Reviewerly/exHarmony huggingface dataset:
Montgomery Reduction for Gaussian Integers

Modular arithmetic over integers is required for many cryptography systems. Montgomery reduction is an efficient algorithm for the modulo reduction after a multiplication. Typically, Montgomery reduction is used for rings of ordinary integers. In contrast, we investigate the modular reduction over rings of Gaussian integers. Gaussian integers are complex numbers where the real and imaginary parts are integers. Rings over Gaussian integers are isomorphic to ordinary integer rings. In this work, we show that Montgomery reduction can be applied to Gaussian integer rings. Two algorithms for the precision reduction are presented. We demonstrate that the proposed Montgomery reduction enables an efficient Gaussian integer arithmetic that is suitable for elliptic curve cryptography. In particular, we consider the elliptic curve point multiplication according to the randomized initial point method which is protected against side-channel attacks. The implementation of this protected point multiplication is significantly faster than comparable algorithms over ordinary prime fields.
Optimal Clustering in Stable Instances Using Combinations of Exact and Noisy Ordinal Queries

This work studies clustering algorithms which operates with ordinal or comparison-based queries (operations), a situation that arises in many active-learning applications where “dissimilarities” between data points are evaluated by humans. Typically, exact answers are costly (or difficult to obtain in large amounts) while possibly erroneous answers have low cost. Motivated by these considerations, we study algorithms with non-trivial trade-offs between the number of exact (high-cost) operations and noisy (low-cost) operations with provable performance guarantees. Specifically, we study a class of polynomial-time graph-based clustering algorithms (termed Single-Linkage) which are widely used in practice and that guarantee exact solutions for stable instances in several clustering problems (these problems are NP-hard in the worst case). We provide several variants of these algorithms using ordinal operations and, in particular, non-trivial trade-offs between the number of high-cost and low-cost operations that are used. Our algorithms still guarantee exact solutions for stable instances of k-medoids clustering, and they use a rather small number of high-cost operations, without increasing the low-cost operations too much.
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization

Most reinforcement learning (RL) algorithms assume online access to the environment, in which one may readily interleave updates to the policy with experience collection using that policy. However, in many real-world applications such as health, education, dialogue agents, and robotics, the cost or potential risk of deploying a new data-collection policy is high, to the point that it can become prohibitive to update the data-collection policy more than a few times during learning. With this view, we propose a novel concept of deployment efficiency, measuring the number of distinct data-collection policies that are used during policy learning. We observe that naively applying existing model-free offline RL algorithms recursively does not lead to a practical deployment-efficient and sample-efficient algorithm. We propose a novel model-based algorithm, Behavior-Regularized Model-ENsemble (BREMEN), that not only performs better than or comparably as the state-of-the-art dynamic-programming-based and concurrently-proposed model-based offline approaches on existing benchmarks, but can also effectively optimize a policy offline using 10-20 times fewer data than prior works. Furthermore, the recursive application of BREMEN achieves impressive deployment efficiency while maintaining the same or better sample efficiency, learning successful policies from scratch on simulated robotic environments with only 5-10 deployments, compared to typical values of hundreds to millions in standard RL baselines.
Analysis of influencing factors and teaching reform of nuclear professional English based on logistic regression

Abstract Nuclear professional English course can help students of nuclear energy engineering major to understand the corresponding course content accurately and effectively while learning and researching frontier topics. This course has an important guiding role for their future development. This experiment obtained a series of data from students who had completed the course by questionnaire survey, and logistic regression algorithm was used to analyse the influencing factors of professional English test, then obtained the P value. The significant relationship between the independent variable and the dependent variable can be seen from the P value, thus reflecting the problems existing in the current course. The odds ratio (OR value) value of 0.030 is the biggest influence on the passing rate of the examination factor. In the questionnaire survey on the distribution of students’ intention, 96.25% of the students hope to have a new teaching mode, while 38.10% of the students do not take the initiative to read English books.
Cyber Situational Awareness in Critical Infrastructure Protection

The European Union promotes collaboration between authorities and the private sector, and the providers of the most critical services to society face security related obligations. In this paper, critical infrastructure is seen as a system of systems that can be subject to cyber-attacks and other disturbances. Situational awareness (SA) enhances preparations for and decision-making during assessed and unforeseen disruptive incidents, and promoting Cyber effective situational awareness (CSA) requires information sharing between the different interest groups. This research is constructive in nature, where innovative constructions developed as solutions for domain-specific real world problems, while the research question is: “How can cyber situational awareness protect critical infrastructures?” The Observe – Orient – Decide – Act (OODA) loop is examined as a way to promote collaboration towards a shared situational picture, awareness and understanding to meet challenges of forming CSA in relation to risk assessment (RA) and improving resilience. Three levels of organizational decision-making are examined in relation a five-layer cyber structure of an organization to provide a more comprehensive systems view of organizational cyber security. Successful, crisis-management efforts enable organizations to sustain and resume operations, minimize losses, and adapt to manage future incidents, as many critical infrastructures typically lack resilience and may easily lose essential functionality when hit by an adverse event. Situation awareness is the main prerequisite towards cyber security. Without situation awareness, it is impossible to systematically prevent, identify, and protect the system from cyber incidents.
Hybrid Quantum-Classical Eigensolver without Variation or Parametric Gates

The use of near-term quantum devices that lack quantum error correction, for addressing quantum chemistry and physics problems, requires hybrid quantum-classical algorithms and techniques. Here we present a process for obtaining the eigenenergy spectrum of electronic quantum systems. This is achieved by projecting the Hamiltonian of a quantum system onto a limited effective Hilbert space specified by a set of computational bases. From this projection an effective Hamiltonian is obtained. Furthermore, a process for preparing short depth quantum circuits to measure the corresponding diagonal and off-diagonal terms of the effective Hamiltonian is given, whereby quantum entanglement and ancilla qubits are used. The effective Hamiltonian is then diagonalized on a classical computer using numerical algorithms to obtain the eigenvalues. The use case of this approach is demonstrated for ground sate and excited states of BeH$_2$ and LiH molecules, and the density of states, which agrees well with exact solutions. Additionally, hardware demonstration is presented using IBM quantum devices for H$_2$ molecule.
Cloud platform security protection framework technology

The cloud platform is the company's new generation of information basic platform, and it is an important support for driving the company's IT and data resource construction, application and operation and maintenance to the enterprise level. This paper proposes a security protection framework model in the cloud environment, combined with the power cloud platform framework, design The security protection framework system applicable to electric power companies elaborated the key technologies and system architecture of security protection in the cloud environment. Finally, based on the power cloud platform architecture, the feasibility and reliability of cloud platform security protection were verified. Finally, summarize the paper and point out further work.
Image defogging method combining light field depth estimation and dark channel

In order to improve the defogging effect of fogged images, the traditional dark channel prior theory is easy to cause halo effect in abrupt edge region and color distortion in sky area. In this paper, an image defogging method combining light depth estimation with dark channel theory is proposed. Firstly, block and point transmittance estimation of dark channel are used for multiscale fusion. Secondly, the final transmittance is obtained by combining the transmittance calculated from the depth information of light scene and the transmittance of dark channel. Finally, the sky region is segmented by using the depth information of light scene, and the atmospheric light is obtained in the sky area. Experimental results show that, compared with the traditional de fogging algorithm, the proposed algorithm is optimal in both subjective and objective evaluation.
THE INFLUENCE OF GENDER ON READING COMPREHENSION

This study deals with the effect of gender differences on reading comprehension. The purpose is to answer the question: Does female students perform more significantly on reading comprehension of neutral text than do male students? The study that was conducted in the academic year 2019/2020, is pre-experimental in nature intact group design, and involved 60 grade students purposely selected based on their gender as the sample. They are then assigned into two treatment groups. The data are collected using an objective type test in multiple-choice format, and are analyzed statistically using t-test for independent sample. Result of the analysis showed that at p .05 and df 58, the t-observed is larger than t-table [3.333 ≥ 2.000). This indicates that female students outperformed their male counterparts in reading comprehension. In other words, it can be stated that gender differences affect reading comprehension. Keywords: gender, reading comprehension, neutral text, EFL
Output gap determinants in Ethiopia

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An Improved wNAF Scalar-Multiplication Algorithm With Low Computational Complexity by Using Prime Precomputation

To reduce the computational complexity of scalar multiplication, an improved wNAF scalar-multiplication algorithm is proposed by using prime precomputation. It replaces odd number with prime number in the precomputation stage, and makes up the difference between prime and odd by building the tiny multi-base chains. Thanks to the proposed algorithm, the computational complexity, the cost of precomputation and the creation time of addition chains are all reduced. Compared with the wNAF, the computational complexity and the running speed are optimized by 28.37% and 77.38% respectively at the fittest case. Also, lower computational complexity and higher running speed are outstanding features of the introduced algorithm than other existing algorithms.
Feature Selection using Simulated Annealing with Optimal Neighborhood Approach

Abstract The one of the metaheuristic approaches that can be used was simulated annealing (SA) algorithm which inspired by annealing metallurgical process. This algorithm shows advantages in finding global optimum of given function which will be used in feature selection. In this study, we will trying to combine the neighborhood size and limited approach by using data simulation comparing between two function which is Akaike Index Criterion (AIC) function and Bayesian Index Criterion (BIC) function. The result of this experiment shows that the selected variables using optimal neighborhood size and limit the selected variable provide the result of goodness model around 98% of accuracy and specificity and 94% of sensitivity compared with simulated annealing algorithms without any modification using both AIC function and BIC function, and in the simulation also shows that BIC function give better result than AIC function.
Improving Randomness of Symmetric Encryption for Consumer Privacy Using Metaheuristic-Based Framework

In order to maintain data privacy and security for consumers, data encryption is closely related to our daily lives. Symmetric encryption is currently the most widely used encryption method in consumer electronics fields, especially in services involving Internet-of-Thing devices due to easy usage, and quick and lower computing costs. However, most of symmetric encryptions adopt a quite linear key generation scheme so that the generated keys still have higher opportunity to be cracked. In this article, we define a model to identify the generated key's randomness and further propose a novel metaheuristic-based key generation framework to improve the symmetric encryption drawbacks.
Analysis Photovoltaic System in Relation to Tracking and Non-Tracking System

The increasing demand of electricity has been a great concern in recent years. The increasing demand and environmental(global warming) issues urged scientists to evolve in the field of renewable energy. Solar energy is one of the majorsources of renewable energy. Electrical energy is produced by photovoltaic cells when they allow light particles to knockfree electrons from atoms. The amount of electrical output produced by the system is dependent on amount of solarenergy received by PV cells. To increase solar energy output, a fixed solar panel inclined towards the optimal point isusually used. The collection of solar energy is increased by using solar tracking systems i.e. single axis or dual axis, whichcontinuously track the sun using incidence angle of sunlight. The analysis is carried out to compare the performancebetween tracking and non-tracking photovoltaic systems. Data of specific solar panel systems is analysed and comparedwith simulations and actual outputs to compute performance ratios and deduce conclusions. The average performanceratio is found out to be 0.73 for non-tracking system and 0.90 (17% more than non-tracking systems) for trackingsystems. The accuracy of estimated output of a PV system can be improved by using more accurate solar irradiance data,accurate weather conditions, exact system losses and matched inverter efficiency. The efficiency of a PV system can beimproved by using solar trackers, using more efficient solar panels, installing them in a less shaded area, cleaning thepanels on regular intervals, and using more efficient electricalcomponents.
Analysis on RAMS Information for Metro Vehicles Using Natural Language Processing Algorithm: Evidence From China

Abstract The RAMS information of rail vehicles is an important data for the operation and maintenance of rail transit, and is the key to improving the performance and reliability level of rail vehicle equipment. At present, there are a large number of colloquial, hybridized and subjective data records in the RAMS information of metro trains; especially in the vehicle fault record, this phenomenon is widespread, which brings great difficulties to subsequent data analysis. Therefore, how to convert these irregular fault records into computer-readable fault texts is of great significance. This paper proposes the RAMS information structuring algorithm based on the Jieba word segmentation and Doc2Vec technology. Besides, we compile a professional dictionary of metro vehicles and a standard fault statement library for metro vehicles.
Super Learning with Repeated Cross Validation

Super learner algorithm was created to combine results of multiple base learners with the use of cross validation. However, in many cases it does not outperform significantly a simple average of the base results. We propose to apply multiple repeats of cross validation to improve the performance of super learning. Two approaches to application of repeated cross validation were tested on artificial data sets and on real-life, biomedical data sets. One of the approaches, MEAN OUTPUT strategy, proved to significantly improve the results. To reduce the computational complexity of the algorithm, we suggest the use of 3-fold, rather than the previously recommended 10-fold validation. The tests showed, that this simplification does not affect the super learning results.
Property Controllable Variational Autoencoder via Invertible Mutual Dependence

Deep generative models have made important progress towards modeling complex, high dimensional data via learning latent representations. Their usefulness is nevertheless often limited by a lack of control over the generative process or a poor understanding of the latent representation. To overcome these issues, attention is now focused on discovering latent variables correlated to the data properties and ways to manipulate these properties. This paper presents the new Property controllable VAE (PCVAE), where a new Bayesian model is proposed to inductively bias the latent representation using explicit data properties via novel group-wise and property-wise disentanglement. Each data property corresponds seamlessly to a latent variable, by innovatively enforcing invertible mutual dependence between them. This allows us to move along the learned latent dimensions to control specific properties of the generated data with great precision. Quantitative and qualitative evaluations confirm that the PCVAE outperforms the existing models by up to 28% in capturing and 65% in manipulating the desired properties.
Evaluation of the Use of Artificial Neural Networks to Predict the Photovoltaic Power Generation Factors by Using Feed Forward Back Propagation (FFBP) Technique

The solar power generation (renewable energy) is the cleanest form of energy generation method and the solar power plant has a very long life and also is maintenance-free, but due to the high unpredictability of the generated solar power due to dynamically changing environmental factors it cannot be used as the reliable source of power. This prevents the maximum utilization of solar energy. In this project we are designing the artificial neural network model to predict the power generated depending on the various environmental factors like visibility, cloud cover (sky cover), etc. the intensity of the incident of the solar radiation decreases and thus the plant is not able to work at its rated capacity. We use Artificial Neural Network (ANN) with Feed Forward Back Propagation (FFBP) technique and predicted the percentage of the maximum plant capacity which will be generated by considering the environmental factors like temperature, pressure, distance to solar noon, day light, sky cover, visibility, humidity, wind speed, wind direction and compared our results with available data and find quite encouraging results.
Sequential Generative Exploration Model for Partially Observable Reinforcement Learning

Many challenging partially observable reinforcement learning problems have sparse rewards and most existing model-free algorithms struggle with such reward sparsity. In this paper, we propose a novel reward shaping approach to infer the intrinsic rewards for the agent from a sequential generative model. Specifically, the sequential generative model processes a sequence of partial observations and actions from the agent's historical transitions to compile a belief state for performing forward dynamics prediction. Then we utilize the error of the dynamics prediction task to infer the intrinsic rewards for the agent. Our proposed method is able to derive intrinsic rewards that could better reflect the agent's surprise or curiosity over its ground-truth state by taking a sequential inference procedure. Furthermore, we formulate the inference procedure for dynamics prediction as a multi-step forward prediction task, where the time abstraction that has been incorporated could effectively help to increase the expressiveness of the intrinsic reward signals. To evaluate our method, we conduct extensive experiments on challenging 3D navigation tasks in ViZDoom and DeepMind Lab. Empirical evaluation results show that our proposed exploration method could lead to significantly faster convergence than various state-of-the-art exploration approaches in the testified navigation domains.
The $$2^\mathrm{nd}$$ 106-Point Lightweight Facial Landmark Localization Grand Challenge

Facial landmark localization has been applied to numerous face related applications, such as face recognition and face image synthesis. It is a very crucial step for achieving high performance in these applications. We host the \(2^\mathrm{nd}\) 106-point lightweight facial landmark localization grand challenge in conjunction with ICPR 2020. The purpose is to make effort towards benchmarking lightweight facial landmark localization, which enables efficient system deployment. Compared with the \(1^\mathrm{st}\) grand challenge (https://facial-landmarks-localization-challenge.github.io/), the JD-landmark-v2 dataset contains more than 24,000 images with larger variations in identity, pose, expression and occlusion. Besides, strict limits of model size (\(\le \)20M) and computational complexity (\(\le \)1G Flops) are employed for computational efficiency. The challenge has attracted attention from academia and industrial practitioners. More than 70 teams participate in the competition, and nine of them involve in the final evaluation. We give a detailed introduction of the competition and the solution by the winners in this paper.
A Lightweight Secure Image Super Resolution using Network Coding

Images play an important part in our daily life.They convey our personal stories and maintain meaningful objects, events, emotions etc. People, therefore, mostly use images as visual information for their communication with each other.Data size and privacy are, however, two of important aspects whilst transmitting data through network like internet, i.e. the time prolongs when the amount of data are increased and the risk of exposing private data when being captured and accessed by irrelevant people.In this paper, we introduce a unified framework, namely Deep-NC, to address these problems seamlessly.Our method contains three important components: the first component, adopted from Random Linear Network Coding (RLNC), to protect the sharing of private image from the eavesdropper; the second component to remove noise causing to image data due to transmission over wireless media; and the third component, utilising Image Super-Resolution (ISR) with Deep Learning (DL), to recover high-resolution images from low-resolution ones due to image sizes reduced.This is a general framework in which each component can be enhanced by sophisticated methods.Simulation results show that an outperformance of up to 32 dB, in terms of Peak Signal-to-Noise Ratio (PSNR), can be obtained when the eavesdropper does not have any knowledge of parameters and the reference image used in the mixing schemes.Various impacts of the method are deeply evaluated to show its effectiveness in securing transmitted images.Furthermore, the original image is shown to be able to downscale to a much lower resolution for saving significantly the transmission bandwidth with negligible performance loss.