Reviewerly/exHarmony huggingface dataset:
Automatic Preimage Attack Framework on Ascon Using a Linearize-and-Guess Approach

Ascon is the final winner of the lightweight cryptography standardization competition (2018 − 2023). In this paper, we focus on preimage attacks against round-reduced Ascon. The preimage attack framework, utilizing the linear structure with the allocating model, was initially proposed by Guo et al. at ASIACRYPT 2016 and subsequently improved by Li et al. at EUROCRYPT 2019, demonstrating high effectiveness in breaking the preimage resistance of Keccak. In this paper, we extend this preimage attack framework to Ascon from two aspects. Firstly, we propose a linearize-and-guess approach by analyzing the algebraic properties of the Ascon permutation. As a result, the complexity of finding a preimage for 2-round Ascon-Xof with a 64-bit hash value can be significantly reduced from 239 guesses to 227.56 guesses. To support the effectiveness of our approach, we find an actual preimage of all ‘0’ hash in practical time. Secondly, we develop a SAT-based automatic preimage attack framework using the linearize-and-guess approach, which is efficient to search for the optimal structures exhaustively. Consequently, we present the best theoretical preimage attacks on 3-round and 4-round Ascon-Xof so far.
Empowering deep learning based organizational decision making: A Survey

The advent of deep learning has revolutionized the landscape of organizational decision-making by offering powerful tools for data analysis and prediction. In this comprehensive survey, we explore the intersection of deep learning and organizational decision-making, elucidating the theoretical underpinnings, empirical evidence, and practical implications of this synergy. Theoretical foundations and research hypotheses are rigorously examined, providing a solid framework for understanding the role of deep learning models in enhancing decision-making processes. We delve into the systematic survey, which encompasses a wide spectrum of applications across various industries and domains, showcasing how deep learning empowers decision support systems, augments data-driven decision-making, and refines decision-making frameworks. Drawing inspiration from the Egyptian Vision 2030, we explore the implications of deep learning-based decision-making on national development strategies and policy implementation. Our analysis sheds light on the transformative potential of these technologies, offering insights into how organizations, particularly in Egypt, can harness these advancements to achieve their developmental goals. Finally, we outline future directions in this field, highlighting emerging trends, technological advancements, and potential areas for further research. As the digital age continues to reshape the landscape of decision-making, this survey serves as a valuable resource for researchers, policymakers, and practitioners seeking to leverage deep learning for empowered, data-driven, and informed organizational decisions.
Total Factor Productivity and Economic Freedom in Former Socialistic Countries

Total Factor Productivity (TFP) is the portion of the country's output not explained by the amount of inputs used in production. The main goal of the present paper is to estimate the Total Factor Productivity levels and then determine the long-term impact of Economic freedom on the TFP in ten former socialistic countries, which are full EU members since 2000. To estimate the TFP we have applied the fixed effect panel on standard Cobb-Douglas production function in per capita terms. In the second iteration we have used PMG ARDL model to estimate the long-term impact of economic freedom on the TFP. This research has proven that higher level of economic freedom, which is used as a proxy of the quality of the institutions and institutional framework, caused higher level of total factor productivity in the period 2000-2018 in the case of ten former socialistic countries which are full EU members since 2000. The obtained results enable us an insight in policies which are important for efficiency increase and economic performance. Our finding could be very useful for policymakers, stressing which policies are contributing to efficiency, and which are not. So that policymakers could intervene in the way to increase the quality of institutional framework and economic institutions. Many other studies investigate the TFP and growth, or growth and institutional framework for the countries of Central and East Europe. Our survey is among the first to investigate the long-term impact of the institutional framework and economic institutions on the countries efficiency for this countries. Our survey enables us an insight into the mechanism through which the institutions can positively impact the TFP through increasing the predictability and reducing the uncertainty for CE countries.
Photonic advantage of optical encoders

Abstract Light’s ability to perform massive linear operations in parallel has recently inspired numerous demonstrations of optics-assisted artificial neural networks (ANN). However, a clear system-level advantage of optics over purely digital ANN has not yet been established. While linear operations can indeed be optically performed very efficiently, the lack of nonlinearity and signal regeneration require high-power, low-latency signal transduction between optics and electronics. Additionally, a large power is needed for lasers and photodetectors, which are often neglected in the calculation of the total energy consumption. Here, instead of mapping traditional digital operations to optics, we co-designed a hybrid optical-digital ANN, that operates on incoherent light, and is thus amenable to operations under ambient light. Keeping the latency and power constant between a purely digital ANN and a hybrid optical-digital ANN, we identified a low-power/latency regime, where an optical encoder provides higher classification accuracy than a purely digital ANN. We estimate our optical encoder enables ∼10 kHz rate operation of a hybrid ANN with a power of only 23 mW. However, in that regime, the overall classification accuracy is lower than what is achievable with higher power and latency. Our results indicate that optics can be advantageous over digital ANN in applications, where the overall performance of the ANN can be relaxed to prioritize lower power and latency.
Image Processing and Acquisition using Python

Introduction to Images and Computing using Python Introduction to Python Introduction What Is Python? Python Environments Running a Python Program Basic Python Statements and Data Types Computing using Python Modules Introduction Python Modules Numpy Scipy Matplotlib Python Imaging Library Scikits Python OpenCV Module Image and Its Properties Introduction Image and Its Properties Image Types Data Structures for Image Analysis Programming Paradigm Image Processing using Python Spatial Filters Introduction Filtering Edge Detection using Derivatives Image Enhancement Introduction Pixel Transformation Image Inverse Power Law Transformation Log Transformation Histogram Equalization Contrast Stretching Fourier Transform Introduction Definition of Fourier Transform Two-Dimensional Fourier Transform Convolution Filtering in Frequency Domain Segmentation Introduction Histogram-Based Segmentation Region-Based Segmentation Segmentation Algorithm for Various Modalities Morphological Operations Introduction History Dilation Erosion Grayscale Dilation and Erosion Opening and Closing Hit-or-Miss Thickening and Thinning Image Measurements Introduction Labeling Hough Transform Template Matching Image Acquisition X-Ray and Computed Tomography Introduction History X-Ray Generation Material Properties X-Ray Detection X-Ray Imaging Modes Computed Tomography (CT) Hounsfield Unit (HU) Artifacts Magnetic Resonance Imaging Introduction Laws Governing NMR and MRI Material Properties NMR Signal Detection MRI Signal Detection or MRI Imaging MRI Construction T1, T2, and Proton Density Image MRI Modes or Pulse Sequence MRI Artifacts Light Microscopes Introduction Physical Principles Construction of a Wide-Field Microscope Epi-Illumination Fluorescence Microscope Confocal Microscopes Nipkow Disk Microscopes Confocal or Wide-Field? Electron Microscopes Introduction Physical Principles Construction of EM Specimen Preparations Construction of TEM Construction of SEM Appendix A: Installing Python Distributions Appendix B: Parallel Programming Using MPI4Py Appendix C: Introduction to ImageJ Appendix D: MATLAB and Numpy Functions Index A Summary and Exercises appear at the end of each chapter.
An Efficient Chaotic Interleaver for Image Transmission over IEEE 802.15.4 Zigbee Network

This paper studies a vital issue in wireless communications, which is the transmission of images over wireless networks. IEEE ZigBee 802.15.4 is a short-range communication standard that could be used for small distance multimedia transmissions. In fact, the ZigBee network is a wireless personal area network (WPAN), which needs a strong interleaving mechanism for protection against error bursts. Thispaper presents a novel chaotic interleaving scheme for this purpose. This scheme depends on the chaotic Baker map. A comparison study between the proposed chaotic interleaving scheme and the traditional block and convolutional interleaving schemes for image transmission over a correlated fading channel is presented. The simulation results show the superiority of the proposed chaotic interleaving scheme over the traditional schemes.
Authentication in VoIP Telephony with Use of the Echo Hiding Method

The paper describes the method intended to authenticate identity of a VoIP subscriber with use of the data hiding technique that is specifically implemented by means of the echo hiding method. The scope includes presentation of experimental results related to transmission of information via a hidden channel with use of the SIP/SDP signalling protocol as well as results of subjective assessment on quality of a signal with an embedded watermark.
Business communication plans and strategies

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Principles we talk by

This paper takes two behavioural principles which have been suggested as explanatory models for human conversation, and tests them on a corpus of task-oriented dialogues (the HCRC Map Task Corpus). The principles chosen are Clark’s Collaborative Theory and Shadbolt’s Principle of Parsimony, which are both interested in notions of effort although they come from entirely different subfields of linguistics. The aim of the study is to compare the explanatory power of each of these principles when they are applied to real language data. Each of the principles was converted into a set of representative hypotheses about the types of behaviour which they would predict in dialogue. Then, a way of coding dialogue behaviour was developed, in order that the hypotheses could be tested on a suitably sized dataset. In particular, the coding system tried to distinguish between the levels of effort which participants used in their utterances. Finally, a series of statistical tests was undertaken to test the predictions of the hypotheses on the information generated by the coding system. The strongest support was found for the Principle of Parsimony and its associate Principle of Least Individual Effort, at the expense of the Collaborative Principle and the Principle of Least Collaborative Effort. There is certainly evidence that speakers try to minimise effort, but this seems to be occurring on an individual basis – which can be to the cost of the overall dialogue and task performance – rather than on a collaborative basis.
Amazon Elastic Compute Cloud (EC2) versus In-House HPC Platform: A Cost Analysis

While High Performance Computing (HPC) centers continuously evolve to provide more computing power to their users, we observe a wish for the convergence between Cloud Computing (CC) and High Performance Computing (HPC) platforms, with the commercial hope to see Cloud Computing (CC) infrastructures to eventually replace in-house facilities. If we exclude the performance point of view where many previous studies highlight a non-negligible overhead induced by the virtualization layer at the heart of every Cloud middleware when running a HPC workload, the question of the real cost-effectiveness is often left aside with the intuition that, most probably, the instances offered by the Cloud providers are competitive from a cost point of view. In this article, we wanted to assert (or infirm) this intuition by analyzing what composes the Total Cost of Ownership (TCO) of an in-house HPC facility operated internally since 2007. This Total Cost of Ownership (TCO) model is then used to compare with the induced cost that would have been required to run the same platform (and the same workload) over a competitive Cloud IaaS offer. Our approach to address this price comparison is three-fold. First we propose a theoretical price-performance model based on the study of the actual Cloud instances proposed by one of the major Cloud IaaS actors: Amazon Elastic Compute Cloud (EC2). Then, based on the HPC facility TCO analysis we propose a hourly price comparison between our in-house cluster and the equivalent EC2 instances. Finally, based on the experimental benchmarking on the local cluster and on the Cloud instances we propose an update of the former theoretical price model to reflect the real system performance. The results obtained advocate in general for the acquisition of an in-house HPC facility, which balances the common intuition in favor of Cloud Computing platforms, would they be provided by the reference Cloud provider worldwide.
Cost-aware Multi Data-Center Bulk Transfers in the Cloud from a Customer-Side Perspective

Many cloud applications (e.g., data backup and replication, video distribution) require dissemination of large volumes of data from a source data-center to multiple geographically distributed data-centers. Given the high costs of wide-area bandwidth, the overall cost of inter-data-center communication is a major concern in such scenarios. While previous works have focused on optimizing the costs of bulk transfer, most of them use the charging models of Internet service providers, typically based on the 95th percentile of bandwidth consumption. However, public Cloud Service Providers (CSP) follow very different models to charge their customers. First, the cost for transmission is flat and depends on the location of the source and receiver data-centers. Second, CSPs offer discounts once customer transfers exceed certain volume thresholds per data-center. We present a systematic framework, CloudMPcast, that exploits these two aspects of cloud pricing schemes. CloudMPcast constructs overlay distribution trees for bulk-data transfer that both optimizes dollar costs of distribution, and ensures end-to-end data transfer times are not affected. CloudMPCast monitors TCP throughputs between data-centers and only proposes alternative trees that respect original transfer times. After an extensive measurement study, the cost savings range from 10 to 60 percent for both Azure and EC2 infrastructures, which potentially translates to millions of dollars a year assuming realistic demands.
Bayesian Repulsive Gaussian Mixture Model

We develop a general class of Bayesian repulsive Gaussian mixture models that encourage well-separated clusters, aiming at reducing potentially redundant components produced by independent priors for locations (such as the Dirichlet process). The asymptotic results for the posterior distribution of the proposed models are derived, including posterior consistency and posterior contraction rate in the context of nonparametric density estimation. More importantly, we show that compared to the independent prior on the component centers, the repulsive prior introduces additional shrinkage effect on the tail probability of the posterior number of components, which serves as a measurement of the model complexity. In addition, a generalized urn model that allows a random number of components and correlated component centers is developed based on the exchangeable partition distribution, which gives rise to the corresponding blocked-collapsed Gibbs sampler for posterior inference. We evaluate the performance and demonstrate the advantages of the proposed methodology through extensive simulation studies and real data analysis. Supplementary materials for this article are available online.
EnsembleLens: Ensemble-based Visual Exploration of Anomaly Detection Algorithms with Multidimensional Data

The results of anomaly detection are sensitive to the choice of detection algorithms as they are specialized for different properties of data, especially for multidimensional data. Thus, it is vital to select the algorithm appropriately. To systematically select the algorithms, ensemble analysis techniques have been developed to support the assembly and comparison of heterogeneous algorithms. However, challenges remain due to the absence of the ground truth, interpretation, or evaluation of these anomaly detectors. In this paper, we present a visual analytics system named EnsembleLens that evaluates anomaly detection algorithms based on the ensemble analysis process. The system visualizes the ensemble processes and results by a set of novel visual designs and multiple coordinated contextual views to meet the requirements of correlation analysis, assessment and reasoning of anomaly detection algorithms. We also introduce an interactive analysis workflow that dynamically produces contextualized and interpretable data summaries that allow further refinements of exploration results based on user feedback. We demonstrate the effectiveness of EnsembleLens through a quantitative evaluation, three case studies with real-world data and interviews with two domain experts.
Duet: Helping Data Analysis Novices Conduct Pairwise Comparisons by Minimal Specification

Data analysis novices often encounter barriers in executing low-level operations for pairwise comparisons. They may also run into barriers in interpreting the artifacts (e.g., visualizations) created as a result of the operations. We developed Duet, a visual analysis system designed to help data analysis novices conduct pairwise comparisons by addressing execution and interpretation barriers. To reduce the barriers in executing low-level operations during pairwise comparison, Duet employs minimal specification: when one object group (i.e. a group of records in a data table) is specified, Duet recommends object groups that are similar to or different from the specified one; when two object groups are specified, Duet recommends similar and different attributes between them. To lower the barriers in interpreting its recommendations, Duet explains the recommended groups and attributes using both visualizations and textual descriptions. We conducted a qualitative evaluation with eight participants to understand the effectiveness of Duet. The results suggest that minimal specification is easy to use and Duet's explanations are helpful for interpreting the recommendations despite some usability issues.
Collective radiance effects in the ultrastrong-coupling regime

We investigate the collective radiance characteristics of qubits in the ultrastrong coupling regime, where the radiance witness is defined based on the resonator-qubit dressed basis. The ultrastrong hyperradiance effect is demonstrated when the dressed state of system is resonantly driven. Interestingly, we show that, besides the resonator-qubit coupling strength, the parity-symmetry-breaking induced cascade-transition can significantly enhance the collective radiance of qubits, which allows us to manipulate the transitions between subradiance, superradiance, and hyperradiance via adjusting the parity symmetry of system with an external magnetic field. This work extends the collective radiance theory to the ultrastrong coupling regime, and offers the potential applications in the engineering of new laser devices.
AED-Net: An Abnormal Event Detection Network

It has long been a challenging task to detect an anomaly in a crowded scene. In this paper, a self-supervised framework called the abnormal event detection network (AED-Net), which is composed of a principal component analysis network (PCAnet) and kernel principal component analysis (kPCA), is proposed to address this problem. Using surveillance video sequences of different scenes as raw data, the PCAnet is trained to extract high-level semantics of the crowd’s situation. Next, kPCA, a one-class classifier, is trained to identify anomalies within the scene. In contrast to some prevailing deep learning methods, this framework is completely self-supervised because it utilizes only video sequences of a normal situation. Experiments in global and local abnormal event detection are carried out on Monitoring Human Activity dataset from University of Minnesota (UMN dataset) and Anomaly Detection dataset from University of California, San Diego (UCSD dataset), and competitive results that yield a better equal error rate (EER) and area under curve (AUC) than other state-of-the-art methods are observed. Furthermore, by adding a local response normalization (LRN) layer, we propose an improvement to the original AED-Net. The results demonstrate that this proposed version performs better by promoting the framework’s generalization capacity.
Lower Bounds on Adversarial Robustness from Optimal Transport

While progress has been made in understanding the robustness of machine learning classifiers to test-time adversaries (evasion attacks), fundamental questions remain unresolved. In this paper, we use optimal transport to characterize the maximum achievable accuracy in an adversarial classification scenario. In this setting, an adversary receives a random labeled example from one of two classes, perturbs the example subject to a neighborhood constraint, and presents the modified example to the classifier. We define an appropriate cost function such that the minimum transportation cost between the distributions of the two classes determines the \emph{minimum $0-1$ loss for any classifier}. When the classifier comes from a restricted hypothesis class, the optimal transportation cost provides a lower bound. We apply our framework to the case of Gaussian data with norm-bounded adversaries and explicitly show matching bounds for the classification and transport problems and the optimality of linear classifiers. We also characterize the sample complexity of learning in this setting, deriving and extending previously known results as a special case. Finally, we use our framework to study the gap between the optimal classification performance possible and that currently achieved by state-of-the-art robustly trained neural networks for datasets of interest, namely, MNIST, Fashion MNIST and CIFAR-10.
Experimental Two‐Way Communication with One Photon

Abstract Superposition of two or more states is one of the most fundamental concepts of quantum mechanics and provides a basis for several advantages offered by quantum information processing. This work reports the experimental demonstration of two‐way communication between two distant parties that can exchange only a single particle once, an impossible task in classical physics. This is achieved through preparation of a single photon in a coherent superposition of the two parties' locations. Furthermore, it is shown that this concept allows the parties to perform secure and anonymous quantum communication employing one particle per transmitted bit. These important features can lead to the realization of new quantum communication schemes, which are simultaneously anonymous, secure, and resource‐efficient.