Reviewerly/exHarmony huggingface dataset:
Nonlinearity of photonic quantum memristors in high-frequency regime

Over the last decades, the combination of quantum computing and machine learning has opened many possibilities, for example enhancing machine learning algorithms through quantum platforms. However, one of the current challenges consists in combining the linear unitary evolution of closed quantum systems with the nonlinearity required by neural networks, which are currently the most widely used and versatile machine learning algorithms. This issue can now be addressed by a novel photonic tool, the quantum memristor,1 which displays a nonlinear behavior, while preserving quantum coherence, through a weak controlled interaction of its input state with the environment. Here, we show how its operation can be extended to deal with higher frequency modulations of the input and, possibly, with a simplification in its scheme. This method can prove useful for the future implementation of memristor-based quantum neural networks.
Continuous-variable quantum key distribution in a multi-way setting

We extend the asymptotic security of continuous variable quantum key distribution using one-way and two-way quantum communication to a multi-way setting: Quantum signals travel many times between the parties before the final detection. We assume that the encoded signals are Gaussian-modulated coherent states that travel each time through the same Gaussian channel characterized by loss and thermal noise. We then quantify the reverse-reconciliation performance of the protocol presenting results for even or odd numbers of signal exchange.
Laplacian pair state transfer in vertex coronas

Pair state transfer was introduced very recently, and there are only few results on the existence of pair state transfer in graphs. In this paper, we mainly study the existence of Laplacian pair state transfer in vertex coronas. We give sufficient conditions for vertex corona to have or to not have Laplacian perfect pair state transfer. We also give a sufficient condition for vertex corona to have Laplacian pretty good pair state transfer.
Architectures for QKD networks

In this general brief overview, we discuss the different types of nodes and associated levels of security that can be considered in a quantum key distribution (QKD) network. This classification lead to different possible scenarios for QKD repeater chains and, more generally, for QKD network architectures.
Conditional generation of medical time series for extrapolation to underrepresented populations

The widespread adoption of electronic health records (EHRs) and subsequent increased availability of longitudinal healthcare data has led to significant advances in our understanding of health and disease with direct and immediate impact on the development of new diagnostics and therapeutic treatment options. However, access to EHRs is often restricted due to their perceived sensitive nature and associated legal concerns, and the cohorts therein typically are those seen at a specific hospital or network of hospitals and therefore not representative of the wider population of patients. Here, we present HealthGen, a new approach for the conditional generation of synthetic EHRs that maintains an accurate representation of real patient characteristics, temporal information and missingness patterns. We demonstrate experimentally that HealthGen generates synthetic cohorts that are significantly more faithful to real patient EHRs than the current state-of-the-art, and that augmenting real data sets with conditionally generated cohorts of underrepresented subpopulations of patients can significantly enhance the generalisability of models derived from these data sets to different patient populations. Synthetic conditionally generated EHRs could help increase the accessibility of longitudinal healthcare data sets and improve the generalisability of inferences made from these data sets to underrepresented populations.
CDNs’ Dark Side: Security Problems in CDN-to-Origin Connections

Content Delivery Networks (CDNs) play a vital role in today’s Internet ecosystem. To reduce the latency of loading a website’s content, CDNs deploy edge servers in different geographic locations. CDN providers also offer important security features including protection against Denial of Service (DoS) attacks, Web Application Firewalls (WAFs), and recently, issuing and managing certificates for their customers. Many popular websites use CDNs to benefit from both the security and the performance advantages. For HTTPS websites, Transport Layer Security (TLS) security choices may differ in the connections between end-users and a CDN (front-end or user-to-CDN), and between the CDN and the origin server (back-end or CDN-to-Origin). Modern browsers can stop/warn users if weak or insecure TLS/HTTPS options are used in the front-end connections. However, such problems in the back-end connections are not visible to browsers or end-users, and lead to serious security issues (e.g., not validating the certificate can lead to MitM attacks). In this article, we primarily analyze TLS/HTTPS security issues in the back-end communication; such issues include inadequate certificate validation and support for vulnerable TLS configurations. We develop a test framework and investigate the back-end connection of 14 leading CDNs (including Cloudflare, Microsoft Azure, Amazon, and Fastly), where we could create an account. Surprisingly, for all the 14 CDNs, we found that the back-end TLS connections are vulnerable to security issues prevented/warned by modern browsers; examples include failing to validate the origin server’s certificate, and using insecure cipher suites such as RC4, MD5, SHA-1, and even allowing plain HTTP connections to the origin. We also identified 168,795 websites in the Alexa top 1 million that are potentially vulnerable to Man-in-the-Middle (MitM) attacks in their back-end connections regardless of the origin/CDN configurations chosen by the origin owner.
Structured Dynamic Precision for Deep Neural Networks Quantization

Deep Neural Networks (DNNs) have achieved remarkable success in various Artificial Intelligence applications. Quantization is a critical step in DNNs compression and acceleration for deployment. To further boost DNN execution efficiency, many works explore to leverage the input-dependent redundancy with dynamic quantization for different regions. However, the sensitive regions in the feature map are irregularly distributed, which restricts the real speed up for existing accelerators. To this end, we propose an algorithm-architecture co-design, named Structured Dynamic Precision (SDP). Specifically, we propose a quantization scheme in which the high-order bit part and the low-order bit part of data can be masked independently. And a fixed number of term parts are dynamically selected for computation based on the importance of each term in the group. We also present a hardware design to enable the algorithm efficiently with small overheads, whose inference time mainly scales with the precision proportionally. Evaluation experiments on extensive networks demonstrate that compared to the state-of-the-art dynamic quantization accelerator DRQ, our SDP can achieve 29% performance gain and 51% energy reduction for the same level of model accuracy.
Relevance Feedback and Deep Neural Network-Based Semantic Method for Query Expansion

Machine learning techniques have been widely used in almost every area of arts, science, and technology for the last two decades. Document analysis and query expansion also use machine learning techniques at a broad scale for information retrieval tasks. The state-of-the-art models like the Bo1 model, Bo2 model, KL divergence model, and chi-square model are probabilistic, and they work on DFR-based retrieval models. These models are much focused on term frequency and do not care about the semantic relationship among the terms. The proposed model applies the semantic method to find the semantic similarity among the terms to expand the query. The proposed method uses the relevance feedback method that selects a user-assisted most relevant document from top “ <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>k</mi> </math> ” initially retrieved documents and then applies deep neural network technique to select the most informative terms related to original query terms. The results are evaluated at FIRE 2011 ad hoc English test collection. The mean average precision of the proposed method is 0.3568. The proposed method also compares the state-of-the-art models. The proposed model observed 19.77% and 8.05% improvement on the mean average precision (MAP) parameter with respect to the original query and Bo1 model, respectively.
Plagiarism and “self-plagiarism” in scientific works in the age of digital technologies

As well as streamlining academic research activities, contemporary technologies provide opportunities to infringe on the intellectual property of others through plagiarism. However, plagiarism has yet to be adequately dealt with in national legislations, which either do not contain any provision in this connection or fail to adequately define the relevant terms. Moreover, there continues to be much discussion as to what plagiarism is, as well as how and in what cases it should be punishable. The situation is further complicated by the various approaches to plagiarism and the lack of consensus on whether so-called “self-plagiarism” should be treated separately from the intentional infringement of other people’s intellectual property or be deemed as a form of plagiarism. With the aim of clarifying these questions, the authors of the present paper compare different approaches taken to the problem of plagiarism and consider some contemporary approaches to plagiarism detection.
Implementation of a noise-robust quantum algorithm for multivariate polynomial factorization

We implement in an IBM quantum computer a quantum algorithm for multivariate polynomial factoring and propose a noise-avoiding protocol to distill the experimental results in the presence of noise. This algorithm uses single-qubit quantum state tomography (QST) processes to factor a specific type of multivariate polynomials. In one-to-one correspondence, it encodes each multivariate polynomial to one quantum state. While the validity of the algorithm is experimentally verified, the quality of the final results is subjected to the decoherence levels of the preparation of the quantum states. In this paper we propose a protocol to ensure the validity of factors found by our algorithm in the presence of such decoherence and noise. This method might be, in fact, part of a larger class of methods based on that same premise and useful outside the implementation of this specific algorithm. In combination with the noise robustness of the single-qubit QST, our factorization algorithm performs perfectly, even reversing the effects of weak noise, for the second- to fifth-order polynomial cases for which it has been implemented.
CRYPTOCURRENCY, PROFITABILITY, AND TWEETER: A MGARCH FRAMEWORK

This paper used two frames based on the Multivariate General Autoregressive Conditional Heteroscedasticity (MGARCH) model, namely the Dynamic Conditional Correlation (DCC) and the Baba, Engle, Kraft, and Kroner (BEKK) models. DCC parameters confirmed the significant results to assess the spillover effects for return volatilities of five cryptocurrencies (Bitcoin, Dogecoin, Ethereum, Monero, and Peercoin). It indicated that cryptocurrency market returns would be volatile, connected with the time-varying pattern. Most ARCH and GARCH effects were significant in estimating the three pairs of return-mining profitability, return-Tweet, and mining profitability-Tweet. For the cryptocurrency return and profitability pair, returns depended on future price returns and cross-volatility spillover and were greater than their own volatility spillover effect. Moreover, the BEKK diagonal model was found to be the best model for return-mining profitability. The research community can also gain valuable insights into cryptocurrency investment models, offering wider future areas of research.
What is "Decimal number system"? (About two terms of informatics)

The article discusses the concept of "decimal number system". Based on the analysis of a number of sources, it is concluded that this concept can be used in two cases: 1) when 10 characters (numbers) are used to write numbers, this option will be positional in nature; 2) when the number is represented as signs corresponding to powers of 10 (this will be a non-positional decimal system). In this regard, such a concept of informatics as "mixed number system" is also specifed. It is argued that we can talk about two types of mixed number systems: 1) "mixed" in terms of the positional nature of individual systems; 2) "mixed" in terms of the basis of individual systems. Attention is drawn to an erroneous example of a mixed number system given in a number of sources related to school informatics (on teacher sites, forums, etc.) and on the popular Wikipedia website.
Some aspects of developing the course of graphic design for additional education of the basic school students

The article presents the rationale and relevance of developing a graphic design course “Digital Art” as a project that contributes to the development of students’ creative abilities, their social and professional self-determination and self-realization. The author’s view on the application of various teaching technologies at the stages of studying theoretical material and mastering practical skills is presented. The results of the analysis of the capabilities of the CoreApp service, designed to create online courses, are presented. CoreApp is an education-focused platform that provides tools for creating educational content based on pedagogical templates and improving learning outcomes with an analytics recommender system. The article considers the importance of additional education for basic school students as a factor in the development of a variable component of general education that contributes to the improvement of the individual.
The best choice between gold and bitcoin

In this paper, we use Analytic Hierarchy Process (AHP) to determine the appropriate weights and establish a bull market and bear market judgment indicator and investment risk models. Secondly, the Autoregressive Integrated Moving Average model (ARIMA) is constructed to make the expected trend for the next five years, and the optimal asset portfolio of gold assets and bitcoin assets is constructed through quadratic programming, and the Dynamic Programming (DP) model is used to compare strategies between two trades. Finally, the rationality of the model calculation is verified by the test of Long-Short-Term Memory (LSTM).
Constructing Adaptive Multi-Scale Feature via Transformer-Aware Patch for Occluded Person Re-Identification

Person re-identification (Re-ID) aims to retrieve a specific pedestrian across a multi-disjoint camera in a surveillance system. Most of the research is based on a strong assumption that images should contain a full human torso. However, it cannot be guaranteed that all the people have a clear foreground because they are out of constraint. In the real world, a variety of occluded situations frequently appear in video monitoring, which impedes the recognition process. To settle the occluded person Re-ID issue, a new Dual-Transformer symmetric architecture is proposed in this work, which can reduce the occluded impact and build a multi-scale feature. There are two contributions to our proposed model. (i) A Transformer-Aware Patch Searching (TAPS) module is devised to learn visible human region distribution using a multiheaded self-attention mechanism and construct a branch of distributed information attention scale. (ii) An Adaptive Visible-Part Cropping (AVPC) Strategy, with two steps of cropping and weakly-supervised learning, is used to generate a fine-scale visible image for another branch. Only ID labels are utilized to restrain TAPS and AVPC without any extra visible-part annotation. Extensive experiments are conducted on two occluded person Re-ID benchmarks, confirming that our approach performs a SOTA or comparable effect.
Analysis of Garbage Collection Patterns to Extend Microbenchmarks for Big Data Workloads

Java uses automatic memory allocation where the user does not have to explicitly free used memory. This is done by the garbage collector. Garbage Collection (GC) can take up a significant amount of time, especially in Big Data applications running large workloads where garbage collection can take up to 50 percent of the application's run time. Although benchmarks have been designed to trace garbage collection events, these are not specifically suited for Big Data workloads, due to their unique memory usage patterns. We have developed a free and open source pipeline to extract and analyze object-level details from any Java program including benchmarks and Big Data applications such as Hadoop. The data contains information such as lifetime, class and allocation site of every object allocated by the program. Through the analysis of this data, we propose a small set of benchmarks designed to emulate some of the patterns observed in Big Data applications. These benchmarks also allow us to experiment and compare some Java programming patterns.
Performance Evaluation of GraphCore IPU-M2000 Accelerator for Text Detection Application

The large compute load and memory footprint of modern deep neural networks motivates the use of accelerators for high through- put deployments in application spanning multiple domains. In this paper, we evaluate throughput capabilities of a comparatively new hardware from Graphcore, IPU-M2000 that supports massive par- allelism and in-memory compute. For a text detection model, we measured the throughput and power variations with batch size. We also evaluate compressed versions of this model and analyze perfor- mance variation with model precision. Additionally, we compare IPU (Intelligence Processing Unit) results with state-of-the-art GPU and FPGA deployments of a compute intensive text region detec- tion application. Our experiments suggest, IPU supports superior throughput, 27×, 1.89×, and 1.56× as compared to CPU, FPGA DPU and A100 GPU, respectively for text detection application.
Measuring Baseline Overheads in Different Orchestration Mechanisms for Large FaaS Workflows

Serverless environments have attracted significant attention in recent years as a result of their agility in execution as well as inherent scaling capabilities as a cloud-native execution model. While extensive analysis has been performed in various critical performance aspects of these environments, such as cold start times, the aspect of workflow orchestration delays has been neglected. Given that this paradigm has become more mature in recent years and application complexity has started to rise from a few functions to more complex application structures, the issue of delays in orchestrating these functions may become severe. In this work, one of the main open source FaaS platforms, Openwhisk, is utilized in order to measure and investigate its orchestration delays for the main sequence operator of the platform. These are compared to delays included in orchestration of functions through two alternative means, including the execution of orchestrator logic functions in supporting runtimes based on Node-RED. The delays inserted by each different orchestration mode are measured and modeled, while boundary points of selection between each mode are presented, based on the number and expected delay of the functions that constitute the workflow. It is indicative that in certain cases, the orchestration overheads might range from 0.29% to 235% compared to the beneficial computational time needed for the workflow functions. The results can extend simulation and estimation mechanisms with information on the orchestration overheads.
MAPLE

Many Artificial Intelligence (AI) applications are composed of multiple machine learning (ML) and deep learning (DL) models. Intelligent process automation (IPA) requires a combination (sequential or parallel) of models to complete an inference task. These models have unique resource requirements and hence exploring cost-efficient high performance deployment architecture especially on multiple clouds, is a challenge. We propose a high performance framework MAPLE, to support the building of applications using composable models. The MAPLE framework is an innovative system for AI applications to (1) recommend various model compositions (2) recommend appropriate system configuration based on the application's non-functional requirements (3) estimate the performance and cost of deployment on cloud for the chosen design.
Minimal criterion artist collective

Minimal criterion co-evolution (MCC) is an evolutionary algorithm that uses a simple reproduction constraint between two interacting populations to drive an open-ended search process. While it has previously been applied to parameterise simple agents and environments, in this work we extend its use to the generation of art: synthesising both images and music. As a creative AI tool which does not require any data, the use of MCC emphasises the design of the creative medium.
On the effect of the sampling ratio of past trajectories in the combination of evolutionary algorithm and deep reinforcement learning

Combinations of evolutionary approaches (EA) and deep reinforcement learning approaches (DRL) have been proposed to take advantages of two approaches: stability of EA and sample efficiency of DRL. CEM-RL is a promising approach combining cross-entropy method (CEM) and twin delayed deep deterministic policy gradient (TD3), showing competitive performance in this fields. In the TD3 part, interaction histories obtained by CEM-oriented agents and TD3-oriented agents are mixed in a single replay buffer, and mini-batch samples are drawn uniform-randomly from the replay buffer. The quality of the training in the TD3 part must depend on the distribution of samples in the replay buffer, hence it depends on the ratio of CEM-oriented and TD3-oriented samples, called replay sample ratio. In this paper we investigate the impact of the replay sample ratio in CEM-RL on 5 MuJoCo environments and confirm that a good ratio leads to outperforming the original CEM-RL on some environments. Based on this observation, we propose an adaptation mechanism for the replay sample ratio, and empirically show that the proposed variant, RSR-CEM-RL, outperforms the original CEM-RL on some environments.
Opportunities for genetic improvement of cryptographic code

Cryptography is one of the main tools underlying the security of our connected world. Cryptographic code must achieve both high security requirements and high performance. Automatic generation and genetic improvement of such code are underexplored, making cryptographic code a prime target for future research.