Reviewerly/exHarmony huggingface dataset:
Stable high-order cubature formulas for experimental data

In many applications, it is impractical -- if not even impossible -- to obtain data to fit a known cubature formula (CF). Instead, experimental data is often acquired at equidistant or even scattered locations. In this work, stable (in the sense of nonnegative only cubature weights) high-order CFs are developed for this purpose. These are based on the approach to allow the number of data points N to be larger than the number of basis functions K which are integrated exactly by the CF. This yields an (N-K)-dimensional affine linear subspace from which cubature weights are selected that minimize certain norms corresponding to stability of the CF. In the process, two novel classes of stable high-order CFs are proposed and carefully investigated.
Measuring Software Testability Modulo Test Quality

Comprehending the degree to which software components support testing is important to accurately schedule testing activities, train developers, and plan effective refactoring actions. Software testability estimates such property by relating code characteristics to the test effort. The main studies of testability reported in the literature investigate the relation between class metrics and test effort in terms of the size and complexity of the associated test suites. They report a moderate correlation of some class metrics to test-effort metrics, but suffer from two main limitations: (i) the results hardly generalize due to the small empirical evidence (datasets with no more than eight software projects); and (ii) mostly ignore the quality of the tests. However, considering the quality of the tests is important. Indeed, a class may have a low test effort because the associated tests are of poor quality, and not because the class is easier to test. In this paper, we propose an approach to measure testability that normalizes the test effort with respect to the test quality, which we quantify in terms of code coverage and mutation score. We present the results of a set of experiments on a dataset of 9,861 Java classes, belonging to 1,186 open source projects, with around 1.5 million of lines of code overall. The results confirm that normalizing the test effort with respect to the test quality largely improves the correlation between class metrics and the test effort. Better correlations result in better prediction power and thus better prediction of the test effort.
Point of interest recommendations based on the anchoring effect in location-based social network services

A point of interest (POI) recommender system (RS) is one of the representative research areas based on the location-based social network (LBSN). Most POI RS studies utilized various implicit information or social information to improve recommendation accuracy. However, majority of these studies overlooked the importance of users' initial check-in information. Users are affected by their first input data in online services, and this phenomenon is called the anchoring effect. In POI RSs, few studies have analyzed the association with the anchoring effect while other RS domains already verified this effect. In particular, a research area, including POI RS, that focuses on the importance of the initial input does not exist. In this paper, we propose a latent Dirichlet allocation (LDA) model based on the anchoring effect for POI RS. This model emphasizes the importance of initial check-in data and is called the anchor-LDA. Experimental results showed that the anchor-LDA outperformed existing LDA-based POI recommender algorithms. Furthermore, we validated the importance of initial check-in information on the LBSN.
INDFORG: Industrial Forgery Detection Using Automatic Rotation Angle Detection and Correction

Internet and other online media networks have emerged as the most important platforms for the sharing of digital information. However, the readily available editing tools provide an easy way for adversaries to manipulate the data and affect decision-making in various industrial applications. This malicious modification of the content, which has reduced the credibility of information delivery, is a commonly prevalent issue and hence needs serious attention. It also initiates an extreme need for industrial cyber–physical systems (ICPS), which can compare the transferred and received images for correct orientation to ensure that it conveys meaningful information and assists in correct decision-making in industrial automation. In this article, we propose “INDFORG”, which employs a novel and highly accurate automatic rotation angle detection and correction algorithm (ARADC) for intelligent detection of forgery in industrial images. ARADC uses basic geometrical concepts, such as Pythagorean theorem and intensity correlation computation and works without any digital signature or watermark. It performs accurately even under several simultaneous signal-processing manipulations. The proposed framework detects the rotation angles blindly with a 99% accuracy rate for rotation up to ±89°. Experimental results prove that the proposed algorithm is highly efficient compared to various state-of-the-art approaches and is a preferred ICPS for trustworthy media delivery in industrial automation.
Using Wearable Physiological Sensors for Affect-Aware Intelligent Tutoring Systems

Intelligent Tutoring Systems (ITS) have shown great potential in enhancing the learning process by being able to adapt to the learner's knowledge level, abilities, and difficulties. An aspect that can affect the learning process but is not taken into consideration by traditional ITS is the affective state of the learner. In this work, we propose the use of physiological signals and machine learning for the task of detecting a learner's affective state during test taking. To this end, wearable physiological sensors were used to record electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG) signals from 27 individuals while participating in a computerised English language test. Features extracted from the acquired signals were used in order to train machine learning models for the prediction of the self-reported difficulty level of the test's questions, as well as for the prediction of whether the questions would be answered correctly. Supervised classification experiments showed that there is a relation between the acquired signals and the examined tasks, reaching a classification F1-score of 74.21% for the prediction of the self-reported question difficulty level, and a classification F1-score of 59.14% for predicting whether a question was answered correctly. The acquired results demonstrate the potential of the examined approach for enhancing ITS with information relating to the affective state of the learners.
Designing a DDS-Based SoC for High-Fidelity Multi-Qubit Control

The design of a large-scale quantum computer requires co-optimization of both the quantum bits (qubits) and their control electronics.This work presents the first systematic design of such a controller to simultaneously and accurately manipulate the states of multiple spin qubits or transmons.By employing both analytical and simulation techniques, the detailed electrical specifications of the controller have been derived for a single-qubit gate fidelity of 99.99% and validated using a qubit Hamiltonian simulator.Trade-offs between several architectures with different levels of digitization are discussed, resulting in the selection of a highly digital DDS-based solution.Initiating from the system specifications, a complete error budget for the various analog and digital circuit blocks is drafted and their detailed electrical specifications, such as signal power, linearity, spurs and noise, are derived to obtain a digital-intensive power-optimized multi-qubit controller.A power consumption estimate demonstrates the feasibility of such a system in a nanometer CMOS technology node.Finally, application examples, including qubit calibration and multiqubit excitation, are simulated with the proposed controller to demonstrate its efficacy.The proposed methodology, and more specifically, the proposed error budget lay the foundations for the design of a scalable electronic controller enabling large-scale quantum computers with practical applications.
Height Estimation From Single Aerial Images Using a Deep Ordinal Regression Network

Understanding the 3D geometric structure of the Earth's surface has been an active research topic in photogrammetry and remote sensing community for decades, serving as an essential building block for various applications such as 3D digital city modeling, change detection, and city management. Previous researches have extensively studied the problem of height estimation from aerial images based on stereo or multi-view image matching. These methods require two or more images from different perspectives to reconstruct 3D coordinates with camera information provided. In this paper, we deal with the ambiguous and unsolved problem of height estimation from a single aerial image. Driven by the great success of deep learning, especially deep convolution neural networks (CNNs), some researches have proposed to estimate height information from a single aerial image by training a deep CNN model with large-scale annotated datasets. These methods treat height estimation as a regression problem and directly use an encoder-decoder network to regress the height values. In this paper, we proposed to divide height values into spacing-increasing intervals and transform the regression problem into an ordinal regression problem, using an ordinal loss for network training. To enable multi-scale feature extraction, we further incorporate an Atrous Spatial Pyramid Pooling (ASPP) module to extract features from multiple dilated convolution layers. After that, a post-processing technique is designed to transform the predicted height map of each patch into a seamless height map. Finally, we conduct extensive experiments on ISPRS Vaihingen and Potsdam datasets. Experimental results demonstrate significantly better performance of our method compared to the state-of-the-art methods.
Multiplicative Noise Removal: Nonlocal Low-Rank Model and Its Proximal Alternating Reweighted Minimization Algorithm

The goal of this paper is to develop a novel numerical method for efficient multiplicative noise removal.The nonlocal self-similarity of natural images implies that the matrices formed by their nonlocal similar patches are low-rank.By exploiting this low-rank prior with application to multiplicative noise removal, we propose a nonlocal low-rank model for this task and develop a proximal alternating reweighted minimization (PARM) algorithm to solve the optimization problem resulting from the model.Specifically, we utilize a generalized nonconvex surrogate of the rank function to regularize the patch matrices and develop a new nonlocal low-rank model, which is a nonconvex nonsmooth optimization problem having a patchwise data fidelity and a generalized nonlocal low-rank regularization term.To solve this optimization problem, we propose the PARM algorithm, which has a proximal alternating scheme with a reweighted approximation of its subproblem.A theoretical analysis of the proposed PARM algorithm is conducted to guarantee its global convergence to a critical point.Numerical experiments demonstrate that the proposed method for multiplicative noise removal significantly outperforms existing methods, such as the benchmark SAR-BM3D method, in terms of the visual quality of the denoised images, and of the peak-signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) values.
Fast and slow curiosity for high-level exploration in reinforcement learning

Abstract Deep reinforcement learning (DRL) algorithms rely on carefully designed environment rewards that are extrinsic to the agent. However, in many real-world scenarios rewards are sparse or delayed, motivating the need for discovering efficient exploration strategies. While intrinsically motivated agents hold promise of better local exploration, solving problems that require coordinated decisions over long-time horizons remains an open problem. We postulate that to discover such strategies, a DRL agent should be able to combine local and high-level exploration behaviors. To this end, we introduce the concept of fast and slow curiosity that aims to incentivize long-time horizon exploration. Our method decomposes the curiosity bonus into a fast reward that deals with local exploration and a slow reward that encourages global exploration. We formulate this bonus as the error in an agent’s ability to reconstruct the observations given their contexts. We further propose to dynamically weight local and high-level strategies by measuring state diversity. We evaluate our method on a variety of benchmark environments, including Minigrid, Super Mario Bros, and Atari games. Experimental results show that our agent outperforms prior approaches in most tasks in terms of exploration efficiency and mean scores.
Non-Gaussian nature and entanglement of spontaneous parametric nondegenerate triple-photon generation

How to prepare deterministically non-Gaussian entangled states is a fundamental question for continuous-variable quantum information technology. Here, we theoretically demonstrate through numerical methods that the triple-photon state generated by three-photon spontaneous parametric down-conversion is a pure super-Gaussian resource of non-Gaussian entanglement. Interestingly, the degree of entanglement between the modes of the triple-photon state is stronger than that corresponding to the two-mode squeezed vacuum state produced by a quadratic Hamiltonian with the same parameters. Furthermore, we propose a model to prepare two-mode non-Gaussian entangled states with tunable non-Gaussianity based on quadrature projection measurements.
AutoToken

Right-sizing resource allocation for big-data queries, particularly in serverless environments, is critical for improving infrastructure operational efficiency, capacity availability, query performance predictability, and for reducing unnecessary wait times. In this paper, we present AutoToken --- a simple and effective predictor for estimating the peak resource usage of recurring big data queries. It uses multiple query plan identifiers to identify recurring query templates and to learn models with the goal of reducing over-allocation in future instances of those queries. AutoToken is computationally light, for both training and scoring, is easily deployable at scale, and is integrated with the Peregrine workload optimization infrastructure at Microsoft. We extensively evaluate AutoToken on SCOPE jobs from our production clusters and show that it outperforms state-of-the-art solutions for peak resource estimation. We also discuss our plans towards supporting repeatable and extensible research on resource prediction for SCOPE jobs, including describing a simulation methodology for generating arbitrary-sized datasets with similar characteristics as the production datasets.
Dig users’ intentions via attention flow network for personalized recommendation

Accurately forecasting user’s purchase intention over time is a huge challenge for personalized recommend systems, of which a critical problem is how to model the changes of user preference and temporal correlation of items. In this paper, aiming at addressing this question, we first introduce attention flow network to model users’ purchase records by leveraging attention flow that describes the changing process of purchase intention. Then based on the attention flow network and individuals’ attention flows, we propose a novel personalized recommendation algorithm named Attention Flow Network based Personalized Recommendation (AFNPR). Our method integrates all the purchase sequences of users into a weighted attention flow network, and recommends items based on transition probabilities related to attention flow network with user’s attention decay, which is efficient in linear time. The experiments demonstrate its superior performance on several real datasets.
Recommendation of users in social networks: A semantic and social based classification approach

Abstract Recently, the study of social network‐based recommender systems has become an active research topic. The integration of the social relationships that exist between users can improve the accuracy of recommendation results since the users' preferences are similar or influenced by their connected friends. We focus in this article on the recommendation of users in social networks. Our approach is based on semantic and social representations of the users' profiles. We have formalized and illustrated these two dimensions using the Yelp social network. The novelty of our approach concerns the modelling of the credibility of the user, through his/her trust and commitment in the social network. Moreover, in order to optimize the performance of the recommendation process, we have used two classification techniques: an unsupervised technique that uses the K‐means algorithm (applied initially to all users); and a supervised technique that uses the K‐Nearest Neighbours algorithm (applied to newly added users). A recommendation algorithm has been proposed taking into account the cold‐start and sparsity problems. A prototype of a recommender system has been developed and tested using two publicly available datasets: the Yelp database and the Rich Epinions database. The comparative evaluation results show the effectiveness of combining the semantic, the social and the credibility information in an approach that appropriately uses the K‐means and K‐Nearest Neighbours algorithms.
DeNERT-KG: Named Entity and Relation Extraction Model Using DQN, Knowledge Graph, and BERT

Along with studies on artificial intelligence technology, research is also being carried out actively in the field of natural language processing to understand and process people’s language, in other words, natural language. For computers to learn on their own, the skill of understanding natural language is very important. There are a wide variety of tasks involved in the field of natural language processing, but we would like to focus on the named entity registration and relation extraction task, which is considered to be the most important in understanding sentences. We propose DeNERT-KG, a model that can extract subject, object, and relationships, to grasp the meaning inherent in a sentence. Based on the BERT language model and Deep Q-Network, the named entity recognition (NER) model for extracting subject and object is established, and a knowledge graph is applied for relation extraction. Using the DeNERT-KG model, it is possible to extract the subject, type of subject, object, type of object, and relationship from a sentence, and verify this model through experiments.
Inexact SARAH algorithm for stochastic optimization

We develop and analyse a variant of the SARAH algorithm, which does not require computation of the exact gradient. Thus this new method can be applied to general expectation minimization problems rather than only finite sum problems. While the original SARAH algorithm, as well as its predecessor, SVRG, requires an exact gradient computation on each outer iteration, the inexact variant of SARAH (iSARAH), which we develop here, requires only stochastic gradient computed on a mini-batch of sufficient size. The proposed method combines variance reduction via sample size selection and iterative stochastic gradient updates. We analyse the convergence rate of the algorithms for strongly convex and non-strongly convex cases, under smooth assumption with appropriate mini-batch size selected for each case. We show that with an additional, reasonable, assumption iSARAH achieves the best-known complexity among stochastic methods in the case of non-strongly convex stochastic functions.
Teaching learning-based whale optimization algorithm for multi-layer perceptron neural network training

This paper presents an improved teaching learning-based whale optimization algorithm (TSWOA) used the simplex method. First of all, the combination of WOA algorithm and teaching learning-based algorithm not only achieves a better balance between exploration and exploitation of WOA, but also makes whales have self-learning ability from the biological background, and greatly enriches the theory of the original WOA algorithm. Secondly, the WOA algorithm adds the simplex method to optimize the current worst unit, averting the agents to search at the boundary, and increasing the convergence accuracy and speed of the algorithm. To evaluate the performance of the improved algorithm, the TSWOA algorithm is employed to train the multi-layer perceptron (MLP) neural network. It is a difficult thing to propose a well-pleasing and valid algorithm to optimize the multi-layer perceptron neural network. Fifteen different data sets were selected from the UCI machine learning knowledge and the statistical results were compared with GOA, GSO, SSO, FPA, GA and WOA, severally. The statistical results display that better performance of TSWOA compared to WOA and several well-established algorithms for training multi-layer perceptron neural networks.
Exploring the structure of digital literacy competence assessed using authentic software applications

Abstract Digital literacy competence (DL) is an important capacity for students’ learning in a rapidly changing world. However, little is known about the empirical structure of DL. In this paper, we review major DL assessment frameworks and explore the dimensionality of DL from an empirical perspective using assessment data collected using authentic software applications, rather than simulated assessment environments. Secondary analysis on representative data collected from primary and secondary school students in Hong Kong using unidimensional and multidimensional item response theory reveals a general dimension of digital literacy performance and four specific, tool-dependent dimensions. These specific DL dimensions are defined by the software applications that students use and capture commonality among students’ performance that is due to their familiarity with the assessment tools and contexts. The design of DL assessment is discussed in light of these findings, with particular emphasis on the influence of the nature of digital applications and environments used in assessment on the DL achievement scores measured.
Deep Rényi entropy graph kernel

Graph kernels are applied heavily for the classification of structured data. In this paper, we propose a deep Rényi entropy graph kernel for this purpose. We gauge the deep information through a family of h-layer expansion subgraphs rooted at a vertex, and define a h-layer depth-based second-order Rényi entropy representation for each vertex. The second-order Rényi entropy representation is used together with Euclidean distance to build a deep second-order Rényi entropy graph kernel (SREGK). For graphs with n vertices, the time complexity for our kernel is O(n3). This low-order polynomial complexity enables our subgraph kernels to easily scale up to graphs of reasonably large sizes and thus overcome the size limits arising in state-of-the-art graph kernels. Experimental results on fourteen real world graph datasets are shown to demonstrate the overall superior performance of our approach over a number of state-of-the-art methods.