Reviewerly/exHarmony huggingface dataset:
Multiple discriminant analysis for collaborative representation-based classification

Collaborative Representation-based Classifier (CRC) has shown its advantages and impressive results in face recognition. To further imporve the performance of CRC, we propose a novel dimensionality reduction method termed Multiple Discriminant Analysis for Collaborative Representation-based Classification (MDA-CRC). Considering the labeling criterion of CRC is class-specific, MDA-CRC solves a group of binary classification problems where specific feature subspaces are learned for each class. In each binary classification problem, an orthogonal discriminant analysis method based on collaborative representation is adopted. Hence, MDA-CRC can improve the discriminant ability of collaborative representation and be consistent with the labeling criterion of CRC simultaneously. Further, the convergence of MDA-CRC is proven. Extensive experiments on several benchmark datasets demonstrate the effectiveness of MDA-CRC.
RETRACTED: Research on the coordinated development of tourism economy based on embedded dynamic data

Tourism is one of the most important and growing industries in the global economy. This sector plays an important role in promoting the national economy. Increased tourism streams can have positive and economic consequences for the country, especially in terms of Gross Domestic Product (GDP) and business openings. In South Asian nations, the travel industry is the driving force of monetary turn of events and GDP development. This examination inspected the effect of the travel industry on financial development and work. Radial Neural Network (RNN) technology and FPGA tools are used for data analysis. This shows the study, with major findings, including the positive and significant impact of tourism on economic growth and the long-term relationship between the employment and research variables. The study shows that lawmakers should focus on policies that emphasize tourism promotion, especially due to the country's huge potential. The impact of this latest policy study and recommendations for future studies is also mentioned.
Fake-Face Image Classification using Improved Quantum-Inspired Evolutionary-based Feature Selection Method

Deep learning models have been quite successful in discriminating synthesized or edited fake-face images. However, in the case of small training data, transfer-learning is rather preferable. This is a complex process for high dimensional feature space due to the curse of dimensionality. To mitigate the same, this paper proposes a new feature selection method for the classification of manually created fake-face images. In the proposed method, a pre-trained deep learning model is used to extract features of an image. Next, an optimal feature subset is selected from the extracted features through an improved quantum-inspired evolutionary algorithm. Lastly, the elicited features are considered to perform the classification. Experiments are conducted on a publicly available manually created fake-face image dataset, namely Real and Fake Face Detection by Yonsei University. The performance of the proposed method is compared with two methods in terms of classification accuracy and the number of selected features. The experimental comparisons exhibit that the proposed method achieves promising results among the considered methods.
Speech Processing: MFCC Based Feature Extraction Techniques- An Investigation

Abstract In this digital world, there are many applications to secure and legalize their data and all of these emissions by various techniques and there are many algorithms and methods to process their data. Some extensive method used is biometric authentication and voice recognition is better, since it paves the convenient manner to the user and it merely acquires the voice from the user. Also the background noise is in crisis with Mel Frequency Cepstrum Coefficient (MFCC) which is recognition algorithm where overcome by other tools such as smoothening filter etc. The main focus of this project is to investigate the feature extraction scheme.
Arrays vs. Decision Diagrams: A Case Study on Quantum Circuit Simulators

Despite the recent progress in the physical implementation of quantum computers, a significant amount of research still depends on the use of quantum circuit simulators running on classical hardware. While there are several techniques for quantum circuit simulation, many state-of-the-art simulators rely on an array-based simulation approach. However, this array-based approach has exponential memory complexity with respect to the number of simulated qubits. To address this drawback, complementary approaches based on decision diagrams have been proposed. While these approaches allow simulating circuits that could not be simulated before, they come with their own drawbacks. Unfortunately, no detailed case study has been conducted to date, which compares those complementary approaches and their respective strengths and weaknesses. In this work, we are addressing this by providing a survey on both approaches as well as a detailed case study on their respective performances.
What changes in where?

A deep understanding of the common patterns of bug-fixing changes is useful in several ways: (a) such knowledge can help developers in proactively avoiding coding patterns that lead to bugs and (b) bug-fixing patterns are exploited in devising techniques for automatic bug localization and program repair. This work includes an in-depth quantitative and qualitative analysis over 4,653 buggy revisions of five software systems. Our study identifies 38 bug-fixing edit patterns and discovers 37 new patterns of nested code structures, which frequently host the bug-fixing edits. While some of the edit patterns were reported in earlier studies, these nesting patterns are new and were never targeted before.
ShiSha

The importance of remote collaboration grows in an interconnected world as the reasons to avoid travel increase. The spatial rendering and collaboration capabilities of virtual and augmented reality systems are well suited for tasks such as support or training. Users can take a shared perspective to build a common understanding. Also, users may engage in face-to-face cooperation to support interpersonal communication. However, a shared perspective and face-to-face collaboration are both desirable but naturally exclude each other. We place all users at the same location to provide a shared perspective. To avoid overlapping body parts, the avatars of the other connected users are shifted to the side. A redirected body pose modification corrects the resulting inconsistencies. The implemented system is compared to a baseline of two users standing in the same location and working with overlapping avatars. The results of a user study show that the proposed modifications provide an easy to use, efficient collaboration and yield higher co-presence and the feeling of teamwork. Applying redirection techniques to other users opens up novel ways to increase social presence for local or remote collaboration.
Local Network Identifiability with Partial Excitation and Measurement

This work focuses on the identifiability of dynamical networks with partial excitation and measurement: a set of nodes are interconnected by unknown transfer functions according to a known topology, some nodes are subject to external excitation, and some nodes are measured. The goal is to determine which transfer functions in the network can be recovered based on the input-output data collected from the excited and measured nodes. We propose a local version of network identifiability, representing the ability to recover transfer functions which are approximately known, or to recover them up to a discrete ambiguity. We show that local identifiability is a generic property, establish a necessary and sufficient condition in terms of matrix generic ranks, and exploit this condition to develop an algorithm determining, with probability 1, which transfer functions are locally identifiable. Our implementation presents the results graphically, and is publicly available.
CIT-GAN: Cyclic Image Translation Generative Adversarial Network With Application in Iris Presentation Attack Detection

In this work, we propose a novel Cyclic Image Translation Generative Adversarial Network (CIT-GAN) for multi-domain style transfer. To facilitate this, we introduce a Styling Network that has the capability to learn style characteristics of each domain represented in the training dataset. The Styling Network helps the generator to drive the translation of images from a source domain to a reference domain and generate synthetic images with style characteristics of the reference domain. The learned style characteristics for each domain depend on both the style loss and domain classification loss. This induces variability in style characteristics within each domain. The proposed CIT-GAN is used in the context of iris presentation attack detection (PAD) to generate synthetic presentation attack (PA) samples for classes that are under-represented in the training set. Evaluation using current state-of-the-art iris PAD methods demonstrates the efficacy of using such synthetically generated PA samples for training PAD methods. Further, the quality of the synthetically generated samples is evaluated using Frechet Inception Distance (FID) score. Results show that the quality of synthetic images generated by the proposed method is superior to that of other competing methods, including StarGan.
Knowledge distillation methods for efficient unsupervised adaptation across multiple domains

Beyond the complexity of CNNs that require training on large annotated datasets, the domain shift between design and operational data has limited the adoption of CNNs in many real-world applications. For instance, in person re-identification, videos are captured over a distributed set of cameras with non-overlapping viewpoints. The shift between the source (e.g. lab setting) and target (e.g. cameras) domains may lead to a significant decline in recognition accuracy. Additionally, state-of-the-art CNNs may not be suitable for such real-time applications given their computational requirements. Although several techniques have recently been proposed to address domain shift problems through unsupervised domain adaptation (UDA), or to accelerate/compress CNNs through knowledge distillation (KD), we seek to simultaneously adapt and compress CNNs to generalize well across multiple target domains. In this paper, we propose a progressive KD approach for unsupervised single-target DA (STDA) and multi-target DA (MTDA) of CNNs. Our method for KD-STDA adapts a CNN to a single target domain by distilling from a larger teacher CNN, trained on both target and source domain data in order to maintain its consistency with a common representation. Our proposed approach is compared against state-of-the-art methods for compression and STDA of CNNs on the Office31 and ImageClef-DA image classification datasets. It is also compared against state-of-the-art methods for MTDA on Digits, Office31, and OfficeHome. In both settings -- KD-STDA and KD-MTDA -- results indicate that our approach can achieve the highest level of accuracy across target domains, while requiring a comparable or lower CNN complexity.
An Ergodic Measure for Active Learning From Equilibrium

This article develops KL-ergodic exploration from equilibrium (KL-E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ), a method for robotic systems to integrate stability into actively generating informative measurements through ergodic exploration. Ergodic exploration enables robotic systems to indirectly sample from informative spatial distributions globally, avoiding local optima, and without the need to evaluate the derivatives of the distribution against the robot dynamics. Using a hybrid systems theory, we derive a controller that allows a robot to exploit equilibrium policies (i.e., policies that solve a task) while allowing the robot to explore and generate informative data using an ergodic measure that can extend to high-dimensional states. We show that our method is able to maintain Lyapunov attractiveness with respect to the equilibrium task while actively generating data for learning tasks such, as Bayesian optimization, model learning, and off-policy reinforcement learning. In each example, we show that our proposed method is capable of generating an informative distribution of data while synthesizing smooth control signals. We illustrate these examples using simulated systems and provide simplification of our method for real-time online learning in robotic systems. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Robotic systems need to adapt to sensor measurements and learn to exploit an understanding of the world around them such that they can truly begin to experiment in the real world. Standard learning methods do not have any restrictions on how the robot can explore and learn, making the robot dynamically volatile. Those that do are often too restrictive in terms of the stability of the robot, resulting in a lack of improved learning due to poor data collection. Applying our method would allow robotic systems to be able to adapt online without the need for human intervention. We show that considering both the dynamics of the robot and the statistics of where the robot has been, we are able to naturally encode where the robot needs to explore and collect measurements for efficient learning that is dynamically safe. With our method, we are able to effectively learn while being energetically efficient compared with state-of-the-art active learning methods. Our approach accomplishes such tasks in a single execution of the robotic system, i.e., the robot does not need human intervention to reset it. Future work will consider multiagent robotic systems that actively learn and explore in a team of collaborative robots.
Cost Analysis of Smart Contracts Via Parametric Resource Analysis

The very nature of smart contracts and blockchain platforms, where program execution and storage are replicated across a large number of nodes, makes resource consumption analysis highly relevant. This has led to the development of analyzers for specific platforms and languages. However, blockchain platforms present significant variability in languages and cost models, as well as over time. Approaches that facilitate the quick development and adaptation of cost analyses are thus potentially attractive in this context. We explore the application of a generic approach and tool for cost analysis to the problem of static inference of gas consumption bounds in smart contracts. The approach is based on Parametric Resource Analysis, a method that simplifies the implementation of analyzers for inferring safe bounds on different resources and with different resource consumption models. In addition, to support different input languages, the approach also makes use of translation into a Horn clause-based intermediate representation. To assess practicality we develop an analyzer for the Tezos platform and its Michelson language. We argue that this approach offers a rapid, flexible, and effective method for the development of cost analyses for smart contracts.
GlocalNet: Class-aware Long-term Human Motion Synthesis

Synthesis of long-term human motion skeleton sequences is essential to aid human-centric video generation [8] with potential applications in Augmented Reality, 3D character animations, pedestrian trajectory prediction, etc. Long-term human motion synthesis is a challenging task due to multiple factors like, long-term temporal dependencies among poses, cyclic repetition across poses, bi-directional and multi-scale dependencies among poses, variable speed of actions, and a large as well as partially overlapping space of temporal pose variations across multiple class/types of human activities. This paper aims to address these challenges to synthesize a long-term (> 6000 ms) human motion trajectory across a large variety of human activity classes (> 50). We propose a two-stage activity generation method to achieve this goal, where the first stage deals with learning the long-term global pose dependencies in activity sequences by learning to synthesize a sparse motion trajectory while the second stage addresses the generation of dense motion trajectories taking the output of the first stage. We demonstrate the superiority of the proposed method over SOTA methods using various quantitative evaluation metrics on publicly available datasets.
A Poisson multi-Bernoulli mixture filter with spawning based on Kullback–Leibler divergence minimization

In the classical form, the Poisson Multi-Bernoulli Mixture (PMBM) filter uses a PMBM density to describe target birth, surviving, and death, which does not model the appearance of spawned targets. Although such a model can handle target birth, surviving, and death well, its performance may degrade when target spawning arises. The reason for this is that the original PMBM filter treats the spawned targets as birth targets, ignoring the surviving targets’ information. In this paper, we propose a Kullback–Leibler Divergence (KLD) minimization based derivation for the PMBM prediction step, including target spawning, in which the spawned targets are modeled using a Poisson Point Process (PPP). Furthermore, to improve the computational efficiency, three approximations are used to implement the proposed algorithm, such as the Variational Multi-Bernoulli (VMB) filter, the Measurement-Oriented marginal MeMBer/Poisson (MOMB/P) filter, and the Track-Oriented marginal MeMBer/Poisson (TOMB/P) filter. Finally, simulation results demonstrate the validity of the proposed filter by using the spawning model in these three approximations.
Riemannian Adaptive Optimization Algorithm and its Application to Natural Language Processing

This article proposes a Riemannian adaptive optimization algorithm to optimize the parameters of deep neural networks. The algorithm is an extension of both AMSGrad in Euclidean space and RAMSGrad on a Riemannian manifold. The algorithm helps to resolve two issues affecting RAMSGrad. The first is that it can solve the Riemannian stochastic optimization problem directly, in contrast to RAMSGrad which only achieves a low regret. The other is that it can use constant learning rates, which makes it implementable in practice. Additionally, we apply the proposed algorithm to Poincaré embeddings that embed the transitive closure of the WordNet nouns into the Poincaré ball model of hyperbolic space. Numerical experiments show that regardless of the initial value of the learning rate, our algorithm stably converges to the optimal solution and converges faster than the existing algorithms.
Algorithmic properties of first-order superintuitionistic logics of finite Kripke frames in restricted languages

Abstract We consider the effect of restricting the number of individual variables, as well as the number and arity of predicate letters, in languages of first-order predicate superintuitionistic logics of finite Kripke frames on the logics’ algorithmic properties. By a finite frame we mean a frame with a finite set of possible worlds. The languages we consider have no constants, function symbols or the equality symbol. We show that positive fragments of many predicate superintuitionistic logics of natural classes of finite Kripke frames are not recursively enumerable—more precisely, $\varPi ^0_1$-hard—in languages with three individual variables and a single monadic predicate letter; this applies to the logics of finite frames of the predicate counterparts of propositional logics lying between the intuitionistic logic and the logic of the weak law of the excluded middle.
Tweets Sentiment Analysis During COVID-19 Pandemic

The aim of this paper is to investigate the impact of social distance on people during COVID-19 pandemic using twitter sentiment analysis through a comparison between the kmeans clustering and Mini-Batch k-means clustering approaches.To find the most common frequent words, two datasets have been investigated (WHO and Bahrain ministry of health datasets) to be as data preparation and exploration.Another two datasets (English and Arabic datasets) are used in the clustering of k-means.In this paper, a comparison between k-means and Mini-Batch k-means is performed to find a pattern.The word frequency shows that there are several words related to the pandemic.The sentiment analysis result show that in USA, Australia, Nigeria, Canada, and England, most tweets are neutral.However, the majority of tweets are positive tweets from both Italy and India.In addition, the k-means cluster in the English dataset reveals several cluster trends where COVID-19 pandemic procedures are addressed in cluster 1, and health workers are encouraged in cluster 3.
An Uncertainty-Driven and Observability-Based State Estimator for Nonholonomic Robots

The problem addressed in this article is the localization of a mobile robot using a combination of onboard sensors and ultrawideband (UWB) beacons. By using a discrete-time formulation of the system's kinematics, we identify the geometric conditions that make the system globally observable and cast the state estimation problem into the framework of least-squares optimization. The observability filter thus obtained is remarkably different from classic Bayesian filters, such as the Kalman Filter, since it does not need a priori stochastic models of process and measurement uncertainty contributions and thus proves to have better performance than the Kalman filters if such contributions are partly unknown or differ from the expected values. The second important outcome of this work is the analytical study of uncertainty propagation. The effectiveness of the designed filter, the validity of the theoretical analysis of estimation uncertainties, and the comparisons with a state-of-the-art extended Kalman filter (EKF) are corroborated by extensive simulations and validated experimentally.
Quantum Bloom Filter and Its Applications

A quantum Bloom filter is a spatially more efficient data structure which is used to represent a set of n elements by using O(lognk) qubits.In this article, we define and design a quantum Bloom filter and its corresponding algorithms.Due to the reversibility of quantum operators, it can not only add a new element to a quantum Bloom filter but also delete an existing element from the quantum Bloom filter.Furthermore, we employ the quantum Bloom filter to solve two private issues, i.e., oblivious set-member decision and multiparty private set intersection cardinality.The results show that the quantum Bloom filter has inherent advantages in privacy-preserving applications concerning set operations.
A Basic Framework for Explanations in Argumentation

We discuss explanations for formal (abstract and structured) argumentation-the question of whether and why a certain argument or claim can be accepted (or not) under various extension-based semantics. We introduce a flexible framework, which can act as the basis for many different types of explanations. For example, we can have simple or comprehensive explanations in terms of arguments for or against a claim, arguments that (indirectly) defend a claim, the evidence (knowledge base) that supports or is incompatible with a claim, and so on. We show how different types of explanations can be captured in our basic framework, discuss a real-life application and formally compare our framework to existing work.