Reviewerly/exHarmony huggingface dataset:
Intra-City Traffic Data Visualization: A Systematic Literature Review

The increasing proportion of people living in urban areas causes well-known mobility issues such as pollution and congested roads. In addition to their heavy environmental impact, these issues negatively affect citizens’ quality of life. Emerging technologies allow gathering huge amounts of data regarding traffic which can in turn be analyzed to diagnose mobility issues and propose solutions. Information visualization is increasingly leveraged as a medium to make the most out of this traffic data. In this paper, we present the systematic literature review we conducted to analyze the existing research in intra-city traffic data visualization. Following a well-defined and rigorous protocol, we have made a selection of 146 relevant works which we analyzed in depth under multiple perspectives: the data used, the domain problems, the visualization techniques and interaction, and the target end-user. We were able to identify common practices such as the most used visualization techniques and the problems tackled the most often. This allowed us to uncover little explored research directions and to suggest avenues for innovative research. In particular, we noted a lack of visualizations engaging citizens in the reflection on mobility-related issues and a lack of consideration for user studies.
A 20x28 Spins Hybrid In-Memory Annealing Computer Featuring Voltage-Mode Analog Spin Operator for Solving Combinatorial Optimization Problems

Computationally-expensive combinatorial optimization problems can be solved effectively via finding the ground state of the system by annealing computers. This work proposes a hybrid analog-digital implementation of an annealing computer that achieves 1.58x improvement in the area and >3x reduction in annealing time compared with recent works. The test-chip is fabricated using the 65nm process, and the measured power consumption is 9.9mW at 0.8V and 320MHZ.
Augmented Reality in Virtual Classroom for Higher Education During COVID-19 Pandemic

In this study, a virtual learning application is developed by blending Augmented Reality (AR) with the instructional strategies in the virtual classroom. This research work adopts virtual learning using 3D models into the existing pedagogical models for the higher education courses. The AR mobile app is developed to impart Internet of Things (IoT) course for undergraduate students. The virtual 3D modelling environment is developed to teach the basic concepts of IoT lab experiments which enables the learners to develop better understanding of the hardware/embedded related concepts. The objective of this research work is focussed to analyse the learner's performances in augmented reality course that are integrated with the existing pedagogical model in the virtual classroom. The student's performance is analysed using Linear Regression and KNN algorithm which resulted in an accuracy of 0.95 using this model. Additionally, the K-Means clustering algorithm is also incorporated to measure the learner's performances in the assessments.
Moving Basis Decomposition for Precomputed Light Transport

Abstract We study the problem of efficient representation of potentially high‐dimensional, spatially coherent signals in the context of precomputed light transport. We present a basis decomposition framework, Moving Basis Decomposition (MBD), that generalizes many existing basis expansion methods and enables high‐performance, seamless reconstruction of compressed data. We develop an algorithm for solving large‐scale MBD problems. We evaluate MBD against state‐of‐the‐art in a series of controlled experiments and describe a real‐world application, where MBD serves as the backbone of a scalable global illumination system powering multiple, current and upcoming 60Hz AAA‐titles running on a wide range of hardware platforms.
Dynamical Perceptual-Motor Primitives for Better Deep Reinforcement Learning Agents

Recent innovations in Deep Reinforcement Learning (DRL) and Artificial Intelligence (AI) techniques have allowed for the development of artificial agents that can outperform human counterparts. But when it comes to multiagent task contexts, the behavioral patterning of AI agents is just as important as their performance. Indeed, successful multi-agent interaction requires that co-actors behave reciprocally, anticipate each other’s behaviors, and readily perceive each other’s behavioral intentions. Thus, developing AI agents that can produce behaviors compatible with human co-actors is of vital importance. Of particular relevance here, research exploring the dynamics of human behavior has demonstrated that many human behaviors and actions can be modeled using a small set of dynamical perceptual-motor primitives (DPMPs) and, moreover, that these primitives can also capture the complex behavior of humans in multiagent scenarios. Motived by this understanding, the current paper proposes methodologies which use DPMPs to augment the training and action dynamics of DRL agents to ensure that the agents inherit the essential pattering of human behavior while still allowing for optimal exploration of the task solution space during training. The feasibility of these methodologies is demonstrated by creating hybrid DPMP-DRL agents for a multiagent herding task. Overall, this approach leads to faster training of DRL agents while also exhibiting behavior characteristics of expert human actors.
BiodivOnto: Towards a Core Ontology for Biodiversity

Biodiversity is the variety of life on earth which covers the evolutionary, ecological, and cultural processes that sustain life. Therefore, it is important to understand where biodiversity is, how it is changing over space and time, the driving factors of these changes and the resulting consequences on the diversity of life. To do so, it is necessary to describe and integrate the conditions and measures of biodiversity to fully capture the domain. In this paper, we present the design of a core ontology for biodiversity aiming to establish a link between the foundational and domain-specific ontologies. The proposed ontology is designed using the fusion/merge strategy by reusing existing ontologies and it is guided by data from several resources in the biodiversity domain.
A Machine Learning Approach to Sentiment Analysis on Web Based Feedback

The advent of this new era of technology has brought forward new and convenient ways to express views and opinions. This is a major factor for the vast influx of data that we experience every day. People have found out new ways to communicate their feelings and emotions to others through written texts sent over the Internet. This is exactly where the field of sentiment analysis comes into existence. This paper focuses on analyzing the reviews of various applications on the Internet and to understand whether they are positive or negative. For achieving this objective, we initially pre-process the data by performing data cleaning and removal of stop words. TF-IDF method is used to convert the cleaned data into a vectorised form. Finally, the machine learning algorithms: Naïve Bayes, Support Vector Machine and Logistic Regression are applied and their comparative analysis is performed on the basis of accuracy, precision and recall parameters. Our proposed approach has achieved an accuracy of 92.1% and has outperformed many other existing approaches.
3D SUSAN Filtering of CT Images With Adaptive Selection of the Brightness Threshold

In this paper a new application of the Smallest Univalue Segment Assimilating Nucleus (SUSAN) filter is presented aimed at reducing the Additive White Gaussian Noise (AWGN) in Computed Tomography (CT) images. The original 2D implementation of the filter is extended in 3D and scheme for adaptive selection of the brightness threshold is proposed. Testing over large set of CT images reveal its applicability for the purpose and better performance than the 3D Gaussian and Average filters. The current modification of the 3D SUSAN filter is considered also applicable for filtering of other types of 3D images, such as MRI, multispectral and others.
Online Estimation of Sparse Inverse Covariances

Gaussian graphical models have been well studied as a way to represent the relationships between various entities, and numerous algorithms have been proposed to learn the dependencies in such models. However, these algorithms process data in a batch, and may not be suitable for realtime estimation. In this paper, we propose an online sparse inverse covariance algorithm to infer the network structure (i.e., dependencies between nodes) in real-time from time-series data. Our approach is based on an alternating minimization algorithm and allows users to select the number of iterations per data point. We provide theoretical guarantees showing that the online estimates converge to that of the batch mode as the number of data increases and characterize its asymptotic rate of convergence. Finally, we evaluate our online algorithm on synthetic data sets.
Assamese Word Sense Disambiguation using Cuckoo Search Algorithm

Natural language processing is associated with human-computer interaction, where several challenges require natural language understanding. The Word sense disambiguation problem comprises the computational assignment of meaning to a word according to a specific context in which it occurs. There are numerous natural language processing applications, such as machine translation, information retrieval, and information extraction, which require this task which takes place at the semantic level. To solve this problem unsupervised computation proposals can be effective since they have been successfully used for many real-world optimization problems. In this paper, we propose to solve the word sense disambiguation problem using the cuckoo search algorithm in the Assamese language. We illustrate the performance of our algorithm by carrying out experiments on an Assamese corpus. And comparing them against an unsupervised genetic algorithm that is implemented in the Assamese language. Results of the experiment show that the cuckoo algorithm can achieve more precision, recall and F-measure, attaining 87.5, 84, and 85.71 percentages respectively.
Offline Bengali Handwritten Sentence Recognition Using BiLSTM and CTC Networks

It is a very challenging task to recognize unconstrained Bengali handwritten text due to its cursive nature. This paper introduces an offline technique of recognizing handwritten Bengali sentences based on BiLSTM architecture and connectionist temporal classification (CTC) output layer. The traditional approach of detecting handwritten sentence recognition rises the computational complexity due to the difficulty of combining isolated characters. The proposed method only segments the sentences into words, recognizes separately and combines them to represents the output sentence. The proposed technique is evaluated with 450 Bengali sentences and achieved 92% accuracy for recognizing sentences considering 15 distinct writing styles.
Software Architectural Migration

Software architectural designs are usually changed over time to support emerging technologies and to adhere to new principles. Architectural migration is an important activity that helps to transform the architectural styles applied during a system’s design with the result of modernising the system. If not performed correctly, this process could lead to potential system failures. This article presents an automated approach to refactoring architectural design and to planning the evolution process. With our solution, the architectural design can be refactored, ensuring that system functionality is preserved. Furthermore, the architectural migration process allows the system to be safely and incrementally transformed. We have evaluated our approach with five real-world software applications. The results prove the effectiveness of our approach and identify factors that impact the performance of architectural verification and migration planning. An interesting finding is that planning algorithms generate migration plans that differ in term of their relative efficiency.
Building a Question and Answer System for News Domain

This project attempts to build a Question-Answering system in the News Domain, where Passages will be News articles, and anyone can ask a Question against it. We have built a span-based model using an Attention mechanism, where the model predicts the answer to a question as to the position of the start and end tokens in a paragraph. For training our model, we have used the Stanford Question and Answer (SQuAD 2.0) dataset[1]. To do well on SQuAD 2.0, systems must not only answer questions when possible but also determine when no answer is supported by the paragraph and abstain from answering. Our model architecture comprises three layers- Embedding Layer, RNN Layer, and the Attention Layer. For the Embedding layer, we used GloVe and the Universal Sentence Encoder. For the RNN Layer, we built variations of the RNN Layer including bi-LSTM and Stacked LSTM and we built an Attention Layer using a Context to Question Attention and also improvised on the innovative Bidirectional Attention Layer. Our best performing model which uses GloVe Embedding combined with Bi-LSTM and Context to Question Attention achieved an F1 Score and EM of 33.095 and 33.094 respectively. We also leveraged transfer learning and built a Transformer based model using BERT. The BERT-based model achieved an F1 Score and EM of 57.513 and 49.769 respectively. We concluded that the BERT model is superior in all aspects of answering various types of questions.
Multi-Pass Sequential Mini-Batch Stochastic Gradient Descent Algorithms for Noise Covariance Estimation in Adaptive Kalman Filtering

Estimation of unknown noise covariances in a Kalman filter is a problem of significant practical interest in a wide array of applications. Although this problem has a long history, reliable algorithms for their estimation were scant, and necessary and sufficient conditions for identifiability of the covariances were in dispute until recently. Necessary and sufficient conditions for covariance estimation and a batch estimation algorithm were presented in our previous study. This paper presents stochastic gradient descent algorithms for noise covariance estimation in adaptive Kalman filters that are an order of magnitude faster than the batch method for similar or better root mean square error. More significantly, these algorithms are applicable to non-stationary systems where the noise covariances can occasionally jump up or down by an unknown magnitude. The computational efficiency of the new algorithms stems from adaptive thresholds for convergence, recursive fading memory estimation of the sample cross-correlations of the innovations, and accelerated stochastic gradient descent algorithms. The comparative evaluation of the proposed methods on a number of test cases demonstrates their computational efficiency and accuracy.
Video Decolorization Based on the CNN and LSTM Neural Network

Video decolorization is the process of transferring three-channel color videos into single-channel grayscale videos, which is essentially the decolorization operation of video frames. Most existing video decolorization algorithms directly apply image decolorization methods to decolorize video frames. However, if we only take the single-frame decolorization result into account, it will inevitably cause temporal inconsistency and flicker phenomenon meaning that the same local content between continuous video frames may display different gray values. In addition, there are often similar local content features between video frames, which indicates redundant information. To solve the preceding problems, this article proposes a novel video decolorization algorithm based on the convolutional neural network and the long short-term memory neural network. First, we design a local semantic content encoder to learn and extract the same local content of continuous video frames, which can better preserve the contrast of video frames. Second, a temporal feature controller based on the bi-directional recurrent neural networks with Long short-term memory units is employed to refine the local semantic features, which can greatly maintain temporal consistency of the video sequence to eliminate the flicker phenomenon. Finally, we take advantages of deconvolution to decode the features to produce the grayscale video sequence. Experiments have indicated that our method can better preserve the local contrast of video frames and the temporal consistency over the state of the-art.
A Generalized Walsh System and Its Fast Algorithm

In this paper, we propose a new class of discontinuous orthogonal system called Walsh-U system, which is composed of piecewise polynomials of degree <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> in Hilbert space <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L^{2}[0, 1]$</tex-math></inline-formula> . The Walsh-U system generalizes the Walsh system from piecewise constant to piecewise polynomials, which is capable of representing discontinuous signals. Besides, the basis functions of the Walsh-U system not only possess a more concise construction, but also have stronger sparse representation capabilities than the U-system. In the construction of the Walsh-U system, the Legendre polynomials are adopted as the generators at first, and then a series of basis functions are produced through the generators and basic operations of scaling and replication. These basis functions are formed into different groups based on the generation order. Furthermore, a fast algorithm for the Walsh-U system is designed in this paper. The experimental results demonstrate that when compared with Fourier series, Walsh functions, wavelet functions, and U-system, the Walsh-U system has fast convergence and high approximation precision in analog signal reconstruction and denoising. It is verified that the fast algorithm for the Walsh-U system can effectively improve the computation speed for discontinuous signals.
Analysis of Blood Cancer Using Microscopic Image Processing

Blood cancer or leukemia is the cancer which leads to abnormal formation of white blood cells in the blood by the blood marrow; it can be fatal disease if not diagnosed early. It can be detected and diagnosed at the earliest by using the image processing of microscopic images of blood cell. This method is cheap and very fast and accurate. This technique is currently being used in the hospitals to detect blood cancer of all types. In this paper, a method is prepared in which the white blood cells are separated from blood and its color, textural, geometrical features are extracted and fed into different types of classifiers like SVM, LDA, etc. These classifiers are used to detect whether cells are healthy or cancerous. To improve the accuracy of detection, we have checked the detection without principal component analysis (PCA) and also with PCA. Further, if cancer is detected, then KNN classifier is used to detect the type of the cancer. The counting of white blood cells is also done to make the method more accurate. This automated method is found to be highly accurate, fast, and cheap as compared to manual detection.
Straight-Line Detection with the Hough Transform Method Based on a Rectangular Grid

This paper deals with the reduction of straight-line detection time with the standard Hough transform method. The number of processed pixels is reduced and therefore contributes to improve the processing time. In a preprocessing step, the image is divided into rectangular boxes and only boxes with a certain percentage of high intensity pixels are considered in computing Hough transform. The proposed method is evaluated with examples for edge detection in Lena image. The main purpose is to show that how straight-line detection can be done rapidly.
PRIVATT - A Closer Look at People’s Data Privacy Attitudes in Times of COVID-19

The current Covid-19 global pandemic led to a proliferation of contact-tracing applications meant to help control and suppress the spread of the virus. However, the success of these contact-tracing apps relies on obtaining access to sensitive data stored on citizen’s mobile devices. The approaches taken are different around the world. While the countries with a strong democratic and civil liberty ethos are encouraging voluntary adoption of contact-tracing apps by their citizens, other countries opted for forced mass surveillance methods that limit individual freedoms. As a result, the attempt to fight the global pandemic is actually testing people’s attitudes towards privacy and government surveillance. In this context, this research introduces a pilot study examining people’s privacy concerns in a time of Covid-19. The results show that people are willing to share their personal data in the interest of controlling the spread of the virus and save lives.
An Automatic Facial Age Proression Estimation System

Linear age progression models which are largely used in prototype and conventional approaches usually produce synthesized images that are lack of quality because of the aging variations. Therefore, in this paper, a facial age progression model that captures non-linear age variances is designed by using a deep learning-based method called Generative Adversarial Network. The proposed face aging model aims to achieve convincing and visually plausible aging effects by controlling the age attribute. The model first maps the face via a convolutional encoder to a latent vector, then the vector is projected by a deconvolutional generator to the face manifold based on age, and finally the encoder and the generator are imposed on two adversarial networks respectively. The proposed model is trained on UTKFace dataset using Pytorch machine learning library. The experimental results demonstrate the capability of the proposed Generative Advanced Network (GAN) model of generating photorealistic aging faces and preserving the original identity property.
Dynamic immune cooperative scheduling of agricultural machineries

Abstract Considering the low flexibility and efficiency of the scheduling problem, an improved multi-objective immune algorithm with non-dominated neighbor-based selection and Tabu search (NNITSA) is proposed. A novel Tabu search algorithm (TSA)-based operator is introduced in both the local search and mutation stage, which improves the climbing performance of the NNTSA. Special local search strategies can prevent the algorithm from being caught in the optimal solution. In addition, considering the time costs of the TSA, an adapted mutation strategy is proposed to operate the TSA mutation according to the scale of Pareto solutions. Random mutations may be applied to other conditions. Then, a robust evaluation is adopted to choose an appropriate solution from the obtained Pareto solutions set. NNITSA is used to solve the problems of static partitioning optimization and dynamic cross-regional co-operative scheduling of agricultural machinery. The simulation results show that NNITSA outperforms the other two algorithms, NNIA and NSGA-II. The performance indicator C-metric also shows significant improvements in the efficiency of optimizing search.