Reviewerly/exHarmony huggingface dataset:
Optimal N-ary ECOC Matrices for Ensemble Classification

A new recursive construction of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> -ary error-correcting output code (ECOC) matrices for ensemble classification methods is presented, generalizing the classic doubling construction for binary Hadamard matrices. Given any prime integer <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> , this deterministic construction generates base- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> symmetric square matrices <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M$</tex> of prime-power dimension having optimal minimum Hamming distance between any two of its rows and columns. Experimental results for six datasets demonstrate that using these deterministic coding matrices for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> -ary ECOC classification yields comparable and in many cases higher accuracy compared to using randomly generated coding matrices. This is particular true when <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> is adaptively chosen so that the dimension of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M$</tex> matches closely with the number of classes in a dataset, which reduces the loss in minimum Hamming distance when <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M$</tex> is truncated to fit the dataset. This is verified through a distance formula for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M$</tex> which shows that these adaptive matrices have significantly higher minimum Hamming distance in comparison to randomly generated ones.
Online Learning on Non-Stationary Data Streams for Image Recognition using Deep Embeddings

Deep neural networks offer state-of-the-art technologies for highly nonlinear domains such as image processing; yet their initial training requires large amounts of data, such that they are not directly suited for online learning scenarios for streaming data where class distributions or class labels may change over time. In this contribution, we investigate the suitability of a combination of recent online learning technologies, which have been proposed for learning with streaming data and concept drift in simpler settings, and deep representations of image data as provided by deep networks trained in batch mode, to offer flexible learning technologies for streaming data from the image domain.
ScottishFold: CatBoost-Enabled Lightweight Autonomous Smart Home Device Classification

Massive deployed IoT devices in smart homes pose security management challenges due to different hardware and firmware. To address these challenges, an automated IoT-device-type identification has been used as the first step in autonomous networks in which IoT security policy is automatically managed. Although machine-learning-based traffic patterns were proven high accuracy on a prediction while heavy on preprocessing, we argue that good stability and less computational cost should be considered. We focus on constructing an IoT-device-type classification based on the common features extracted from the network traffic. Inspired by the categorical boosting algorithm, we propose ScottishFold, a classification model, that is constructed from the dirty categorical IoT traffic features with less preprocessing. ScottishFold can classify the IoT device type from a single packet (either a signaling handshake or a data packet) that makes the classification process light. We boost the classification accuracy by integrating a machine reasoning approach into the classification process. The experimental results confirm the effectiveness and robustness of our proposed technique.
A Quantum-inspired Particle Swarm Optimization K-means++ Clustering Algorithm

Hybrid clustering algorithm that combine the swarm intelligence algorithm and K-means is widely used in clustering areas. Such as But, the hybrid particle swarm optimization clustering algorithm, the hybrid genetic clustering algorithm and ant colony algorithm. In which, the hybrid particle swarm optimization algorithm clustering algorithm may appear empty cluster in the iteration process, which will result in a bad clustering results. To improve this phenomenon, we combine the particle swarm algorithm with K-means++ (PSOK-means++), to some extent, which improve the clustering result. But, empty clusters may appear during the iteration of PSOK-means++, as a remedy, we introduce the empty-cluster-reassignment technique and use it to modify particle swarm optimization K-means++, resulting in a particle swarm optimization K-means++ clustering algorithm with empty cluster reassignment (EPSOK-means++). Furthermore, we combine the EPSOK-means++ with quantum computing theory, referred to as QEPSOK-means++ clustering algorithm. The experimental results show that QEPSOK-means++ is effective and promising.
An Algorithm for Randomized Nonnegative Matrix Factorization and Its Global Convergence

Nonnegative Matrix Factorization (NMF) is to decompose a given nonnegative matrix into two nonnegative factor matrices. Recently, randomized NMF has been proposed as an approach to fast NMF of large nonnegative matrices. The main idea of this approach is to perform NMF after reducing the dimensionality of the given nonnegative matrix by multiplying it by a random matrix. Since randomized NMF is formulated as a constrained optimization problem which is slightly different from the one for original NMF, it is necessary to develop suitable algorithms for solving it. However, the conventional algorithm has a serious drawback that the constraints are not satisfied. In addition, the convergence of the algorithm has not been analyzed. In this paper, in order to overcome these drawbacks, we propose to modify the optimization problem and design an algorithm based on the hierarchical alternating least squares method to solve the modified optimization problem. We also prove the global convergence of the designed algorithm.
A Q-matrix Model Based on Binarized Neural Network

Abstract Q-matrix theory plays an important role in the field of cognitive diagnosis assessment. It is time-consuming and laborious for experts to define Q-matrix from a large number of data. In order to deal with this problem, this article comes up with a Q- matrix generation model based on binarized neural network. We combine the neural network with the Boolean operation relations of Q-matrix, A matrix and R matrix in item response theory, so that the model can mine Q-matrix more effectively.
An Analysis of the factors affecting Cybercrime against individuals in Pakistan

The number of internet users in Pakistan is sharply on the rise. This acceleration of digital adoption coupled with a general lack of awareness about cybersecurity in the Pakistani population has created fertile ground for cyber criminals and bad actors to exploit the digital medium thus harming both individuals and society in the process. This paper presents an overview of the state of cybercrime in Pakistan. We discuss the various types of cybercrime, the different groups of people falling victim to these crimes, the factors motivating cybercrime, the factors supporting cybercrime, and the measures necessary to reduce cybercrime activity. The insights presented have been drawn through a process of in-depth interviews with cybercrime experts, followed by a broad-based survey of individuals regarding their experiences with cybercrime.
On-board UAV Pilots Identification in Counter UAV Images

Among Unmanned Aerial Vehicles (UAV) countermeasures, the detection of the drone position and the identification of the human pilot represent two crucial tasks, as demonstrated by the attention already obtained from security agencies in different countries. Many research works focus on the UAV detection but they rarely take into account the problem of the detection of the pilot of another approaching UAV. This work proposes a full autonomous pipeline that, taking images from a flying UAV, can detect the humans in the scene and recognizing the eventual presence of the pilot(s). The system has been designed to be run on-board of the UAV, and tests have been performed on an NVIDIA Jetson TX2. Moreover, the SnT-ARG-PilotDetect dataset, designed to assess the capabilities to identify the UAV pilots in realistic scenarios, is introduced for the first time and made publicly available. An accurate comparison of different classification approaches on the pilot and non-pilot images of the proposed dataset has been performed, and results show the validity of the proposed pipeline for piloting behavior classification.
Undermining democracy and development

This chapter considers how academic economics undermines democracy and development. It begins by exploring the history of the subdiscipline of development economics, highlighting its birth in efforts to help the ‘Third World’ free itself from ‘backwardness’ and then its evolution, often mirroring broader shifts in economics and politics up to the present. The chapter argues that academic economics has become focused on internal explanations for the poor economic performance of low-income countries, such as corruption. As a result, it tends to suggest interventions that focus on the symptoms and not root causes, which would require an analysis that incorporates how history and power have reproduced structural inequalities. Randomised control trials are currently the method in vogue in development economics and its application is presented as exemplifying the above. Here the book suggests that the whole framework of development and underdevelopment is rooted in a colonial world view. This has been a mechanism through which ideas and values from the US and Europe have been imposed on other parts of the world through the internationalisation of neoclassical economics traced in Chapter 2. Rather than a single pathway predetermined by economists and powerful countries, economic development must have principles of democracy and self-determination at its core, and be led by the people who are doing it so that it reflects their needs, priorities, values, and culture.
Best Security Measures to Reduce Cyber-Incident and Data Breach Risks

Corporations plan to adopt appropriate combinations of data privacy managements to mitigate the risk of data breach. Examples of such well-established measures include the certification of an information security management system, a periodic security auditing, and dedicated positions such as a Chief Information Officer (CIO). However, the effectiveness of introducing each of these measures to reduce the risk of data breach is unclear. To assess the effective risk reduction, this work combines the big data of cyber incidents with the attributes of corporations and computes the relative risk with respect to these security measures. Our analysis of five-year data from about 6,000 corporations reveals a negative effect for most measures. The results must be biased by industry characteristics associated with the risk of cyber incidents such as business style and company scale, which are known confounding factors. After investigating company attributes individually, we identify the significant confounding factors that represent obstacles to risk analysis. Using hypothesis testing and multiple logistic regression analysis, we adjust odds ratios for 17 security measures, social responsibilities, environmental conditions, and employment arrangements. The results confirm that an environmental auditing reduces the risk by one-third at a statistically significant level.
Tuning Accuracy-Diversity Trade-off in Neural Network Ensemble via Novel Entropy Loss Function

Ensemble methods are used in machine learning by combining several models which produce an optimal predictive model. Neural network ensemble learning is a technique, which uses multiple individual deep neural networks (DNNs). Ensemble pruning methods are used to reduce the computational complexity of ensemble models. In this study, a novel optimization model is proposed to increase error independence in classifiers via entropy measurement and thus better prune the ensemble. An ensemble of 300 DNNs is trained and tested on the CIFAR-10 dataset and results show an increase in accuracy while main-taining a level of relative entropy measured by Kullback-Leibler divergence (KL-divergence).
Cross-Departmental Authority Heading Creation: A Case Study

The Cataloging and the Reference and Research Services departments at the University of Louisiana at Lafayette’s Edith Garland Dupré Library collaborated to create name authority headings for the Library of Congress Name Authority File. The project’s primary goal was to increase the number of the library’s contributions as a Name Authority Cooperative Program participant. The Head of Cataloging determined which Louisiana persons needed name authority headings and compiled a list of names for Reference & Research Services staff to investigate. After using various online resources to collect biographical information, staff submitted their findings via a Google Form. The Head of Cataloging compiled and edited the data using MarcEdit. The non-traditional methods discussed in this article can be incorporated by catalogers at other libraries and institutions to simplify the authority heading creation process.
Human identification by means of optoelectronic reservoir computing

As an improvement of the traditional recurrent neural networks (RNN), the reservoir computing (RC) only needs to train one output connection weight matrix linearly, which greatly reduces the number of machine learning network calculations. The optoelectronic RC can be realized with a delay feedback loop composed of optical and electrical devices. It has the advantages of lower power consumption and faster speed than the all-electric RC scheme. At the same time, it is easier to be controlled than the all-optical RC scheme. In this paper, we propose to employ the optoelectronic RC to process radar signals to distinguish different persons in the indoor environment. The radar signal required for the simulation is referred from the IDRad data set, which contains the echo signals of the frequency modulated continuous wave (FMCW) radar, and five persons of different ages are free to move around in the room, which is close to the real scene. First, the echo signal is processed and the micro-Doppler features are extracted, and each frame corresponds to a row vector. Then, this vector is used as the input signal of the optoelectronic RC. We numerically studied the impact of parameters such as the size of the RC and the regularization coefficient in the system. Finally, the classification accuracy of five targets reaches 87%.
Random Utility Models with Status Quo Bias

Status quo bias is one of the most commonly observed and attested behavioral biases in economics studies. I incorporate status quo bias into the Random Utility Model (RUM) and three of its special cases, including the Luce model, the Dual Random Utility Model (dRUM) and the Single-Crossing Random Utility Model (SCRUM). These behavioral random utility models generalize their non-behavioral counterparts, in which decision-makers (DM) randomize over alternatives that maximize a set of utility functions. Instead, in the behavioral models, DMs are only drawn to alternatives that are better than the status quo alternative and randomize his choice over utility maximizers among such alternatives. An axiomatic approach is proposed to identify DMs who choose stochastically according to these random utility models but are subject to status quo bias. I also discuss the relationship between these models with other well-known behavioral RUMs in the literature.
T<sup>3</sup>: Domain-Agnostic Neural Time-series Narration

The task of generating rich and fluent narratives that aptly describe the characteristics, trends, and anomalies of time-series data is invaluable to the sciences (geology, meteorology, epidemiology) or finance (trades, stocks). The efforts for time-series narration hitherto are domain-specific and use predefined templates that offer consistency but lead to mechanical narratives. We present $\mathrm{T}^{3}$ (Time-series-To-Text), a domain-agnostic neural framework for time-series narration, that couples the representation of essential time-series elements in the form of a dense knowledge graph and the translation of said knowledge graph into rich and fluent narratives through the transfer-learning capabilities of PLMs (Pre-trained Language Models). To the best of our knowledge, $\mathrm{T}^{3}$ is the first investigation of the use of neural strategies for time-series narration. We showcase that $\mathrm{T}^{3}$ can improve the lexical diversity of the generated narratives by up to 65.38% while still maintaining grammatical integrity. The performance and practicality of $\mathrm{T}^{3}$ is further validated through an expert review $(n=21)$ where 76.2% of participating experts wary of auto-generated narratives favored $\mathrm{T}^{3}$ as a deployable system for time-series narration due to its rich and diverse narratives. Our code-base and the datasets used with detailed instructions for reproducibility is publicly hosted <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/Mandar-Sharma/TCube
Sustainable and Secure IoT-Chain Architecture Integrated with Blockchain Technology

In the recent period, the Internet of Things (IoT) becomes an emerging and vast courtesy area in the smart environment. It rapidly increased the number of intelligent nodes or devices in the system. Due to the huge number of devices, the IoT network is vulnerable with its security and privacy, etc. In this circumstance, Blockchain becomes the most rising, emerging, efficient, and sustainable technology for reducing the security encounters of the IoT applications, because of its Peer-to-Peer, transparent, immutable, decentralized, and distributed nature. The distributed behavior of this technology makes blockchain feasible to integrate the IoT network. This manuscript proposed an IoT layer architecture integrating blockchain technology. The proposed paradigm IoT-Chain architecture has one more Cryptographic layer to increase the security of IoT system. Finally, this manuscript provides the future path of Blockchain in IoT architecture as well as précis the conclusion of this manuscript.
Numerical Analysis of Possibilistic Answer Set

Possibilistic logic program (PLP) is a logic system for representing incomplete and uncertain knowledge in a non-probabilistic way. One of the existing semantics is less expressive and the other one is not tools-oriented, none of them characterises the possibilistic integrity constraints, resulting in the semantics are not compatible for software development. In this paper, we consider a monotonic PLP-a fully grounded possibilistic definite logic program (PDLP)-and formalise a tools-oriented numerical method to determine the answer set of the PDLP. To do so, firstly, we define an atom-wise transformation of a PDLP into a system of equality constraints of necessity degrees. Secondly, we formalise iterative formulas to determine those necessity degrees. Finally, we extend our method to characterise the possibilistic integrity constraints appearing in PDLP. We consider the theoretical aspect to understand the efficiency and the computational complexity of the proposed method and prove that the number of iterations required here for a PDLP is at best the number of rules of the program.
Different Haze Image Conditions for Single Image Dehazing Method

The dust, mist, haze, and smokiness of the atmosphere typically degrade images from the light and absorption. These effects have poor visibility, dimmed luminosity, low contrast, and distortion of colour. As a result, restoring a degraded image is difficult, especially in hazy conditions. The image dehazing method focuses on improving the visibility of image details while preserving image colours without causing data loss. Many image dehazing methods achieve the goal of removing haze while also addressing other issues such as oversaturation, colour distortion, and halo artefacts. However, the limitation of haze level rendered these approaches ineffective. A volume of various haze level data is required to demonstrate the efficiency of the image dehazing method in removing haze at all haze levels and obtaining the image's quality. This paper introduced a dynamic scattering coefficient to the dehazing algorithm for determining an applicable visibility range for different haze conditions. These proposed methods improve on the current state-of-the-art dehazing method in terms of image quality measurement results.
A Study on the Establishment of a Security and Customs Cooperation System for Reinforcement of the International Air Cargo Supply Chain Security

The International Civil Aviation Organization (ICAO) and the World Customs Organization (WCO) emphasize securing supply chain security through mutual cooperation between aviation security and customs by establishing a standardized security system by regulations, procedures and practices of international air cargo. Accordingly, in accordance with the Aviation Security Act, the known consignors system aims to secure cargo security before loading air cargo into the aircraft, while the customs AEO system is a public-private cooperation program that focuses on simplification of customs clearance procedures. These systems basically have the same purpose of effectively identifying high-risk cargo through a risk-based approach in international air cargo transportation and preventing risks in advance, and the content that a common basic standard for cargo security must be established is also similar. Therefore, it is necessary to establish a cooperation system by simplifying problems such as cumbersome and redundant authentication procedures and on-site verification through coordination of security requirements for mutual recognition between the two systems. As a result, it is necessary to establish a process for coordinating security and customs
CloudNPlay: Resource Optimization for A Cloud-Native Gaming System

Cloud gaming enables people playing graphically intensive games from their less powerful, or even outdated computing devices. It is challenging to realize cloud gaming as it requires minimal latency in server-side processing, rendering and streaming, which are expensive in terms of resource requirements, e.g., powerful GPU servers. Commercial gaming providers, e.g., Google Stadia, Amazon Luna, etc., hardly disclose any information on how they optimize gaming performance and cloud cost. In this work, we aim to investigate resource cost optimization for such cloud gaming systems. In contrast to previous work which have been focusing more on theoretical approaches, we deliver a fully functional, cost-optimized cloud-native gaming system, called CloudNPlay, implemented entirely on AWS Lambda, EC2 and other AWS services. We have conducted extensive evaluations on the gaming performance including latency and frame rates; as well as cloud resource cost reduction. The results indicate that games hosted on CloudNPlay are highly playable. More importantly, CloudNPlay reduces the resource cost by around 24% compared to a non-optimized deployment. We plan to open-source CloudNPlay to facilitate further research in cloud gaming.