data owners can track their data while it's shared amongst the various data custodians ensuring data security. The consensus algorithm enables the users to trust the network as malicious nodes fail to get approval from all nodes, thereby causing the transaction to be aborted. The use case chosen to demonstrate the proposed consensus algorithm is the college placement system. The algorithm has been extended to implement a diversified, decentralized, automated placement system, wherein the data owner i.e. the student, maintains an immutable certificate vault and the student's data has been validated by a verifier network i.e. the academic department and placement department. The data transfer from student to companies is recorded as transactions in the distributed ledger or blockchain allowing the data to be tracked by the student.
Developing a Framework for Vehicle Detection, Tracking and Classification in Traffic Video Surveillance

Intelligent Transportation System and safety driver assistance systems are significant topics of research in the field of transport and traffic management. The most challenging of moving vehicle detection, tracking and classification is in the Intelligent Transportation System (ITS) and smart vehicles sector. This paper provides a method for vision based tracking and classify different classes of vehicle by controlling the video surveillance system. Several verification techniques were investigated based on matching templates and classifying images. This paper focused on improving the performance of a single camera vehicle detection, tracking and classification system and proposed a method based on a Histogram of Oriented Gradient (HOG) function that is one of the most discriminatory features to extract the object features and trained for classification and object on the Linear Support Vector Machine (SVM) classifier. Also categorize vehicles on the shape or dimension based feature extraction with cascade based Adaboost classifier which has the high predictive accuracy and the low cost of storage affirm the efficacy for real-time vehicle classification. In the final stage, for minimizing the number of missing vehicles, Kalman Filter was used to track the moving vehicles in video frame. Our proposed system is checked using different videos and provided the best output with appropriate processing time. The experimental results shows the efficiency of the algorithm.
An Intelligent Text Reader based on Python

As per the World Health Organization (WHO), 285 million people are visually impaired of whom 39 million are completely blind. Though there exist enough remedies to the problems of assisting individuals who are visually impaired to read, there is a requirement for an intelligent text reader which is economical, accurate and easily accessible in order to help them read for day to day activities. This paper proposes an intelligent text reader using python. This product is built on a Raspberry Pi module connected with camera that is used to capture the input image. The input image is enhanced using Image processing techniques. The Tesseract OCR (Optical Character Recognition) engine embedded in the Raspberry Pi searches for the text in an improved image and converts it into digital document. The digital document is then analyzed using semantic check module. After the analysis, text is converted to speech by a Python based TTS (text to speech) conversion unit embedded in the Raspberry Pi. Finally, the audio output is given to the Audio Amplifier for it to be read out.
K-Means Clustering Algorithm Based on Chaotic Adaptive Artificial Bee Colony

K-Means Clustering is a popular technique in data analysis and data mining. To remedy the defects of relying on the initialization and converging towards the local minimum in the K-Means Clustering (KMC) algorithm, a chaotic adaptive artificial bee colony algorithm (CAABC) clustering algorithm is presented to optimally partition objects into K clusters in this study. This algorithm adopts the max–min distance product method for initialization. In addition, a new fitness function is adapted to the KMC algorithm. This paper also reports that the iteration abides by the adaptive search strategy, and Fuch chaotic disturbance is added to avoid converging on local optimum. The step length decreases linearly during the iteration. In order to overcome the shortcomings of the classic ABC algorithm, the simulated annealing criterion is introduced to the CAABC. Finally, the confluent algorithm is compared with other stochastic heuristic algorithms on the 20 standard test functions and 11 datasets. The results demonstrate that improvements in CAABA-K-means have an advantage on speed and accuracy of convergence over some conventional algorithms for solving clustering problems.
Extracting Moving Objects More Accurately: A CDA Contour Optimizer

In the area of change detection, there were a rare number of optimization methods. Most of the optimization methods that are used by change detection are morphological transformation or median filtering, which cannot best optimize change detection algorithm. In this paper, a general post-processing algorithm for change detection is proposed. We believe that some problems cannot be avoided in the area of change detection such as 1) region of moving object generated by change detection is slightly larger than the ground-truth and 2) there are always some disjoint and small regions that are independent from the moving objects. To address the problem, our method can optimize the change detection algorithm bases on the idea of edge detection, which can remove the wrong edge or pixel. In the experiments, more than 20 change detection algorithms that include the best algorithm in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ChangeDetection.net</i> are selected. Most of these change detection algorithms are optimized by the proposed method on PWC, Precision, and FMeasure, where, our optimized algorithm named FgSegNet_v2 is better than all other algorithms in the CDnet. The best-optimized margin of PWC is 0.64, and the fast speed is 548FPS on CPU. Our approach can better resolve the afore-mentioned problems that cannot be avoided and is general and fast. The experiments can be reproduced with C++ on Github <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/walty19950301/CDA-contour-optimizer</uri> .
LFC: Combining Autonomous Agents and Automated Planning in the Multi-Agent Programming Contest

The 2019 Multi-Agent Programming Contest introduced a new scenario, Agents Assemble, where two teams of agents move around a 2D grid and compete to assemble complex block structures. In this paper, we describe the strategies used by our team that led us to achieve first place in the contest. Our strategies tackle some of the major challenges in the 2019 contest: how to explore and build a map when agents only have access to local vision and no global coordinates; how to move around the map efficiently even though there are dynamic events that can change the cells in the grid; and how to assemble and submit complex block structures given that the opposing team may try to sabotage us. To implement our strategies, we use the multi-agent systems development platform JaCaMo to program our agents and the Fast Downward planner to plan the movement of the agent in the grid. We also provide a brief discussion of our matches in the contest and give our analysis of how our team performed in each match.
The INUS Platform: A Modular Solution for Object Detection and Tracking from UAVs and Terrestrial Surveillance Assets

Situational awareness is a critical aspect of the decision-making process in emergency response and civil protection and requires the availability of up-to-date information on the current situation. In this context, the related research should not only encompass developing innovative single solutions for (real-time) data collection, but also on the aspect of transforming data into information so that the latter can be considered as a basis for action and decision making. Unmanned systems (UxV) as data acquisition platforms and autonomous or semi-autonomous measurement instruments have become attractive for many applications in emergency operations. This paper proposes a multipurpose situational awareness platform by exploiting advanced on-board processing capabilities and efficient computer vision, image processing, and machine learning techniques. The main pillars of the proposed platform are: (1) a modular architecture that exploits unmanned aerial vehicle (UAV) and terrestrial assets; (2) deployment of on-board data capturing and processing; (3) provision of geolocalized object detection and tracking events; and (4) a user-friendly operational interface for standalone deployment and seamless integration with external systems. Experimental results are provided using RGB and thermal video datasets and applying novel object detection and tracking algorithms. The results show the utility and the potential of the proposed platform, and future directions for extension and optimization are presented.
A Generalizable Deep-Learning Approach for Cardiac Magnetic Resonance Image Segmentation Using Image Augmentation and Attention U-Net

Cardiac cine magnetic resonance imaging (CMRI) is the reference standard for assessing cardiac structure as well as function. However, CMRI data presents large variations among different centers, vendors, and patients with various cardiovascular diseases. Since typical deep-learning-based segmentation methods are usually trained using a limited number of ground truth annotations, they may not generalize well to unseen MR images, due to the variations between the training and testing data. In this study, we proposed an approach towards building a generalizable deep-learning-based model for cardiac structure segmentations from multi-vendor,multi-center and multi-diseases CMRI data. We used a novel combination of image augmentation and a consistency loss function to improve model robustness to typical variations in CMRI data. The proposed image augmentation strategy leverages un-labeled data by a) using CycleGAN to generate images in different styles and b) exchanging the low-frequency features of images from different vendors. Our model architecture was based on an attention-gated U-Net model that learns to focus on cardiac structures of varying shapes and sizes while suppressing irrelevant regions. The proposed augmentation and consistency training method demonstrated improved performance on CMRI images from new vendors and centers. When evaluated using CMRI data from 4 vendors and 6 clinical center, our method was generally able to produce accurate segmentations of cardiac structures.
Human behavioral pattern analysis-based anomaly detection system in residential space

Abstract Increasingly, research has analyzed human behavior in various fields. The fourth industrial revolution technology is very useful for analyzing human behavior. From the viewpoint of the residential space monitoring system, the life patterns in human living spaces vary widely, and it is very difficult to find abnormal situations. Therefore, this study proposes a living space-based monitoring system. The system includes the behavioral analysis of monitored subjects using a deep learning methodology, behavioral pattern derivation using the PrefixSpan algorithm, and the anomaly detection technique using sequence alignment. Objectivity was obtained through behavioral recognition using deep learning rather than subjective behavioral recording, and the time to derive a pattern was shortened using the PrefixSpan algorithm among sequential pattern algorithms. The proposed system provides personalized monitoring services by applying the methodology of other fields to human behavior. Thus, the system can be extended using another methodology or fourth industrial revolution technology.
Design Of Led’s For Interactive Court Lines

A novel method of Multiple lines of e-courts for various sports application controlled by Light Emitting Diode strips was proposed and experimentally analysed which accurately displays the court lines for the selected game. Here, an aurdino based microcontroller is used which supervises the work of the block chain and RFID tag which serves as an electronic key for accessing the sport court. The distinct court rule lines are shown for various sports like basketball, badminton, volleyball and kabaddi are set up and stored in the Application through which one can select particular court of interest. In this paper, we propose an efficient system design by providing security and quick access to multiple games at the same location. The designed module projects a series of lines in one touch onto the surface and also further transforms a basketball court to a volleyball court in seconds hence it utilize the space efficiently with multi-purpose single sporting space. This novel idea replaces the traditional court marking complexity for various games under single roof. The usage of various sensors like pressure sensor, IR sensors were utilized for monitoring the score points which offers added assistance to referees. Hence the idea is novel and the design concept is demonstrated and verified. This electronic court rule line system not only helps sportsmen and referees, but also connects them with audiences.
Unsupervised Learning of Video Representations via Dense Trajectory Clustering

This paper addresses the task of unsupervised learning of representations for action recognition in videos. Previous works proposed to utilize future prediction, or other domain-specific objectives to train a network, but achieved only limited success. In contrast, in the relevant field of image representation learning, simpler, discrimination-based methods have recently bridged the gap to fully-supervised performance. We first propose to adapt two top performing objectives in this class - instance recognition and local aggregation, to the video domain. In particular, the latter approach iterates between clustering the videos in the feature space of a network and updating it to respect the cluster with a non-parametric classification loss. We observe promising performance, but qualitative analysis shows that the learned representations fail to capture motion patterns, grouping the videos based on appearance. To mitigate this issue, we turn to the heuristic-based IDT descriptors, that were manually designed to encode motion patterns in videos. We form the clusters in the IDT space, using these descriptors as a an unsupervised prior in the iterative local aggregation algorithm. Our experiments demonstrates that this approach outperform prior work on UCF101 and HMDB51 action recognition benchmarks. We also qualitatively analyze the learned representations and show that they successfully capture video dynamics.
Neural Architecture Search for LF-MMI Trained Time Delay Neural Networks

State-of-the-art automatic speech recognition (ASR) system development is data and computation intensive. The optimal design of deep neural networks (DNNs) for these systems often require expert knowledge and empirical evaluation. In this paper, a range of neural architecture search (NAS) techniques are used to automatically learn two types of hyper-parameters of factored time delay neural networks (TDNN-Fs): i) the left and right splicing context offsets; and ii) the dimensionality of the bottleneck linear projection at each hidden layer. These techniques include the differentiable neural architecture search (DARTS) method integrating architecture learning with lattice-free MMI training; Gumbel-Softmax and pipelined DARTS methods reducing the confusion over candidate architectures and improving the generalization of architecture selection; and Penalized DARTS incorporating resource constraints to balance the trade-off between performance and system complexity. Parameter sharing among TDNN-F architectures allows an efficient search over up to <inline-formula><tex-math notation="LaTeX">$7^{28}$</tex-math></inline-formula> different systems. Statistically significant word error rate (WER) reductions of up to 1.2&#x0025; absolute and relative model size reduction of 31&#x0025; were obtained over a state-of-the-art 300-hour Switchboard corpus trained baseline LF-MMI TDNN-F system featuring speed perturbation, i-Vector and learning hidden unit contribution (LHUC) based speaker adaptation as well as RNNLM rescoring. Performance contrasts on the same task against recent end-to-end systems reported in the literature suggest the best NAS auto-configured system achieves state-of-the-art WERs of 9.9&#x0025; and 11.1&#x0025; on the NIST Hub5&#x2019; 00 and Rt03 s test sets respectively with up to 96&#x0025; model size reduction. Further analysis using Bayesian learning shows that the proposed NAS approaches can effectively minimize the structural redundancy in the TDNN-F systems and reduce their model parameter uncertainty. Consistent performance improvements were also obtained on a UASpeech dysarthric speech recognition task.
Black hole Entropic Fuzzy Clustering-based image indexing and Tversky index-feature matching for image retrieval in cloud computing environment

Due to the expansion of social websites, the data owner accumulates multimedia data and stores in cloud server. The owner encrypts the images before uploading it in cloud server for security. However, the conventional encryption method failed to support feasible retrieval on the encrypted images. This paper proposes novel technique, namely Black Hole Entropic Fuzzy Clustering-based Tversky index for effective image retrieval. Here, the SLBP (Spatial Local Binary Pattern) features, semantic features, statistical features, and low image features are considered in the feature extraction. In addition, encryption of feature vector using Elliptic Curve Cryptography (ECC) is done for encrypting the images contained in cloud server. The Black Hole Entropic Fuzzy Clustering (BHEFC) is adapted for grouping the images. Whenever users request an input query then, the query image is fed to feature extraction, and encryption phase, wherein the Tversky index is applied for matching the similarity between the images for retrieval. The proposed BHEFC-based Tversky index outperformed other methods with maximal accuracy of 95.737%, maximal precision of 83.563%, maximal recall of 94.697%, and maximal F-measure of 83.014%.
ML Criterion Based Signal Detection of a MIMO-OFDM System Using Quantum and Semi-Quantum Assisted Modified DHA/BBHT Search Algorithm

The quantum search algorithm (QSA) is one of the most recent developments in the era of quantum signal processing (QSP) and quantum information theory (QIT). It shows a faster performance in finding the solutions of system such as multiple-input multiple-output (MIMO) communication system, quantum error correction and others with a polynomial order over the classical one. Grover's algorithm is one of the first proposed QSA in identifying the index number of the solution of a system, whose solutions are known to the oracle. A further improved QSA called Boyer-Brassard-Høyer-Tapp (BBHT) is followed by the Durr-Hoyer algorithm (DHA), which proposes to find the solutions, while the exact number of the solutions is not known. In this treatise, we propose two modifications to the existing DHA and BBHT algorithms. In DHA, we propose a semi-quantum framework, where after each iteration in DHA, the non-solution points are removed in classical framework. This makes the data set iteratively shorter. In the case of BBHT algorithm, we propose to choose the Grover search number based on the Gamma distribution instead of the uniform one, which gives more chances to the new larger values to improve probability of success. The performance of the proposed QSAs demonstrates plausible improvement in search success performance as compared to their existing BBHT and DHA counterparts. Numerical simulations are conducted with the proposed QSA algorithms on a MIMO orthogonal frequency division multiplexing (OFDM) system to demonstrate the performance improvement.
Reference-frame-independent measurement-device-independent quantum key distribution using fewer states

Reference-frame-independent measurement-device-independent quantum key distribution (RFI-MDI-QKD) is immune to both detector side channel attacks and slow drifts of reference frames, and thus possesses a high level of security. In the traditional protocol of RFI-MDI-QKD, each legitimate user, Alice or Bob, has to prepare six encoding states in three orthogonal bases ($X, Y$, and $Z$), which are sent to the untrusted third party, Charlie. Then all the successful counting events are utilized for parameter estimations, which suffer from a severe finite-size effect. In this paper, we propose a scheme on RFI-MDI-QKD with fewer states and demonstrate that it can be secured and made useful by utilizing semidefinite programming. Numerical simulation results reveal the equivalence between those groups of different monitoring states from Alice and Bob. In addition, when considering practical experimental conditions and taking finite-size effects into account, our proposal can achieve a better performance than the traditional six-state RFI-MDI scheme, which provides a resource-saving and simpler state-preparation approach for practical implementations without sacrificing key rates.
A Bayesian Latent Variable Selection Model for Nonignorable Missingness

Missing data are exceedingly common across a variety of disciplines, such as educational, social, and behavioral science areas. Missing not at random (MNAR) mechanism where missingness is related to unobserved data is widespread in real data and has detrimental consequence. However, the existing MNAR-based methods have potential problems such as leaving the data incomplete and failing to accommodate incomplete covariates with interactions, non-linear terms, and random slopes. We propose a Bayesian latent variable imputation approach to impute missing data due to MNAR (and other missingness mechanisms) and estimate the model of substantive interest simultaneously. In addition, even when the incomplete covariates involves interactions, non-linear terms, and random slopes, the proposed method can handle missingness appropriately. Computer simulation results suggested that the proposed Bayesian latent variable selection model (BLVSM) was quite effective when the outcome and/or covariates were MNAR. Except when the sample size was small, estimates from the proposed BLVSM tracked closely with those from the complete data analysis. With a small sample size, when the outcome was less predictable from the covariates, the missingness proportions of the covariates and the outcome were larger, and the missingness selection processes of the covariates and the outcome were more MNAR and MAR, the performance of BLVSM was less satisfactory. When the sample size was large, BLVSM always performed well. In contrast, the method with an MAR assumption provided biased estimates and undercoverage confidence intervals when the missingness was MNAR. The robustness and the implementation of BLVSM in real data were also illustrated. The proposed method is available in the Blimp software application, and the paper includes a data analysis example illustrating its use.
Design-theoretic analogies between codes, lattices, and vertex operator algebras

There are many analogies between codes, lattices, and vertex operator algebras. For example, extremal objects are good examples of combinatorial, spherical, and conformal designs. In this study, we investigated these objects from the aspect of design theory.