Reviewerly/exHarmony huggingface dataset:
Dynamic Unmanned Aircraft System Traffic Volume Reservation Based on Multi-Scale A* Algorithm

With the recent advancements in unmanned aircraft system (UAS) technology, unmanned aerial vehicles (UAVs) are widely used or proposed to carry out various daily tasks in the low altitude airspace, such as delivery and urban air mobility. In order to safely integrate the UAS traffic into the congested airspace in the urban area, the current UAS traffic management system proposed by NASA will reserve a static traffic volume for the whole planned trajectory, which is safe but not efficient. In this paper, we propose a dynamic traffic volume reservation method for the UAS traffic management system based on a multi-scale A* algorithm. The planning airspace is represented as a multi-resolution grid, where the resolution will get coarser as the distance to the planning drone gets larger. Therefore, each drone will only need to reserve a temporary traffic volume along the finest flight path in its local area, which helps release the airspace back to others on the far side. Moreover, the multi-scale A* can run nearly in real-time due to a much smaller search space, which enables dynamically rolling planning to consider updated information. The presented numerical results support the advantages of the proposed approach.
Hybrid PSO-α BB Global Optimisation for <i>C</i>² Box-Constrained Multimodal NLPs

<inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> BB is an elegant deterministic branch and bound global optimisation that guarantees global optimum convergence with minimal parameter tuning. However, the method suffers from a slow convergence speed calling for computational improvements in several areas. The current paper proposes hybridising the branch and bound process with particle swarm optimisation to improve its global convergence speed when solving twice differentiable (<inline-formula> <tex-math notation="LaTeX">$C^{2}$ </tex-math></inline-formula>) box-constrained multimodal functions. This hybridisation complemented with interval analysis leads to an early discovery of the global optimum, quicker pruning of suboptimal regions in the problem space, thus improving global convergence. Also, when used as a heuristic search algorithm, the hybrid algorithm yields superior solution accuracy owing to the combined search capabilities of PSO and the branch and bound framework. Computational experiments have been conducted on CEC 2017/2019 test sets and on n-dimensional classical test sets yielding improved convergence speed in the complete search configuration and superior solution accuracy in the heuristic search configuration.
FPGA Implementation of Chaos based Pseudo Random Number Generator

A PRNG (pseudo-random number generator) is proposed in this paper. The algorithm which is proposed here is dynamically adjusts the chaotic system’s parameters to keep the device from falling into short-duration orbits and to make the formed sequences more random. This PRNG is implemented with a fixed point precision of 32-bit using Xilinx Vivado Design Suite Version 2019.2. By checking the randomness of the proposed PRNG to the sequences created by a Standard RNG(using $urandom) of size 32-bit, the proposed PRNG achieves significantly better randomness. Later, the following bitwise dynamical pseudo-random number generator is compared to standard randomization realizations using Monobit test Method which is used to check randomness.
When do they offend together? Comparing co-offending between different types of cyber-offenses and traditional offenses

Research on cyber-co-offending tends to focus on either the organizational structure of organized cybercrime or social learning processes among individuals. This paper provides a new perspective on co-offending by examining the extent to which individuals co-offend for different types of cybercrime compared to traditional crime. Additionally, differences in the type of co-offender (friends, family, or others) and relationships between IT-knowledge and cyber-co-offending are examined. This paper is based on individual self-report survey data from a judicial sample of Dutch adult cyber-dependent offenders and traditional offenders. It includes information on 466 different crimes (51.50% cybercrime) self-reported by 164 individuals. Results indicate that cybercrime and traditional crime show similar patterns of co-offending. The majority of offenders prefer to commit their crimes alone, but some types of crime are more often committed with co-offenders than other types of crime. For cybercrime results suggest that limitations in an offender's IT-knowledge may be a reason to seek co-offenders with strong IT-skills.
Effective by precision algorithms for approximation of functions from the Gelder class by Fourier’s series

Effective by precision algorithms for approximation of functions from the Gelder class by Fourier’s series, using the Fourier’s coefficients, calculated with high precision, are constructed, and the evaluations of their basic characteristics (precision and speed) are obtained.
Analyzing the Cause of Students Reluctant to Participate in the Classroom: Machine Learning Approach

in the twenty-first century, seems many students are reluctant to participate in classroom matters day by day. Some days ago, a course teacher is providing a lecture, on the most important topics that will be implemented in every step in life that class majority students were nonexistent. We got motivation why it’s happened and find out a lot of reasons behind uninterested in class after interrogation. Who are belonging age range 20-25, most of the time they face an obstacle and suffer and other teenagers. The fundamental purpose of this research to take action based on behind reasons, so that students can move in classroom learning matters. In this article, solve will be described and how to motivate students. Then it will be much helpful and far informative for teacher, students, guardians and also education specialist [8]. In this research shown causes, and also recommended how to overcome and solve those reluctant and spread knowledge and enlighten the nation. The aim of the research to help and inform provide to students avoid his/her predicted absent in classroom using Machine learning, particularly we used Liner Algorithm. Then, students will participate in classroom lectures as well as social activity and world would be more peaceful. This research achieved more than the accuracy of 95.9%.
Interaction Aware Relational Representations for Video Prediction

Video prediction is an active machine learning problem to use past information in a video sequence to acquire human-like understanding and then predicting future consequences of object states and actions. The existing prediction frameworks integrated the decomposition and disentanglement techniques to observe object interaction and use them to predict future video scenes. However, the previous works did not consider physical interaction among objects in the prediction. Thus, this research utilizes the physical reasoning concept to represent object dynamics in the real world and estimate future sequences enclosing object dependencies. This paper addresses the investigation of object interaction using stochastic video prediction with physical reasoning representation. We propose a self-supervised framework called Relational Prediction Auto-Encoder (RPAE). Extensive experiments demonstrate that the proposed RPAE can effectively improve the generation and prediction of the near future sequences. We also confirmed the predicted object dynamics by measuring the velocity of each object and its physical interaction in the experiments.
State of the art

This chapter presents a comprehensive discussion on the state of the-art of grammatical inference (GI). The fundamental definitions of language and grammar are explained in a simplified manner for beginners and intermediate-level researchers. The first part of Chapter 2 is dedicated to basic grammar definitions. The discussion starts with the standard grammar presentation referred as Backus–Naur form. After that, standard grammar representation is discussed along with the Chomsky hierarchy. Various grammar representations such as unstructured grammar, context sensitive grammar, context-free grammar, and regular grammar are covered. Other forms of grammar such as matrix grammar, random context-free grammar, valence grammar, and bag context grammar are explained using the rewriting mechanism. Learning algorithms, with their strengths and weakness, are presented later in the chapter. Finally, a comparative classification of different learning algorithms is presented. The key concepts presented in this chapter are a detailed and comprehensive review of approaches that have been proposed for GI, with the strengths and weaknesses of each approach. The approaches are classified based on the various factors. The analysis of each approach is conducted and presented in tabulated form for quick review.
Fast Traffic Sign and Light Detection using Deep Learning for Automotive Applications

Traffic sign and light detections are core components of Advanced Driver Assistance Systems (ADAS) and self-driving vehicles. To this end, the automotive industry is widely exploiting computer vision (CV) and deep learning (DL) techniques. This paper presents a lightweight traffic sign and light detector by harnessing a single-stage, single-shot multi-object detector (SSD). For accelerating the inference speed of the detector, its original backbone, VGG16 is replaced by MobileNet V2 that expertly manages detection speed and network size. In autonomous driving, quicker detection performance with respect to the distance of an object is of particular interest, for a comfortable braking. However, farther distance makes the objects to be detected appear smaller. Unfortunately, the original SSD struggles to detect small objects. Thus, this work further optimizes the number of feature map layers of the SSD for the detection of small objects along with a better trade-off between detection precision and inference time. Experimental analysis confirms the effectiveness of the proposed model, which achieves 2 times (or more) faster detection time than the baseline SSD models and a competitive precision of 76.7%.
Cascade or Direct Speech Translation? A Case Study

Speech translation has been traditionally tackled under a cascade approach, chaining speech recognition and machine translation components to translate from an audio source in a given language into text or speech in a target language. Leveraging on deep learning approaches to natural language processing, recent studies have explored the potential of direct end-to-end neural modelling to perform the speech translation task. Though several benefits may come from end-to-end modelling, such as a reduction in latency and error propagation, the comparative merits of each approach still deserve detailed evaluations and analyses. In this work, we compared state-of-the-art cascade and direct approaches on the under-resourced Basque–Spanish language pair, which features challenging phenomena such as marked differences in morphology and word order. This case study thus complements other studies in the field, which mostly revolve around the English language. We describe and analysed in detail the mintzai-ST corpus, prepared from the sessions of the Basque Parliament, and evaluated the strengths and limitations of cascade and direct speech translation models trained on this corpus, with variants exploiting additional data as well. Our results indicated that, despite significant progress with end-to-end models, which may outperform alternatives in some cases in terms of automated metrics, a cascade approach proved optimal overall in our experiments and manual evaluations.
Gaze dynamics are sensitive to target orienting for working memory encoding in virtual reality

Numerous studies have demonstrated that visuospatial attention is a requirement for successful working memory encoding. It is unknown, however, whether this established relationship manifests in consistent gaze dynamics as people orient their visuospatial attention toward an encoding target when searching for information in naturalistic environments. To test this hypothesis, participants' eye movements were recorded while they searched for and encoded objects in a virtual apartment (Experiment 1). We decomposed gaze into 61 features that capture gaze dynamics and a trained sliding window logistic regression model that has potential for use in real-time systems to predict when participants found target objects for working memory encoding. A model trained on group data successfully predicted when people oriented to a target for encoding for the trained task (Experiment 1) and for a novel task (Experiment 2), where a new set of participants found objects and encoded an associated nonword in a cluttered virtual kitchen. Six of these features were predictive of target orienting for encoding, even during the novel task, including decreased distances between subsequent fixation/saccade events, increased fixation probabilities, and slower saccade decelerations before encoding. This suggests that as people orient toward a target to encode new information at the end of search, they decrease task-irrelevant, exploratory sampling behaviors. This behavior was common across the two studies. Together, this research demonstrates how gaze dynamics can be used to capture target orienting for working memory encoding and has implications for real-world use in technology and special populations.
Emerging Business Models for IoT-Based Smart Distribution Systems

Recently, Internet of Things (IoT) has been developing in several social areas such as energy, transportation, health, etc., through the physical and virtual connection of objects using the communication and information technologies. In the IoT technology, it is possible to provide different services with high reliability and security levels based on data identification, recording, storing, and processing as well as communication of objects. Meanwhile, traditional power systems are changing towards smart grids to overcome problems regarding the one-way data transmission, energy losses, security and reliability of the system, demand increases, Distributed Generations (DGs) penetration, changing nature of electricity consumers, etc. Moreover, the smart grids use several equipment for monitoring and control of the network. The IoT development as the reliable, accurate, and fast communication and information infrastructure, has led to the belief that the architecture of the future smart distribution networks would be based on this technology. It will make a huge difference in distribution networks due to the use of various equipment such as sensors, actuators, and smart meters. Not only it will cause some technical changes, but also cause structural changes such as stakeholders and their role and relationships, as well as their economic issues. Considering these technical and structural concepts, the existing businesses in distribution networks will be changed. Therefore, new business model canvases should be developed to satisfy the upcoming requirements based on the new business environment. Although several remarkable studies have been conducted to investigate the IoT technology and its applications, its business models have not been developed. This chapter aims to investigate the IoT-based smart distribution grids, and also identify the emerging players and their roles, as well as their communications in the new environment. Finally, the appropriate business model canvases will be proposed for the future IoT-based smart distribution grids.
Does Code Complexity Affect the Quality of Real-Time Projects?

Code smell targets to identify bugs that occur due to incorrect analysis of code during software development life cycle. It is the task of analyzing a code design problem. The primary causes of code smell are complexity in structural design, violation of programming paradigm, and lack of unit-level testing by the software programmer. Our research focuses on the identification of code smell using different machine learning classifiers. We have considered 15 software code metrics of the Junit open source project and developed a hybrid model for code smell detection. Our dataset consists of 45 features which is further reduced by 15 using various feature selection techniques. Random sampling is used to handle the imbalance in the dataset. The project's performance is evaluated using 10 machine learning techniques which including regression, ensemble methods, and classification. Based on the statistical analysis, it is analyzed that the Random forest ensemble classifiers give best result with an accuracy of 99.12 % is the most appropriate technique for detecting different types of bad smells like god class, duplicate code, long method, large class, and refused bequest.
A Performance Evaluation of Spark GraphFrames for Fast and Scalable Graph Analytics at Twitter

Graph analytics demand is emerging rapidly and has become one of the key parts of Twitter machine learning for driving engagement, serving most relevant content, and promoting healthier conversations. However, due to lack of infrastructure support for graph analytics, we are suffering from a long timeline and huge engineering effort for each project to deal with graphs at the Twitter scale, which blocks us from fast iteration. To bring fast and scalable graph analytics capability into Twitter, we adopted Spark GraphFrames and conducted a performance evaluation for a typical graph analytics use case -Connected Component - on one of the largest graphs at Twitter. Compared to our existing Scalding solution, Spark GraphFrames achieved more than 33x speedup in running time.
HyperMaze: Towards Privacy-Preserving and Scalable Permissioned Blockchain

Blockchain systems face two emergent problems, namely scalability and privacy, each of which has been addressed independently. However, how to achieve privacy and scalability simultaneously remains a challenging problem for blockchains. In this article, we propose a privacy-preserving and scalable permissioned blockchain system called HyperMaze employing the zero knowledge proof technique and a hierarchical system architecture. It gains scalability by adopting a hierarchy of multiple blockchains that processes transactions in parallel. We design an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ID-based dual-balance account model</i> where an identity-based account is associated with two types of balances–a plaintext balance and a private (zero-knowledge) balance. Furthermore, we design a two-phase cross-chain transaction mechanism (2PXT) to achieve transaction privacy for both intra-chain and cross-chain transactions. We rigorously formulate a security model for HyperMaze under the universal composability framework, and then provide a simulation-based security proof. A prototype of HyperMaze is implemented and a series of experiments are conducted over up to 2,600 nodes to evaluate its performance. The experimental results show that a 4-level, (7,10)-threshold, 6-ary HyperMaze system can reach 19,440 TPS and the transaction confirmation latency is only 9.5 seconds. To our best knowledge, HyperMaze is the first high-throughput privacy-preserving blockchain whose throughput is over 19,000 TPS.
Detecting moving object from dynamic background video sequences via simulating heat conduction

Moving object detection is one of the essential tasks for surveillance video analysis. The dynamic background often composed by waving trees, rippling water or fountains, etc. in nature scene greatly interferes with the detection of moving objects in the form of noise. In this paper, a method simulating heat conduction is proposed to extract moving objects from dynamic background video sequences. Based on the visual background extractor (ViBe) with an adaptable distance threshold, we design a temperature field relying on the generated mask image to distinguish between the moving objects and the noise caused by dynamic background. In temperature field, a brighter pixel is associated with more energy. It will transfer a certain amount of energy to its neighboring darker pixels. Through multiple steps of energy transfer the noise regions loss more energy so that they become darker than the detected moving objects. After heat conduction, K-Means algorithm with the customized initial clustering centers is utilized to separate the moving objects from background. We test our method on many videos with dynamic background from public datasets. The results show that the proposed method is feasible and effective for moving object detection from dynamic background sequences.
Dry waste segregation using seamless integration of deep learning and industrial machine vision

Municipal solid waste management has been one of the most critical issues of urban cities today. Increasing population, constructions, industries, etc. are the major factors creating a large amount of waste that is dumped onto the landfill sites. Various systems have been proposed and are under the utilization for the management of municipal waste which includes mechanical vibration-based size-based sorters, eddy current sensor-based sorting of metallic waste, automatic optical waste sorters, etc. This paper focuses on a novel solution for solid waste segregation using the concepts of machine vision and deep learning. The proposed concept is tested for the segregation of solid dry waste particularly plastic bottles, aluminum cans, and tetra packs. The prototype system developed for the segregations works at high speed and accuracy. The prototype system sorts 250 objects per minute with an average accuracy of 96%. The proposed novel idea be extended and implemented for other types of waste segregation and can include more categories of solid dry waste. The system provides a solution for the ever-challenging municipal waste management problem.
Macroprudential policies and current account balance

Macroprudential policies have become essential tools for policymakers to maintain financial stability. We investigate the impact of macroprudential policies on the current account balance, considering the link between external imbalances and financial stability. Building on a panel VAR model, we further document that usage of a macroprudential instrument is associated with an improvement in the current account balance. Our findings suggest that the positive impact of macroprudential policy measures on the current account balance is more substantial in the deficit countries.
Single-Trace Attack on NIST Round 3 Candidate Dilithium Using Machine Learning-Based Profiling

In this paper, we propose single-trace side-channel attacks against CRYSTALS-DILITHIUM.CRYSTALS-DILITHIUM is a lattice-based digital signature algorithm, one of the third round finalists of the national institute of standards and technology (NIST) standardization project.We attack number-theoretic transform (NTT) in the signing procedure and key generation of CRYSTALS-DILITHIUM to obtain a secret key.When targeting the signing procedure, we can recover both secret key vectors s 1 and s 2 .This enables forgery of signatures.However, only the secret key vector s 1 can be recovered when targeting the key generation.Thus, we additionally attack four operations, sampling, addition, rounding, and packing, to find s 2 .We applied a machine learning-based profiling attack method to find the secret key vectors s 1 and s 2 with a single trace. INDEX TERMS Digital signature, lattice-based