Reviewerly/exHarmony huggingface dataset:
Sentiment analysis tracking of COVID-19 vaccine through tweets

Recent studies on the COVID-19 pandemic indicated an increase in the level of anxiety, stress, and depression among people of all ages. The World Health Organization (WHO) recently warned that even with the approval of vaccines by the Food and Drug Administration (FDA), population immunity is highly unlikely to be achieved this year. This paper aims to analyze people's sentiments during the pandemic by combining sentiment analysis and natural language processing algorithms to classify texts and extract the polarity, emotion, or consensus on COVID-19 vaccines based on tweets. The method used is based on the collection of tweets under the hashtag #COVIDVaccine while the nltk toolkit parses the texts, and the tf-idf algorithm generates the keywords. Both n-gram keywords and hashtags mentioned in the tweets are collected and counted. The results indicate that the sentiments are divided into positive and negative emotions, with the negative ones dominating.
Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication

Static authentication methods, like passwords, grow increasingly weak with advancements in technology and attack strategies. Continuous authentication has been proposed as a solution, in which users who have gained access to an account are still monitored in order to continuously verify that the user is not an imposter who had access to the user credentials. Mouse dynamics is the behavior of a user’s mouse movements and is a biometric that has shown great promise for continuous authentication schemes. This article builds upon our previous published work by evaluating our dataset of 40 users using three machine learning and three deep learning algorithms. Two evaluation scenarios are considered: binary classifiers are used for user authentication, with the top performer being a 1-dimensional convolutional neural network (1D-CNN) with a peak average test accuracy of 85.73% across the top-10 users. Multi-class classification is also examined using an artificial neural network (ANN) which reaches an astounding peak accuracy of 92.48%, the highest accuracy we have seen for any classifier on this dataset.
Discovering Urban Governance Parameters for Online Learning in Saudi Arabia During COVID-19 Using Topic Modeling of Twitter Data

Smart cities are a relatively recent phenomenon that has rapidly grown in the last decade due to several political, economic, environmental, and technological factors. Data-driven artificial intelligence is becoming so fundamentally ingrained in these developments that smart cities have been called artificially intelligent cities and autonomous cities. The COVID-19 pandemic has increased the physical isolation of people and consequently escalated the pace of human migration to digital and virtual spaces. This paper investigates the use of AI in urban governance as to how AI could help governments learn about urban governance parameters on various subject matters for the governments to develop better governance instruments. To this end, we develop a case study on online learning in Saudi Arabia. We discover ten urban governance parameters using unsupervised machine learning and Twitter data in Arabic. We group these ten governance parameters into four governance macro-parameters namely Strategies and Success Factors, Economic Sustainability, Accountability, and Challenges. The case study shows that the use of data-driven AI can help the government autonomously learn about public feedback and reactions on government matters, the success or failure of government programs, the challenges people are facing in adapting to the government measures, new economic, social, and other opportunities arising out of the situation, and more. The study shows that the use of AI does not have to necessarily replace humans in urban governance, rather governments can use AI, under human supervision, to monitor, learn and improve decision-making processes using continuous feedback from the public and other stakeholders. Challenges are part of life and we believe that the challenges humanity is facing during the COVID-19 pandemic will create new economic, social, and other opportunities nationally and internationally.
Partially Observable Markov Decision Processes in Robotics: A Survey

Noisy sensing, imperfect control, and environment changes are defining characteristics of many real-world robot tasks. The partially observable Markov decision process (POMDP) provides a principled mathematical framework for modeling and solving robot decision and control tasks under uncertainty. Over the last decade, it has seen many successful applications, spanning localization and navigation, search and tracking, autonomous driving, multi-robot systems, manipulation, and human-robot interaction. This survey aims to bridge the gap between the development of POMDP models and algorithms at one end and application to diverse robot decision tasks at the other. It analyzes the characteristics of these tasks and connects them with the mathematical and algorithmic properties of the POMDP framework for effective modeling and solution. For practitioners, the survey provides some of the key task characteristics in deciding when and how to apply POMDPs to robot tasks successfully. For POMDP algorithm designers, the survey provides new insights into the unique challenges of applying POMDPs to robot systems and points to promising new directions for further research.
Boosting Broader Receptive Fields for Salient Object Detection

Salient Object Detection has boomed in recent years and achieved impressive performance on regular-scale targets. However, existing methods encounter performance bottlenecks in processing objects with scale variation, especially extremely large- or small-scale objects with asymmetric segmentation requirements, since they are inefficient in obtaining more comprehensive receptive fields. With this issue in mind, this paper proposes a framework named BBRF for Boosting Broader Receptive Fields, which includes a Bilateral Extreme Stripping (BES) encoder, a Dynamic Complementary Attention Module (DCAM) and a Switch-Path Decoder (SPD) with a new boosting loss under the guidance of Loop Compensation Strategy (LCS). Specifically, we rethink the characteristics of the bilateral networks, and construct a BES encoder that separates semantics and details in an extreme way so as to get the broader receptive fields and obtain the ability to perceive extreme large- or small-scale objects. Then, the bilateral features generated by the proposed BES encoder can be dynamically filtered by the newly proposed DCAM. This module interactively provides spacial-wise and channel-wise dynamic attention weights for the semantic and detail branches of our BES encoder. Furthermore, we subsequently propose a Loop Compensation Strategy to boost the scale-specific features of multiple decision paths in SPD. These decision paths form a feature loop chain, which creates mutually compensating features under the supervision of boosting loss. Experiments on five benchmark datasets demonstrate that the proposed BBRF has a great advantage to cope with scale variation and can reduce the Mean Absolute Error over 20% compared with the state-of-the-art methods.
Digital Image Watermarking Technique Using Discrete Wavelet Transform and Discrete Cosine Transform

Digital watermarking has been proposed as a viable solution to the need of copyright protection and authentication of multimedia data in a networked environment, since it makes possible to identify the author, owner, distributor or authorized consumer of a document. In this paper a new watermarking technique to add a code to digital images is presented: the method operates in the frequency domain embedding a pseudo-random sequence of real numbers in a selected set of DCT coefficient. And a new method for digital image watermarking which does not require the original image for watermark detection. Advantages of the proposed method include: improved resistance to attacks on the watermark, implicit visual masking utilizing the time-frequency localization property of wavelet transform and a robust definition for the threshold which validates the watermark.. Experimental results demonstrate that this proposed technique is robust to most of the signal processing techniques and geometric distortions.
YAMATO: Yet-another more advanced top-level ontology

yamato sharply distinguishes itself from other existing upper ontologies in the following respects. (1) Most importantly, yamato is designed with both engineering and philosophical minds. (2) yamato is based on a sophisticated theory of roles, given that the world is full of roles. (3) yamato has a tenable theory of functions which helps to deal with artifacts effectively. (4) Information is a ‘content-bearing’ entity and it differs significantly from the entities that philosophers have traditionally discussed. Taking into account the modern society in which a flood of information occurs, yamato has a sophisticated theory of informational objects (representations). (5) Quality and quantity are carefully organized for the sake of greater interoperability of real-world data. (6) The philosophical contribution of yamato includes a theory of objects, processes, and events. Those features are illustrated with several case studies. These features lead to the intensive application of yamato in some domains such as biomedicine and learning engineering.
An improved bootstrap test of density ratio ordering

Two probability distributions with common support are said to exhibit density ratio ordering when they admit a nonincreasing density ratio. Existing statistical tests of the null hypothesis of density ratio ordering are known to be conservative, with null limiting rejection rates below the nominal significance level whenever the two distributions are unequal. It is shown that a bootstrap procedure can be used to raise the pointwise limiting rejection rate to the nominal significance level on the boundary of the null. This improves power against nearby alternatives. The proposed procedure is based on preliminary estimation of a contact set, the form of which is obtained from a novel representation of the Hadamard directional derivative of the least concave majorant operator. Numerical simulations indicate that improvements to power can be very large in moderately sized samples.
A game characterisation of tree-like Q-Resolution size

We provide a characterisation for the size of proofs in tree-like Q-Resolution and tree-like QU-Resolution by a Prover–Delayer game, which is inspired by a similar characterisation for the proof size in classical tree-like Resolution. This gives one of the first successful transfers of one of the lower bound techniques for classical proof systems to QBF proof systems. We apply our technique to show the hardness of three classes of formulas for tree-like Q-Resolution. In particular, we give a proof of the hardness of the parity formulas from Beyersdorff et al. (2015) [10] for tree-like Q-Resolution and of the formulas of Kleine Büning et al. (1995) [29] for tree-like QU-Resolution.
Resource Renting for Periodical Cloud Workflow Applications

Cloud computing is a new resource provisioning mechanism, which represents a convenient way for users to access different computing resources. Periodical workflow applications commonly exist in scientific and business analysis, among many other fields. One of the most challenging problems is to determine the right amount of resources for multiple periodical workflow applications. In this paper, the periodical workflow applications scheduling problem with total renting cost minimization is considered. The novelty of this work relies precisely on this objective function, which is more realistic in practice than the more commonly considered makespan minimization. An integer programming model is constructed for the problem under study. A Precedence Tree based Heuristic (PTH) is developed which considers three types of initial schedule construction methods. Based on the initial schedule, two improvement procedures are presented. The proposed methods are compared with existing algorithms for the related makespan based multiple workflow scheduling problem. Experimental and statistical results demonstrate the effectiveness and efficiency of the proposed algorithm.
Decentralised Monte Carlo Tree Search for Active Perception

We propose a decentralised variant of Monte Carlo tree search (MCTS) that is suitable for a variety of tasks in multi-robot active perception. Our algorithm allows each robot to optimise its own individual action space by maintaining a probability distribution over plans in the joint-action space. Robots periodically communicate a compressed form of these search trees, which are used to update the locally-stored joint distributions using an optimisation approach inspired by variational methods. Our method admits any objective function defined over robot actions, assumes intermittent communication, and is anytime.We extend the analysis of the standard MCTS for our algorithm and characterise asymptotic convergence under reasonable assumptions. We evaluate the practical performance of our method for generalised team orienteering and active object recognition using real data, and show that it compares favourably to centralised MCTS even with severely degraded communication. These examples support the relevance of our algorithm for real-world active perception with multi-robot systems.
A Dynamic-Shape-Prior Guided Snake Model With Application in Visually Tracking Dense Cell Populations

This work proposes a dynamic-shape-prior guided snake model (DSP G-snake) that is designed to improve the overall stability of the point-based snake model. The dynamic shape prior is first proposed for snakes, that efficiently unifies different types of high-level priors into a new force term. To be specific, a global-topology regularity is first introduced that settles the inherent self-intersection problem with snakes. The problem that a snake's snaxels tend to unevenly distribute along the contour is also handled, leading to good parameterization. Unlike existing methods that employ learning templates or commonly enforce hard priors, the dynamic-template scheme strongly respects the deformation flexibility of the model, while retaining a decent global topology for the snake. It is verified by experiments that the proposed algorithm can effectively prevent snakes from self-crossing, or automatically untie an already selfintersected contour. In addition, the proposed model is combined with existing forces and applied to the very challenging task of tracking dense biological cell populations. The DSP G-snake model has enabled an improvement of up to 30% in tracking accuracy with respect to regular model-based approaches. Through experiments on real cellular datasets, with highly dense populations and relatively large displacements, it is confirmed that the proposed approach has enabled superior performance, in comparison to modern active-contour competitors as well as state-of-the-art cell tracking frameworks.
The density-based clustering method for privacy-preserving data mining

Privacy-preserving data mining has become an interesting and emerging issue in recent years since it can, not only hide the sensitive information but still mine the meaningful knowledge at the same time. Since privacy-preserving data mining is a non-trivial task, which is also concerned as a NP-hard problem, several evolutionary algorithms were presented to find the optimized solutions but most of them focus on considering a single-objective function with the pre-defined weight values of three side effects (<i>hiding failure, missing cost, and artificial cost</i>). In this paper, we aim at designing a multiple objective particle swarm optimization method for hiding the sensitive information based on the density clustering approach (named CMPSO). The presented CMPSO is more flexible to select the most appropriate solutions for hiding the sensitive information based on user's preference. Extensive experiments are carried on two datasets to show that the designed CMPSO algorithm has good performance than the traditional single-objective evolutionary approaches in terms of three side effects.
Simulating Anisotropic quantum Rabi model via frequency modulation

Abstract Anisotropic quantum Rabi model is a generalization of quantum Rabi model, which allows its rotating and counter-rotating terms to have two different coupling constants. It provides us with a fundamental model to understand various physical features concerning quantum optics, solid-state physics, and mesoscopic physics. In this paper, we propose an experimental feasible scheme to implement anisotropic quantum Rabi model in a circuit quantum electrodynamics system via periodic frequency modulation. An effective Hamiltonian describing the tunable anisotropic quantum Rabi model can be derived from a qubit-resonator coupling system modulated by two periodic driving fields. All effective parameters of the simulated system can be adjusted by tuning the initial phases, the frequencies and the amplitudes of the driving fields. We show that the periodic driving is able to drive a coupled system in dispersive regime to ultrastrong coupling regime, and even deep-strong coupling regime. The derived effective Hamiltonian allows us to obtain pure rotating term and counter-rotating term. Numerical simulation shows that such effective Hamiltonian is valid in ultrastrong coupling regime, and stronger coupling regime. Moreover, our scheme can be generalized to the multi-qubit case. We also give some applications of the simulated system to the Schrödinger cat states and quantum gate generalization. The presented proposal will pave a way to further study the stronger anisotropic Rabi model whose coupling strength is far away from ultrastrong coupling and deep-strong coupling regimes in quantum optics.
Big Data in academic libraries: literature review and future research directions

Recently, Big Data studies have attracted considerable attention. However, Big Data analytics in academic libraries confront two fundamental challenges: the huge volume, velocity, and variety of data and the complexity of its techniques and algorithms. The primary aim of this study is to explore which techniques and tools can be applied in academic libraries in order to analyze Big Data, and then determine its profits in academic libraries. In addition, this study attempts to answer the following research questions: how should librarians be made to involve in Big Data? What are the future research developments in Big Data? What are the gaps in Big Data studies related to academic libraries? To provide a considerably better understanding of the advantages of Big Data in academic libraries and their future research directions, a comprehensive literature review of Big Data analysis of academic libraries over the last seven years was conducted. The results yielded a total of 37 papers related to Big Data in academic libraries. These results indicated that despite the large amount of research conducted on this topic, only a few studies discussed the implication of Big Data in academic libraries, including the analyzing tools and techniques. The benefits of Big Data in academic libraries and its implications on methodology in future studies are discussed. The present study also highlights the evolving field of Big Data research in academic libraries.
Image Processing Using FPGAs

Nine articles have been published in this Special Issue on image processing using field programmable gate arrays (FPGAs). The papers address a diverse range of topics relating to the application of FPGA technology to accelerate image processing tasks. The range includes: Custom processor design to reduce the programming burden; memory management for full frames, line buffers, and image border management; image segmentation through background modelling, online K-means clustering, and generalised Laplacian of Gaussian filtering; connected components analysis; and visually lossless image compression.
Application of the semi-analytical Fourier transform to electromagnetic modeling

The Fast Fourier Transform (FFT) algorithm makes up the backbone of fast physical optics modeling. Its numerical effort, approximately linear on the sample number of the function to be transformed, already constitutes a huge improvement on the original Discrete Fourier Transform. However, even this orders-of-magnitude improvement in the number of operations required can fall short in optics, where the tendency is to work with field components that present strong wavefront phases: this translates, as per the Nyquist-Shannon sampling theorem, into a huge sample number. So much so, in fact, that even with the reduced effort of the FFT, the operation becomes impracticable. Finding a workaround that allows us to evade, at least in part, these stringent sampling requirements is then fundamental for the practical feasibility of the Fourier transform in optics. In this work we propose, precisely, a way to tackle the Fourier transform that eschews the sampling of second-order polynomial phase terms, handling them analytically instead: it is for this reason that we refer to this method as the "semi-analytical Fourier transform". We present here the theory behind this concept and show the algorithm in action at several examples which serve to illustrate the vast potential of this approach.
Image Encryption Algorithm Based on the H-Fractal and Dynamic Self-Invertible Matrix

In this paper, an image encryption algorithm based on the H-fractal and dynamic self-invertible matrix is proposed. The H-fractal diffusion encryption method is firstly used in this encryption algorithm. This method crosses the pixels at both ends of the H-fractal, and it can enrich the means of pixel diffusion. The encryption algorithm we propose uses the Lorenz hyperchaotic system to generate pseudorandom sequences for pixel location scrambling and self-invertible matrix construction to scramble and diffuse images. To link the cipher image with the original image, the initial values of the Lorenz hyperchaotic system are determined using the original image, and it can enhance the security of the encryption algorithm. The security analysis shows that this algorithm is easy to implement. It has a large key space and strong key sensitivity and can effectively resist plaintext attacks.
Engaging with Robotic Swarms

In recent years, researchers have explored human body posture and motion to control robots in more natural ways. These interfaces require the ability to track the body movements of the user in three dimensions. Deploying motion capture systems for tracking tends to be costly and intrusive and requires a clear line of sight, making them ill adapted for applications that need fast deployment. In this article, we use consumer-grade armbands, capturing orientation information and muscle activity, to interact with a robotic system through a state machine controlled by a body motion classifier. To compensate for the low quality of the information of these sensors, and to allow a wider range of dynamic control, our approach relies on machine learning. We train our classifier directly on the user to recognize (within minutes) which physiological state his or her body motion expresses. We demonstrate that on top of guaranteeing faster field deployment, our algorithm performs better than all comparable algorithms, and we detail its configuration and the most significant features extracted. As the use of large groups of robots is growing, we postulate that their interaction with humans can be eased by our approach. We identified the key factors to stimulate engagement using our system on 27 participants, each creating his or her own set of expressive motions to control a swarm of desk robots. The resulting unique dataset is available online together with the classifier and the robot control scripts.