Reviewerly/exHarmony huggingface dataset:
Basic Cryptography: Symmetric Key Encryption

Symmetric Key (or “secret key”) cryptography is one of the two main branches of cryptography (the other being Asymmetric Key (or “public/private key”) cryptography. Symmetric Key cryptography scrambles “plaintext” (human-readable) information via one of various algorithms, along with a single cryptographic key, into the garbled form (“ciphertext”). Decryption with symmetric key recovers the original plaintext from the ciphertext through use of the same algorithm and key that was used to encrypt the information. Use of any other key in decryption will result in gibberish. This moves the need for secrecy from the algorithm, which can be published and peer reviewed, to the key. Symmetric Key cryptography is very fast and well suited to encrypting large amount of information. It is however, very poorly suited to key management. Asymmetric Key cryptography on the other hand is very slow, and suitable only for encrypting small amounts of information (e.g. one 256-bit key) but is ideally suited to key management. Most real-world cryptographic systems use both kinds of cryptography. We will build more and more complex structures on these simple concepts until we reach a full Public Key Infrastructure (PKI).
Fractal dimension measured over areas and perimeters using “Box Counting” technique applied over a Mandelbrot figure.

Measuring fractal dimension in general is made over edges of a figure, however this kind of calculations could be made over a 1D, 2D or even a 3D images. With the FracLac plugin of ImageJ application, it has been possible to measure both dimensions, over the area and over the edge of a Mandelbrot fractal, using the Box Counting technique.
The Crime Beat

This chapter examines the future of crime journalism, specifically centered on how technologies, data, and virtual worlds might shape the investigation and help to capture criminals. With cameras everywhere, there may be a witness on any corner. Smart phones, television, and robotic pets record subversively every minute of one's life. Facial recognition can be used to ferret through a large crowd seeking repeat offenders amidst a monitored society.
Research on Active Sampling with Self-supervised Model

In the case of insufficient labeled data, active learning performs very well by actively querying more valuable samples. Traditional active learning models usually use the target model to select samples in the image classification tasks. The target model is not only used to find out the crucial unlabeled samples with massive information but also used to perform prediction. This method does not use the information of the remaining unlabeled samples, and the efficiency will also be restricted when using a single model to solve these two tasks simultaneously. We put forward a self-supervised active learning framework to help query samples in this paper. First, we introduce contrastive learning to construct a new feature space. Second, a special active sampling strategy is proposed based on the distance between unlabeled samples and the center of the category clusters. We update the clusters at each iteration to ensure that the examples most needed can be selected for labeling. Experiments show the success of our method.
Research on Verifiable Access Technology of Mixed Database in Distributed System under Big Data

With the rapid development of Internet applications and the explosive growth of information data, the mixed database of distributed storage management has high requirements for verifiable access technology. The problem of poor application security efficiency in traditional technology is adopted, and the research on verifiable access technology of distributed system mixed database is proposed. According to the principle of verifiable access technology, the mixed database access model is analyzed. Through mixed database access model, a set of access allocation and use strategy can be provided for the distributed system. Configure the data source, design the structure of the access model service module, and complete the research on the application of the verifiable access technology of the mixed database in the distributed system according to the implementation process. By comparing the results of the experiment, we can see that the security efficiency of the verifiable access technology of the hybrid database is 90%, and the efficient utilization of the mixed database in the distributed system is realized.
Local Directional Strength Pattern for Effective Offline Handwritten Signature Verification

Offline handwritten signature verification is one the oldest and widespread biometric identification tools. In various daily life applications, the handwritten signature is used for documents approval and identity verification. Systems that automatically achieve this task are mainly composed of feature generation and verification modules. The more features are robust the best the verification score is. Presently, we introduce the use of the Local Directional Strength Patterns (LDSP) for handwritten signature characterization. This descriptor is associated with SVM classifier to perform the signature verification. Experiments conducted on two public datasets reveal the effectiveness of the proposed descriptor which outperforms several state-of-the-art techniques.
Challenges and Solutions in Teaching Practicum During COVID-19 Pandemic

In 2020, due to objective reasons related to the COVID‐19 pandemic, universities faced the problem of adjusting the entire educational process. Apart from the introduction of distance learning, there appeared the challenge of organizing the teaching practicum online. This paper aims to provide a critical insight into teacher education in Russia during coronavirus pandemic; to identify and highlight challenges and solutions to organizing the practicum of student teachers during the pandemic lockdown (the case of Kazan Federal University). The following research questions were posed: (1) What organizational solutions need to be offered to transfer students to the remote format of conducting their practicum? (2) What individual assignments can be offered to students in on-line format of practicum? (3) How effective is this format of practicum? (4) What difficulties do teachers and students face during on-line practicum? The main data sources are two questionnaires for teachers and students. 82 student teachers and 23 professors of the Institute of Psychology and Education (Kazan Federal University) completed the questionnaires. These professors organized the teaching practicum online during the lockdown. The qualitative analysis that followed helped to reveal the challenges as well as the positive points they learned from the on-line practicum format and allowed to evaluate the effectiveness of distance format.
Conversational Image Captioning Using LSTM and YOLO for Visually Impaired

According to the WHO, as of 2021, there are 2.2 billion people with distance or near vision impairment. The biggest challenge they face is to navigate around places. The main purpose is to build a guidance system for the visually impaired which will take imagery input from the camera, describe it, and give a speech-driven output to its users. This paper describes the process of image captioning based on artificial intelligence by showcasing some image preprocessing techniques, CNN and RNN. However, as improvisations on conventional methods, we have implemented CNN inspired from the YOLO algorithm and LSTM for captioning along with a text-to-speech system to dictate information about the surroundings to the user. This paper presents how the YOLO v1-LSTM model serves the purpose and delivers the faster output with minimum loss and less weight while maintaining comparable accuracy against conventional methods.
Markets Alone Make No Development

Based on the macroeconomic principles introduced in the previous chapter, we can now better understand why markets alone are not able to increase low incomes and low productivity in developing countries. To improve growth conditions, active economic policies with public investment are needed such that the private sector is crowded in and becomes more active. This chapter explains why the arguments of the economic mainstream against such policies are analytically misguided.
An Image Dehazing Method Based on the Optimum of Image Quality Assessment

The fog seriously affects the quality of UAV images and influences the performances of reconnaissance and recognition. In order to improve the effective utilization of UAV images, this paper proposes an UAV image dehazing method based on the optimum of image quality assessment. By the combination of three image quality assessment parameters, including information entropy, standard deviation and image Fourier amplitude, the comprehensive image quality assessment function is established. The best global atomspheric transmission value corresponds to maximum of accessing function. The experiments shows that the UAV image dehazing based on the optimum of image quality assessment has low computational complexity, simple operation and good real-time performance, and the obtained dehazing image has clear visual effects and minor result errors.
A Practical Approach to Quantum Circuit Design for Singlet State Preparation

In this contribution we present a method to generate a quantum circuit that implements the preparation of the so-called Cabello state which is a quantum state important for a number of applications, in particular the implementation of a quantum byzantine fault tolerant consensus protocol. Compared to state of the art canonical generation methods our approach makes it possible to reduce the number of C-NOT circuits from 15 to 5 which has significant benefits in the context of noisy intermediate-scale quantum computers. First we assess the computational complexity of our method over state of the art classical commercial hardware - while it is true that for a fixed number of nodes the circuit has to be generated only once, but distributed systems may be built of basically any number of nodes from 3 to hundreds of thousands. Then the generated circuits are analyzed using three variants of the fidelity metric defined between two quantum states. The assessment is made first in an ideal scenario, then we assess the deviation of the fidelity metrics from the ideal case over several quantum computer instances, each instance being characterized by a slightly distinct qubit topology from the others.
Smart Cyber-Physical System-of-Systems Using Intelligent Agents and MAS

The Cyber-Physical Systems (CPS) are complex, multi- disciplinary, physically-aware future’s paradigms which are integrating embedded computing technologies (cyber part) into the physical world (physical part). The interaction requirement with the physical world makes the CPS unpredictable because of the real-world’s dynamic behaviours. So a CPS needs to reason these changes and adapt its behaviour accordingly. Moreover, a CPS can cooperate with multiple CPSs to establish cyber-physical system-of-systems (CPSoS). This creates a distributed and heterogeneous environment where we are challenged by unpredictability. To address the challenges of the CPSoS, new methodologies and new approaches need to be developed. One way to tackle these challenges is by making them smart with intelligent agents and modelling them explicitly. To make intelligent decisions it is needed to do reasoning and to use decision-making mechanisms. In this way, they can handle the unpredictable changes encountered both internally and externally. Nevertheless, suitable reasoning, smartness, and awareness mechanisms must be studied, implemented, and applied to achieve smart CPSoS.
Tumor detection using spatial hybrid clustering (SHCM) from brain MRI scans

This paper presents a strong division technique in mind MRI picture to recognize cerebrum tumor. To invalidate the unpredictability of cerebrum tissues we have utilized K-implies inserted in fuzzy C-means with spatial data, named as spatial hybrid fuzzy C-means (SHCM), grouping calculation that diminishes administrators and gear blunder. We have contrasted this and three others regular bunching technique and henceforth it is demonstrated that it gives us a superior outcome in recognizing the tumor. K-means algorithm emphasizes initial segmentation by selecting the proper template. This paper mirrors an effective picture division approach utilizing fuzzy C-implies calculation incorporated with K-implies bunching procedure and utilizing the spatial data named spatial hybrid fuzzy C-means (SHCM). The proposed technique can get the benefits of K-implies grouping notwithstanding the upsides of the fuzzy C-implies in the parts of precision. The proposed spatial participation work manages the clamor affectability by the heterogeneity and inclination field. The relative investigation of SHCM with other grouping strategies explains us with the best possible differentiation for the improvement of SHCM. Along these lines by utilizing this ad libbed division method we can distinguish the unpredictable tumor inserted in the MRI outputs of cerebrum adequately.
Minimization of Ambulance Response Time Using Image Processing and Critical Path Mapping Based on Traffic Control

Object visual detection (OVD) intends to extricate precise ongoing on-street traffic signs, which includes three stages discovery of objects of interest and acknowledgment of recognized different object. The main objective of this research paper is detecting ambulance in between different object. Here OpenCV instrument gives the calculation backing to various item identifications. For the prototype consideration, this used static ambulance image and trained dataset. On detection of an ambulance, the traffic light is automatically changed to green. Item discovery is a PC innovation that is associated with picture handling and PC vision that manage recognizing occasion objects of certain class in computerized pictures and recordings. This paper describes how object recognition is a difficult work in image processing-based PC applications, and here CNN and RCNN algorithms are used to recognize objects. It is accustomed to distinguishing that whether in scene or picture object has been there or not. In this paper, we will introduce procedures and techniques for distinguishing or perceiving object with different advantages like effectiveness, precision, power, and so forth.
Power-Law Type Dynamic Behaviours

The designation “fractional behaviour” used in the title of this book is actually a shorthand for “long memoryMemory power-law type behaviours” which expresses well that the studied behaviours admit, in a limited frequency or time domain a power-law type response.
TCLF-Based Obstacle Avoidance Path Planning for HSV Using Pigeon-Inspired Optimization

This paper proposes an obstacle avoidance planning method based on the penalty function of the truncated Coulomb-like force (TCLF) for Hypersonic vehicle (HSV) in multiple threat zones. The main point of the proposed method is to to put forward a TCLF so that an flight-fit trajectory is obtained which can avoid obstacles autonomously while ensuring landing accuracy. Analogous to the Coulomb force, this paper proposes a TCLF penalty function to characterize the impact of the threat zone on ballistic planning. The pigeon swarm algorithm is used to solve the ballistic impact point optimization problem with penalty function. The simulation results show that the method can automatically generate the bank angle sequence and the angle of attack sequence to form a trajectory, effectively solving the problem of obstacle avoidance for HSV.
An Overview of the Methods Used to Recognize Garbage

This article offers an overview of methods employed to recognize garbage. There are methods discussed, which rely on machine vision to detect objects, as well as hardware for garbage sorting intelligence systems. There has been a comparative analysis carried out, which embraces various methods based on machine vision and optical sensors aimed at detecting metal in garbage. There have been technologies identified, which feature the best ratio of indicators. The main criteria included the cost of building a system based on the method, and accuracy. Further on, there are plans to carry out research focusing on developing an original system for the recognition of garbage patterns, its classification and sorting.
Numerical Parameter Estimates of Beta-Uniform Mixture Models

When analyzing biomedical data, researchers often need to apply the same statistical test or procedure to many variables resulting in a multiple comparisons setup. A portion of the tests are statistically significant, their unadjusted p-values form a spike near the origin and as such they can be modeled by a suitable Beta distribution. The unadjusted p-values of the non-significant tests are drawn form a uniform distribution in the unit interval. Therefore the set of all unadjusted p-values can be represented by a beta-uniform mixture model. Finding the parameters of that model plays an important role in estimating the statistical power of the subsequent Benjamini-Hochberg correction for multiple comparisons. To empirically investigate the properties of some parameter estimation procedures we carried out a series of computationally intensive numerical simulations on a high-performance computing facility. As a result of these simulations, in this article we have identified the overall optimal method for estimating the mixture parameters. We also show an asymptotic property of one of the parameter estimates.
Ensuring User Interface Adaptation Consistency Through Triple Graph Grammars

As modern User Interfaces (UIs) are used in varying context-of-use situations, sophisticated mechanisms to control UI adaptations are needed. UI adaptations describe the process of manipulating the initial UI to fit the current context-of-use. Typically, the UI and context-of-use are formalized by models. If these adaptations are not well-defined or checked properly, undesirable application of multiple adaptations may threaten consistency. To prevent conflicting UI adaptations and ease the specification of UI adaptation rules, we introduce a notion of adaptation consistency based on Triple Graph Grammars (TGGs) augmented with a 0–1 priority system. We choose TGGs because it is a formalism for consistency maintenance in model-driven engineering. We extend the TGG semantics with a 0–1 priority system to assess for consistency regarding application order. Based on this solution idea, we present the implementation of a prototypical TGG interpreter to suggest a design- and run-time solution for ensuring consistency of UI adaptations.
The base, stats, and graphics Packages

In this chapter, we take a quick look at the base, stats, and graphics packages (three of the packages loaded by default in R). The base package contains things such as the trigonometric function and other mathematical functions, many of the as. and is. functions, the arithmetic operators, the flow control statements, some apply functions, and many other basic functions in R.
AVX2 C++ Programming: Part 1

In Chapter 2, you learned how to use AVX C++ SIMD intrinsic functions to perform packed integer arithmetic and other data manipulation operations using 128-bit wide operands. In this chapter, you will learn how to exploit AVX2 to perform similar operations using 256-bit wide packed integer operands. Chapter 4 is partitioned into two sections. The first section highlights SIMD integer arithmetic, unpacking and packing, and size promotions. The second section includes source code examples that illustrate additional image processing techniques including pixel clipping, RGB to grayscale conversions, and thresholding.
OpenPGP Email Forwarding via Diverted Elliptic Curve Diffie-Hellman Key Exchanges

An offline OpenPGP user might want to forward part or all of their email messages to third parties. Given that messages are encrypted, this requires transforming them into ciphertexts decryptable by the intended forwarded parties, while maintaining confidentiality and authentication. It is shown in recent lines of work that this can be achieved by means of proxy re-encryption schemes, however, while encrypted email forwarding is the most mentioned application of proxy re-encryption, it has not been implemented in the OpenPGP context, to the best of our knowledge. In this paper, we adapt the seminal technique introduced by Blaze, Bleumer, and Strauss in EUROCRYPT’98, allowing a Mail Transfer Agent to transform and forward OpenPGP messages without access to decryption keys or plaintexts. We also provide implementation details and a security analysis.
A Quantum Architecture Based Decoherence Model

Quantum computers give hope that they will be able to solve computational problems that conventional computers cannot. They will do this by concepts not applicable to conventional computers such as entanglement, superposition, and not being limited to the states 0 and 1. Entanglement is the ability to connect two or more qubits in such a way that an operation on one qubit affects not only the operational qubit but also its entangled twin. Two proven uses of entanglement are superdense coding and quantum teleportation. This document describes a quantum computing architecture-based de-coherence model developed on the available IBM Q-Experience quantum computers. By using entangled qubits in superdense coding, it is possible to explore how the error rate between two qubits and the individual qubit error rate affects the accuracy of the outcome.
Security

Security for web applications is a broad field and includes a number of topics such as protecting access to resources, shielding against injection attacks of various kinds, and preventing users from being tricked into doing malicious actions on behalf of an attacker.
Language Basics

The fundamental elements of the Python language—variable assignment, indentation, array indexing, for and while loops, if statements, functions, comments, exceptions, modules—are covered in this chapter. Although classes and object-oriented programming are also fundamental aspects of Python, these will be covered in Chapter 10 .
A Novel Deep ConvNets Architecture for Traffic Sign Recognition

The transportation system has become a fascinating and active research topic due to its multiple problems; most prior research has focused on traffic forecasting, the advanced driver assistant system (ADAS), and self-driving vehicles. Traffic sign recognition (TSR) is an essential sub-system in ADAS that helps a driver better understand the surrounding environment (obstacles, frost, pedestrians). Automatic recognition of traffic signs is a real-world computer vision challenge and pattern recognition problem. Recently, deep architecture neural networks have shown robust solutions in many areas (health care, agriculture, transportation) due to their ability to handle large amounts of data and excel in complex systems. Therefore, a convolutional neural network (CNN) has been adopted, one of the best deep learning approaches in pattern recognition and image classification for TSR. The proposed architecture has been trained and tested on the German traffic sign recognition benchmark dataset (GTSRB). The results reported accuracy of 99.43% that outperformed human accuracy.