Reviewerly/exHarmony huggingface dataset:
Faster Enumeration-Based Lattice Reduction: Root Hermite Factor $$k^{1/(2k)}$$ Time $$k^{k/8+o(k)}$$

We give a lattice reduction algorithm that achieves root Hermite factor $$k^{1/(2k)}$$ in time $$k^{k/8+o(k)}$$ and polynomial memory. This improves on the previously best known enumeration-based algorithms which achieve the same quality, but in time $$k^{k/(2e) + o(k)}$$ . A cost of $$k^{k/8 + o(k)}$$ was previously mentioned as potentially achievable (Hanrot-Stehlé’10) or as a heuristic lower bound (Nguyen’10) for enumeration algorithms. We prove the complexity and quality of our algorithm under a heuristic assumption and provide empirical evidence from simulation and implementation experiments attesting to its performance for practical and cryptographic parameter sizes. Our work also suggests potential avenues for achieving costs below $$k^{k/8 + o(k)}$$ for the same root Hermite factor, based on the geometry of SDBKZ-reduced bases.
Compositional Generalization by Factorizing Alignment and Translation

Standard methods in deep learning for natural language processing fail to capture the compositional structure of human language that allows for systematic generalization outside of the training distribution.However, human learners readily generalize in this way, e.g. by applying known grammatical rules to novel words.Inspired by work in cognitive science suggesting a functional distinction between systems for syntactic and semantic processing, we implement a modification to an existing approach in neural machine translation, imposing an analogous separation between alignment and translation.The resulting architecture substantially outperforms standard recurrent networks on the SCAN dataset, a compositional generalization task, without any additional supervision.Our work suggests that learning to align and to translate in separate modules may be a useful heuristic for capturing compositional structure.
A Curvature-Based Saliency Method for Ship Detection in SAR Images

According to the theory and interpretation method of information geometry, this work presents a novel curvature-based saliency method for ship detection in synthetic aperture radar (SAR) images. First, the nonlinear anisotropic diffusive process has been adopted to eliminate clutter while preserving the local ship target structure in SAR images. Then, a novel curvature-based saliency method for super-pixels in the filtered image is presented, which is used to exploit the microstructure feature of statistical manifold. Finally, a statistical classification method is used to realize the location of targets. The experimental results on real SAR images show that the proposed method can achieve good performance.
Automated diagnosis of intestinal parasites: A new hybrid approach and its benefits

Intestinal parasites are responsible for several diseases in human beings. In order to eliminate the error-prone visual analysis of optical microscopy slides, we have investigated automated, fast, and low-cost systems for the diagnosis of human intestinal parasites. In this work, we present a hybrid approach that combines the opinion of two decision-making systems with complementary properties: ($DS_1$) a simpler system based on very fast handcrafted image feature extraction and support vector machine classification and ($DS_2$) a more complex system based on a deep neural network, Vgg-16, for image feature extraction and classification. $DS_1$ is much faster than $DS_2$, but it is less accurate than $DS_2$. Fortunately, the errors of $DS_1$ are not the same of $DS_2$. During training, we use a validation set to learn the probabilities of misclassification by $DS_1$ on each class based on its confidence values. When $DS_1$ quickly classifies all images from a microscopy slide, the method selects a number of images with higher chances of misclassification for characterization and reclassification by $DS_2$. Our hybrid system can improve the overall effectiveness without compromising efficiency, being suitable for the clinical routine -- a strategy that might be suitable for other real applications. As demonstrated on large datasets, the proposed system can achieve, on average, 94.9%, 87.8%, and 92.5% of Cohen's Kappa on helminth eggs, helminth larvae, and protozoa cysts, respectively.
Investigation effects of environmental and operating factors on <scp>PV</scp> panel efficiency using by multivariate linear regression

The aim of this study is to show the investigation effects of environmental and operating factors on photovoltaic (PV) panel efficiency using by multivariate linear regression. Also in this study, the relationship between PV panel efficiency and some environmental and operating factors (solar radiation, open-circuit voltage, short circuit current (Isc), power, fill factor, outside temperature, humidity, wind speed, and voltage) were investigated using multivariate linear regression. The effect of binary parameter interactions on panel performance was investigated using the backward elimination (or deletion) method. The data used in the analysis were obtained from monocrystal and polycrystal panels in December in the winter season. In all analyzes, the regression values are close to 1, which indicates that the compatibility between binary parameters and performance characteristics is perfect. In addition, the three most effective parameters on panel efficiency were found to be solar radiation, maximum power (Pmax), and Isc, respectively.
Ten Research Challenge Areas in Data Science

To drive progress in the field of data science, we propose 10 challenge areas for the research community to pursue.Since data science is broad, with methods drawing from computer science, statistics, and other disciplines, and with applications appearing in all sectors, these challenge areas speak to the breadth of issues spanning science, technology, and society.We preface our enumeration with meta-questions about whether data science is a discipline.We then describe each of the 10 challenge areas.The goal of this article is to start a discussion on what could constitute a basis for a research agenda in data science, while recognizing that the field of data science is still evolving.
User location privacy protection mechanism for location-based services

With the rapid development of the Internet of Things (IoT), Location-Based Services (LBS) are becoming more and more popular. However, for the users being served, how to protect their location privacy has become a growing concern. This has led to great difficulty in establishing trust between the users and the service providers, hindering the development of LBS for more comprehensive functions. In this paper, we first establish a strong identity verification mechanism to ensure the authentication security of the system and then design a new location privacy protection mechanism based on the privacy proximity test problem. This mechanism not only guarantees the confidentiality of the user’s information during the subsequent information interaction and dynamic data transmission, but also meets the service provider’s requirements for related data.
COVID-19: A scholarly production dataset report for research analysis

COVID-2019 has been recognized as a global threat, and several studies are being conducted in order to contribute to the fight and prevention of this pandemic. This work presents a scholarly production dataset focused on COVID-19, providing an overview of scientific research activities, making it possible to identify countries, scientists and research groups most active in this task force to combat the coronavirus disease. The dataset is composed of 40,212 records of articles' metadata collected from Scopus, PubMed, arXiv and bioRxiv databases from January 2019 to July 2020. Those data were extracted by using the techniques of Python Web Scraping and preprocessed with Pandas Data Wrangling. In addition, the pipeline to preprocess and generate the dataset are versioned with the Data Version Control tool (DVC) and are thus easily reproducible and auditable.
METHWORK: An Approach for Ranking of Research Trends with a Case Study for IoET

Objective: Ranking in many areas has been a big problem for a long time. The authors tried to use a novel ranking approach to find out the most popular research interest among all the research fields. The authors have assumed that the selection of a research area is a tedious task. Methods: Therefore, they tried to propose a mechanism named METHWORK (Methodology With the Opted Related Keywords) to choose popular research trends. Google-based searching was applied to find samples in the initial state of this approach. The METHWORK was tested with respect to a case of ranking of research trends within the IoET (Internet of Environmental Things). To find ranks, the first phase is to assess the popularity of the topics in the existing published research papers. To find out the correctness of the ranking found using METHWORK, the authors performed χ2 hypothesis testing while making a comparison with current ranking techniques. Results: The methodology proposed is a milestone to find out the research trends within a broad research area. Conclusion: The results of the test indicated that the approach could be applied to determine trends in any research discipline and proved the applicability of the proposed approach.
Weakly Private Information Retrieval Under the Maximal Leakage Metric

In the canonical private information retrieval (PIR) problem, a user retrieves a message from a set of databases without allowing any individual database to obtain any knowledge regarding the identity of the requested message. This perfect privacy requirement may be too stringent in many cases, and the user may only wish to control the amount of the privacy leakage to below a given level, and in return, can retrieve the message at a lower communication cost. In this work, we study the tradeoff between the download cost and the amount of privacy leakage under the maximal leakage metric. A new scheme is proposed by allowing a more flexible query structure and probability distributions in a code previously proposed by Tian et al., which utilized a fixed query set and a uniform distribution. It is shown that the optimal probability distribution in the proposed scheme has a particularly simple structure, which leads to a closed form achievability bound for the optimal tradeoff between the download cost and the privacy leakage. The proposed scheme includes several known schemes, such as those proposed by Lin et al., by Samy et al., and by Jia, as special cases.
The processing of multiword expressions in children and adults: An eye-tracking study of Chinese

Abstract The processing advantage for multiword expressions over novel language has long been attested in the literature. However, the evidence pertains almost exclusively to multiword expression processing in adults. Whether or not other populations are sensitive to phrase frequency effects is largely unknown. Here, we sought to address this gap by recording the eye movements of third and fourth graders, as well as adults (first-language Mandarin) as they read phrases varying in frequency embedded in sentence context. We were interested in how phrase frequency, operationalized as phrase type (collocation vs. control) or (continuous) phrase frequency, and age might influence participants’ reading. Adults read collocations and higher frequency phrases consistently faster than control and lower frequency phrases, respectively. Critically, fourth, but not third, graders read collocations and higher frequency phrases faster than control and lower frequency sequences, respectively, although this effect was largely confined to a late measure. Our results reaffirm phrase frequency effects in adults and point to emerging phrase frequency effects in primary school children. The use of eye tracking has further allowed us to tap into early versus late stages of phrasal processing, to explore different areas of interest, and to probe possible differences between phrase frequency conceptualized as a dichotomy versus a continuum.
Psychometric Properties of ‘Attitude towards e-Learning Scale’ among Nursing Students

As nursing schools embrace e-learning during and beyond the COVID-19 crisis, academic nursing institutions must first assess the attitude or perception of students to achieve successful transition and transformation to e-learning.However, there is limited information about the psychometric properties that measure the attitudes towards e-learning among nursing students in the Philippines.The objective of the study is to determine the validity and reliability of the attitudes towards e-learning scale Filipino nursing students.This descriptive cross-sectional study included 111 Filipino nursing students in selected public and private universities were selected through purposive sampling.The attitude toward e-learning scale was assessed for construct validity through exploratory factor analysis using varimax rotation and internal consistency reliability using Cronbach's á and -item-total correlation.The nineitem attitude towards e-learning scale was loaded on one component with factor loadings ranging from -0.907 to 0.893, explaining 61.92 percent of the total variance.The KMO test revealed a value of 0.900, while Bartlett's test was 644.380 (df =55, p=0.000).The corrected item-total correlation of each item varied in between 0.409 to 0.854, and the Cronbach's alpha coefficient of the 9-item scale was 0.917.The 9-item instrument was valid and reliable in assessing the attitude towards e-learning among nursing students.Further studies should be done in order to test it among varied health science students.
A Hierarchical Learning Approach for Human Action Recognition

In the domain of human action recognition, existing works mainly focus on using RGB, depth, skeleton and infrared data for analysis. While these methods have the benefit of being non-invasive, they can only be used within limited setups, are prone to issues such as occlusion and often need substantial computational resources. In this work, we address human action recognition through inertial sensor signals, which have a vast quantity of practical applications in fields such as sports analysis and human-machine interfaces. For that purpose, we propose a new learning framework built around a 1D-CNN architecture, which we validated by achieving very competitive results on the publicly available UTD-MHAD dataset. Moreover, the proposed method provides some answers to two of the greatest challenges currently faced by action recognition algorithms, which are (1) the recognition of high-level activities and (2) the reduction of their computational cost in order to make them accessible to embedded devices. Finally, this paper also investigates the tractability of the features throughout the proposed framework, both in time and duration, as we believe it could play an important role in future works in order to make the solution more intelligible, hardware-friendly and accurate.
Multi-χ: Identifying Multiple Authors from Source Code Files

Abstract Most authorship identification schemes assume that code samples are written by a single author. However, real software projects are typically the result of a team effort, making it essential to consider a finegrained multi-author identification in a single code sample, which we address with Multi-χ. Multi-χ leverages a deep learning-based approach for multi-author identification in source code, is lightweight, uses a compact representation for efficiency, and does not require any code parsing, syntax tree extraction, nor feature selection. In Multi-χ, code samples are divided into small segments, which are then represented as a sequence of n -dimensional term representations. The sequence is fed into an RNN-based verification model to assist a segment integration process which integrates positively verified segments, i.e., integrates segments that have a high probability of being written by one author. Finally, the resulting segments from the integration process are represented using word2vec or TF-IDF and fed into the identification model. We evaluate Multi-χ with several Github projects (Caffe, Facebook’s Folly, Tensor-Flow, etc.) and show remarkable accuracy. For example, Multi-χ achieves an authorship example-based accuracy (A-EBA) of 86.41% and per-segment authorship identification of 93.18% for identifying 562 programmers. We examine the performance against multiple dimensions and design choices, and demonstrate its effectiveness.
Modality-Free Feature Detector and Descriptor for Multimodal Remote Sensing Image Registration

The nonlinear radiation distortions (NRD) among multimodal remote sensing images bring enormous challenges to image registration. The traditional feature-based registration methods commonly use the image intensity or gradient information to detect and describe the features that are sensitive to NRD. However, the nonlinear mapping of the corresponding features of the multimodal images often results in failure of the feature matching, as well as the image registration. In this paper, a modality-free multimodal remote sensing image registration method (SRIFT) is proposed for the registration of multimodal remote sensing images, which is invariant to scale, radiation, and rotation. In SRIFT, the nonlinear diffusion scale (NDS) space is first established to construct a multi-scale space. A local orientation and scale phase congruency (LOSPC) algorithm are then used so that the features of the images with NRD are mapped to establish a one-to-one correspondence, to obtain sufficiently stable key points. In the feature description stage, a rotation-invariant coordinate (RIC) system is adopted to build a descriptor, without requiring estimation of the main direction. The experiments undertaken in this study included one set of simulated data experiments and nine groups of experiments with different types of real multimodal remote sensing images with rotation and scale differences (including synthetic aperture radar (SAR)/optical, digital surface model (DSM)/optical, light detection and ranging (LiDAR) intensity/optical, near-infrared (NIR)/optical, short-wave infrared (SWIR)/optical, classification/optical, and map/optical image pairs), to test the proposed algorithm from both quantitative and qualitative aspects. The experimental results showed that the proposed method has strong robustness to NRD, being invariant to scale, radiation, and rotation, and the achieved registration precision was better than that of the state-of-the-art methods.
Automatic image annotation method based on a convolutional neural network with threshold optimization

In this study, a convolutional neural network with threshold optimization (CNN-THOP) is proposed to solve the issue of overlabeling or downlabeling arising during the multilabel image annotation process in the use of a ranking function for label annotation along with prediction probability. This model fuses the threshold optimization algorithm to the CNN structure. First, an optimal model trained by the CNN is used to predict the test set images, and batch normalization (BN) is added to the CNN structure to effectively accelerate the convergence speed and obtain a group of prediction probabilities. Second, threshold optimization is performed on the obtained prediction probability to derive an optimal threshold for each class of labels to form a group of optimal thresholds. When the prediction probability for this class of labels is greater than or equal to the corresponding optimal threshold, this class of labels is used as the annotation result for the image. During the annotation process, the multilabel annotation for the image to be annotated is realized by loading the optimal model and the optimal threshold. Verification experiments are performed on the MIML, COREL5K, and MSRC datasets. Compared with the MBRM, the CNN-THOP increases the average precision on MIML, COREL5K, and MSRC by 27%, 28% and 33%, respectively. Compared with the E2E-DCNN, the CNN-THOP increases the average recall rate by 3% on both COREL5K and MSRC. The most precise annotation effect for CNN-THOP is observed on the MIML dataset, with a complete matching degree reaching 64.8%.
HyperBSA: A High-Performance Consortium Blockchain Storage Architecture for Massive Data

With the considerable exploration of blockchain in various industrial fields, the storage architectures of mainstream consortium blockchains exhibit significant performance limitations, which can't meet the requirements of efficient data access with massive data storage in enterprise-level business scenarios.In this paper, we creatively divided the underlying data of the consortium blockchain into two categories: continuous data and state data and proposed a new storage architecture to store and operate these two types of data efficiently.For continuous data, we designed a specialized index-based storage engine.For state data, we proposed a multi-level cache mechanism with a secure and integrated data persistence policy.In addition, a pluggable Client/Server mode is employed to achieve flexible distributed extension.A series of experiments are conducted to show the effectiveness of our architecture.Compared with mainstream consortium blockchain storage architecture based on LevelDB, the average time-consuming decreases 81.85%/82.47%for reading/writing continuous data and 22.21%/48.99%for reading/writing state data.Compared with the storage architecture based on distributed database TiKV, the time-consuming decreases more significantly.This storage architecture has been integrated into the enterprise-level consortium blockchain platform Hyperchain, which has supported the efficient running of dozens of large-scale commercial blockchain projects with massive data.
A Bilevel Learning Model and Algorithm for Self-Organizing Feed-Forward Neural Networks for Pattern Classification

Conventional artificial neural network (ANN) learning algorithms for classification tasks, either derivative-based optimization algorithms or derivative-free optimization algorithms work by training ANN first (or training and validating ANN) and then testing ANN, which are a two-stage and one-pass learning mechanism. Thus, this learning mechanism may not guarantee the generalization ability of a trained ANN. In this article, a novel bilevel learning model is constructed for self-organizing feed-forward neural network (FFNN), in which the training and testing processes are integrated into a unified framework. In this bilevel model, the upper level optimization problem is built for testing error on testing data set and network architecture based on network complexity, whereas the lower level optimization problem is constructed for network weights based on training error on training data set. For the bilevel framework, an interactive learning algorithm is proposed to optimize the architecture and weights of an FFNN with consideration of both training error and testing error. In this interactive learning algorithm, a hybrid binary particle swarm optimization (BPSO) taken as an upper level optimizer is used to self-organize network architecture, whereas the Levenberg–Marquardt (LM) algorithm as a lower level optimizer is utilized to optimize the connection weights of an FFNN. The bilevel learning model and algorithm have been tested on 20 benchmark classification problems. Experimental results demonstrate that the bilevel learning algorithm can significantly produce more compact FFNNs with more excellent generalization ability when compared with conventional learning algorithms.