Reviewerly/exHarmony huggingface dataset:
Comparative of Advanced Sorting Algorithms (Quick Sort, Heap Sort, Merge Sort, Intro Sort, Radix Sort) Based on Time and Memory Usage

Every algorithm has its own best-case as well as its worst-case scenario, so it is difficult to determine the best sorting algorithm just by its Big-O. Not only that, the amount of memory required also affect the algorithm's efficiency. This research provides an overview for the advanced sorting algorithms, namely Radix Sort, Heap Sort, Quick Sort, Merge Sort, and Introspective Sort, that are used directly in real life work to sort 11K GoodRead's data and compare each algorithm, in terms of time required and memory usage to complete the sort. The test is completed by using visual studio code to write the application and is implemented using python programming language. The program will do the testing for each algorithm up to 5 times in a row and will be recorded. This research show that Introspective sort is the best at time and Heap sort is the best at memory usage.
Craft

Craft, like creativity, has existed for millennia and represents the hallmark of our existence as a species that builds tools, environments, and culture. In this entry, we explore the multiple connections between crafting and creating first through the lens of cognitive science and then with an expanded view of cognition and of creativity itself. By using Material Engagement Theory, and with the help of a case example, we argue that the creative process of craft is embodied, socio-material, and deeply cultural. Moreover, we conclude that these characteristics can be applied to creative activity beyond craft and that a focus on craft practice can help us to reflect on the nature of agency and the relation of the mind in the world during the act of making.
Nonlife Insurance Risk Classification Using Categorical Embedding

In nonlife insurance risk classification, many rating factors are naturally categorical and often the categorical variables have a large number of levels. The high cardinality of categorical rating variables presents challenges in actuarial applications of both traditional generalized linear models and modern deep learning methods. To address these challenges, we introduce the method of categorical embedding to the practice of risk classification. The method is inspired by the neural network language models for learning text data and maps a categorical variable into a real-valued representation in the Euclidean space. Using a property insurance claims data set, we demonstrate the use of categorical embedding in three applications. The first shows how embeddings are used to construct rating classes and calculate rating relativities for a single insurance risk. The second concerns predictive modeling for multivariate insurance risks and emphasizes the effects of dependence on tail risks. The third focuses on pricing new products where transfer learning is used to gather knowledge from existing products.
A Random Finite Set Sensor Control Approach for Vision-based Multi-object Search-While-Tracking

Through automatic control, intelligent sensors can be manipulated to obtain the most informative measurements about objects in their environment. In object tracking applications, sensor actions are chosen based on the predicted improvement in estimation accuracy, or information gain. Although random finite set theory provides a formalism for measuring information gain for multi-object tracking problems, predicting the information gain remains computationally challenging. This paper presents a new tractable approximation of the random finite set expected information gain applicable to multi-object search and tracking. The approximation presented in this paper accounts for noisy measurements, missed detections, false alarms, and object appearance/disappearance. The effectiveness of the approach is demonstrated through a ground vehicle tracking problem using real video data from a remote optical sensor.
Automatic Narrative Summarization for Visualizing Cyber Security Logs and Incident Reports

Cyber security logs and incident reports describe a narrative, but in practice analysts view the data in tables where it can be difficult to follow the narrative. Narrative visualizations are useful, but common examples use a summarized narrative instead of the full story's narrative; it is unclear how to automatically generate these summaries. This paper presents (1) a narrative summarization algorithm to reduce the size and complexity of cyber security narratives with a user-customizable summarization level, and (2) a narrative visualization tailored for incident reports and network logs. An evaluation on real incident reports shows that the summarization algorithm reduces false positives and improves average precision by 41% while reducing average incident report size up to 79%. Together, the visualization and summarization algorithm generate compact representations of cyber narratives that earned praise from a SOC analyst. We further demonstrate that the summarization algorithm can apply to other types of dynamic graphs by automatically generating a summary of the Les Misérables character interaction graph. We find that the list of main characters in the automatically generated summary has substantial agreement with human-generated summaries. A version of this paper, data, and code is freely available at https://osf.io/ekzbp/.
Research on Improvement of Blockchain DPOS Consensus Mechanism Based on HK Clustering

Aiming at the problem of low voting enthusiasm of nodes in the Delegated Proof of Stake (DPOS) consensus mechanism in the blockchain, an incentive mechanism based on a clustering algorithm is proposed. By establishing a node positivity model, the nodes are classified by a clustering algorithm, and rewards are distributed to different nodes according to the classification results, so as to improve the enthusiasm of nodes in voting. The traditional K-means clustering algorithm has problems such as the need to predict the number of clusters K and the inability to deal with outliers, so it is not suitable for the incentive mechanism of node classification. Aiming at the problem of K-means clustering algorithm, an Hegselmann-Krause (HK) opinion dynamics clustering algorithm is introduced, which can meet the needs of the reward distribution mechanism in this paper. Through simulation and comparison with the existing schemes, the effectiveness of the improved algorithm is verified.
Integrating Content Authentication Support in Media Services

The chapter investigates content authentication strategies and their use in media practice. Remarkable research progress has been conducted on media veracity methods and algorithms, however without providing that much straightforward tools to users involved in real-world applications. Hence, there is an urgent need for further support content verification by exploiting all the available methods in properly integrated online environments, forming a media authentication network. On-demand training (and feedback) on these technologies is considered of major importance, enabling users to collaborate with media and forgery experts towards adoption, refinement, and widespread dissemination of best practices. Better comprehension of the involved tools and algorithms would propel their broad exploitation in practice, gaining valuable feedback for further improvements. Thus, a continuously updated online repository, containing documented examples, learning resources, and media veracity tools could be adaptively accommodated, better supporting various users and applications needs.
On the binarization of Grey Wolf optimizer: a novel binary optimizer algorithm

Grey Wolf Optimizer (GWO) is a nature-inspired swarm intelligence algorithm that mimics the hunting behavior of grey wolves. GWO, in its basic form, is a real coded algorithm that needs modifications to deal with binary optimization problems. In this paper, previous work on the binarization of GWO are reviewed, and are classified with respect to their encoding scheme, updating strategy, and transfer function. Then, we propose a novel binary GWO algorithm (named SetGWO), which is based on set encoding and uses set operations in its updating strategy. The proposed algorithm uses a completely different encoding scheme that eliminates the need for the transfer function and boundary checking, and also uses lower-dimensional agents; therefore, decreases the running time. Also, by using an exclusive exploration set for each agent, defining a different distance measure and an encircling strategy in discrete spaces, the quality of solutions has been improved. Experimental results on different real-world combinatorial optimization problems and datasets show that SetGWO outperforms other existing binary GWO algorithms in terms of quality of solutions, running time, and scalability.
Rando: A General-purpose True Random Number Generator for Conventional Computers

Designing and developing a true random number generator is a grand challenge for all time. It is highly necessitated in cryptography and various domains, such as simulation and other scientific applications. Therefore, various random number generators are emerging. Many true random number algorithms have already been proposed, which are very complex and require specific devices; for instance, a quantum random number generator depends on quantum devices. Moreover, diverse true random number generators are devised based on light, image, voltage, currents, etc., to achieve true randomness. Thus, it is a challenging task to generate a truly random number in any computing device. Therefore, we propose a general-purpose true random number generator for a conventional computing device, called Rando. The time complexity of Rando is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(m)$</tex> , where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> is the bit size of a random number, but it does not use extra spaces. Our proposed algorithm is a simple and straightforward true random number generator based on hashing techniques and system clocks yet powerful. Our experimental results show that Rando outperforms state-of-the-art techniques in randomness. Rando is validated its true randomness in NIST SP 800–22 and passes all 15 statistical tests of randomness.
An Effective Encrypted Scheme Over Outsourcing Data for Query on Cloud Platform

With the proliferation of cloud platforms, users are inclined to outsource their encrypted data to cloud platforms. However, the existing encryption schemes only provide users with limited query types over encrypted data. Meanwhile, if users encrypt their data by deterministic encryption, their encrypted data will be easily subject to the frequency attack. Besides, users' data privacy is likely to be disclosed to the cloud server when their encrypted data is updated on cloud platforms. To address these problems, in this paper, we propose an effective encrypted query scheme over outsourcing data on cloud platforms. In our scheme, users' data is encrypted based on all possible queries to meet users' diverse query demands. Furthermore, a double AES encryption method is adopted to cope with the frequency attack existing in deterministic encryption. To protect users' privacy when their data is updated, a neighbor rows exchange method is designed in our scheme. The theoretical analysis and comparative experiments demonstrate the effectiveness of our scheme.
An investigation of trace elements' behavior during chemical preparation of ultramafic matrix rock samples using bomb digestion for analysis by ICP-MS

A chemical treatment scheme providing complete dissolution of a 300 mg test portion of ultramafic rocks with various MgO contents is proposed.
Secure Document Verification System Using Blockchain

Document verification is a complex domain that involves processes to authenticate original documents. Some original documents like birth certificate, university diploma, contract, certificate of occupancy, Will etc. may involve serious verification and authentication practices, because fake documents can easily be created. A skillfully generated fake document is always difficult to detect and can be treated as original. With the increase of forged documents, the integrity of both the document holder and the issuing authority is jeopardized. This research is intended to address the issue of electronic document forgery and provide an alternative secure means for storing documents. The aim of this research is to design and implement a secure document verification system using blockchain. The result of this study shows how the user documents are stored securely in the blockchain, and if any change or adjustment are made to the documents the chain becomes invalid and the user will be notified
Vulnerability of the Synchronization Process in the Quantum Key Distribution System

A typical structure of an auto-compensation system for quantum key distribution is given. The principle of operation of a fiber-optic system for the distribution of quantum keys with phase coding of photon states is described. The operation of the system in the synchronization mode and the formation of quantum keys was investigated. The process of detecting a time interval with an optical synchronization pulse is analyzed. The structural scheme of the experimental stand of the quantum-cryptographic network is given. Data are obtained that attest to the presence of a multiphoton signal during the transmission of sync pulses from the transceiver station to the coding and backward direction. The results of experimental studies are presented, which prove the existence of a vulnerability in the process of synchronization of the quantum key distribution system. It is shown that the use of a multiphoton optical pulse as a sync signal makes it possible for an attacker to unauthorized access to a quantum communication channel. The experimental results show that tapping a portion of the optical power from the quantum communication channel during the synchronization process allows an attacker to remain unnoticed while the quantum protocol is operating. Experimentally proved the possibility of introducing malfunctions into the operation of the quantum communication system at the stage of key formation, while remaining invisible for control means.
Image Processing

Over the years, the field of computer vision that encompasses techniques for acquiring, processing, analyzing, and understanding the visual world has created many lines of research. Of these lines, one of the main aims to electronically duplicate the skills of the human vision system. However, the same challenge has been complicated by the increase in the number and resolution of images and videos available for processing. With the need to process vast amounts of visual information, computer vision systems now must work efficiently with the limited computational resources available. In this chapter, the basic concepts of image processing are presented while focusing on the topic of image segmentation.
Artificial Intelligence

The dual headed disruption tidal wave of blockchain enabled activities and artificial intelligence will invariably lead to anxiety, stress, and potentially misunderstanding of just what these technologies represent for financial services. Blockchain, hopefully, at this point has been demystified to a certain extent, but the idea of artificial intelligence may appear and seem like a more amorphous concept that is both difficult to understand but potentially disruptive in nature. While artificial intelligence has been featured in numerous media outlets, movies, and T.V. shows, the image that is most often presented to audiences and market actors is one that, almost invariably, has negative connotations and implications for the developers and users. Fortunately, while there have been numerous advances in the development and implementation of artificial intelligence, the limits of current iterations are still substantial. In other words there is no need to fear the Terminator coming for financial practitioner roles. Prior to diving into what the applications and implications of AI may very well be, however, it seems appropriate to first put forward a definition that makes sense in the context of this discussion. Not meant to be overly technical, but rather a working definition to assist financial professionals seeking to understand and explain the implications of AI, a working definition as follows is a workable option:
Secure Ranked Choice Online Voting System via Intel SGX and Blockchain

Establishing trust in the election outcome is a very important problem. In order to solve it, we propose a practical ranked choice online voting system where every user can verify the validity of the outcomes. The proposed system combines the use of public blockchain and Intel Software Guard Extensions (SGX) to ensure that all standard requirements for voting systems are met, and at the same time to provide protection against malicious adversaries with administrative access. Each cast vote is submitted to the Intel SGX via a secure communication channel. The SGX verifies the eligibility and validity of the vote and encrypts it inside the SGX enclave. Later on, only the fingerprints (i.e., the hash values) of the encrypted votes are published on the public blockchain ledger before the deadline of the election. All encrypted votes are released after the deadline. Every voter can self-tally all submissions in encrypted form using the homomorphic property of our encryption system. The proposed protocol also achieves end-to-end voter verification. Our theoretical security analysis and experimental results show that the protocol is secure and is more efficient than previous alternative voting protocols.
Fuzzy Logic for Refining the Evaluation of Learners’ Performance in Online Engineering Education

Intelligent tutoring systems have been widely used for optimizing the educational process by creating a student-centered learning environment. As a matter of fact, an integral part of intelligent tutoring systems is the evaluation of the learners’ performance. In traditional learning, the instructors calculate the grade of the students derived from the assessment units and other factors, such as the difficulty of the exercises or their effort, in order to produce the final students’ score in the course. However, in most cases, the evaluation of learners’ performance in intelligent tutoring systems takes place by calculating an average grade of students without taking into account the aforementioned factors. In view of the above, this paper presents a novel way for refining the evaluation of students’ performance using fuzzy logic. As a testbed for our research, we have designed and implemented an intelligent tutoring system holding social networking characteristic for teaching the engineering course of “Compilers”. More specifically, the system is responsible for acquiring information about students such as their grades, the kinds of misconceptions, the level of tests’ difficulty as well as their effort including their social interaction, i.e. participation in forums, making comments in posts and posting regarding the educational process. Taking these into consideration, fuzzy logic model diagnoses the accuracy of students’ grade and the system suggests that the instructor redefine students’ grade properly. Our system was evaluated using t-test and the results show high accuracy and objectivity in the evaluation of students’ performance.
Distribution Models for Falsification and Verification of DNNs

DNN validation and verification approaches that are input distribution agnostic waste effort on irrelevant inputs and report false property violations. Drawing on the large body of work on model-based validation and verification of traditional systems, we introduce the first approach that leverages environmental models to focus DNN falsification and verification on the relevant input space. Our approach, DFV, automatically builds an input distribution model using unsupervised learning, prefixes that model to the DNN to force all inputs to come from the learned distribution, and reformulates the property to the input space of the distribution model. This transformed verification problem allows existing DNN falsification and verification tools to target the input distribution – avoiding consideration of infeasible inputs. Our study of DFV with 7 falsification and verification tools, two DNNs defined over different data sets, and 93 distinct distribution models, provides clear evidence that the counterexamples found by the tools are much more representative of the data distribution, and it shows how the performance of DFV varies across domains, models, and tools.
An Overview of Machine Learning Methods for Multiple Target Tracking

Traditional multiple target tracking (MTT) algorithms are model-based. Target and sensor models are used to associate measurements, perform track filtering, score possible associations, and find the best association hypothesis. Recent advances in machine learning (ML) have resulted in data-driven model-free methods for MTT, especially in computer vision, where MTT is called multiple object tracking (MOT). This paper presents an overview of ML methods for detection, track filtering, data association, and end-to-end MTT. It assesses the state-of-the-art and presents future research directions.
Introduction and terminology

This chapter begins with a short history of the development of acoustics from the time of Helmholtz and Lord Rayleigh to today's concert halls and cell phones. A visual picture of sound in air is presented to help the reader to understand the basic equations that follow. Vital to the acoustics field is an understanding of its terminology: sound pressure, density, speed and velocity, impedance, intensity, energy density, and levels. The reader is urged to carry the material of this chapter in mind as these concepts occur over and over in the subsequent chapters.
Troublemakers, Bystanders, and Pathbreakers

Abstract Do youth protest more than their older counterparts and is their main motivation for protest their preoccupation with unemployment? This chapter aims to answer these questions using various data sources of micro- and macro-economic time series data. Protest trends across cohorts from all Sub-Saharan African countries are first presented. A multivariate regression approach is then used to identify the significant drivers of mobilization using public opinion data. Although there are clear life cycle effects underlying protest behaviour such that younger individuals are more likely to protest than older ones, protest activity is a form of mobilization used by all age groups. For both young and old, education, engagement in activism, and deprivation of basic goods are strong predictors of protest regardless of time period. More recently, however, young people are more likely to protest if they are unemployed and if they lack trust in political institutions.