Reviewerly/exHarmony huggingface dataset:
Quantum hub and authority centrality measures for directed networks based on continuous-time quantum walks

In this work we introduce, test and discuss three quantum methods for computing hub and authority centrality scores in directed networks. The methods are based on unitary, continuous-time quantum walks; the construction of a suitable Hermitian Hamiltonian is achieved by performing a quantum walk on the associated bipartite graph. Two methods, called CQAu and CQAw, use the same evolution operator, inspired by the classical HITS algorithm, but with different initial states; the computation of hub and authority scores is performed simultaneously. The third method, called CQG and inspired by classical PageRank, requires instead two separate runs with different evolution operators, one for hub and one for authority scores. The methods are tested on several directed graphs with different sizes and properties; a comparison with other well-established ranking algorithms is provided. CQAw emerges as the most reliable of the three methods and yields rankings that are largely compatible with results from HITS, although CQAu and CQG also present interesting features and potential for applications.
Why Modelling the COVID-19 pandemic using Fuzzy Cognitive Maps (FCM)?

The coronavirus disease known today as COVID-19, has created tremendous chaos around the world, affecting people's lives and causing a large number of deaths. The WHO has accepted COVID-19 as a pandemic leading to a global health emergency. Global collaboration is sought in numerous quarters. Research efforts have been intensified all around the humankind. Most studies for COVID-19 are done based on statistical models which depend solely on correlation factors. The factor of causality has not been considered appropriately. The approach of Fuzzy Cognitive Maps (FCM) that is considering the causality factors is proposed, to investigate the whole spectrum of COVID-19. An FCM COVID-19 model is proposed having 10 symptoms-concepts. Early theoretical simulation studies using an FCM COVID-19 model and real data from the local hospital, have been conducted. Simulations with real patient data give excellent results. Future research directions are provided.
Audio CAPTCHA for Visually Impaired

Completely Automated Public Turing Tests (CAPTCHA) have been used to differentiate between computers and humans for quite some time now. There are many different varieties of CAPTCHAs - text-based, image-based, audio, video, arithmetic, etc. However, not all varieties are suitable for the visually impaired. As time goes by and Spambots and APIs grow more accurate, the CAPTCHA tests have been constantly updated to stay relevant, but that has not happened with the audio CAPTCHA. There exists an audio CAPTCHA intended for the blind/visually impaired but many blind/visually impaired find it difficult to solve. We propose an alternative to the existing system, which would make use of unique sound samples layered with music generated through GANs (Generative Adversarial Networks) along with noise and other layers of sounds to make it difficult to dissect. The user has to count the number of times the unique sound was heard in the sample and then input that number. Since there are no letters or numbers involved in the samples, speech-to-text bots/APIs cannot be used directly to decipher this system. Also, any user regardless of their native language can comfortably use this system.
Batch quadratic programming network with maximum entropy constraint for anomaly detection

The difficulty of anomaly detection lies in balancing the impact of noise on the network (noise suppression) and distinguishing the real anomaly from the noise (abnormal exposure). So, a deep anomaly detector Batch Quadratic Programming (BQP) network with Maximum Entropy Constraint is proposed. It imposes quadratic programming constraints on Support Vector Data Description through the BQP output layer to achieve noise suppression. In BQP network processes’ batch data, Maximum Entropy Constraint is used to balance abnormal samples and noise. The experiment compared the shallow method with the currently popular deep method on MNIST and CIFAR-10 data sets and proved that the BQP network with Maximum Entropy Constraint has excellent performance.
Performance Comparison of Multiple Supervised Learning Algorithms for YouTube Exaggerated Bangla Titles Classification

Exaggerated titles of YouTube videos are annoying. YouTube has been the most popular online video service for several years, with billions of subscribers and viewers. These videos have titles. To attract audiences, the YouTuber uses the most interesting words in the video's title. On YouTube, there are two types of titles: consistent and exaggerated. Usually, these exaggerated titles encourage people to watch videos but in reality, people don't get much entertainment by watching these exaggerated titles videos. To overcome this problem, we present a Bangla text classification analysis approach based on Natural Language Processing (NLP) on YouTube titles. We collect two different phases of video data on YouTube after watching so many videos. This classification assists in the discovery of YouTube videos with exaggerated titles. We used a total of six models in this research. Based on the results of research conducted, Convolutional Neural Network (CNN) has successfully classified the exaggerated Bangla title videos because it achieves results of 80%, 81.25%, and 76.47% for accuracy, precision, and F1 Score respectively.
Coronavirus Legislative Responses in the UK: Regression to Panic and Disdain of Constitutionalism

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Quantum rebound attack to D-M structure based on ARIA algorithm

Abstract With the increasing application of quantum computing, quantum technology is increasingly used in the security analysis and research of multiple symmetric cryptographic algorithms such as block ciphers and hash functions. In 2020, Sasaki et al. proposed a dedicated quantum collision attack against hash functions in EUROCRYPT. Some differential trajectories with a probability of 2 −2 n /3 that cannot be used in the classical environment may be used to launch collision attacks in the quantum environment. The ARIA algorithm is a block cipher proposed by the Korean researcher Kwon et al. on ICISC 2003. The block cipher algorithm is similar to AES in structure. This article mainly analyzes the security of Davies-Meyer structure, and uses AIRA as the permutation function to construct ARIA hash function based on the DM hash model. A new AIRA differential path was found based on MILP, and 7 rounds of ARIA-DM hash function quantum rebound attacks were given.
Information Extraction from Visually Rich Documents with Font Style Embeddings

Information extraction (IE) from documents is an intensive area of research with a large set of industrial applications. Current state-of-the-art methods focus on scanned documents with approaches combining computer vision, natural language processing and layout representation. We propose to challenge the usage of computer vision in the case where both token style and visual representation are available (i.e native PDF documents). Our experiments on three real-world complex datasets demonstrate that using token style attributes based embedding instead of a raw visual embedding in LayoutLM model is beneficial. Depending on the dataset, such an embedding yields an improvement of 0.18% to 2.29% in weighted F1-score with a decrease of 30.7% in the final number of trainable parameters of the model, leading to an improvement in both efficiency and effectiveness.
Selective Lossy Image Compression for Autonomous Systems

The main objective of this paper was to effectively interface object detection based on Convolution Neural Networks (CNNs) with selective lossy image compression techniques to improve the efficiency of subsequent image operations and reduce the memory requirement for storing the images in autonomous applications of self-driving vehicles. Object detection and localization was performed using 2 state-of-the-art CNN based models from the Tensorflow 2.0 Object Detection API - Faster R-CNN ResNet152 V1 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1024\times 1024$</tex> and CenterNet HourGlass104 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1024\times 1024$</tex> . Lossy Image Compression centred around the most prominent detected object (which is preserved) is done through 3 techniques - K-Means Clustering (KM), Genetic Algorithm (GA), Discrete Cosine Transform (DCT). The compressed and preserved parts were recombined to produce the final image. Analysis of the results obtained from different models and compression techniques was carried out. It was found that DCT produced the best results on both the models.
m-Stability

Use of game theory and mechanism design in cloud security is a well-studied topic. When applicable, it has the advantages of being efficient and simple compared to cryptography alone. Most analyses consider two-party settings, or multi-party settings where coalitions are not allowed. However, many cloud security problems that we face are in the multi-party setting and the involved parties can almost freely collaborate with each other. To formalize the study of disincentivizing coalitions from deviating strategies, a well-known definition named k-resiliency has been proposed by Abraham et al. (ACM PODC '06). Since its proposal, k-resiliency and related definitions are used extensively for mechanism design. However, in this work we observe the shortcoming of k-resiliency. That is, although this definition is secure, it is too strict to use for many cases and rule out secure mechanisms as insecure. To overcome this issue, we propose a new definition named ℓ-repellence against the presence of a single coalition to replace k-resiliency. Our definition incorporates transferable utility in game theory as it is realistic in many distributed and multi-party computing settings. We also propose m-stability definition against the presence of multiple coalitions, which is inspired by threshold security in cryptography. We then show the advantages of our novel definitions on three mechanisms, none of which were previously analyzed against coalitions: incentivized cloud computation, forwarding data packages in ad hoc networks, and connectivity in ad hoc networks. Regarding the former, our concepts improve the proposal by Küpçü (IEEE TDSC '17), by ensuring a coalition-proof mechanism.
How diverse is the ACII community? Analysing gender, geographical and business diversity of Affective Computing research

ACII is the premier international forum for presenting the latest research on affective computing. In this work, we monitor, quantify and reflect on the diversity in ACII conference across time by computing a set of indexes. We measure diversity in terms of gender, geographic location and academia vs research centres vs industry, and consider three different actors: authors, keynote speakers and organizers. Results raise awareness on the limited diversity in the field, in all studied facets, and compared to other AI conferences. While gender diversity is relatively high, equality is far from being reached. The community is dominated by European, Asian and North American researchers, leading the rest of continents under-represented. There is also a strong absence of companies and research centres focusing on applied research and products. This study fosters discussion in the community on the need for diversity and related challenges in terms of minimizing potential biases of the developed systems to the represented groups. We intend our paper to contribute with a first analysis to consider as a monitoring tool when implementing diversity initiatives. The data collected for this study are publicly released through the European divinAI initiative.
Understanding Linear Style Transfer Auto-Encoders

Style transfer auto-encoder has recently been shown to be highly effective in synthesizing images with styles transferred from another image. In this work, we aim to provide an answer to this question by studying a simpler variant of STAE, namely, the linear style transfer auto-encoders (LinSTAEs), where the encoder and decoders are all linear models. We show that the objective function of LinSTAE, under the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\ell_{2}$</tex> loss, affords a simple form, and the optimal solutions reveal the mechanism how the encoder capture joint characteristics from the input and the target domain, and the decoders restore their idiosyncrasies. We further show that at least for the linear case, the cycle reconstruction loss is not necessary - the vanilla LinSTAE objective function is already effective. We use numerical experiments on the synthetic and the MNIST dataset to showcase our findings.
Grassmannian learning mutual subspace method for image set recognition

This paper addresses the problem of object recognition given a set of images as input (e.g., multiple camera sources and video frames). Convolutional neural network (CNN)-based frameworks do not exploit these sets effectively, processing a pattern as observed, not capturing the underlying feature distribution as it does not consider the variance of images in the set. To address this issue, we propose the Grassmannian learning mutual subspace method (G-LMSM), a NN layer embedded on top of CNNs that can process image sets more effectively and can be trained in an end-to-end manner. The image set is first represented by a low-dimensional input subspace and then this input subspace is matched with dictionary subspaces by a similarity of their canonical angles, an interpretable and easy to compute metric. The key idea of G-LMSM is that the dictionary subspaces are learned as points on the Grassmann manifold, optimized with Riemannian stochastic gradient descent. This learning is stable, efficient and theoretically well-grounded. We demonstrate the effectiveness of our proposed method on hand shape recognition, face identification, and facial emotion recognition.
Blind VQA on 360° Video via Progressively Learning From Pixels, Frames, and Video

Blind visual quality assessment (BVQA) on 360° video plays a key role in optimizing immersive multimedia systems. When assessing the quality of 360° video, human tends to perceive its quality degradation from the viewport-based spatial distortion of each spherical frame to motion artifact across adjacent frames, ending with the video-level quality score, i.e., a progressive quality assessment paradigm. However, the existing BVQA approaches for 360° video neglect this paradigm. In this paper, we take into account the progressive paradigm of human perception towards spherical video quality, and thus propose a novel BVQA approach (namely ProVQA) for 360° video via progressively learning from pixels, frames and video. Corresponding to the progressive learning of pixels, frames and video, three sub-nets are designed in our ProVQA approach, i.e., the spherical perception aware quality prediction (SPAQ), motion perception aware quality prediction (MPAQ) and multi-frame temporal non-local (MFTN) sub-nets. The SPAQ sub-net first models the spatial quality degradation based on spherical perception mechanism of human. Then, by exploiting motion cues across adjacent frames, the MPAQ sub-net properly incorporates motion contextual information for quality assessment on 360° video. Finally, the MFTN sub-net aggregates multi-frame quality degradation to yield the final quality score, via exploring long-term quality correlation from multiple frames. The experiments validate that our approach significantly advances the state-of-the-art BVQA performance on 360° video over two datasets, the code of which has been public in https://github.com/yanglixiaoshen/ProVQA.
Improving a Virtual Campus for teaching and learning during COVID-19 and beyond guided by a digital transformation strategy

COVID-19 has accelerated digital transformation of universities by giving them no option but to immediately undertake important technological and methodological changes to guarantee instructional continuity. In addition, faculty and staff have made a great effort to improve their digital capabilities. From a strategic point of view, it is very important higher education institutions to take advantage of these advances as the basis to further adapt and improve their teaching and learning model to fulfill the expectations of learners in a digital society beyond COVID-19. In this paper, we describe the evolution of the ICT ecosystem of University of Murcia (Spain), with a strong focus on our Virtual Campus. In particular, we describe how we have continuously enriched our teaching and learning ecosystem as we were transitioning from emergency remote teaching towards the "new normal". Moreover, we also explain how we are capitalizing the effort in responding to COVID-19 to support the long-term goals of our digital transformation plan at the same time we fulfill the immediate need of offering our faculty an ecosystem adapted to the new needs that can support different teaching models and methodologies.
An Intelligent Trash Can Robt for Early Childhood Green Education

Trash is often defined as unwanted materials that are no longer useful and have been thrown away. To make our environment more sustainable, we as a society need to manage trash more efficiently. Sometimes, recyclable materials are thrown in trash cans and they need to be differentiated from actual trash in order to be reused. The utilization of technology, especially an implementation of robot technology, can help humans in this regard. In addition to the environment sector, there is a high demand for robots in other sectors due to the development of new technologies such as artificial intelligence. The robot industry is in urgent need of professional and applied talents. Additionally, recent social development has put forth urgent requirements for progressive robotic education. In order to cultivate innovative talents, enhance students’ practical ability and promote the intelligent development of robots towards a green environment, an educational trash can robot has been developed. It helps to stimulate students’ interest in scientific research, is cultivates innovative talents and promotes the further development of higher technology such as artificial intelligence. This paper describes the proposed methodology for building a trash can robot that can be easily used in high schools, middle schools, and elementary schools alike to promote robotics education as well as green education.
Automation of a multicomponent assembly of open information systems

In the information infrastructure of any level - global, national, regional, etc. as a basis for integration is necessary to use the general principles of open information systems, the essence of which is to ensure the compatibility of all used components of the system for an agreed set of standards. However, in the process of creating open information systems, problems inevitably arise to ensure the interaction of the components in the environment, as well as the formation of multicomponent structures with the requirements and limitations of information processes. Existing methods for the synthesis of technical systems, as a rule, are aimed at solving the problem of choosing efficient structures from a finite set of options, and the process of forming and formalizing alternative options remains a resource-intensive task. The report discusses the issues of automating the construction of information systems, the components of which are capable of interaction, proposed models and algorithms for the synthesis of multicomponent structures.
An Exponential Decreased Kinematic and PID Low Level Control Schemes for an Omni-Wheeled Mobile Robot

This paper presents a kinematic control algorithm development for a four-omni-wheeled robot such that the robot pose’s error decreases exponentially. A PID control scheme was used to regulate the angular speed of DC motors. Simulation experiments have been carried out to verify the mobile robot motion and the proposed control performances. This simulation would give a good initial robot and control design before realizing a real time robot controller. The results obtained through the simulation program elucidate that the robot is able to reach the specified static target and direction towards the robot in less than 6 seconds with the value of every RMSE below 0.01. These results also reveal that the proposed PID control can be utilized to make the robot moves smoothly as planned. The graphical form is employed to present the results of response, trajectory, and the changes in error values.
Modeling temporally uncorrelated components of complex-valued stationary processes

A complex-valued linear mixture model is considered for discrete weakly stationary processes.Latent components of interest are recovered, which underwent a linear mixing.Asymptotic properties are studied of a classical unmixing estimator which is based on simultaneous diagonalization of the covariance matrix and an autocovariance matrix with lag τ .The main contributions are asymptotic results that can be applied to a large class of processes.In related literature, the processes are typically assumed to have weak correlations.This class is extended, and the unmixing estimator is considered under stronger dependency structures.In particular, the asymptotic behavior of the unmixing estimator is estimated for both long-and short-range dependent complex-valued processes.Consequently, this theory covers unmixing estimators that converge slower than the usual √ T and unmixing estimators that produce non-Gaussian asymptotic distributions.The presented methodology is a powerful preprocessing tool and highly applicable in several fields of statistics.
A Study of Awareness and Perception of Augmented Reality in Education Sector

This study will give the detail understanding of Augmented Reality and the existence of Augmented Reality in the education sector. This paper will also be contributing in understanding that how much user's and teachers are aware about this technology and how many of them have experienced the technology. This study is also giving review on implementation of AR technology and how this is giving benefits to users in different subjects and at different ages as compare to traditional mode of learning. Paper will also help readers in understanding about the perception of users and teachers if they have experienced AR in learning, teaching simultaneously and further scope of research with certain limitations.