Reviewerly/exHarmony huggingface dataset:
Arithmetic Average Based Multi-sensor TPHD Filter for Distributed Multi-target Tracking

Compared with the probability hypothesis density (PHD) filter for sets of targets, the trajectory probability hypothesis density (TPHD) filter can estimate the sets of trajectories in a principle way and has better target tracking performance. This paper aims at extending the TPHD filter to distributed multitarget tracking (MTT) for the multi-sensor system. However, in the trajectory set based distributed fusion implementation, the trajectory state difference phenomenon makes the clustering and merging techniques unfeasible in trajectory state space. To address this problem, this paper studies the space decomposition of the TPHD and proposes a distributed MTT method based on the TPHD filter with the weighted arithmetic average (WAA) fusion rule. First, we prove the rationality of the space decomposition in the posterior density of the TPHD filter. Then, based on the proposed property, we derive the WAA fusion formulation of the TPHD filter by minimizing the weighted sum of Kullback-Leibler divergences (KLD) from local posterior densities, and develop the analytical Gaussian mixture (GM) implementation with the L-scan approximation. Numerical results demonstrate the efficacy of the proposed fusion method.
A Study of Fusions of Multiple Estimates for Limit Cases

Decentralised estimation often sacrifices optimality for solution simplicity, while within the fusion under unknown correlation, a worst-case type of optimality is adopted. This paper studies the gap between the simple solution and the optimal one for special cases. Namely, symmetric configurations are considered for infinite number of estimates and also for infinite dimension of the state to be estimated. In these academic cases, the optimal solution is better than the simple one by low tens percent, if the size of circumscribing balls is considered. In practice, much lower gap can be expected.
Probabilistic Information Matrix Fusion in a Multi-Object Environment

In distributed sensor fusion systems, each of the local sensors has its own tracker processing local measurements for measurement-to-track association and state estimation. Only the processed data, local tracks (LT) comprising state vector estimates and their covariance matrices are transmitted to the fusion center (FC). In this work, a multi-object hybrid probabilistic information matrix fusion (MO-HPIMF) is derived taking into account all association hypotheses. In MO-HPIMF, the association carried out is between the FC track states (prediction) and the LT state estimates from local sensors. When having a large number of objects and sensors in fusion, only the m-best FC-track-to-LT association hypotheses should be incorporated in MO-HPIMF to reduce the computational complexity. A Sequential m-best 2-D method is used for solving the multidimensional assignment problem in this work. It is shown in the simulations that MO-HPIMF can successfully track all targets of interest and is superior to track-to-track fusion (T2TF, a commonly used approach in distributed sensor fusion system) which relies on hard association decisions.
Reducing Viral Transmission through AI-based Crowd Monitoring and Social Distancing Analysis

This paper explores multi-person pose estimation for reducing the risk of airborne pathogens. The recent COVID-19 pandemic highlights these risks in a globally connected world. We developed several techniques which analyse CCTV inputs for crowd analysis. The framework utilised automated homography from pose feature positions to determine interpersonal distance. It also incorporates mask detection by using pose features for an image classification pipeline. A further model predicts the behaviour of each person by using their estimated pose features. We combine the models to assess transmission risk based on recent scientific literature. A custom dashboard displays a risk density heat-map in real time. This system could improve public space management and reduce transmission in future pandemics. This context agnostic system and has many applications for other crowd monitoring problems.
UPC-Faster-RCNN: A Dynamic Self-Labeling Algorithm for Open-Set Object Detection Based on Unknown Proposal Clustering

To promote the development of object detection in a more realistic world, efforts have been made to a new task named open-set object detection. This task aims to increase the model’s ability to recognize unknown classes. In this work, we propose a novel dynamic self-labeling algorithm, named UPC-Faster-RCNN. The wisdom of DBSCAN is applied to build our unknown proposal clustering algorithm, which aims to filter and cluster the unknown objects proposals. An effective dynamic self-labeling algorithm is proposed to generate high-quality pseudo labels from clustered proposals. We evaluate UPC-Faster-RCNN on a composite dataset of PASCAL VOC and COCO. The extensive experiments show that UPC-Faster-RCNN effectively increases the ability upon Faster-RCNN baseline to detect unknown target, while keeping the ability to detect known targets. Specifically, UPC-Faster-RCNN decreases the WI by 23.8%, decreases the A-OSE by 6542, and slightly increase the mAP in known classes by 0.3%.
Noninformative Prior Weights for Dirichlet PDFs

The noninformative prior weight W of a Dirichlet PDF (Probability Density Function) determines the balance between the prior probability and the influence of new observations on the posterior probability distribution. In this work, we propose a method for dynamically converging the weight W in a way that satisfies two constraints. The first constraint is that the prior Dirichlet PDF (i.e. in the absence of evidence) must always be uniform, which dictates that W = k where k is the cardinality of the domain. The second constraint is that the prior weight of large domains must not be so heavy that it prevents new observation evidence from having the expected influence over the shape of the Dirichlet PDF, which dictates that W quickly converges to a low constant C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W</inf> in the presence of observation evidence, where typically C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W</inf> = 2. In the case of a binary domain, the noninformative prior weight is normally set to W = 2, irrespective of the amount of evidence. In the case of a multidimensional domain with arbitrarily large cardinality k, the noninformative prior weight is initially equal to the domain cardinality k, but rapidly decreases to the constant convergence factor C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W</inf> as the amount of evidence increases.
Target Pose Estimation from Dual-Plane Speckle Return

We explore the potential for measuring size, distance, and tilt orientation of a simplified target from its returned speckle patterns in the pupil and image planes of a generalized imaging system. Our results show that such measurements are possible to within a 10% error bound.
High density Photonic Tensor Core for Matrix-Vector Multiplication

With the increasing demanding for data processing and application-specific hardware, photonics can out-perform electrical IC in terms of speed and energy saving. Here, we present our Photonic Tensor Core architecture, showing high components density, with error rate lower than 4% on image edge detection.
Underwater Oculometry for Diver Physiology and Neurocognitive Assessment

An oculometric neurologic assessment tool developed to integrate a non-invasive, diver/subject worn test system that can rapidly and repeatedly evaluate the underseas related efforts on neurologic function. The tool will be used for its ability to capture eye movement patterns while wearing a diver mask.
A Study about solar tracker for the performance of photovoltaic plants

Solar energy represents 2.6% of the electricity matrix in Brazil, according to 2022 data from Operador Nacional do Sistema Elétrico (ONS), a significant increase compared to 2020, when the representation was 2.0%. In addition to the desire to expand this sector, it is necessary to seek different ways to improve generation performance, such as, for example, the search for techniques that enhance energy generation through panels. This article aims to present a simulation study on the characteristics and productivity comparisons between mono and two-face photovoltaic panels, as well as comparisons on the generation of horizontal modules or with the use of trackers, with the Study and Analysis of an Automatic Control System of Trackers - analysis of the functioning of a conventional tracking system to observe functional aspects of interest.The study was carried out using data available in the National Renewable Energy Laboratory (NREL) database on climate information regarding the location of the roof of the CEAR (Center Alternative and Renewable Energies) laboratory located at the Federal University of Paraíba in the city of João Pessoa. The state of Paraíba was chosen because the simulations were carried out to predict the performance of the system that will be assembled in the city of João Pessoa.
Identifying gene expression-based biomarkers in online learning environments

Gene expression-based classifiers are often developed using historical data by training a model on a small set of patients and a large set of features. Models trained in such a way can be afterwards applied for predicting the output for new unseen patient data. However, very often the accuracy of these models starts to decrease as soon as new data is fed into the trained model. This problem, known as concept drift, complicates the task of learning efficient biomarkers from data and requires special approaches, different from commonly used data mining techniques.Here, we propose an online ensemble learning method to continually validate and adjust gene expression-based biomarker panels over increasing volume of data. We also propose a computational solution to the problem of feature drift where gene expression signatures used to train the classifier become less relevant over time. A benchmark study was conducted to classify the breast tumors into known subtypes by using a large-scale transcriptomic dataset (∼3500 patients), which was obtained by combining two datasets: SCAN-B and TCGA-BRCA. Remarkably, the proposed strategy improves the classification performances of gold-standard biomarker panels (e.g. PAM50, OncotypeDX and Endopredict) by adding features that are clinically relevant. Moreover, test results show that newly discovered biomarker models can retain a high classification accuracy rate when changing the source generating the gene expression profiles.github.com/UEFBiomedicalInformaticsLab/OnlineLearningBD.Supplementary data are available at Bioinformatics Advances online.
MERGE-LABELING, Transfer, and CED Effects

Jaejun Kim and Myung-Kwan Park. 2022. MERGE-LABELING, Transfer, and CED Effects. Studies in Modern Grammar 115, 27-50. Since the postulation of Huang’s (1982) condition on extraction domain (CED), adjuncts and subjects have been standardly assumed to be opaque for extraction. However, the CED is empirically contradicted by cases where certain adjuncts and subjects are not islands to movement. Given this background, we propose that adjuncts in general are immediately transferred to resolve a problematic configuration {XP, YP} where labeling cannot be determined (i.e., problem of projection (POP)). Being sent to Transfer, extraction from adjuncts is barred. However, as uncovered by Truswell (2007), there are exceptional adjuncts in which extraction from them is occasionally permitted. In such cases, we claim that labeling via feature sharing can be achieved in the mode of restructuring or reanalysis (cf. Choe 1988; Wurmbrand 2001). Since labeling is properly determined, sub-extraction from them naturally follows. Concerning the typical subjects that ban movement from them, we argue that it is due to the labeling-wise defectiveness of T in English; movement from Spec,TP to Spec,CP is not allowed owing to the prior Transfer of typical subjects in Spec,TP position. With respect to certain exceptional subjects in which sub-extraction from them is licit, we recourse to the determination of labeling at their landing sites; thus, the island-hood of such subjects is voided. All in all, the present paper deduces CED effects within the system of MERGE through labeling and Transfer.
Continuous Variable Quantum Secret Sharing with Security Enhancement in Practical Quantum Communications

Quantum communications can be conveniently implemented by two participants, but quantum secret key sharing (QSS) through multi-participant communication seems difficult in practice. In this paper, we propose a multi-participant QSS scheme with the local local oscillator (LLO) in continuous variable (CV) quantum communications. It allows an honest participant called a dealer to share a secret key with the others, making it possible to establish a secret key if and only if all participants gather together. The LLO scheme, which eliminates the need for the local oscillator (LO) to be propagated in insecure channels, can be used to avoid the potential LO attack in the traditional CVQSS scheme. Numerical simulation shows that the performance of the LLO-based CVQSS can be improved in terms of the maximal transmission distance even if it is performed with the partially trusted noise.
A Dialogue System That Models User Opinions Based on Information Content

When designing rule-based dialogue systems, the need for the creation of an elaborate design by the designer is a challenge. One way to reduce the cost of creating content is to generate utterances from data collected in an objective and reproducible manner. This study focuses on rule-based dialogue systems using survey data and, more specifically, on opinion dialogue in which the system models the user. In the field of opinion dialogue, there has been little study on the topic of transition methods for modeling users while maintaining their motivation to engage in dialogue. To model them, we adopted information content. Our contribution includes the design of a rule-based dialogue system that does not require an elaborate design. We also reported an appropriate topic transition method based on information content. This is confirmed by the influence of the user’s personality characteristics. The content of the questions gives the user a sense of the system’s intention to understand them. We also reported the possibility that the system’s rational intention contributes to the user’s motivation to engage in dialogue with the system.
Complete algebraic semantics for second-order rewriting systems based on abstract syntax with variable binding

Abstract By using algebraic structures in a presheaf category over finite sets, following Fiore, Plotkin and Turi, we develop sound and complete models of second-order rewriting systems called second-order computation systems (CSs). Restricting the algebraic structures to those equipped with well-founded relations, we obtain a complete characterisation of terminating CSs. We also extend the characterisation to rewriting on meta-terms using the notion of $\Sigma$ -monoid.
BP Network Model Based on SCLBOA for House Price Forecasting

Butterfly optimization algorithm (BOA) is a new swarm intelligence algorithm mimicking the behaviors of butterflies. However, there is still much room for improvement. In order to enhance the convergence speed and accuracy of the BOA, we present an improved algorithm SCLBOA based on SIBOA, which incorporates a logical mapping and a Lévy flight mechanism. The logical chaotic map is used for population initialization, and then the Lévy flight mechanism is integrated into the SCLBOA algorithm. To evaluate the performance of the SCLBOA, we conducted many experiments on standard test functions. The simulation results suggest that the SCLBOA is capable of high-precision optimization, fast convergence, and effective global optimization, all of which show that our method outperforms other methods in solving mathematical optimization problems. Finally, the BP network is optimized according to the SCLBOA (SCLBOA-BP) to further verify the availability of the algorithm. Simulation experiments prove the practicability of this method by building a Boston housing price prediction model for training.
Blockchain-Based Web Framework for Real Estate Transactions

The onset of blockchain generation within Bitcoin has generated significant interest by showing an opportunity to eliminate the middle ground need and transform communication between people and machines by increasing trust. Initially restricted to the integrated currency domain, people began to see the power of a generation beyond just the cryptocurrencies, which brought the acceptance of the blockchain era to erase the world’s problems. One such situation is problems for e-governance companies in other areas of the public sphere. In the scope of this thesis, we have specifically addressed the issues within the traditional property registration system. This thesis discusses the new design and architecture for real estate transactions and implements it using a blockchain-based solution and addressing issues including record integrity, privacy, and most importantly the lack of common platforms among concerned government organizations. The advent of the blockchain era led to the creation of blockchain-enabled platforms like Hyperledger Fabric. It is one of the most popular open-source permissioned blockchain frameworks, created and supported by the Linux Foundation and IBM, used in many industrial scenarios. So, it is used to create a network with one ordering organization with an ordering node and one peer organization with two peer nodes to prove the concept. Chaincode similar to Ethereum’s smart contract contains the logic to perform all operations and modify the ledger data. All the methods of chaincode are accessed using the Fabric gateway in the web application to perform various operations.
Identifying Genre of a Book from Its Summary Using Machine Learning Approach

Categorical search and category-wise book recommendation are two common tasks for online booksellers. But for a machine to understand this category from a given text is still challenging work, where machine learning is a widely used tool at present. Though in the English language, with the availability of rich datasets and corpus, machine learning-based categorization and recommendation have reached a standard level, in the Bengali language, to reach the standard, still needs a long way to go. One key reason is the lack of availability of a rich Bengali dataset. The aim of this research was to make a dataset first for the book’s genre identification from its given summary and to explore which supervised classifier performed best on that dataset for classifying the genres. Before that, we performed several essential preprocessing steps essential to prepare our dataset fit for the algorithms. Six machine learning classifiers were applied to the dataset, and it was observed that Naive Bayes performed best with an accuracy of 68% followed by XGB with an accuracy of 67%.
K-Nearest Neighbor and Collaborative Filtering-Based Movie Recommendation System

Over the past several years, the recommendation system has become one of the important aspects in our day to day life. Recommendation systems are used in various areas like YouTube, Amazon, etc. Collaborative filtering algorithms have been used in recommendation systems for a long time. They have been effective in resolving a number of issues with commercially available systems. Methods based on a user’s neighborhood have showed potential in forecasting user ratings. The aim of this paper is to design and evaluate ‘KNN algorithm and Collaborative Filtering algorithm’ for producing movie recommendations. The dataset used in this paper is ‘Movielens dataset’ which is downloaded from Kaggle. The system was implemented using ‘Python programming language’. Initially, compared different distance measures and then performed correlation. Based on the correlation value, developed system will recommend similar movies/users. Performances of both the developed algorithms were analyzed in terms of accuracy. Finally the result shows that the accuracy of the recommendations is very good and we will get more accurate movie recommendations based on the combination of “KNN algorithm and collaborative filtering algorithms”.
Semantic-Based Feature Extraction and Feature Selection in Digital Library User Behaviour Dataset

World Wide Web has become a universal environment for human interface, collaboration, communication, data storage and data sharing. Information retrieval is a technique to understand the text in the web pages. The purpose of Semantic Web is to allow the machines to advance knowledge itself by recognizing its meaning. The essential intention of this research is to predict variation in the directional behaviour of the digital library user, based on their recommended data. The significance of this research lies in enlightening the user behaviour in digital library using semantic-driven approach. An important contribution of this research work corresponds to retrieve accurate data from websites by using semantically enhanced various algorithms. This research concerns on identifying educational digital library web user’s behaviour. The user behaviour unstructured data are pre-processed and semantically extracted using suggested weighted TF-IDF normalization method. Then the extracted features are selected using improved Ensemble-based FS method. It improves the performance and reliability of classification by eliminating unnecessary and superfluous features from the extracted user behaviour datasets.
Influencer Profiling to Identify the Top Keywords Using LDA

In line with the strong growth of the social data volume, a huge number of social contents are created in various forms every day. As a result, it is often difficult for users to find or keep up with current popular keywords. Many researchers are interested to analyze the social data and identify the current popular keywords and topics. Thus, these researches are more complex for profiling the target topics with their keywords. The main purpose of this paper is to identify the most trending keywords in social media based on profiling influencers. So, for achieving this objective. Our approach illustrates determining influential nodes on a social network graph. After that, due to the latent Dirichlet allocation model, we could profile the trending keywords based on influential posts content. In any case, our proposal is more relevant and optimal compared to the previous approaches.
Landmark Recognition and Retrieval Using ResNet50 and DELF

Landmark recognition is a method that predicts landmark labels directly from image pixels. This model is designed to detect the proper landmark in a dataset of complex and challenging test images, allowing users to better understand and organize their photograph collections. A dataset containing historic monuments of India of around 100 classes is created. The dataset is divided into training, testing, and validation data. Data augmentation is performed on training and validation data in the dataset. ResNet50 algorithm is used here to train the dataset. A sample image is given to the ResNet50 model for prediction, and the model gives the top ten labels of the sample image. Consider two images for each label from the training dataset, and apply DELF on these images and sample image. The inliers are calculated for these images using the Ransac algorithm. Image label which has a good number of inliers with sample image is the landmark of that sample image.