Reviewerly/exHarmony huggingface dataset:
Performance optimization of computing task scheduling based on the Hadoop big data platform

Abstract Hadoop, a distributed computing framework that can efficiently process large-scale datasets, has been used by an increasing number of organizations as the basic computing framework to build cloud computing platforms. Improving its execution efficiency is a hot research direction in the industry, and the scheduling problem is a key factor affecting the execution efficiency of Hadoop. It is very important to identify its shortcomings and improve them. This paper examines and analyses the optimization of computing task scheduling performance based on the Hadoop big data platform. This paper first analyses Hadoop big data processing. Hadoop has high scalability. Computing nodes can be added at any time, and they can participate in cluster work through simple configuration. The paper discusses the improvement in the Hadoop resource scheduling algorithm. The task scheduling algorithm in the Hadoop-based data task localization proposed in this paper is compared with the default algorithm used in the Hadoop task scheduling algorithm. The former shows better local data in all four jobs, there are more data localization tasks, and the expected goal is achieved. The effectiveness of the algorithm is verified, and the performance is improved by 30%.
Developing an Interactive Digital Reality Module for Simulating Physical Laboratories in Fluid Mechanics

Fundamental engineering units such as fluid mechanics are usually challenging for students to learn since they are practical and linked with mathematics. Traditional physical laboratories are useful for enhancing students' learning; however, they are expensive and require large space. New technologies such as virtual reality (VR) could benefit students without limiting enrolment capacity or laboratory availability. This paper presents a case study focusing on the development of VR experience for the Fluid Mechanics unit at the School of Engineering, Macquarie University. The VR module is an extension of the physical laboratory session where students use wind tunnel facilities to understand the flow features around an object. 3D computational fluid dynamics simulation datasets for a wing are incorporated into the VR platform that uses SteamVR, Oculus and Ansys/EnSight. A survey is conducted to assess students' opinions about the VR experience. Preliminary feedback was highly positive, as most students (>86%) indicated that the VR module was enjoyable, engaging, and interactive. 37% of students agreed that the VR session directly helps them to improve their performance in the Fluid Mechanics unit, while most students (>93%) like to see more VR sessions in Fluid Mechanics.
Checklist Approach for the Development of Educational Applications by Novice Software Developers

Innovation and challenges are significant factors that lead to the improvement in technology involving various sectors, including the educational field.New methods and techniques have been introduced in teaching and learning among learners and educators.Modern technology generates an effective learning process that increases the students' interest and understanding of learning activities.Hence, software engineers need to develop high-quality educational applications that include the required elements such as learning materials and types of assessments.In addition, it should align with stipulated guidelines, timelines, budgets, and policies.Adaptation of a checklist approach in developing educational software can improve the quality of the software significantly.The checklist approach appears to be more effective for developers, especially novices with limited knowledge in relevant evidence-based principles.This study investigates the checklist approach for novice software developers in developing educational applications.A survey was employed to gather data from a group of respondents.This study used two types of sampling methods which were purposive sampling and Yamane formula.It shows that 89.19% of the respondents had understood and benefited from the provided checklist.
ROME: A Graph Contrastive Multi-View Framework From Hyperbolic Angular Space for MOOCs Recommendation

As Massive Open Online Courses (MOOCs) expand and diversify, more and more researchers study recommender systems that take advantage of interaction data to keep students interested and boost their performance. In a typical roadmap, courses and videos are recommended using a graph model, but this does not take into account the user’s learning needs with some particular subjects. However, all existing graph models degrade performances either by ignoring the data sparsity issue caused by a large number of concepts, which may lead to biased recommendations, or by constructing improper contrasting pairs, which may result in graph noise. To overcome both challenges, we propose a g <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> aph c <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</u> ntrastive <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</u> ulti-view fram <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> work (ROME) from hyperbolic angular space to learn user and concept representations based on user-user and concept-concept relationships. The first step is to use hyperbolic and Euclidean space representations as different views of graph and maximize the mutual information between them. Furthermore, we maximize the angular decision margin in graph contrastive training objects to enhance pairwise discriminative power. Our experiments on a large-scale real-world MOOC dataset show that the proposed approach outperforms several baselines and state-of-the-art methods for predicting and recommending concepts of interest to users.
Linear Contrast Enhancement Network for Low-Illumination Image Enhancement

Images captured under low-illumination conditions usually suffer from severe degradations, such as fading and low contrast, drastically affecting the performance of systems relying on images under low-illumination conditions. To address such problems, this study proposes a linear contrast enhancement network (LCENet) for low-illumination image enhancement. It consists of three subnets: two encoder–decoder-based subnets for gradient map restoration and brightness enhancement, respectively, and a backbone network for adaptive brightness and contrast adjustment. In addition, a linear contrast enhancement adaptive instance normalization (LCEAIN) module with linear contrast enhancement ability is proposed in the backbone network, which can avoid the problem of ignoring contrast enhancement when enhancing image brightness. Considerable evaluations on both synthetic and real low-illumination images show that the proposed method performs favorably against other existing similar methods. Moreover, our method can handle complex low-illuminance conditions and has good generalization for low-illuminance scenes with backlighting, night scenes with light sources, as well as underwater scenes with low illuminance. Code: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zhouzhaorun/LCENet</uri> .
Time efficient variants of Twin Extreme Learning Machine

Twin Extreme Learning Machine models can obtain better generalization ability than the standard Extreme Learning Machine model. But, they require to solve a pair of quadratic programming problems for this. It makes them more complex and computationally expensive than the standard Extreme Learning Machine model. In this paper, we propose two novel time-efficient formulations of the Twin Extreme Learning Machine, which only require the solution of systems of linear equations for obtaining the final classifier. In this sense, they can combine the benefits of the Twin Support Vector Machine and standard Extreme Learning Machine in the true sense. We term our first formulation as ‘Least Squared Twin Extreme Learning Machine’. It minimizes the L2-norm of error variables in its optimization problem. Our second formulation ‘Weighted Linear loss Twin Extreme Learning Machine’ uses the weighted linear loss function for calculating the empirical error, which makes it insensitive towards outliers. Numerical results obtained with multiple benchmark datasets show that proposed formulations are time efficient with better generalization ability. Further, we have used the proposed formulations in the detection of phishing websites and shown that they are much more effective in the detection of phishing websites than other Extreme Learning Machine models.
AdaAnn: ADAPTIVE ANNEALING SCHEDULER FOR PROBABILITY DENSITY APPROXIMATION

Approximating probability distributions can be a challenging task, particularly when they are supported over regions of high geometrical complexity or exhibit multiple modes. Annealing can be used to facilitate this task which is often combined with constant a priori selected increments in inverse temperature. However, using constant increments limits the computational efficiency due to the inability to adapt to situations where smooth changes in the annealed density could be handled equally well with larger increments. We introduce AdaAnn, an adaptive annealing scheduler that automatically adjusts the temperature increments based on the expected change in the Kullback-Leibler divergence between two distributions with a sufficiently close annealing temperature. AdaAnn is easy to implement and can be integrated into existing sampling approaches such as normalizing flows for variational inference and Markov chain Monte Carlo. We demonstrate the computational efficiency of the AdaAnn scheduler for variational inference with normalizing flows on a number of examples, including posterior estimation of parameters for dynamical systems and probability density approximation in multimodal and high-dimensional settings.
Parallel Photonic Convolutional Processing on-Chip With Cross-Connect Architecture and Cyclic AWGs

Convolutional neural network (CNN) is one of the best neural network structures for solving classification problems. The convolutional processing of the network dominates processing time and computing power. Parallel computing for convolutional processing is essential to accelerate the computing speed of the neural network. In this paper, we introduce another domain of parallelism on top of the already demonstrated parallelisms suggested for photonic integrated processors with WDM approaches, to further accelerate the convolutional operation on chip. The operation of the novel parallelism is introduced with an updated cross-connect architecture, exploiting cyclic array waveguide grating. The photonic CNN system is demonstrated for the handwritten digit classification problem in simulation, with a speed of 2.56 Tera operation/s and end-to-end system energy efficiency of 3.75 pJ/operation, using 16 weighting elements and 10 Giga sample/s inputs. The proposed parallelism improves CNN acceleration by 4-16 times with respect to state-of-the-art integrated convolutional processors, depending on the available weighting elements per convolutional core.
Two-phase self-supervised pretraining for object re-identification

Self-supervised pretraining (e.g., MAE) based on reconstructing masked image patches has become increasingly popular in recent months. It commonly pretrains models on the large-scale ImageNet dataset with a considerably wider range of image categories, and then finetunes on downstream tasks such as classification, detection, segmentation, etc. However, we discover that for finetuning domain-specific tasks usually with a few or limited semantic spaces (e.g., object re-identification (ReID)), the model pretrained on a wider variety of sources (e.g., ImageNet) fails to be a good initialization. To address the problem, we propose a second-phase self-supervised pretraining on the specific domain, based on the self-supervised ImageNet pretraining models in the first phase. We perform studies on 2 domains of object ReID, i.e., person and vehicle ReID, where person ReID domain involves four datasets (Market-1501, DukeMTMC, MSMT17, Occluded-Duke) and vehicle ReID domain contains 2 datasets (VeRi-776, VehicleID). Through extensive experiments, we observe significant improvements in every single dataset based on the second-phase self-supervised pretraining over the entire unlabeled domain data. Specifically in person ReID task, compared to the self-supervised model in the first phase, our approach achieves +8.5%/+11.5%/+13.3%/+17.2% mAP improvements in Market-1501, DukeMTMC, MSMT17 and Occluded-Duke datasets, respectively. Furthermore, it also outperforms the supervised pretraining baselines, achieving improved performance by +1.5%/+4.3%/+3.9%/+4.2% mAP. We observe similar inspiring improvements in vehicle ReID tasks. Additionally, we also perform the second-phase pretraining on the specific tasks using its own unlabeled data, namely task-specific pretraining (TSP), which also consistently shows encouraging results across object ReID tasks.
A multi-unmanned aerial vehicle fast path-planning method based on non-rigid hierarchical discrete grid voxel environment modeling

Multi-unmanned aerial vehicle (multi-UAV) path planning involves determining no-conflicting paths for multiple UAVs. Given the shortcomings of existing planning algorithms, such as number limitations and low efficiency, we studied an undirected graph environment construction method based on a non-rigid grid data model to unify conflict descriptions and simplify the conflict detection process. Accordingly, we propose Conflict-based Objective-oriented Prioritization (CBOP), a fast multi-UAV path-planning algorithm. CBOP takes the whole UAV as the resolution unit in the high-level solver and uses priority calculation to avoid massive branch calculations. Experiments show that: (1) Compared with the voxel environment constructed by the rigid grid data model, adopting our undirected graph can reduce the planning time by 6.7% and the planned path length by 7.4% on average when the number of UAVs exceeds 100. (2) Compared with the Conflict-based Search with heuristic (CBSH), one of the most effective variants of CBS, the CBOP is more suitable for solving large-scale problems. The efficiency can be increased by 35.1 times on average when the number of UAVs is between 100 and 500.
Optimal mining in proof-of-work blockchain protocols

This paper examines the economic mechanism of cryptocurrency mining. By presenting a profit function, a maximization equilibrium is obtained. The model provides a formal approach to the demand for hashing power as a function of revenues, mining costs and the number of miners. We consider how the equilibrium is affected by passive miners. We use these results to introduce a formulation of the price elasticity of the demand for hashing power with respect to the cost of energy. The model is simulated using Reinforcement Learning algorithms that arrive to similar equilibrium results. The article concludes with implications of the model for policymaking.
Assessing the feasibility of Blockchain Technology in Automotive Industry

Blockchain is a decentralised, distributed public ledger that has recently garnered enormous traction. The automotive industry, one of the most profitable markets worldwide, is undoubtedly responsible for the transformations in modern civilisation over the past century. This paper primarily focuses on the issue of the complexity of existing technologies in the field and the possibility of applying Blockchain technology as a replacement in the automotive sector. In addition, the researcher has considered compatible precedents for identifying lacunae in the existing technologies. As a result, the adoption of Blockchain technology provides access to a wide range of potential automotive applications that can develop modern business models moreover even transform the established automotive partaking economy. Further, after examining the advantages, limitations, opportunities, and risks, some suggestions are made to guide enterprises and researchers in future advancements in the cyber-resilient automotive sector.
Automatic Speaker Verification Spoof Detection and Countermeasures Using Gaussian Mixture Model

Automatic Speaker Verification (ASV) is an emerging biometric authentication technique with the process of accepting/rejecting the users' claimed identity based on his/her speech samples. Robust countermeasures for spoofing attack detections are required to secure biometric systems from intruders. Anti-spoofing is also called replay detection in which voice is recorded, stored and replayed to deceive ASV systems. The ASVspoof series of challenge provides a shared anti-spoofing attack, ASVspoof 2019 focused on both synthetic and replay speech that are referred to as physical and logical access attacks, respectively. To build the robust system, we considered separate data for bonafide and spoofed voice data and implemented separate models for both classes. We addressed our system based on Gaussian Mixture Model, which is performed on ASVspoof 2019 Database. Finally, the experiments focused on both MFCC features and machine learned features have a comparable results with an equal error rate (EER) of 5.64% and 7.56 %.
Solar Photovoltaic Energy Forecasting Using Machine Learning and Deep Learning Technique

The whole world is going through electrical fuel transition, from traditional to renewable energy (RE) sources. Natural resources like coal, natural gas, fossil fuels are still dominant energy sources to produce electrical energy throughout the world. If the switching towards RE source does not take place, these natural sources will deplete sooner, and heavy energy crises will come into picture. The paper addresses the issue of forecasting short-term renewable energy supply. The stochastic nature of RE sources has an impact on power system planning procedures, lowering the reliability as well as security of power supply for end users [1]. In this paper solar photovoltaic (PV) energy forecasting is performed using two dependent data variables such as (a) solar irradiance and (b) temperature, and past solar PV energy output using machine learning and deep learning (DL) algorithms. DL is a kind of complex learning inspired by human learning. Long Short Term Memory (LSTM) network and Gated Recurrent Unit (GRU) network are the examples of it. The paper investigates the issue of identifying features and determining suitable error metrics. DL model was developed and tested on real solar PV energy produced on MNNIT Allahabad, India campus. The forecasting performance of developed models is evaluated in terms of three important measures, (a) mean absolute error (MAE), (b) mean squared error (MSE), and (c) root mean square error (RMSE).
Assess students’ knowledge database formation

This article aims to develop a database of student knowledge assessment using modern software tools that are user-friendly and understandable based on demand and supply. Also, the task of this database is to create a convenient environment for evaluating students’ knowledge and obtaining the necessary information.
Estimating the Longest Increasing Subsequence in Nearly Optimal Time

Longest Increasing Subsequence (LIS) is a fundamental statistic of a sequence, and has been studied for decades. While the LIS of a sequence of length n can be computed exactly in time $O(n\log n)$, the complexity of estimating the (length of the) LIS in sublinear time, especially when LIS $\ll n$, is still open. We show that for any $n\in\mathbb{N}$ and $\lambda=o(1)$, there exists a (randomized) non-adaptive algorithm that, given a sequence of length n with LIS $\geq\lambda n$, approximates the LIS up to a factor of $1/\lambda^{o(1)}$ in $ n^{o(1)}/\lambda$ time. Our algorithm improves upon prior work substantially in terms of both approximation and run-time: (i) we provide the first sub-polynomial approximation for LIS in sub-linear time; and (ii) our run-time complexity essentially matches the trivial sample complexity lower bound of $\Omega(1/\lambda)$, which is required to obtain any non-trivial approximation of the LIS. As part of our solution, we develop two novel ideas which may be of independent interest. First, we define a new Genuine-LIS problem, in which each sequence element may be either genuine or corrupted. In this model, the user receives unrestricted access to the actual sequence, but does not know a priori which elements are genuine. The goal is to estimate the LIS using genuine elements only, with the minimal number of tests for genuineness. The second idea, Precision Tree, enables accurate estimations for composition of general functions from “coarse” (sub-)estimates. Precision Tree essentially generalizes classical precision sampling, which works only for summations. As a central tool, the Precision Tree is pre-processed on a set of samples, which thereafter is repeatedly used by multiple components of the algorithm, improving their amortized complexity.
Almost Ramanujan Expanders from Arbitrary Expanders via Operator Amplification

We give an efficient algorithm that transforms any bounded degree expander graph into another that achieves almost optimal (namely, near-quadratic, $d\leq 1/\lambda^{2+o(1)}$) trade-off between (any desired) spectral expansion $\lambda$ and degree d. Furthermore, the algorithm is local: every vertex can compute its new neighbors as a subset of its original neighborhood of radius $O(\log(1/\lambda))$. The optimal quadratic trade-off is known as the Ramanujan bound, so our construction gives almost Ramanujan expanders from arbitrary expanders. The locality of the transformation preserves structural properties of the original graph, and thus has many consequences. Applied to Cayley graphs, our transformation shows that any expanding finite group has almost Ramanujan expanding generators. Similarly, one can obtain almost optimal explicit constructions of quantum expanders, dimension expanders, monotone expanders, etc., from existing (suboptimal) constructions of such objects. Another consequence is a “derandomized” random walk on the original (suboptimal) expander with almost optimal convergence rate. Our transformation also applies when the degree is not bounded or the expansion is not constant. We obtain our results by a generalization of Ta-Shma’s technique in his breakthrough paper [STOC 2017], used to obtain explicit almost optimal binary codes. Specifically, our spectral amplification extends Ta-Shma’s analysis of bias amplification from scalars to matrices of arbitrary dimension in a very natural way. Curiously, while Ta-Shma’s explicit bias amplification derandomizes a well-known probabilistic argument (underlying the Gilbert-Varshamov bound), there seems to be no known probabilistic (or other existential) way of achieving our explicit (high-dimensional”) spectral amplification.