Reviewerly/exHarmony huggingface dataset:
Multi-Target Tracking with Dependent Likelihood Structures in Labeled Random Finite Set Filters

In multi-target tracking, a data association hypothesis assigns measurements to tracks, and the hypothesis likelihood (of the joint target-measurement associations) is used to compare among all hypotheses for truncation under a finite compute budget. It is often assumed however that an individual target-measurement association likelihood is independent of others, i.e., it remains the same in whichever hypothesis it belongs to. In the case of Track Oriented Multiple Hypothesis Tracking (TO-MHT), this leads to a parsimonious representation of the hypothesis space, with a maximum a posteriori solution obtained through solving an Integer Linear Programming problem. In Labeled Random Finite Set (Labeled RFS) filters, this leads to an efficient way of obtaining the top ranked hypotheses through solving a ranked assignment problem using Murty’s algorithm. In this paper we present a Propose and Verify approach for certain Dependent Likelihood Structures, such that the true hypothesis likelihood is evaluated jointly for the constituent track-measurement associations to account for dependence among them, but at the same time that ranking is still obtained efficiently. This is achieved by proposing a candidate ranking under an assumption of independence, and then evaluating the true likelihood one by one, which is guaranteed, for certain Dependent Likelihood Structures, to not increase from its candidate value, until the desired number of top ranked hypotheses are obtained. Examples of such Dependent Likelihood Structures include the Collision Likelihood Structure and the Occlusion Likelihood Structure, both encountered frequently in applications.
Block-based streaming blind deconvolution for space-variant turbulence mitigation

A block-based, streaming, multi-frame blind deconvolution (MFBD) mitigation method is presented for restoration of imagery impacted by space-variant atmospheric turbulence. An incremental approach is taken, referred to as “streaming”, which operates on each new frame as it arrives. For each new frame, an optimization is initiated to minimize the error between the new frame and forward modeling of the object and point spread function (PSF). To adapt to space-variant turbulence conditions, the fundamental units of operation are small, overlapping blocks of pixels extracted from the entire frame. The optimization for each new block is seeded using the solution from the same block area in the previous frame to produce a cumulative, improved, block solution. For each block, the algorithm implements stochastic gradient descent, and alternates between seeking a solution for the PSF and the object, while holding the other constant. To assist in regularizing the PSF solutions during the processing, the PSF estimate is projected onto a sparse dictionary representation of PSFs. The block solutions are combined using an overlap/add method to produce the object estimate after each frame. Our method can utilize either a Picard iterative process or a limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm with bound constraints. A comparison of results is presented using data simulated with high-fidelity to real systems and environments. While incremental methods for blind deconvolution have been reported in the literature, our method has been developed explicitly for the routine mechanics of blind deconvolution.
A Comparative Analysis of Euclidean, Jaccard and Cosine Similarity Measure and Arabic Wordnet for Automated Arabic Essay Scoring

The Coronavirus crisis has cast a shadow over the education sector; As it pushed schools, universities, and educational institutions to close their doors, to reduce the chances of its spread. This raised great concern among those affiliated with this sector, especially students preparing to take important exams. All this pushed educational institutions to switch to E-learning, as an alternative that has long been talked about over the need to integrate it into the educational process. From this standpoint, the importance of electronic exams as an alternative to the traditional paper exams appears, there are many assessment methods in E-learning, one of these assessments is the essay. The study used a variety of similarity tests and used an Arabic data set of 40 student answers. The results indicate that the Arabic Automated Essay System will improve with Cosine similarity and Arabic WordNet. Automatic Arabic Essay Scoring using WordNet is higher in terms of precision than the Automated Arabic Essay Scoring without using WordNet dependent on the mean absolute error value and Pearson correlation. The results clearly show the Cosine similarity with Arabic WordNet has the lowest error. Cosine similarity with all stemming types has the lowest error compared with the Jaccard and Euclidean similarity. Euclidean similarity has the highest error.
Flight Test Validation of Adaptive Collision Avoidance Algorithms Using Multiple Unmanned Aircraft

With the advent of the unmanned aerial systems (UAS) era, it is important that research be conducted into the reliable and safe operation of UAS in urban areas, where aircraft are flying over people and property in spatially constrained environments. A large focus in recent years has been on adaptable collision avoidance systems capable of avoiding fixed and airborne traffic. This paper presents flight test validation of an adaptive collision algorithm known as the morphing potential field algorithm. The morphing potential field algorithm has been modified to allow implementation of more advanced guidance logics such as L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> guidance. The aim is to validate the morphing collision avoidance path planning algorithm in a complex scenario, with multiple aircraft-two fixed wing and two rotary wing-flying the same spatially constrained area. The aircraft were intentionally put in a collision course with each other and other obstacles. Validation flight tests were successfully conducted to assess performance of morphing potential field navigation algorithms and also to quantify the impact of possible communication delays on the safety of collision avoidance methods for high speed and high inertia aircraft.
Quantum motion control for packaging machines

Abstract In the present work, we give a description of a quantum motion controller to be used in automatic machines for an automated process, especially in packaging machines. The entanglement properties of the quantum systems are applied to have a perfect synchronization among the N slave axes. A detailed description of an architecture for a quantum motion controller is given. A quantum correction unit, included in the quantum motion control device and based on quantum inference, is also defined and described. The paper describes the characteristics of the robust control in the quantum correction unit. A comparison with traditional motion controllers is established showing the improved performances relative to the jitter compensation.
Deep Learning Based Semantic Page Segmentation of Document Images in Chinese and English

Semantic page segmentation of document images is a basic task for document layout analysis which is key to document reconstruction and digitalization. Previous work usually considers only a few semantic types in a page (e.g., text and non-text) and performs mainly on English document images and it is still challenging to make the finer semantic segmentation on Chinese and English document pages. In this paper, we propose a deep learning based method for semantic page segmentation in Chinese and English documents such that a document page can be decomposed into regions of four semantic types such as text, table, figure and formula. Specifically, a deep semantic segmentation neural network is designed to achieve the pixel-wise segmentation where each pixel of an input document page image is labeled as background or one of the four categories above. Then we can obtain the accurate locations of regions in different types by implementing the Connected Component Analysis algorithm on the prediction mask. Moreover, a Non-Intersecting Region Segmentation Algorithm is further designed to generate a series of regions which do not overlap each other, and thus improve the segmentation results and avoid possible location conflicts in the page reconstruction. For the training of the neural network, we manually annotate a dataset whose documents are from Chinese and English language sources and contain various layouts. The experimental results on our collected dataset demonstrate the superiority of our proposed method over the other existing methods. In addition, we utilize transfer learning on public POD dataset and obtain the promising results in comparison with the state-of-the-art methods.
Amazon review classification and sentimental analysis on mobile products with R

Start in today’s time; e-commerce has been growing steadily over the years. Due to the variation of online choices, small prices, and fast sourcing organizations, it has become fashionable to buy products, for most people want to shop online. Meanwhile, the quantity and distribution of goods are unequal, and counterfeit branded products are distributed. We review the product as a dataset at Amazon retailers for customer reviews and rank the review text from Objectivity or Fairness and Negative or Positive buyer perspective. These reviews help somewhat, and they promise buyers and manufacturers of products. In this study, we will analyze the basics of specific, positive, and negative production approach.
Mobile Geo-Fencing Triggers for Alerting Entries Into COVID-19 Containment Zones Using IoT

In a thickly populated nation like India, it is hard to forecast community transmission of COVID-19. Hence, a number of containment zones had been recognized all over the country separated into red, orange, and green zones, individually. People are restricted to move into these containment zones. This chapter focuses on informing the public about the containment zone when they are in travel and also sends an alert to the police when a person enters the containment zone without permission using the containment zone alert system. This chapter suggests a containment zone alert system by means of geo-fencing technology to identify the movement of public, deliver info about the danger to the public in travel and also send an alert to the police when there is an entry or exit detected in the containment zone by the use of location-based services (LBS). By creating a fence virtually called geo-fence at the containment zones established based on the government info, this system monitors public movements like entry and exit to fence.
Hand gesture vocalizer for deaf and dumb people

In recent years, the wearable sensors for hand gesture recognition have increasingly received greater attention. Moreover, with the advancement of sensor and technology, it has various potential applications such as human–computer interaction (HCI), virtual reality (VR), remote control (RC) and industry. One of the main application of wearable sensor is used for speech- and hearing-impaired people to express their views easily. Generally, a communication is an elementary form of interaction between two persons. But for the speech and hearing-impaired people, it becomes a actual challenge to put up their views due to their defect. This paper aims to develope a microcontroller-based vocalizer that converts hand gestures to noticeable sound. The structure is easy to adopt according to the user’s requirements, thus eliminating the trouble of dissimilar hand sign conventions used everywhere around the globe.
Research and Construction of the Evaluation Technology System of Intelligent Analysis and Decision Software

Intelligent analysis and decision software is a computer software system established by using artificial intelligence, especially the principle and technology of expert system. According to the particularity of intelligent analysis and decision software, the evaluation technology system of intelligent analysis and decision software is constructed to provide reference for the evaluation project of intelligent analysis and decision software. Intelligent analysis and decision software evaluation technology system includes: test purpose, quality model, test content, test method and technology, test strategy, test environment, test personnel, test process, test document, etc. The system covers all the factors involved in the quality evaluation of intelligent analysis and decision software, and makes contributions to ensure the quality of intelligent analysis and decision software.
Quantum reservoir computing in bosonic networks

Quantum reservoir computing is an unconventional computing approach that exploits the quantumness of physical systems used as reservoirs to process information, combined with an easy training strategy. An overview is presented about a range of possibilities including quantum inputs, quantum physical substrates and quantum tasks. Recently, the framework of quantum reservoir computing has been proposed using Gaussian quantum states that can be realized e.g. in linear quantum optical systems. The universality and versatility of the system makes it particularly interesting for optical implementations. In particular, full potential of the proposed model can be reached even by encoding into quantum fluctuations, such as squeezed vacuum, instead of classical intense fields or thermal fluctuations. Some examples of the performance of this linear quantum reservoir in temporal tasks are reported.
Recent Developments in Bat Algorithm: A Mini Review

Abstract In present time, meta-heuristic algorithms have been widely adopted for solving diverse classes of optimization problems. These procedures/algorithms have advantage over classical algorithms due to strong exploration and exploitation capabilities. In recent time, a Bat algorithm is developed on the basis of echolocation characteristics of bat. This algorithm has applicability to solve number of constrained and unconstrained optimization problems. The bat algorithm gives state of art results as compared to same class of metaheuristics. The objective of this paper is to explore bat algorithm in terms of modifications, improvements, search strategies and nature of problems being solved. Hence, this paper presents the mini review on bat algorithm. Further, the applicability of bat algorithm in diverse filed has been discussed.
Designing convolutional neural networks with constrained evolutionary piecemeal training

Abstract The automated architecture search methodology for neural networks is known as Neural Architecture Search (NAS). In recent times, Convolutional Neural Networks (CNNs) designed through NAS methodologies have achieved very high performance in several fields, for instance image classification and natural language processing. Our work is in the same domain of NAS, where we traverse the search space of neural network architectures with the help of an evolutionary algorithm which has been augmented with a novel approach of piecemeal-training. In contrast to the previously published NAS techniques, wherein the training with given data is considered an isolated task to estimate the performance of neural networks, our work demonstrates that a neural network architecture and the related weights can be jointly learned by combining concepts of the traditional training process and evolutionary architecture search in a single algorithm. The consolidation has been realised by breaking down the conventional training technique into smaller slices and collating them together with an integrated evolutionary architecture search algorithm. The constraints on architecture search space are placed by limiting its various parameters within a specified range of values, consequently regulating the neural network’s size and memory requirements. We validate this concept on two vastly different datasets, namely, the CIFAR-10 dataset in the domain of image classification, and PAMAP2 dataset in the Human Activity Recognition (HAR) domain. Starting from randomly initialized and untrained CNNs, the algorithm discovers models with competent architectures, which after complete training, reach an accuracy of of 92.5% for CIFAR-10 and 94.36% PAMAP2. We further extend the algorithm to include an additional conflicting search objective: the number of parameters of the neural network. Our multi-objective algorithm produces a Pareto optimal set of neural networks, by optimizing the search for both the accuracy and the parameter count, thus emphasizing the versatility of our approach.
Combining Multi-Robot Motion Planning and Goal Allocation using Roadmaps

This paper addresses the problem of automating fleets of robots with non-holonomic dynamics. Previously studied methods either specialize in facets of this problem, that is, one or a combination of multi-robot goal allocation, motion planning, and coordination, and typically acrifice optimality and completeness for scalability. We propose an approach that constructs an abstract multi-robot roadmap in a reduced configuration space, where we account for environment connectivity and interference cost between robots occupying the same polygons. Querying the road-map results in a robot-goal assignment and abstract multi-robot trajectory. This is then exploited to de-compose the original problem into smaller problems, each of which is solved with a multi-robot motion planner that accounts for kinodynamic constraints. We validate the approach experimentally to demonstrate the advantage of considering task assignment and motion planning holistically, and explore some methods for balancing solution quality and computational efficiency.
Probelab ReImager: A Multi-Platform, Open Source Software for Electron Image and X-ray Map Visualization and Customization

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
A Hyperledger Fabric Based Organizational Decentralized Access Control Solution

Currently, various organizations rely on third-party access control and resource management applications or develop their centralized application for resource management. Considering the pitfalls of present centralized applications, the proposed solution presents a decentralized approach towards organizational resource management and access control provisioning within or across organizational units. The research attempts to provide solutions for problems and risks associated with a centralized resource and access control management systems. This approach leverages the advantages of the permissioned Blockchain framework - Hyperledger Fabric and peer-to-peer network. The proposed solution also provides Single Sign-On authentication mechanism (SSO) for various organizational resources. Using the approach presented in the paper we can develop an efficient way of Team management with which we can achieve a higher degree of autonomy of subdivisions within an organization.
Squeezing of spin-1 quantum states via a one-axis twisting Hamiltonian

Squeezing of quantum states is of great interest due to its application in high-precision measurement that can exceed standard quantum noise limit described by Heisenberg uncertainty relations. Here, we study the squeezing of quantum systems with $\text{SU}(3)$ symmetry via a one-axis twisting Hamiltonian and examine the intriguing connections between the squeezing of spin-1/2 and spin-1 systems. There are seven subalgebras of $\text{su}(3)$ Lie algebra. All these subalgebras are identical with $\text{su}(2)$ Lie algebra but not all of them have the same anticommutation relations as $\text{su}(2)$. Interestingly, squeezing parameters corresponding to spin-1 subalgebras depend not only on structure constants but also on anticommutation relations of the subalgebras. Our results are reported for the subalgebras with vanishing anticommutators and nematic squeezing, while in other cases our first-principle calculation recovers known results.
Determining context of association rules by using machine learning

Association rule mining is typically used to uncover the enthralling interdependencies between the set of variables and reveals the hidden pattern within the dataset. The associations are identified based on co-occurring variables with high frequencies. These associations can be positive (A→B) or negative (A→⌐B). The number of these association rules in larger databases are considerably higher which restricted the extraction of valuable insights from the dataset. Some rule pruning strategies are used to reduce the number of rules that can sometimes miss an important, or include an unimportant rule into the final rule set because of not considering the context of the rule. Context-based positive and negative association rule mining (CBPNARM) for the first time included context variable in the algorithms of association rule mining for selection/ de-selection of such rules. In CBPNARM, the selection of context variable and its range of values are done by the user/expert of the system which demands unwanted user interaction and may add some bias to the results. This paper proposes a method to automate the selection of context variable and selection of its value range. The context variable is chosen by using the diversity index and chi-square test, and the range of values for the context variable is set by using box plot analysis. The proposed method on top of it added conditional-probability increment ratio (CPIR) for further pruning uninteresting rules. Experiments show the system can select the context variable automatically and set the right range for the selected context variable. The performance of the proposed method is compared with CBPNARM and other state of the art methods.
Fuzzy Extensions of Isolation Forests for Road Anomaly Detection

In the presented paper the authors are showing the usage of fuzzy extensions of isolations forests for detecting road anomalies like potholes. Using the data acquired by the accelerometer in the smartphone and the proper smartphone application, the vibrations while driving over road were analyzed using multiple variants of extended isolation forests - n-ary (NIF), with fuzzy membership function (MIF), with k-means clustering (KIF), with two fuzzy clusters incorporated (CIF) or two fuzzy clusters and the distance to the cluster center (prototype) utilized (C2DIF). The presented research shows that in comparison to the state-of-the-art methods previously discussed by the authors, the accuracy and false positive rate have improved, while the sensitivity has been improved to reach 100%.