Reviewerly/exHarmony huggingface dataset:
Motion Prediction for First-Person Vision Multi-object Tracking

Tracking multiple independently moving objects with cameras mounted on moving robots is becoming increasingly common. However, most causal trackers rely on linear motion models that may be inaccurate in these scenarios. To overcome this problem, we present a real-time multi-object tracker based on the Early Association Probability Hypothesis Density Particle Filter with a prediction model that disentangles the motion of objects from that of the camera. Moreover, the prediction model allows us to intentionally reduce the video frame rate at which the tracker operates, with only a minor reduction in accuracy. Specifically, the model allows us to halve the processed frames while still outperforming alternative prediction models, including traditional linear motion predictors in moving-camera sequences. Experimental results show that the proposed model improves both accuracy and precision of the tracker.
Improving Keyword Search Performance in Sign Language with Hand Shape Features

Handshapes and human pose estimation are among the most used pretrained features in sign language recognition. In this study, we develop a handshape based keyword search (KWS) system for sign language and compare different pose based and handshape based encoders for the task of large vocabulary sign retrieval. We improved KWS performance in sign language by 3.5% mAP score for gloss search and 1.6% for cross-lingual KWS by combining pose and handshape based KWS models in a late fusion approach.
Multi–Grid Back–Projection Networks

Multi-grid back-projection (MGBP) is a fully-convolutional network architecture that can learn to restore images and videos with upscaling artifacts. Using the same strategy of multi-grid partial differential equation (PDE) solvers this multiscale architecture scales computational complexity efficiently with increasing output resolutions. The basic processing block is inspired in the iterative back-projection (IBP) algorithm and constitutes a type of cross-scale residual block with feedback from low resolution references. The architecture performs in par with state-of-the-arts alternatives for regression targets that aim to recover an exact copy of a high resolution image or video from which only a downscale image is known. A perceptual quality target aims to create more realistic outputs by introducing artificial changes that can be different from a high resolution original content as long as they are consistent with the low resolution input. For this target we propose a strategy using noise inputs in different resolution scales to control the amount of artificial details generated in the output. The noise input controls the amount of innovation that the network uses to create artificial realistic details. The effectiveness of this strategy is shown in benchmarks and it is explained as a particular strategy to traverse the perception-distortion plane.
A Multi-Layered Network Model for Blockchain Based Security Surveillance system

Blockchain technology is a decentralized ledger of all transactions across peer to peer network. Being decentralized in nature, a blockchain is highly secure as no single user can alter or remove an entry in the blockchain. The security of office premises and data is a very major concern for any organization. This paper majorly focuses on its application of blockchain technology in security surveillance. This paper proposes a blockchain based multi level network model for security surveillance system. The proposed system architecture is composed of different blockchain based systems connected to a multi level decentralized blockchain system to insure authentication, secure storage, Integrity and accountability.
An Abstractive Summarization Technique with Variable Length Keywords as per Document Diversity

Summarization is an essential area in text mining, which has procedures for text extraction.In natural language processing, text summarization maps the documents to a representative set of descriptive words.Therefore, the objective of text extraction is to attain reduced expressive contents from the text documents.Text summarization has two main areas such as abstractive, and extractive summarization.Extractive text summarization has further two approaches, in which the first approach applies the sentence score algorithm, and the second approach follows the word embedding principles.All such text extractions have limitations in providing the basic theme of the underlying documents.In this paper, we have employed text summarization by TF-IDF with PageRank keywords, sentence score algorithm, and Word2Vec word embedding.The study compared these forms of the text summarizations with the actual text, by calculating cosine similarities.Furthermore, TF-IDF based PageRank keywords are extracted from the other two extractive summarizations.An intersection over these three types of TD-IDF keywords to generate the more representative set of keywords for each text document is performed.This technique generates variable-length keywords as per document diversity instead of selecting fixedlength keywords for each document.This form of abstractive summarization improves metadata similarity to the original text compared to all other forms of summarized text.It also solves the issue of deciding the number of representative keywords for a specific text document.To evaluate the technique, the study used a sample of more than eighteen hundred text documents.The abstractive summarization follows the principles of deep learning to create uniform similarity of extracted words with actual text and all other forms of text summarization.The proposed technique provides a stable measure of similarity as compared to existing forms of text summarization.
Rapid Blind Denoising Method for Grating Fringe Images Based on Noise Level Estimation

Object profile measurement theory and technology based on grating fringe image projection are popular topics of research. However, due to the presence of noise, the phase extracted from captured grating fringe images may be wrong, affecting the measurement results. To manage noise with unknown intensity levels, we use Gaussian distribution to model the noise, and propose a novel rapid blind denoising method. First, we establish an energy expression model for grating fringe images. After performing singular value decomposition (SVD) on the grating fringe image, we establish a linear relationship between the average energy of the image and the noise variance. Second, we propose a noise variance estimation method based on a single frame grating fringe image. By adding an auxiliary image of zero-mean Gaussian noise, we can obtain the estimated noise variance. Third, a Gaussian filtering method of the phase maps using the estimated variance, is employed to reduces the amount of data processing. The experimental results show that the proposed method can complete noise processing faster than five other methods, and extract the phase profile of two measured models with minimum root-mean-square (RMS) error of 0.0686 and 0.0673 rad.
Sentiment Classification on Twitter and Zomato Dataset Using Supervised Learning Algorithms

Natural Language Processing (NLP) is a special type of Machine Learning that takes care of unstructured data from the real world. Sentiment Analysis is the 'computational' method that decides whether the given sentence or paragraph is positive, negative or neutral. It derives a people's feelings, opinion and attitude etc. Using sentiment analysis tools in Twitter data to evaluate views and attitude which may help companies understand how people think about various events or products. In this paper, we have collected tweets dataset from twitter of hashtag COVID-19 and CORONA VIRUS. For Twitter dataset, we performed sentiment analysis and on Zomato dataset, Supervised Machine Learning Classification algorithms. Random Forest has got highest accuracy as 94.90 %.
Stability Conditions of Bicomplex-Valued Hopfield Neural Networks

Abstract Hopfield neural networks have been extended using hypercomplex numbers. The algebra of bicomplex numbers, also referred to as commutative quaternions, is a number system of dimension 4. Since the multiplication is commutative, many notions and theories of linear algebra, such as determinant, are available, unlike quaternions. A bicomplex-valued Hopfield neural network (BHNN) has been proposed as a multistate neural associative memory. However, the stability conditions have been insufficient for the projection rule. In this work, the stability conditions are extended and applied to improvement of the projection rule. The computer simulations suggest improved noise tolerance.
DB-GAN: Boosting Object Recognition Under Strong Lighting Conditions

Driven by deep learning, object recognition has recently made a tremendous leap forward. Nonetheless, its accuracy often still suffers from several sources of variation that can be found in real-world images. Some of the most challenging variations are induced by changing lighting conditions. This paper presents a novel approach for tackling brightness variation in the domain of 2D object detection and 6D object pose estimation. Existing works aiming at improving robustness towards different lighting conditions are often grounded on classical computer vision contrast normalisation techniques or the acquisition of large amounts of annotated data in order to achieve invariance during training. While the former cannot generalise well to a wide range of illumination conditions, the latter is neither practical nor scalable. Hence, We propose the usage of Generative Adversarial Networks in order to learn how to normalise the illumination of an input image. Thereby, the generator is explicitly designed to normalise illumination in images so to enhance the object recognition performance. Extensive evaluations demonstrate that leveraging the generated data can significantly enhance the detection performance, outperforming all other state-of-the-art methods. We further constitute a natural extension focusing on white balance variations and introduce a new dataset for evaluation.
Financial Fraud Detection with Improved Neural Arithmetic Logic Units

Domain specific neural network architectures have shown to improve the performance of various machine learning tasks by large margin. Financial fraud detection is such an application domain where mathematical relationships are inherently present in the data. However, this domain hasn't attracted much attention for deep learning and the design of specific neural network architectures yet. In this work, we propose a neural network architecture which incorporates recently proposed Improved Neural Arithmetic Logic Units. These units are capable of modelling mathematical relationships implicitly within a neural network. Further, inspired by a real-world credit payment application, we construct a synthetic benchmark dataset, which reflects the problem setting of automatically capturing such mathematical relations within the data. Our novel network architecture is evaluated on two real-world and two synthetic financial fraud datasets for different network parameters. We compare our proposed model with several well-established classification approaches. The results show that the proposed model is able to improve the performance of neural networks. Further, the proposed model performs among the best approaches for each dataset.
On the Minimization of Variables to Represent Partially Defined Classification Functions

A partially defined classification function is a mapping from the set of k distinct vectors of n bite to m elements, wherek <; <; 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> . Such a function can often be represented with fewer variables than n, by appropriately assigning valus to don't cares. The number of variables can be further reduced by a linear transformation of the input variables. This paper shows an efficient method to find a linear transformation that reduces the number of variables. The method is illustrated with examples.
Intelligent Cooperative System for Traffic Monitoring in Smart Cities

Smart cities introduce the strategies of solving the problems faced by the current cities; such as traffic jams, resource management, and climate change. Traffic flow monitoring is considered as one of the major challenges to be solved. Cooperative systems introduced a suitable solution for sharing information between the road elements, which provides a helpful tool for monitoring the traffic. However, most of the solutions are based on the sensors or cameras mounted in fixed locations, which have a limited range of vision and produce blind areas. In order to deal with the problem, this paper presents a cooperative system based on using Unmanned Aerial Vehicles (UAVs); to monitor the traffic flow. This system proposed a lightweight semantic neural network; that takes the RGB images as input and generates a segmented image of the vehicles and its 2D real-world positions. This data then is sent to each vehicle; to have full information about its surroundings. The proposed method has been validated with several experiments of different scenarios and situations, and the obtained results show its robustness and efficiency of the system, illustrating its functionality in traffic monitoring purposes.
Part-of-Speech Tagging in Mizo Language: A Preliminary Study

Part-of-speech tagging is one of the fundamental tasks in language processing. It is the process of marking up words in a corpus, based on the context of the word in the sentences using language-based defined tagsets. Mizo language is at its infant stage in the language processing, and it belongs to under-resourced language but at the same time, it is interesting to discover the basic facts and morphological structures about the language. The Mizo language (Mizo Open image in new window or Mizo) has sundry grammatical structures, and uttering tones make the word to indicate different meanings. In this paper, the general part of speech in Mizo language has been discussed and presented some of the unique features of the Mizo language. The POS tagsets, which could be used to tag corpus in the future, have also been proposed.
A Novel Underwater Image Enhancement Technique using ResNet

Underwater image enhancement is an active area of research due to its wide applications in areas like marine research, automated underwater vehicles, etc. In general, the underwater images have low contrast, blurriness, and color cast due to various effects like absorption, scattering, and refraction. Normally, the underwater images are less clear and not suitable for various applications. The underwater images should be enhanced to use for real-life applications and the natural image enhancement techniques may not work well for underwater images. In this paper, we introduce a scheme for enhancing the quality of the underwater images by using a residual neural network (ResNet). The synthetic underwater images for the experimental study are generated using the underwater generative adversarial network (UWGAN). The experimental results show that the new underwater image enhancement techniques perform well, and it can be used for real-life applications where we need good quality underwater images.
Software Change Proneness Prediction Using Machine Learning

Software change-proneness is one of the vital quality metrics that represents the extent of change of a class across versions of the system. This change may occur due to evolving requirements, bug fixing, or code refactoring. Consequently, change-proneness may have a negative impact on software evolution. For instance, modules that are change-prone tend to produce more defects and accumulate more technical debt. This research work applies different Machine Learning (ML) techniques on a large dataset from a wide commercial software system to investigate the relationships between object-oriented (OO) metrics and change-proneness, and determine which OO metrics are necessary to predict change-prone classes. Moreover, several state-of-the-art combining methods were evaluated that were constructed by combining several heterogeneous single and ensemble classifiers with voting, Select-Best, and staking scheme. The result of the study indicates a high prediction performance of many of the ensemble classifiers as well as the combining methods selected and proved that ML methods are very beneficial for predicting change-prone classes in software. The study also proved that software metrics are significant indicators of class change-proneness and should be monitored regularly during software development and maintenance.
Effective Two-Stage Image Segmentation: A New Non-Lipschitz Decomposition Approach with Convergent Algorithm

Image segmentation is an important median level vision topic. Accurate and efficient multiphase segmentation for images with intensity inhomogeneity is still a great challenge. We present a new two-stage multiphase segmentation method trying to tackle this, where the key is to compute an inhomogeneity-free approximate image. For this, we propose to use a new non-Lipschitz variational decomposition model in the first stage. The minimization problem is solved by an iterative support shrinking algorithm. By assuming that the subproblem at each iteration is exactly solved, we show the global convergence of the iterative algorithm and a lower bound theory of the image gradient of the iterative sequence, which indicates that the generated approximate image (inhomogeneity-corrected component) is with very neat edges and suitable for the following thresholding operation. Implementation details based on the alternating direction method of multipliers for the strongly convex subproblems are also given. In the second stage, the segmentation is done by applying a widely used simple thresholding technique to the piecewise constant approximation. Numerical experiments indicate good convergence properties and effectiveness of our method in multiphase segmentation for either clean or noisy homogeneous and inhomogeneous images. Both visual and quantitative comparisons with some state-of-the-art approaches demonstrate the performance advantages of our non-Lipschitz-based method.
Single Exposure High Dynamic Range Image Reconstruction Based on Deep Dual-Branch Network

In this article, we relate the operation of single-frame-based high dynamic range (HDR) image reconstruction to the following two tasks: 1) highlight suppression in over-exposed areas and 2) noise elimination in under-exposed areas. The common goal of both tasks is to preserve or even enhance the details and improve the visibility of scenes when generating the HDR image. These two tasks can be solved separately with fundamentally different ways. In this article, we propose a dual-branch network to process the over- and under- exposed areas respectively for single-frame-based HDR image reconstruction. First, the low dynamic range (LDR) image is normalized, linearized and inputted into both branches, and the masks of the over- and under- exposed regions are calculated to detect the improper exposed areas. Second, the over- and under- exposed areas are restored and enhanced by the two branches respectively, at the same time, the color distribution is learned to obtain more consistent color saturation between the generated HDR image and the ground truth. Third, the output of the two branches and the linearized input LDR image are combined based on the masks to obtain the reconstructed HDR image. Extensive experiments show that the proposed method can efficiently restore the texture and color of the over-exposed areas, suppress the noise of the under-exposed areas, and obtain the HDR image with good contrast, clear details and high structural fidelity of the ground truth image appearance.
Authentication Based on Blockchain

Blockchain is one of the most trending technologies in past five years and it is called the new generation of the internet. Bitcoin was the first technology that used blockchain concept in its system. Blockchain has intense attention from academic community, developers and programmers, because of its distinctive properties such as decentralization, persistency, anonymity and auditability. Blockchain technology has evolved and is applicable in various applications outside the field. This paper provides background on blockchain technology and presents a suitable and logical solution for user authentication based on blockchain via the unified smart pass platform that allows the user to login with all service providers channels.
DVRNet: Decoupled Visible Region Network for Pedestrian Detection

Pedestrian detection remains a challenging task due to the problems caused by occlusion variance. Visible-body bounding boxes are typically used as an extra supervision signal to improve the performance of pedestrian detection to predict the full-body. However, visible-body assisted approaches produce a large number of false positives, which result from a lack of adequate and discriminative full-body contextual information. In this paper, we propose a new network, dubbed DVRNet, based on the representative visible-body assisted pedestrian detector named Bi-box. Specifically, we extend Bi-box by adding three modules named the attention-based feature interleaver module (AFIM), the binary mask learning module (BMLM), and the head-aware feature enhancement module (HFEM), which play important roles in employing features learned by the visible-body and the head supervision signals to enrich high discriminative contextual information of the full-body and enhance the power of feature representation. Experimental results indicate that the DVRNet achieves promising results on the CityPersons and the CrowdHuman datasets.
Perceived educational usefulness of a virtual-reality work situation depends on the spatial human-environment relation

Virtual reality (VR) may be useful for situating school-based vocational education in work-life by simulating a work situation such that learners viewing this VR work situation are located inside it. The reason for this assumption is that VR can fully spatially include its viewer. Research on the utility of viewer-including VR work situations for learners has, therefore, already started. However, no study has yet investigated their utility for teachers. This is particularly relevant for work situations involving environmental planning, as VR is expected to facilitate such a task. We, therefore, asked horticultural teachers to assess the educational usefulness of a VR work situation when they were located outside and inside it. For this purpose, we enabled them to plan a basic garden in the VR work situation when its environment was spatially excluding them and when it was including them. We found the teachers to perceive the viewer-including VR work situation as more useful for their teaching than its viewer-excluding version. This suggests that the perceived educational usefulness of a VR work situation depends on the spatial relation of its viewer and environment, that is, the spatial human-environment relation it involves.