Reviewerly/exHarmony huggingface dataset:
Role of information literacy in higher education during recent global pandemic: An overview

This paper discusses the concept information literacy, its role in higher education and the importance associated with information literacy in times of recent global health pandemic. Information is taking a new form each day and is continuously evolving with the changing world. People should become aware and make informed choices as a user of information. The use of information literacy helps an individual to recognize when information is required, how it can be located, evaluated and used productively. The entire education system around the globe faced the wrath of the lockdown period triggered due to the spread of Novel Corona virus (nCov-19). The intended purpose of this paper is to address the issues pertaining to information literacy in Higher Education Institutions (HEIs) during pandemic, to understand the role and usefulness of information literacy in times when there is a difficulty in moving ahead with the traditional student-teacher learning system.
Implementation of the Support Vector Machine (SVM) Algorithm in Classifying Website Phishing

The development of the internet is so significant, if we look at the growth of the internet in the world, it has reached more than 4 billion and in Indonesia, there are more than 171 million users out of a total population of more than 273 million people. This is due to the very fast development of information technology and various kinds of media and functions. However, of the advances in internet technology, it did not escape the existing internet attacks. One of them is phishing. Phishing is a form of activity that threatens or traps someone with the concept of luring that person. Namely by tricking someone so that the person indirectly provides all the information the trapper needs. Phishing is included in cybercrime, where crime is rampant through computer networks. Along with the times, crime is also increasingly widespread throughout the world. So that the threats that are happening today are also via computers. With such cases, this study aims to predict phishing sites with a classification algorithm. One of them is by using the SVM (Support Vector Machine) Algorithm. This research was conducted by classifying the phishing website data set and then calculating the accuracy for each kernel. From the study, the results are SVM with Gaussian RBF has the best performance with 88.92% accuracy, and SVM with Sigmoid kernel has the worst performance with 79.33% accuracy.
The role of music education in the development of personal qualities of creative persons

The article is devoted to the results of research and the creation of guidelines on the pedagogical foundations of education and the development of personal qualities, logical thinking and scientific research of creative individuals by means of music.
Oblivious Sketching for Logistic Regression

What guarantees are possible for solving logistic regression in one pass over a data stream? To answer this question, we present the first data oblivious sketch for logistic regression. Our sketch can be computed in input sparsity time over a turnstile data stream and reduces the size of a $d$-dimensional data set from $n$ to only $\operatorname{poly}(\mu d\log n)$ weighted points, where $\mu$ is a useful parameter which captures the complexity of compressing the data. Solving (weighted) logistic regression on the sketch gives an $O(\log n)$-approximation to the original problem on the full data set. We also show how to obtain an $O(1)$-approximation with slight modifications. Our sketches are fast, simple, easy to implement, and our experiments demonstrate their practicality.
Predicting Deep Neural Network Generalization with Perturbation Response Curves

The field of Deep Learning is rich with empirical evidence of human-like performance on a variety of prediction tasks. However, despite these successes, the recent Predicting Generalization in Deep Learning (PGDL) NeurIPS 2020 competition suggests that there is a need for more robust and efficient measures of network generalization. In this work, we propose a new framework for evaluating the generalization capabilities of trained networks. We use perturbation response (PR) curves that capture the accuracy change of a given network as a function of varying levels of training sample perturbation. From these PR curves, we derive novel statistics that capture generalization capability. Specifically, we introduce two new measures for accurately predicting generalization gaps: the Gi-score and Pal-score, that are inspired by the Gini coefficient and Palma ratio (measures of income inequality), that accurately predict generalization gaps. Using our framework applied to intra and inter class sample mixup, we attain better predictive scores than the current state-of-the-art measures on a majority of tasks in the PGDL competition. In addition, we show that our framework and the proposed statistics can be used to capture to what extent a trained network is invariant to a given parametric input transformation, such as rotation or translation. Therefore, these generalization gap prediction statistics also provide a useful means for selecting the optimal network architectures and hyperparameters that are invariant to a certain perturbation.
Amortized Auto-Tuning: Cost-Efficient Transfer Optimization for Hyperparameter Recommendation.

With the surge in the number of hyperparameters and training times of modern machine learning models, hyperparameter tuning is becoming increasingly expensive. Although methods have been proposed to speed up tuning via knowledge transfer, they typically require the final performance of hyperparameters and do not focus on low-fidelity information. Nevertheless, this common practice is suboptimal and can incur an unnecessary use of resources. It is more cost-efficient to instead leverage the low-fidelity tuning observations to measure inter-task similarity and transfer knowledge from existing to new tasks accordingly. However, performing multi-fidelity tuning comes with its own challenges in the transfer setting: the noise in the additional observations and the need for performance forecasting. Therefore, we conduct a thorough analysis of the multi-task multi-fidelity Bayesian optimization framework, which leads to the best instantiation--amortized auto-tuning (AT2). We further present an offline-computed 27-task hyperparameter recommendation (HyperRec) database to serve the community. Extensive experiments on HyperRec and other real-world databases illustrate the effectiveness of our AT2 method.
Smart Derivatives: On-Chain Forwards for Digital Assets

In this paper, we present a framework for the development of on-chain forwards (and futures). This utilizes smart contracts to automate the custody of collateral and settlement of payouts on expiry. Importantly, our framework also enables forwards to be traded without counter party risk or reliance on off-chain assets (such as fiat currencies). To achieve this, we build on our previous work on on-chain options and demonstrate how the relevant mathematical guarantees can be extended to forwards. In addition, we discuss recent trends in cryptoasset derivatives, capital requirements, and other design considerations (such as the use of split contracts). This paper will be of interest to academics and practitioners interested in financial smart contracts.
Machine Learning Approach for Prediction of Cervical Cancer

Women in the world are suffering from many diseases among those diseases Cervical Cancer is also mentioned globally. Every year many cancer cases are being registered throughout the world. Cervical cancer is ranked fourth of all the other common cancers according to WHO. Prediction of this cancer in its early stages can be cured, avoiding the death rate. Many people are less aware of this type of cancer as this disease is symptom less. Performance of screening test in regular bases cancerous cells can be detected in its early stages which reduces the mortality rate of people every year. There are many medical approaches for the prediction of this cancer like pap-smear test, colposcopy, biopsy, HPV test or HPV DNA test and other screening tests are performed. These medical methods are combined with the Artificial Intelligence for less false rate and more accurate results. This paper considers pap-smear test images for the prediction of cancerous cells combined with Deep Learning techniques for more efficient results. Convolution Neural Networks (CNN’s) ResNet50 pre-trained model for the prediction of cancerous cells which produces accurate results. The proposed work classifies the cells from the inputted images. This cancer can be cured when it is in the initial stages, the identified abnormal cells helps us for the further treatment. The proposed methodology classifies all the classes with 74.04% of accuracy in prediction of cells for maximum number of epochs. Also in addition, it specifies the class of the testing image.
Accelerating Inference for Sparse Extreme Multi-Label Ranking Trees.

Tree-based models underpin many modern semantic search engines and recommender systems due to their sub-linear inference times. In industrial applications, these models operate at extreme scales, where every bit of performance is critical. Memory constraints at extreme scales also require that models be sparse, hence tree-based models are often back-ended by sparse matrix algebra routines. However, there are currently no sparse matrix techniques specifically designed for the sparsity structure one encounters in tree-based models for extreme multi-label ranking/classification (XMR/XMC) problems. To address this issue, we present the masked sparse chunk multiplication (MSCM) technique, a sparse matrix technique specifically tailored to XMR trees. MSCM is easy to implement, embarrassingly parallelizable, and offers a significant performance boost to any existing tree inference pipeline at no cost. We perform a comprehensive study of MSCM applied to several different sparse inference schemes and benchmark our methods on a general purpose extreme multi-label ranking framework. We observe that MSCM gives consistently dramatic speedups across both the online and batch inference settings, single- and multi-threaded settings, and on many different tree models and datasets. To demonstrate its utility in industrial applications, we apply MSCM to an enterprise-scale semantic product search problem with 100 million products and achieve sub-millisecond latency of 0.88 ms per query on a single thread -- an 8x reduction in latency over vanilla inference techniques. The MSCM technique requires absolutely no sacrifices to model accuracy as it gives exactly the same results as standard sparse matrix techniques. Therefore, we believe that MSCM will enable users of XMR trees to save a substantial amount of compute resources in their inference pipelines at very little cost.
Measuring the sensitivity of Gaussian processes to kernel choice.

Gaussian processes (GPs) are used to make medical and scientific decisions, including in cardiac care and monitoring of carbon dioxide emissions. But the choice of GP kernel is often somewhat arbitrary. In particular, uncountably many kernels typically align with qualitative prior knowledge (e.g. function smoothness or stationarity). But in practice, data analysts choose among a handful of convenient standard kernels (e.g. squared exponential). In the present work, we ask: Would decisions made with a GP differ under other, qualitatively interchangeable kernels? We show how to formulate this sensitivity analysis as a constrained optimization problem over a finite-dimensional space. We can then use standard optimizers to identify substantive changes in relevant decisions made with a GP. We demonstrate in both synthetic and real-world examples that decisions made with a GP can exhibit substantial sensitivity to kernel choice, even when prior draws are qualitatively interchangeable to a user.
Estimating Aqueous Nanofluids Viscosity Via GEP Modeling: Correlation Development and Data Assessment

This paper focuses on developing a new method that represents user-accessible correlation for estimation of water-based nanofluids viscosity. For this, an evolutionary algorithm, namely Gene Expression Programming (GEP), was adapted based on a wide selection of literature published databanks including 819 water-based nanofluids viscosity points. The developed model utilized the base fluid viscosity as well as volume fraction and size of the nanoparticles as the inputs of the model. Several statistical parameters integrated with graphical plots were employed in order to assess the accuracy of the proposed GEP-based model. Results of the evaluation demonstrate fairly enough accuracy of the developed model with statistical parameters of AARD%=11.7913, RMSE=0.3567, and SD=0.1851. Furthermore, the trend analysis indicates that the GEP calculated points satisfactorily follow the trend of the nanofluid viscosity variation as a function of different model inputs. To provide more verification, the proposed GEP model was compared with some literature theoretical and empirical correlations leading to the supremacy of the developed model here. The applied sensitivity analysis reveals that the highest impact value is assigned to the volume fraction of the nanoparticle. Moreover, the outliers detection by Williams’ technique illustrates that about 96.5% of the GEP estimates are in the applicability domain resulting in the validity of the proposed model in this study. At last, the results of this study demonstrate that the new method here outperforms other literature published correlations from the standpoint of accuracy and reliability.
THE UTILIZATION OF ARTIFICIAL INTELLIGENCE COMPONENTS IN TECHNOLOGY ERA’S LEARNING

This study was to analyze the importance of component utilization of Artificial Intelligence (AI) in learning. This study was acknowledged by the results of a survey to the secondary school education teachers in the Subject Teachers' Association (MGMP) for Citizenship Education field in Cimahi as many as 42 schools and 146 teachers through the Google Form application and 104 teachers responded according to the Krejcie and Morgan tables. The method used in this research was descriptive survey. Based on the survey results, the main findings in this survey were not very encouraging, because most teachers in the learning process were not always even inclined. They sometimes never used technological media, social networks, use current issues, and assigned learning material widely and in depth. Further research on teachers must be able to master technological assiatance in to make the Citizenship Education was more crucial and urgent for students. On the other hand, students must be able to utilize social media in the daily lives. The school should also provide internet access for the continuation of the learning process. Thus, the government must be able to provide internet access for schools in remote areas, so that technology-based learning is no longer a barrier for teachers and students
A Comparison of Distance Bounds for Quasi-Twisted Codes

Spectral bounds on the minimum distance of quasi-twisted codes over finite fields are proposed, based on eigenvalues of polynomial matrices and the corresponding eigenspaces. They generalize the Semenov-Trifonov and Zeh-Ling bounds in a way similar to how the Roos and shift bounds extend the BCH and HT bounds for cyclic codes. The eigencodes of a quasi-twisted code in the spectral theory and the outer codes in its concatenated structure are related. A comparison based on this relation verifies that the Jensen bound always outperforms the spectral bound under special conditions, which yields a similar relation between the Lally and the spectral bounds. The performances of the Lally, Jensen and spectral bounds are presented in comparison with each other.
TempoRL: Learning When to Act

Reinforcement learning is a powerful approach to learn behaviour through interactions with an environment. However, behaviours are usually learned in a purely reactive fashion, where an appropriate action is selected based on an observation. In this form, it is challenging to learn when it is necessary to execute new decisions. This makes learning inefficient, especially in environments that need various degrees of fine and coarse control. To address this, we propose a proactive setting in which the agent not only selects an action in a state but also for how long to commit to that action. Our TempoRL approach introduces skip connections between states and learns a skip-policy for repeating the same action along these skips. We demonstrate the effectiveness of TempoRL on a variety of traditional and deep RL environments, showing that our approach is capable of learning successful policies up to an order of magnitude faster than vanilla Q-learning.
Deep Learning for engineering applications

In this paper, we formulate a new artificial intelligence based deep learning formulation to solve engineering applications in domains, heat transfer (2D,3D), strength of materials (2D,3D), design of machine elements, 3D component analysis, 3D assembly analysis, fluid dynamics (2D,3D), Kinematics/Vibrations/control (2D, 3D). Our deep learning based Distributed Artificial Neural Network (DANN) formulation showed six orders of magnitute speed up in computational time and needed an everyday use laptop, not necessiating high end super computer servers for engineering applications analysis. Further, the accuracy of the engineering solution showed 99.9% accuracy and comparable to the conventional existing engineering applications analysis results.
Change-Point Analysis of Cyberbullying-Related Twitter Discussions During COVID-19

Due to the outbreak of COVID-19, users are increasingly turning to online services. An increase in social media usage has also been observed, leading to the suspicion that this has also raised cyberbullying. In this initial work, we explore the possibility of an increase in cyberbullying incidents due to the pandemic and high social media usage. To evaluate this trend, we collected 454,046 cyberbullying-related public tweets posted between January 1st, 2020 - June 7th, 2020. We summarize the tweets containing multiple keywords into their daily counts. Our analysis showed the existence of at most one statistically significant changepoint for most of these keywords, which were primarily located around the end of March. Almost all these changepoint time-locations can be attributed to COVID-19, which substantiates our initial hypothesis of an increase in cyberbullying through analysis of discussions over Twitter.
A Survey of Human Action Recognition using Accelerometer Data

Recognizing human actions and analyzing human behaviors from accelerometer data has become a challenging task. Hence, Human Action Recognition (HAR) using inertial sensors have been addressed in a plethora of various review papers. This chapter provides a detailed state of art survey of HAR exploiting acceleration data. Considering different modalities, we prove that the accelerometer is one of the most promising sensors in this field by presenting an overview of its applications. In addition, we propose a comprehensive review of recent studies in this domain along different views: from data modalities to feature extraction and classification steps. Moreover, we list the most publicly available databases that include accelerometer data. Afterwards, we used a multi-level fusion framework that includes signal-level, feature-level, score level and the decision level fusion in order to improve the recognition performance. For the classification, we took advantage of the support vector machine with features from the time-frequency domain. The proposed framework was evaluated using three public datasets: WARD, MHAD and Realdisp. The results obtained from the fusion techniques indicate that the score level provides a satisfactory performance compared to the other levels and with the use of each accelerometer separately.
A Design of Web-Based Informatics Journal Information System

Informatics has become among the fastest growing studies with hundreds or more papers and journals being published each year. The ever expanding repository of published and unpublished journals makes it more and more difficult and expensive to manage it with only human capabilities. As such, the need for an information system that can help classify and manage journals and papers on informatics becomes more and more important. This paper discusses the design and implementation of an informatics journal information system using the prototype method and uniform modeling language.
Non-Euclidean Differentially Private Stochastic Convex Optimization

Differentially private (DP) stochastic convex optimization (SCO) is a fundamental problem, where the goal is to approximately minimize the population risk with respect to a convex loss function, given a dataset of i.i.d. samples from a distribution, while satisfying differential privacy with respect to the dataset. Most of the existing works in the literature of private convex optimization focus on the Euclidean (i.e., $\ell_2$) setting, where the loss is assumed to be Lipschitz (and possibly smooth) w.r.t. the $\ell_2$ norm over a constraint set with bounded $\ell_2$ diameter. Algorithms based on noisy stochastic gradient descent (SGD) are known to attain the optimal excess risk in this setting. In this work, we conduct a systematic study of DP-SCO for $\ell_p$-setups. For $p=1$, under a standard smoothness assumption, we give a new algorithm with nearly optimal excess risk. This result also extends to general polyhedral norms and feasible sets. For $p\in(1, 2)$, we give two new algorithms, whose central building block is a novel privacy mechanism, which generalizes the Gaussian mechanism. Moreover, we establish a lower bound on the excess risk for this range of $p$, showing a necessary dependence on $\sqrt{d}$, where $d$ is the dimension of the space. Our lower bound implies a sudden transition of the excess risk at $p=1$, where the dependence on $d$ changes from logarithmic to polynomial, resolving an open question in prior work \citep{TTZ15a}. For $p\in (2, \infty)$, noisy SGD attains optimal excess risk in the low-dimensional regime; in particular, this proves the optimality of noisy SGD for $p=\infty$. Our work draws upon concepts from the geometry of normed spaces, such as the notions of regularity, uniform convexity, and uniform smoothness.
An Efficient Passive-to-Active Compiler for Honest-Majority MPC over Rings.

Multiparty computation (MPC) over rings such as \(\mathbb {Z}_{2^{32}}\) or \(\mathbb {Z}_{2^{64}}\) has received a great deal of attention recently due to its ease of implementation and attractive performance. Several actively secure protocols over these rings have been implemented, for both the dishonest majority setting and the setting of three parties with one corruption. However, in the honest majority setting, no concretely efficient protocol for arithmetic computation over rings has yet been proposed that allows for an arbitrary number of parties.