Reviewerly/exHarmony huggingface dataset:
Quantum attack against post-quantum electronic signature complexity and implementation probability analysis

The paper identifies and analyzes attacks aimed at Rainbow post-quantum electronic signature cryptanalysis. Today, due to advances in the quantum computers development, the need to present new standards for electronic signatures resistant to both quantum and classical cryptanalysis arisen. To solve the lack of such electronic signatures, NIST USA is running the NIST PQC competition. As part of this competition some electronic signatures designed to resist quantum cryptanalysis were presented, including Rainbow electronic signature. CZ-Rainbow and the compressed Rainbow algorithm were also presented along with the regular Rainbow algorithm. This paper analysis attacks on all three types of electronic signature. The possibility of a quantum attack against the Rainbow electronic signature, as well as the complexity of such an attack, defines the possibility of this electronic signature usage during the post-quantum period.
Golay codes,Witt designs, and Leech lattice

In this chapter we construct the Golay codes and the Witt designs, both in several ways. The uniqueness is proved in a self-contained way for the binary case; in the ternary case some details are left out. We then study the associated Witt designs, which are remarkable Steiner systems on 12 and 24 points. We show uniqueness of these, and of the (multiply) derived designs. We define the two standard near polygons from the ternary Golay code and the large Witt design. We discuss the geometry of the projective plane of order 4 providing an alternative construction and uniqueness proof of the Witt designs. Finally, we introduce the Leech lattice and its binary and complex variants.
A JPEG compression-resistant data watermark embedding and detection algorithm

When the image is processed by JPEG compression, the data watermark originally hidden in the image will not be extracted effectively according to resampling and noise addition. This would greatly limit the application of image data watermark in real scenes. In this paper, a novel JPEG compression-resistant data watermark embedding and detection algorithm is proposed for the robustness of data watermark embedding and extraction under lossy channels. The algorithm uses the compression invariance of DCT coefficient symbols to select candidate coefficients in the carrier image that can resist JPEG compression, followed by assigning distortion cost to each candidate coefficient, and then embedding the watermark information into the carrier image using error correction codes and Syndrome-Trellis Code encoding. The tests show that the proposed watermark embedding method improves the detection resistance performance by more than 7.7% when the relative embedding rate is 0.1, and the correct extraction rate of data watermark can reach more than 99% under JPEG compression with different quality factors.
Global Leadership for Optimal Security Operations

Change doesn't occur on its own. Leadership is required to achieve goals the organization sets. While some individuals are better than others naturally at being leaders, these skills can be learned. Security leaders have a list of operational challenges to manage, the most important of which are mentioned in the chapter.
Categorical Semantics of Reversible Pattern-Matching

This paper is concerned with categorical structures for reversible computation.In particular, we focus on a typed, functional reversible language based on Theseus.We discuss how join inverse rig categories do not in general capture pattern-matching, the core construct Theseus uses to enforce reversibility.We then derive a categorical structure to add to join inverse rig categories in order to capture pattern-matching.We show how such a structure makes an adequate model for reversible pattern-matching.
Adaptive Global k-Nearest Neighbors for Hierarchical Classification of Data Streams

Data stream classification differs from batch learning classification methods as data is made available sequentially and may drift over time. Therefore, data stream classification can be simultaneous to all other kinds of classification problems, and it has been revisiting many aspects related to classification in the last years. So far, hierarchical classification was weakly addressed in streaming scenarios despite being a well-established research topic. To fill in this gap between such areas, in this paper, we propose the adaptive global k-Nearest Neighbors for the hierarchical classification of data streams (Global kNN-hDS). Our proposal classifies hierarchical data streams using a constrained memory buffer and a global classification approach. We compare our method against a stateof-the-art local kNN also tailored for streaming scenarios, and results show that our method obtains competitive prediction rates while being statistically faster.
A Cloud-based approach for synchronous multi-pilot multi-UAV mission plan generation in a MUM-T environment

In this article, we present a new concept that allows multiple human users to cooperatively and simultaneously create a mission plan for multiple unmanned systems equally available to all users assisted by a cognitive agent. With this, we aim to reduce both the workload of individual users and the error rate in mission planning. In addition, we expect an increase in battlefield awareness and improved coordination during mission execution. In military air operations, which are currently exclusively manned, there is a strict separation between the specific mission-related tasks allocated to each team. In a Manned-Unmanned-Teaming (MUM-T) environment, some manned assets are replaced by unmanned aerial vehicles (UAVs). In these setups, one of the tasks of the human user is to plan and monitor the mission. In this context our approach uses a cognitive agent that acts as a centralized planning instance. All tasks that the human users want the UAVs to perform are delegated to the agent, which schedules the individual tasks in a feasible order. It must also merge and deconflict the various plans created by individual pilots into an executable joint mission plan. In addition, the agent creates its own logical plan with the mission flow and can use it to suggest plan changes to human users or execute them directly itself. We have finalized the concept containing the corresponding workflows, procedures, deconflicting, and logical planning capabilities of the central agent. We demonstrate the implementation of the concept using our fully functional MUM-T fighter simulator. Currently, the implementation includes the agent's capabilities to (1) allow multiple pilots to simultaneously delegate tasks for the same available UAVs, (2) detect conflicts that arise during mission plan generation, (3) assist pilots in conflict resolution, (4) associated visualizations, and (5) create a logical plan that will be used to check if the mission plan meets the conditions to achieve the main objective.
CoSim: An Approach to Calculate Complex Object Similarity

Complex objects are often described by several attributes of arbitrary types. To calculate the similarity between complex objects, we propose the CoSim process, which provides a composed similarity function. CoSim applies existing similarity functions for each data type of the object. To calculate the overall similarity, we weight these type-specific similarities. We demonstrate the practical application of our approach by using a simple example of objects. The example involves numerical and categorical attributes. However, our presented idea is applicable to objects with attributes of arbitrary types.
Improved Face Gender Identification Using Fusion of Global Thepade’s SBTC and Local OTSU Thresholding Features

In Image Processing, the face gender classification in the real time applications is an interesting area having important significance. Human can recognize the gender easily but machines find it difficult to recognize the gender from facial images. Many researchers are working in order to fill this gap. The recognition of gender is important for the human computer interaction. The goal of this paper is to propose machine learning based face image gender recognition using global Thepade's SBTC and local Otsu's thresholding features which will help to recognize gender. The experimentations performed on Faces94 dataset and face gender recognition accuracy have shown the proposed method has given better face gender recognition capability with feature fusion across considered machine leaning classifiers.
A Comprehensive Study and Analysis of Reversible Image Steganography Techniques

Whether for work or personal reasons, loading and exchanging secret and/or private information has become a part of our daily lives. As a result, several researchers have focused their efforts on secure storage and transmission of sensitive data. Steganography refers to a set of techniques for concealing secret information in unobtrusive digital media such as video, audio, and images. In recent years, reversible image steganography has become a common method for hiding information. Researchers are interested in reversible image steganography because it lets them to recover the initial form of the cover image losslessly after steganography. This study examines the many types of reversible image steganography now in use, as well as recent additions in each classification across multiple modalities. In addition, the paper includes a comprehensive review of reversible image steganography, including its basic operation, needs, various elements, various types, and performance assessments. Various performance study measures for estimating reversible image steganography schemes are also explored in this article.
Fractal Dimension Box-Counting Algorithm Optimization Through Integral Images

The fractal dimension is an index used in computer vision for characterizing fractal patterns, by quantifying their complexity as a ratio of the change in detail to the change in scale. A common approach for fractal dimension estimation in binary images is the box-counting algorithm that counts the number of boxes needed to cover the object at different scales. A new approach for the counting task of the algorithm is proposed using integral images (summed-area tables). We experimented with our proposed algorithm on two distinct datasets (a texture dataset and a medical image dataset). The experiments have proven that our algorithm statistically outperforms the standard approach.
Towards a Robust New Radio Compatible With XR

Extended Reality (XR), an umbrella abbreviation for Cloud Gaming/Augmented Reality/Virtual Reality, is supposed to deliver the commercial outlook for 5G networks. This paper discusses the challenges incurred by this service due to its inherent stringent requirements from a data rate, reliability and latency perspective. To address the challenges, a novel priority-based adaptive preemption/cancellation strategy is proposed to guarantee the new radio quality of service under both the multi-streams XR service and XR in concurrent transmission with other services scenarios. Simulation results demonstrate that a scheduler devised based on the principle of the proposed strategy is quite useful from a capacity perspective. In the meantime, a synthesis of the industrial consideration and vision is provided featuring the ongoing the 3rd Group Partnership Project Release-17 XR study item.
Predictive Crime Analytics Using Data Science (India and the USA)

Predictive analysis is concerned with the branch of data science used to predict future patterns and trends. This modelling technique can be used to aid society. In recent years, crime against women has skyrocketed, and understanding past history can help to come up with insight that describe the current state of crime and assault in these countries. This research aims to foresee the crime patterns against women in India and the USA. The studies were carried out in both countries to better understand the economic bearing, if any, in crimes committed against women. The data of the past years is studied using extensive EDA (Exploratory Data Analysis) techniques to help understand the problems women face. The data is then normalized, and Linear Regression is executed to predict future trends in crime rates. K-means is then used to create clusters of states with the highest crime rates against women in India. This chapter aims to help women and law enforcement by using technological advancements in the area of data science to predict future trends.
Image Feature Generation Using Binary Patterns—LBP, SLBP and GBP

Image features play pivotal role in digital image processing and computer vision problems. To retrieve image features accurately and effectively, various texture descriptors are needed. This paper presents a comprehensive evaluation of various descriptors such as local binary pattern (LBP), significant local binary pattern (SLBP), gradient binary pattern (GBP) and its effectiveness in plethora of applications such as medical image analysis, object detection and classification. Moreover, the descriptors are validated using brain tumorous magnetic resonance image (MRI).
Efficient Huge Page Management with Xpage

An efficient approach to managing big data workloads is to enable applications to work directly with huge pages. This can effectively avoid or reduce the memory fragmentation problem due to high frequent memory allocation and deallocation and significantly minimize the performance degradation of big data applications. This paper presents XPage, a huge page memory management framework, with three novel features. First, XPage by design can provide automated huge page managements with transparency to both OS and applications. Second, Xpage represents a memory management redesign that brings performance and memory saving to memory intensive applications by supporting dynamic huge page memory management without resorting to splitting huge pages for memory fragmentation. Third but not the least, XPage can efficiently minimize the internal fragmentation without impacting performance of applications. We conduct extensive experiments to evaluate the effectiveness of XPage in minimizing internal memory fragmentation in the presence of dynamic memory intensive big data workloads, by comparing XPage with vanilla Linux using 4KB base page and Linux with 2MB huge page.
Visual Analysis of the High-performance Computing Jobs Based on the Comprehensive Load Scoring Algorithm

High-performance computing system is a complex system composed of a large number of computing nodes and storage nodes. The detailed execution of a job on a cluster system is in a black box. Details about system resources such as CPU, network, and I/O used by jobs cannot be perceived by users or cluster administrators. Users and administrators cannot discover problems on time when a job has inefficient running modes, such as load imbalance and I/O bottlenecks, which seriously hinder the maximum performance of the cluster. Therefore, it is very important to provide an effective method to judge the job load balancing. Based on the comprehensive analysis of high-performance cluster data, an integrated load scoring algorithm is proposed to find the computing nodes with performance problems. The algorithm can identify performance anomalies of different dimensions of performance metrics on nodes. Meanwhile, the visualization diagram of job load balance based on force-oriented layout, time area diagram of node health statistics, and river diagram of I/O fluctuation law is designed to facilitate the system operation. It has been proved that our visual analysis system can improve the efficiency of identifying load imbalancing jobs and slow computing nodes, which improves the job performance and system resilience.
Stochastic Backpropagation through Fourier Transforms

Backpropagating gradients through random variables is at the heart of numerous machine learning applications. In this paper, we present a general framework for deriving stochastic backpropagation rules for any continuous distribution. Our approach exploits the link between the characteristic function and the Fourier transform, to transport the derivatives from the parameters of the distribution to the random variable. Our method generalizes previously known estimators, and results in new estimators for the gamma, beta, Dirichlet and Laplace distributions. Furthermore, we show that the classical deterministic backproapagation rule in neural networks, is a special case of stochastic backpropagation with Dirac distributions, thus providing a link between probabilistic graphical models and neural networks.
Spectroscopy and Dispersive Shifts

Originally, cQED was developed for state detection of superconducting qubits. In this situation, the system of interest (the qubit) was well-understood; it could be accurately modeled as a circuit composed of inductors, capacitors, and Josephson junctions. Nowadays, electromagnetic simulations and room temperature measurements can tell us system parameters (frequencies and couplings) to within 10% even before we cool down. However, cQED can also be used as an exploratory tool; we can use it to probe systems where we don’t have a complete understanding. Loosely speaking, there are two operators that we can probe when investigating a system with cQED: the Hamiltonian and the coupling operator. We can probe the system Hamiltonian by performing microwave spectroscopy, thereby determining the system energies. Properties of the coupling operator, on the other hand, can be extracted from the dispersive shifts.
Towards Computational Modeling of Human Goal Recognition

Recently, we are seeing the emergence of plan- and goal-recognition algorithms which are based on the principle of rationality. These avoid the use of a plan library that compactly encodes all possible observable plans, and instead generate plans dynamically to match the observations. However, recent experiments by Berkovitz (Berkovitz, The effect of spatial cognition and context on robot movement legibility in human-robot collaboration, 2018) show that in many cases, humans seem to have reached quick (correct) decisions when observing motions which were far from rational (optimal), while optimal motions were slower to be recognized. Intrigued by these findings, we experimented with a variety of rationality-based recognition algorithms on the same data. The results clearly show that none of the algorithms reported in the literature accounts for human subject decisions, even in this simple task. This is our first contribution. We hypothesize that humans utilize plan-recognition in service of goal recognition, i.e., match observations to known plans, and use the set of recognized plans to conclude as to the likely goals. To test this hypothesis, a second contribution in this paper is the introduction of a novel offline recognition algorithm. While preliminary, the algorithm accounts for the results reported by Berkovitz significantly better than the existing algorithms. Moreover, the proposed algorithm marries rationality-based and plan-library based methods seamlessly.
On the Speed Limit for Imaginary-Time Schrödinger Equation with Application to Quantum Searches

Recently, Okuyama and Ohzek [1] derived a speed limit for the imaginary-time Schrödinger equation, which is inspired by the prior work of Kieu, who had shown a new class of time–energy uncertainty relations suitable for actually evaluating the speed limit of quantum dynamics. In this paper, we apply the result of Okuyama and Ohzek to obtain a generalized speed limit for Grover’s search in imaginary-time quantum annealing. An estimate of the lower bound on the computational time is shown, from which the role of the coefficient function corresponding to the final Hamiltonian played in the quantum dynamics for the problem is sticking out. However, when trying to apply the speed limit to the analogue of Grover’s problem, we find that not only the coefficient of the target Hamiltonian is related to the time complexity of the algorithm, but also the coefficient of the initial Hamiltonian is crucial for determining the time complexity. This is new and generalizes one of the results in our previous work.
Access Control in woodland through Blockchain and LoRaWAN

Access control in remote areas may be difficult given the short ranges of the wireless technology and memory and cpu usage required by local sensors for authentication and authorization processes. In this paper we proposed a system able to verify the accessibility of people or vehicles in restricted areas through LoRaWan technology and record the relevant data using a private Ethereum Blockchain. To this aim we implemented a testbed in a woodland testing and evaluating the potentials of both LoRa and Ethereum technology on limited hardware. The testbed is mounted in a pinewood near Rome called "Riserva di Castel Porziano" and different tree densities and heights have been taken into account during the measurement campaign.
Classifying Plastic Waste on River Surfaces utilising CNN and Tensorflow

Waste in rivers is an ever-increasing problem. This paper will look at Deep Learning and Computer Vision technologies to determine if they can be applied to the problem domain. Usage of Deep Learning and Computer Vision technologies has grown massively in the last few years thanks to increased computational power, the availability of training data such as ImageNet, and the availability more complex and efficient algorithms. This research investigates two models to determine which one is more suited for the problem domain by evaluating their results based on performing training and testing on a developed waste dataset for the purposes of this research. The dataset is developed four times, each variant incurring more implementation of pre-processing techniques than the other. This resulted in the same dataset being tested four times on both models with varying levels of pre-processing. The first variant of the dataset had no pre-processing, the second with aspect ratio adjusting, the third dataset being augmented by the image data generator, and the fourth by way of an independent augmentation pipeline. The developed waste dataset has images of size 100x100 dimensions regardless of variant. Variant one of the waste datasets contained 1000 images and expanded all the way up to 19,973 images after pipeline augmentation in variant 4. Both VGG-16 and DenseNet-201 will have all four variants implemented on them to investigate which CNN best suits this research domain but also investigate the differences of applying different pre-processing techniques and how this affects results yielded by the two CNN models.
Introduction to Systems Security Engineering Vocabulary

ABSTRACT Systems security protects a system from misuse and malicious behavior and makes sure the system can deliver value even under adverse conditions in cyberspace. This article identifies essential systems security vocabulary including systems security engineering, cyberspace, cyber‐attack, cybersecurity, and cyber resiliency. This terminology overview supports effective collaboration with other INCOSE working groups.