Reviewerly/exHarmony huggingface dataset:
SDN Assisted Codec, Path and Quality Selection for HTTP Adaptive Streaming

Adaptive streaming over HTTP is the dominant video streaming technology for more than a decade. HTTP Adaptive Streaming (HAS) systems provide a framework which enables clients to adapt quality with respect to network fluctuations during streaming, hence to optimize the perceived quality on the client side. Recently, network assistance is integrated with HAS in order to improve underlying network conditions and to provide network-related information to the clients. The performance of HAS systems can be further enhanced if the characteristics of the streamed video are considered. In this paper, we propose a HAS system architecture where Software Defined Networking (SDN) technology is utilized for assisting clients to select the most appropriate video codec and bitrate under the constraint of current network conditions as well as routing the video packet over the appropriate paths. In the proposed architecture, layered video is used, where each additional layer improves the quality. The controller estimates the packet loss probability by taking video codec characteristics, the bitrates of the layers and network capacity into account. Based on these estimations, the controller selects the appropriate codec type and video quality for the clients and manage the network. Simulation results show that the performance of the video streaming architecture can be improved significantly when codec, quality and path selection are jointly considered, and combined with SDN flexibility and advantageous.
Numerical scheme for a nonlinear optical response of a metallic nanostructure: quantum hydrodynamic theory solved by adopting an effective Schrödinger equation

Quantum hydrodynamic theory (QHT) can describe some of the characteristic features of quantum electron dynamics that appear in metallic nanostructures, such as spatial nonlocality, electron spill-out, and quantum tunneling. Furthermore, numerical simulations based on QHT are more efficient than fully quantum mechanical approaches, as exemplified by time-dependent density functional theory using a jellium model. However, QHT involves kinetic energy functionals, the practical implementation of which typically induces significant numerical instabilities, particularly in nonlinear optical phenomena. To mitigate this problem, we develop a numerical solution to QHT that is quite stable, even in a nonlinear regime. The key to our approach is to rewrite the dynamical equation of QHT using the effective Schrödinger equation. We apply the new method to the linear and nonlinear responses of a metallic nanoparticle and compare the results with fully quantum mechanical calculations. The results demonstrate the numerical stability of our method, as well as the reliability and limitations of QHT.
Intelligent health system for the investigation of consenting COVID-19 patients and precision medicine

Advancing frontiers of clinical research, we discuss the need for intelligent health systems to support a deeper investigation of COVID-19. We hypothesize that the convergence of the healthcare data and staggering developments in artificial intelligence have the potential to elevate the recovery process with diagnostic and predictive analysis to identify major causes of mortality, modifiable risk factors and actionable information that supports the early detection and prevention of COVID-19. However, current constraints include the recruitment of COVID-19 patients for research; translational integration of electronic health records and diversified public datasets; and the development of artificial intelligence systems for data-intensive computational modeling to assist clinical decision making. We propose a novel nexus of machine learning algorithms to examine COVID-19 data granularity from population studies to subgroups stratification and ensure best modeling strategies within the data continuum.
Large-Precision Homomorphic Sign Evaluation Using FHEW/TFHE Bootstrapping

A comparison of two encrypted numbers is an important operation needed in many machine learning applications, for example, decision tree or neural network inference/training. An efficient instantiation of this operation in the context of fully homomorphic encryption (FHE) can be challenging, especially when a relatively high precision is sought. The conventional FHE way of evaluating the comparison operation, which is based on the sign function evaluation using FHEW/TFHE bootstrapping (often referred in literature as programmable bootstrapping), can only support very small precision (practically limited to 4–5 bits or so). For higher precision, the runtime complexity scales linearly with the ciphertext (plaintext) modulus (i.e., exponentially with the modulus bit size). We propose sign function evaluation algorithms that scale logarithmically with the ciphertext (plaintext) modulus, enabling the support of large-precision comparison in practice. Our sign evaluation algorithms are based on an iterative use of homomorphic floor function algorithms, which are also derived in our work. Further, we generalize our procedures for floor function evaluation to arbitrary function evaluation, which can be used to support both small plaintext moduli (directly) and larger plaintext moduli (by using a homomorphic digit decomposition algorithm, also suggested in our work). We implement all these algorithms using the PALISADE lattice cryptography library, introducing several implementation-specific optimizations along the way, and discuss our experimental results.
Differentiable Stereopsis: Meshes from multiple views using differentiable rendering

We propose Differentiable Stereopsis, a multi-view stereo approach that reconstructs shape and texture from few input views and noisy cameras. We pair traditional stereopsis and modern differentiable rendering to build an end-to-end model which predicts textured 3D meshes of objects with varying topologies and shape. We frame stereopsis as an optimization problem and simultaneously update shape and cameras via simple gradient descent. We run an extensive quantitative analysis and compare to traditional multi-view stereo techniques and state-of-the-art learning based methods. We show compelling reconstructions on challenging real-world scenes and for an abundance of object types with complex shape, topology and texture. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Project webpage: https://shubham-goel.github.io/ds/
On the Influence of Top Journals

We study the evolution of the influence of journals over the period 1970-2017. In the early 1970's, a number of journals had similar influence, but by 1995, the `Top 5' journals, QJE, AER, RES, Econometrica, and JPE, had acquired a major lead. This dominance has remained more or less unchanged since 1995. To place these developments in a broader context, we also study trends in sociology. The trends there have gone the other way; the field journals rose in influence, relative to the Top General journals. A model of journals as platforms is developed to understand these trends across time and across disciplines.
Chapter 5 Application of machine learning algorithms for facial expression analysis

Nowadays, facial expression analysis (FEA) is becoming an important application on various fields such as medicine, education, entertainment and crime analysis because it helps to analyze where no verbal communication is possible. FEA is being done after face recognition and depends on the feature extraction of how efficiently it is generated. Therefore, classification plays a vital role to acquire the necessary output to analyze the correct expression. In addition, machine learning (ML) and deep learning algorithms are useful to classify the data as system requires either structured-like text or unstructured-like images and videos perhaps to analyze the expression, and image input is preferred by the system as well because the face image consists of a kind of information like texture of organized features, age, gender and shape which cannot be described properly by the textual annotation to a corresponding image. The system can be done in different ways: either it can apply the deep learning algorithms on raw data, or can apply ML algorithms on the preprocessed images based on the user requirement. This chapter discusses the challenges and potential ML algorithms and efficient deep learning algorithms to recognize the automatic expression of humans to prop up with the significant areas such as human computer interaction, psychology in medical field, especially to analyze the behavior of suspected people in crowded areas probably in airports and so on. In recent years, ML algorithms had become very popular in the field of data retrieval to improve its efficiency and accuracy. A new state-ofthe- art image retrieval called ML algorithms plays an imperative role to decrease the gap semantically between the user expectation and images available in the database. This chapter presents a comprehensive study of ML algorithms such as supervised, unsupervised and a sequence of both. Furthermore, the demonstration of various ML algorithms is used for image classification, and of clustering which also represents the summary and comparison of ML algorithms for various datasets like COREL and face image database. Finally, the chapter concludes with the challenges and few recommendations of ML algorithms in image retrieval.
General precedes specific in memory representations for structured experience.

Decades of work has shown that learners rapidly extract structure from their environment, later leveraging their knowledge of what is more versus less consistent with prior experience to guide behavior. However, open questions remain about exactly what is remembered after exposure to structure. Memory for specific associations-transitions that unfold over time-is considered a prime candidate for guiding behavior. However, other factors could influence behavior, such as memory for general features like reliable groupings or within-group positions. We also do not yet know whether memory depends upon the amount of experience with the input structure, leaving us with an incomplete understanding of how statistical learning supports behavior. In 4 experiments, we tracked the emergence of memory for item-item transitions, order-independent groups, and positions by having 400 adults watch a stream of shape triplets followed by a recognition memory test. We manipulated how closely test sequences corresponded to the input along each dimension of interest, allowing us to isolate the contribution of each factor. Both item-item transitions and order-independent group information influenced behavior, highlighting statistical learning as a mechanism through which we form both specific and generalized representations. Moreover, these factors drove behavior after different amounts of experience: With limited exposure, only group information impacted old-new judgments specific transitions gained importance later. Our findings suggest statistical learning proceeds by first forming a general representation of structure, with memory being later refined to include specifics after more experience. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
SFSADE: an improved self-adaptive differential evolution algorithm with a shuffled frog-leaping strategy

Abstract The differential evolution (DE) algorithm is an efficient random search algorithm based on swarm intelligence for solving optimization problems. It has the advantages of easy implementation, fast convergence, strong optimization ability and good robustness. However, the performance of DE is very sensitive to the design of different operators and the setting of control parameters. To solve these key problems, this paper proposes an improved self-adaptive differential evolution algorithm with a shuffled frog-leaping strategy (SFSADE). It innovatively incorporates the idea of the shuffled frog-leaping algorithm into DE, and at the same time, it cleverly introduces a new strategy of classification mutation, and also designs a new adaptive adjustment mechanism for control parameters. In addition, we have carried out a large number of simulation experiments on the 25 benchmark functions of CEC 2005 and two nonparametric statistical tests to comprehensively evaluate the performance of SFSADE. Finally, the results of simulation experiments and nonparametric statistical tests show that SFSADE is very effective in improving DE, and significantly improves the overall diversity of the population in the process of dynamic evolution. Compared with other advanced DE variants, its global search speed and optimization performance also has strong competitiveness.
Assessment of heavy metal pollution in soil samples from a gold mining area in Osun State, Nigeria using proton-induced X-ray emission

This study identified and determined the concentration of heavy metal contaminants in Igun, Ijana-Gada and Igbadae gold mining sites using Particle Induced X-ray Emission (PIXE) technique. This was with a view to providing information on various heavy metal concentrations, potential sources of metals contamination and pollution load at Igun, Ijana-Gada and Igbadae gold mining sites. A total of thirty soil samples were collected randomly from Igun, Ijana-Gada and Igbadae gold mining sites in the Ilesha area, Osun State. Twelve samples were collected each from Igun and Ijana-Gada sites; while six samples were collected from Igbadae site. The soil samples were processed and analyzed for selected heavy metals (Fe, Mn, Cu, Zn, Cr, M, V and Fe). A Multivariate statistics approach (Pearson Correlation Matrix and Principal Component Analysis) was used on the data for identification of contamination sources. The heavy metal concentrations in the studied soils decreased in the following order: Fe > Ti > Mn > V > Cr > Zn > Pb > Cu with mean value of Fe (84,470.60 mg/kg), Ti (15,337.30 mg/kg), Mn (576.80 mg/kg), V (465.90 mg/kg), Cr (304.10 mg/kg), Zn (103.90 mg/kg), Pb (38.60 mg/kg), and Cu (26.00 mg/kg). The result obtained from the assessment of heavy metal pollution using Enrichment Factor (EF), Pollution Load Index (PLI), and Pollution Index (PI) confirmed that Igun, Ijana-Gada, and Igbadae soils/mine-tailings were polluted in the range from slightly through moderately polluted up to highly polluted. Principal Component Analysis (PCA) extracted three major components: PC1with loading of Fe, V, Zn, and Cu, PC2 with high loading of Ti and Mn while PC3 with the contribution of Pb and Cr. This was corroborated by Pearson Correlation Matrix (PCM) for the analyzed elements. The study revealed that the soils and mine tailings from the study area were polluted with heavy metals especially V, Ti and Cr from anthropogenic sources with little crustal material contributions thereby posing serious environmental and health concerns.
Federated Transfer Learning for Bearing Fault Diagnosis Based on Averaging Shared Layers

Existing data-driven machinery fault diagnosis methods can obtain high diagnosis accuracy under the condition of abundant labeled data. However, in the actual industrial environment, complete and high-quality training data may often be distributed on multiple mechanical equipment of different regions or institutions, so-called an isolated data island problem. It is often difficult to integrate and utilize these datasets due to limitation of legal regulations or interest conflict, such as privacy protection, security risk and industry competition. Therefore, how to effectively use the separated data of multiple participants to jointly train a reliable intelligent fault diagnosis model is an urgent challenge. To address this problem, a federated transfer learning method based on averaging shared layers for bearing fault diagnosis is proposed in this study. A server-clients architecture with multiple deep transfer networks is constructed to jointly learn the global features from isolated datasets. Then, a modified federated averaging method based on shared layers is adopted to implement federated averaging of distributed feature layers from different diagnosis models, and personalized layers are updated locally. Three different bearing datasets collected by different devices are used for experimental verification. Compared with the current popular federated learning schemes, the experiment results demonstrate the effectiveness and superiority of the proposed method.
The Golden Rule as a Paradigm for Fostering Prosocial Behavior With Virtual Reality

The Golden Rule of ethics in its negative form states that you should not do to others what you would not want others to do to you, and in its positive form states that you should do to others as you would want them to do to you. The Golden Rule is an ethical principle, but in virtual reality (VR), it can also be thought of as a paradigm for the promotion of prosocial behavior. This is because in VR, you can directly experience harm that you inflicted or were complicit in inflicting from the embodied perspective of the victim. This use of what we refer to as the Golden Rule Embodiment Paradigm (GREP) relies on participants in VR having the illusion of body ownership over a virtual body. In this article, we introduce virtual embodiment and the consequent illusion of ownership over the virtual body, and describe how this phenomenon has been utilized to influence implicit attitudes. We then introduce the GREP and give examples of studies in which it enhanced helping behavior.
Public health effectiveness of digital contact tracing in the COVID-19 pandemic: A systematic review of available data.

Background and aim Contact tracing is a key element of epidemiologic investigation and active surveillance during infectious diseases outbreaks. Digital contact tracing (DCT) are new technologies that have been increasingly adopted in different countries to support conventional contact tracing efforts to control the COVID-19 pandemic. However, scant evidence is available on its public health effectiveness. We applied the Indicator Framework issued in 2021 jointly by the World Health Organization (WHO) and the European Centre for Disease Prevention and Control (ECDC) to assess the available evidence on DCT adoption and impact in the context of the COVID-19 pandemic. Methods We carried out a systematic review following the PRISMA guidelines (Prospero registration number: CRD42021253662) to retrieve, pool, and critically appraise studies published in English from November 2019 to April 2021. We excluded mathematical models of effectiveness. Only studies representative of the general population or specific populations were included . In line with the WHO-ECDC indicator framework, outcomes of interest were grouped in indicators of: i) DCT use, ii) DCT success, and iii) DCT performance. Results We identified 1.201 citations searching the PubMed, Embase, Web of Science and The Cochrane Library. After screening, 10 studies were included. All included studies reported measures of DCT use, varying widely by study population and setting (percentage of DCT apps download from 0.01% to 58.3% in included studies). Almost no data quantified an association between DCT adoption rate and infection transmission at the community level. Only one reported measures of DCT success (ratio of exposure notifications received to positive test results entered), while no studies were retrieved reporting measures of DCT performance. Conclusions DCT has large potential to control epidemics. Its adoption is hindered by several normative, technical and acceptance barriers in different regions and countries. Our review shows that while some evidence is available on its adoption and use in selected settings, very scant data is available on its effectiveness in the fight against COVID-19. As digitalization provides new tools for infection control at the population level, solid research is needed to quantify the public health effects of their application.
Randomized Benchmarking for Non-Markovian Noise

Estimating the features of noise is the first step in a chain of protocols that will someday lead to fault-tolerant quantum computers. The randomized benchmarking (RB) protocol is designed with this exact mindset, estimating the average strength of noise in a quantum processor with relative ease in practice. However, RB, along with most other benchmarking and characterization methods, is limited in scope because it assumes that the noise is temporally uncorrelated (Markovian), which is increasingly evident not to be the case. Here, we combine the RB protocol with a recent framework describing non-Markovian quantum phenomena to derive a general analytical expression of the average sequence fidelity (ASF) for non-Markovian RB with the Clifford group. We show that one can identify non-Markovian features of the noise directly from the ASF through its deviations from the Markovian case, proposing a set of methods to collectively estimate these deviations, non-Markovian memory time scales, and diagnose (in)coherence of non-Markovian noise in a RB experiment. Finally, we demonstrate the efficacy of our proposal by means of several proof-of-principle examples. Our methods are directly implementable and pave the way for a better understanding of correlated noise in quantum processors.7 MoreReceived 23 July 2021Revised 19 October 2021Accepted 5 November 2021DOI:https://doi.org/10.1103/PRXQuantum.2.040351Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasMeasurement-based quantum computingOpen quantum systemsQuantum benchmarkingQuantum computationQuantum correlations in quantum informationQuantum gatesQuantum stochastic processesQuantum InformationStatistical Physics
Hybrid image compression-encryption scheme based on multilayer stacked autoencoder and logistic map

Secure transmission of images over a communication channel, with limited data transfer capacity, possesses compression and encryption schemes. A deep learning based hybrid image compression-encryption scheme is proposed by combining stacked auto-encoder with the logistic map. The proposed structure of stacked autoencoder has seven multiple layers, and back propagation algorithm is intended to extend vector portrayal of information into lower vector space. The randomly generated key is used to set initial conditions and control parameters of logistic map. Subsequently, compressed image is encrypted by substituting and scrambling of pixel sequences using key stream sequences generated from logistic map. The proposed algorithms are experimentally tested over five standard grayscale images. Compression and encryption efficiency of proposed algorithms are evaluated and analyzed based on peak signal to noise ratio (PSNR), mean square error (MSE), structural similarity index metrics (SSIM) and statistical, differential, entropy analysis respectively. Simulation results show that proposed algorithms provide high quality reconstructed images with excellent levels of security during transmission.
Security Considerations for a Central Bank Digital Currency

<ns3:p>The concept of a central bank digital currency (CBDC) has gained traction in recent years, with an increasing number of central banks announcing efforts to explore CBDC use cases and designs. Institutions are in various stages of research and development, with some just beginning their research and others already entering pilot testing or even production, albeit on a limited scale.</ns3:p>