Reviewerly/exHarmony huggingface dataset:
Data-driven prediction of a multi-scale Lorenz 96 chaotic system using deep learning methods: Reservoir computing, ANN, and RNN-LSTM

In this paper, the performance of three deep learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multi-scale spatio-temporal Lorenz 96 system is examined. The methods are: echo state network (a type of reservoir computing, RC-ESN), deep feed-forward artificial neural network (ANN), and recurrent neural network with long short-term memory (RNN-LSTM). This Lorenz 96 system has three tiers of nonlinearly interacting variables representing slow/large-scale ($X$), intermediate ($Y$), and fast/small-scale ($Z$) processes. For training or testing, only $X$ is available; $Y$ and $Z$ are never known or used. We show that RC-ESN substantially outperforms ANN and RNN-LSTM for short-term prediction, e.g., accurately forecasting the chaotic trajectories for hundreds of numerical solvers time steps, equivalent to several Lyapunov timescales. The RNN-LSTM and ANN show some prediction skills as well; RNN-LSTM bests ANN. Furthermore, even after losing the trajectory, data predicted by RC-ESN and RNN-LSTM have probability density functions (PDFs) that closely match the true PDF, even at the tails. The PDF of the data predicted using ANN, however, deviates from the true PDF. Implications, caveats, and applications to data-driven and data-assisted surrogate modeling of complex nonlinear dynamical systems such as weather/climate are discussed.
A Brief Explanation of Basic Science Education

This work is to explains basic science education that is focused exclusively on ‘why’ and ‘how’ aspect.
Handwritten Text Recognition using Machine Learning Techniques in Application of NLP

Handwriting Detection is a technique or ability of a Computer to receive and interpret intelligible handwritten input from source such as paper documents, touch screen, photo graphs etc. Handwritten Text recognition is one of area pattern recognition. The purpose of pattern recognition is to categorizing or classification data or object of one of the classes or categories. Handwriting recognition is defined as the task of transforming a language represented in its spatial form of graphical marks into its symbolic representation. Each script has a set of icons, which are known as characters or letters, which have certain basic shapes. The goal of handwriting is to identify input characters or image correctly then analyzed to many automated process systems. This system will be applied to detect the writings of different format. The development of handwriting is more sophisticated, which is found various kinds of handwritten character such as digit, numeral, cursive script, symbols, and scripts including English and other languages. The automatic recognition of handwritten text can be extremely useful in many applications where it is necessary to process large volumes of handwritten data, such as recognition of addresses and postcodes on envelopes, interpretation of amounts on bank checks, document analysis, and verification of signatures. Therefore, computer is needed to be able to read document or data for ease of document processing.
TensorFlow.js

Tensorflow.js is an open source JavaScript library for machine learning. It is developed by Google and is a companion library to Tensorflow, in Python.
Exploring the Playground: Blockchain Prototype Use Cases with Hyperledger Composer

ABSTRACT While the accounting profession determines the possible future impacts of blockchain, universities will need to prepare students for a career using the disruptive technology. This necessitates hands-on training to teach the basics of modeling a blockchain network. Hyperledger is an open source blockchain advancement initiative hosted by The Linux Foundation (IBM n.d.). Hyperledger Composer is a specific Hyperledger project that provides a framework and necessary tools to facilitate new blockchain development. This proposal walks students through three use cases with the outcome of (1) familiarizing them with IBM's Hyperledger Composer (a virtual playground in which they can experiment with numerous blockchains), (2) orienting them to the underlying code that drives blockchains, and (3) improving their technological agility by directing them to manipulate the code and observe the functional effects.
Trends in digital game-based learning in the mobile era: a systematic review of journal publications from 2007 to 2016

The application of mobile learning and the use of game-based instructional strategies have been demonstrated as promoting students' learning performances and motivation. Over the past decade, many researchers and educators have integrated mobile technology and the features of gaming into their instructional designs; however, there has been little systematic review of the applications and trends in mobile technology-supported gaming. In the present study, related international journal publications from 2007 to 2016 were reviewed by searching the WOS database. The aim of this literature review was to highlight the research domain and issues of promoting game-based learning in future mobile technology. Based on this review, trends in mobile game-based learning are proposed from various aspects, such as the adoption of mobile technologies, gaming types, application domains, learning strategies, research issues, research methods, and participants. Accordingly, several suggestions are given for future studies.
How Much Should We Trust the Dictator's GDP Growth Estimates?

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
English To Hindi Translator Using Seq2seq Model

Research in Machine Translation has been going on since the 1950's. It began in the United States. Since then, we have come a long way in this field of research. Various algorithms have been derived and tested to fulfill the requirements for an ideal translation machine which functions in all the languages which are spread throughout the world. Any machine translation system's success depends on a solid translation algorithm. If there is a large quantity of space available, decoding will be sluggish, which is undesirable. There is a trade-off between the precision of the translation and the speed with which it is decoded. Basically, what the machine learning-based approach proposed does is to identify the input words and categorize them under a few metrics after which they are translated and rearranged to form the output. We then present the output in the desired language.
There's always a bigger fish

Machine learning has made it possible to mount powerful attacks through side channels that have traditionally been seen as challenging to exploit. However, due to the black-box nature of machine learning models, these attacks are often difficult to interpret correctly. Models that detect correlations cannot be used to prove causality or understand an attack's various sources of information leakage.
MATURE-Food: Food Recommender System for MAndatory FeaTURE Choices A system for enabling Digital Health

In order to deduce more appropriate and useful recommendation, it is imperative to amalgamate additional information such as the features of the user and the item along with the usual user-item correlation based on rating. In this research, a content-based recommender system, MATURE is further developed, which relies on the additional information related to the user-item to recommend the items for a user who has current mandatory needs to accommodate as opposed to the past preferences. The MATURE-driven recommendations mandatorily include and satisfy the user's mandatory requirements at a given time. This distinguishes it from the existing algorithms, where recommendations are primarily based on the past preferences and fail to satisfy current mandatory requirements of the user, which are opposite to the user's past preferences. Arguably, MATURE is first recommender system, which promisingly undertakes the given mandatory requirements while recommending the most appropriate item set with pertinent justification of the recommendation. This paper expands the applicability of MATURE and focuses on its application as a food recommender system with mandatory nutrient values, required to be consumed daily.
Explaining Artificial Intelligence Generation and Creativity: Human interpretability for novel ideas and artifacts

Creativity is often thought of as the pinnacle of human achievement, but artificial intelligence (AI) is now starting to play a central role in creative processes, whether autonomously or in collaboration with people. Widespread deployment is now pushing for explanations on how creative AI is working, whether to engender trust, enable action, provide a basis for evaluation, or for intrinsic reasons. In this article, we review various motivations, algorithms, and methods for explaining either the workings of generative/creative AI algorithms or the generative/creative artifacts they produce.
An embedded vertical‐federated feature selection algorithm based on particle swarm optimisation

Abstract In real life, a large amount of data describing the same learning task may be stored in different institutions (called participants), and these data cannot be shared among participants due to privacy protection. The case that different attributes/features of the same instance are stored in different institutions is called vertically distributed data. The purpose of vertical‐federated feature selection (FS) is to reduce the feature dimension of vertical distributed data jointly without sharing local original data so that the feature subset obtained has the same or better performance as the original feature set. To solve this problem, in the paper, an embedded vertical‐federated FS algorithm based on particle swarm optimisation (PSO‐EVFFS) is proposed by incorporating evolutionary FS into the SecureBoost framework for the first time. By optimising both hyper‐parameters of the XGBoost model and feature subsets, PSO‐EVFFS can obtain a feature subset, which makes the XGBoost model more accurate. At the same time, since different participants only share insensitive parameters such as model loss function, PSO‐EVFFS can effectively ensure the privacy of participants' data. Moreover, an ensemble ranking strategy of feature importance based on the XGBoost tree model is developed to effectively remove irrelevant features on each participant. Finally, the proposed algorithm is applied to 10 test datasets and compared with three typical vertical‐federated learning frameworks and two variants of the proposed algorithm with different initialisation strategies. Experimental results show that the proposed algorithm can significantly improve the classification performance of selected feature subsets while fully protecting the data privacy of all participants.
Security Optimization of Resource-Constrained Internet of Healthcare Things (IoHT) Devices Using Asymmetric Cryptography for Blockchain Network

The term “Internet of Things” is becoming increasingly popular and promising, ushering in a new era of smarter connectivity across billions of gadgets. In the foreseeable future, IoT's potential is boundless. The healthcare industry, often known as IoHT, is the most demanding application of IoT. Any healthcare-based IoT system starts with sensors, RFID, and smart tags, all of which are limited in terms of resources. When these devices are integrated, there is a significant need for safe information transformation since they carry sensitive patient information that might be extremely dangerous if it falls into the hands of an unauthorized person. The internet of things based on blockchain is a new technology that combines the benefits of both blockchain and cryptography to protect data at the physical layer. It is lightweight compared to other traditional approaches and does not compromise security levels, as the name implies. This paper explains how to safeguard data using elliptic curve cryptography for blockchain network to encrypt and store the data. On the basis of energy and memory efficiency, as well as latency, the proposed method is evaluated.
Orientations and the Supersingular Endomorphism Ring Problem

We study two important families of problems in isogeny-based cryptography and how they relate to each other: computing the endomorphism ring of supersingular elliptic curves, and inverting the action of class groups on oriented supersingular curves. We prove that these two families of problems are closely related through polynomial-time reductions, assuming the generalised Riemann hypothesis. We identify two classes of essentially equivalent problems. The first class corresponds to the problem of computing the endomorphism ring of oriented curves. The security of a large family of cryptosystems (such as CSIDH) reduces to (and sometimes from) this class, for which there are heuristic quantum algorithms running in subexponential time. The second class corresponds to computing the endomorphism ring of orientable curves. The security of essentially all isogeny-based cryptosystems reduces to (and sometimes from) this second class, for which the best known algorithms are still exponential. Some of our reductions not only generalise, but also strengthen previously known results. For instance, it was known that in the particular case of curves defined over $$\mathbf {F}_p$$ , the security of CSIDH reduces to the endomorphism ring problem in subexponential time. Our reductions imply that the security of CSIDH is actually equivalent to the endomorphism ring problem, under polynomial time reductions (circumventing arguments that proved such reductions unlikely).
Towards Reliable Online Just-in-Time Software Defect Prediction

Throughout its development period, a software project experiences different phases, comprises modules with different complexities and is touched by many different developers. Hence, it is natural that problems such as Just-in-Time Software Defect Prediction (JIT-SDP) are affected by changes in the defect generating process (concept drifts), potentially hindering predictive performance. JIT-SDP also suffers from delays in receiving the labels of training examples (verification latency), potentially exacerbating the challenges posed by concept drift and further hindering predictive performance. However, little is known about what types of concept drift affect JIT-SDP and how they affect JIT-SDP classifiers in view of verification latency. This work performs the first detailed analysis of that. Among others, it reveals that different types of concept drift together with verification latency significantly impair the stability of the predictive performance of existing JIT-SDP approaches, drastically affecting their reliability over time. Based on the findings, a new JIT-SDP approach is proposed, aimed at providing higher and more stable predictive performance (i.e., reliable) over time. Experiments based on ten GitHub open source projects show that our approach was capable of produce significantly more stable predictive performances in all investigated datasets while maintaining or improving the predictive performance obtained by state-of-art methods.
Incomplete Multi-view Clustering with Sample-level Auto-weighted Graph Fusion

Incomplete multi-view clustering (IMC) has received considerable attention due to its flexibility in fusing the multi-view information when the view samples are partly missing. However, existing methods seldom consider the affection of the missing samples to the contributions of the views. In this paper, we propose a novel graph fusion based IMC model (SAGF_IMC) to handle this problem. Instead of directly weighting the whole view, SAGF_IMC learns the sample-level auto weight, which allows considering both the contributions of different views and the affection of the missing samples. An effective iterative algorithm is developed, together with its convergence analysis. Experiments are provided to demonstrate that SAGF_IMC is superior to the related state-of-the-art methods by using several real-world datasets.
Design and application of improved sparrow search algorithm based on sine cosine and firefly perturbation

&lt;abstract&gt; &lt;p&gt;Swarm intelligence algorithms are relatively simple and highly applicable algorithms, especially for solving optimization problems with high reentrancy, high stochasticity, large scale, multi-objective and multi-constraint characteristics. The sparrow search algorithm (SSA) is a kind of swarm intelligence algorithm with strong search capability, but SSA has the drawback of easily falling into local optimum in the iterative process. Therefore, a sine cosine and firefly perturbed sparrow search algorithm (SFSSA) is proposed for addressing this deficiency. Firstly, the Tent chaos mapping is invoked in the initialization population stage to improve the population diversity; secondly, the positive cosine algorithm incorporating random inertia weights is introduced in the discoverer position update, so as to improve the probability of the algorithm jumping out of the local optimum and speed up the convergence; finally, the firefly perturbation is used to firefly perturb the sparrows, and all sparrows are updated with the optimal sparrows using the firefly perturbation method to improve their search-ability. Thirteen benchmark test functions were chosen to evaluate SFSSA, and the results were compared to those computed by existing swarm intelligence algorithms, as well as the proposed method was submitted to the Wilcoxon rank sum test. Furthermore, the aforesaid methods were evaluated in the CEC 2017 test functions to further validate the optimization efficiency of the algorithm when the optimal solution is not zero. The findings show that SFSSA is more favorable in terms of algorithm performance, and the method's searchability is boosted. Finally, the suggested algorithm is used to the locating problem of emergency material distribution centers to further validate the feasibility and efficacy of SFSSA.&lt;/p&gt; &lt;/abstract&gt;
Multiview feature augmented neural network for knowledge graph embedding

Link prediction in knowledge graph embedding is a meaningful research topic. Knowledge graph embedding (KGE) focuses on the problem of predicting missing links based on triples. Neural networks have been the paradigm of choice in knowledge graph tasks. However, the general network KGE models lack attention to the spatial location connection between entities and relations and have a weakness in that they cannot capture global information. We found that multiview feature construction can obtain more feature information corresponding to entities and relations. Aggregation of multiple types of multiview spatial transform information is a critical issue. Therefore, we propose a knowledge graph embedding method called the multiview feature augmented neural network (MFAE), which involves three components: multiview spatial transform, feature fusion convolution and feature information augmentation. To precisely augment the fusion of the vector spatial transform, a feature augmented convolutional network with attentive information calculation is introduced as a triple prediction. The multiview spatial transform is combined with a feature augmented convolutional network, which captures global feature information, obtains multiple views of entity and relation information, and improves the effectiveness of KGE. We conduct extensive experiments on link prediction, the effect of different views and feature augmented neural network comparison on benchmark datasets such as FB15k-237 and WN18RR. Experiments show that MFAE delivers significant performance compared to the classical link prediction methods.
BP-triplet net for unsupervised domain adaptation: A Bayesian perspective

Triplet loss, one of the deep metric learning (DML) methods, is to learn the embeddings where examples from the same class are closer than examples from different classes. Motivated by DML, we propose an effective BP-triplet Loss for unsupervised domain adaption (UDA) from the perspective of Bayesian learning and we name the model as BP-Triplet Net. In previous metric learning based methods for UDA, sample pairs across domains are treated equally, which is not appropriate due to the domain bias. In our work, considering the different importance of pair-wise samples for both feature learning and domain alignment, we deduce our BP-triplet loss for effective UDA from the perspective of Bayesian learning. Our BP-triplet loss adjusts the weights of pair-wise samples in intra-domain and inter-domain. Especially, it can self attend to the hard pairs (including hard positive pair and hard negative pair). Together with the commonly used adversarial loss for domain alignment, the quality of target pseudo labels is progressively improved. Our method achieved low joint error of the ideal source and target hypothesis. The expected target error can then be upper bounded following Ben-David's theorem. Comprehensive evaluations on four benchmark datasets demonstrate the effectiveness of the proposed approach for UDA. Code is available at https://github.com/wangshanshanAHU/BP-Triplet-Net.
From Understanding the Population Dynamics of the NSGA-II to the First Proven Lower Bounds

Due to the more complicated population dynamics of the NSGA-II, none of the existing runtime guarantees for this algorithm is accompanied by a non-trivial lower bound. Via a first mathematical understanding of the population dynamics of the NSGA-II, that is, by estimating the expected number of individuals having a certain objective value, we prove that the NSGA-II with suitable population size needs $\Omega(Nn\log n)$ function evaluations to find the Pareto front of the OneMinMax problem and $\Omega(Nn^k)$ evaluations on the OneJumpZeroJump problem with jump size $k$. These bounds are asymptotically tight (that is, they match previously shown upper bounds) and show that the NSGA-II here does not even in terms of the parallel runtime (number of iterations) profit from larger population sizes. For the OneJumpZeroJump problem and when the same sorting is used for the computation of the crowding distance contributions of the two objectives, we even obtain a runtime estimate that is tight including the leading constant.
Structure- and Texture-Aware Learning for Low-Light Image Enhancement

Structure and texture information is critically important for low-light image enhancement, in terms of stable global adjustment and fine details recovery. However, most existing methods tend to learn the structure and texture of low-light images in a coupled manner, without well considering the heterogeneity between them, which challenges the capability of the model to learn both adequately. In this paper, we tackle this problem in a divide and conquer strategy, based on the observation that the structure and texture representations are highly separated in the frequency spectrum. Specifically, we propose a Structure and Texture Aware Network (STAN) for low-light image enhancement, which consists of a structure sub-network and a texture sub-network. The former exploits the low-pass characteristic of the transformer to capture low-frequency-related structural representation. While the latter builds upon central difference convolution to capture high-frequency-related texture representation. We establish the Multi-Spectrum Interaction (MSI) module between two sub-networks to bidirectionally provide complementary information. In addition, to further elevate the capability of the model, we introduce a dual distillation scheme that assists the learning process of two sub-networks via counterparts' normal-light structure and texture representations. Comprehensive experiments show that the proposed STAN outperforms the state-of-the-art methods qualitatively and quantitatively.