Reviewerly/exHarmony huggingface dataset:
Weighted Ensemble Object Detection with Optimized Coefficients for Remote Sensing Images

The detection of objects in very high-resolution (VHR) remote sensing images has become increasingly popular with the enhancement of remote sensing technologies. High-resolution images from aircrafts or satellites contain highly detailed and mixed backgrounds that decrease the success of object detection in remote sensing images. In this study, a model that performs weighted ensemble object detection using optimized coefficients is proposed. This model uses the outputs of three different object detection models trained on the same dataset. The model’s structure takes two or more object detection methods as its input and provides an output with an optimized coefficient-weighted ensemble. The Northwestern Polytechnical University Very High Resolution 10 (NWPU-VHR10) and Remote Sensing Object Detection (RSOD) datasets were used to measure the object detection success of the proposed model. Our experiments reveal that the proposed model improved the Mean Average Precision (mAP) performance by 0.78%–16.5% compared to stand-alone models and presents better mean average precision than other state-of-the-art methods (3.55% higher on the NWPU-VHR-10 dataset and 1.49% higher when using the RSOD dataset).
The Top 10 Risks of Machine Learning Security

Our recent architectural risk analysis of machine learning systems identified 78 particular risks associated with nine specific components found in most machine learning systems. In this article, we describe and discuss the 10 most important security risks of those 78.
Deep Learning and Implementations in Banking

Data-driven technologies have been changing every aspect of human life and the fast-developing banking sector with its data-rich nature has become the implementation field of these fast-evolving technologies. Deep learning, as one of the emerging technologies in recent years, has also been inevitably adopted for various improvements in banking. To the best of our knowledge, there is no comprehensive literature review, which focuses on specifically deep learning and its implementations in banking. Therefore, this paper investigates the deep learning technology in-depth and summarizes the relevant applications in banking so to contribute to the existing literature. Moreover, by providing a reliable and up-to-date review, it is also aimed to serve as the one-stop repository for banks and researchers who are interested in embracing deep learning, whilst bringing insights for the directions of future research and implementation.
A Neural Network Based Intelligent Support Model for Program Code Completion

In recent years, millions of source codes are generated in different languages on a daily basis all over the world. A deep neural network-based intelligent support model for source code completion would be a great advantage in software engineering and programming education fields. Vast numbers of syntax, logical, and other critical errors that cannot be detected by normal compilers continue to exist in source codes, and the development of an intelligent evaluation methodology that does not rely on manual compilation has become essential. Even experienced programmers often find it necessary to analyze an entire program in order to find a single error and are thus being forced to waste valuable time debugging their source codes. With this point in mind, we proposed an intelligent model that is based on long short-term memory (LSTM) and combined it with an attention mechanism for source code completion. Thus, the proposed model can detect source code errors with locations and then predict the correct words. In addition, the proposed model can classify the source codes as to whether they are erroneous or not. We trained our proposed model using the source code and then evaluated the performance. All of the data used in our experiments were extracted from Aizu Online Judge (AOJ) system. The experimental results obtained show that the accuracy in terms of error detection and prediction of our proposed model approximately is 62% and source code classification accuracy is approximately 96% which outperformed a standard LSTM and other state-of-the-art models. Moreover, in comparison to state-of-the-art models, our proposed model achieved an interesting level of success in terms of error detection, prediction, and classification when applied to long source code sequences. Overall, these experimental results indicate the usefulness of our proposed model in software engineering and programming education arena.
Augmented reality and visuospatial bootstrapping for second-language vocabulary recall

This paper examines second-language vocabulary memorisation using two technology-driven flashcard-based vocabulary learning tools.The use of augmented reality (AR) under visuospatial bootstrapping (VSB), a novel approach developed from work on the cognitive psychology of working memory, was contrasted with an application, Quizlet.Both were implemented using mobile devices.Quizlet has been extensively used in foreign-language teaching and learning practice.The experimental AR-VSB technique offered superior vocabulary learning compared with the Quizlet method in delayed post-tests, although statistical data indicate a somewhat higher forgetting rate after a week in the AR-VSB method.Even so, the experimental technique still offers superior retention compared with the method used in the control group and could be used as an effective initial input method for acquiring vocabulary items in second-language learning.These results imply communication between cognitive systems involved in storing short-term memory for verbal and visual information alongside connections to and from knowledge held in long-term memory when the target information is shown in a familiar array, which are deployed during the AR task and which support enhanced vocabulary learning.The main novel finding in this research has been that the integration of immersive AR experiences into familiar physical space has been seen to improve vocabulary recall test performance among a sample of twenty-first-century university students attempting to learn a second language.The evidence gathered from the experiment can have future practical applications and might contribute to immersive educational technology and innovative material development in second-language instruction.
Need for Tweet

Social media, especially Twitter, has always been a part of the professional lives of software developers, with prior work reporting on a diversity of usage scenarios, including sharing information, staying current, and promoting one's work. However, previous studies of Twitter use by software developers typically lack information about activities of the study subjects (and their outcomes) on other platforms. To enable such future research, in this paper we propose a computational approach to cross-link users across Twitter and GitHub, revealing (at least) 70,427 users active on both. As a preliminary analysis of this dataset, we report on a case study of 786 tweets by open-source developers about GitHub work, combining automatic characterization of tweet authors in terms of their relationship to the GitHub items linked in their tweets with qualitative analysis of the tweet contents. We find that different developer roles tend to have different tweeting behaviors, with repository owners being perhaps the most distinctive group compared to other project contributors and followers. We also note a sizeable group of people who follow others on GitHub and tweet about these people's work, but do not otherwise contribute to those open-source projects. Our results and public dataset open up multiple future research directions.
Head pose estimation by regression algorithm

Head pose estimation is a very in-depth topic in the context of biometric recognition, especially in video surveillance, because the rotation of the head can affect the recognition of some features of the face. Being able to recognize in advance the pose of the head in pitch, yaw and roll enable frontalization or the extraction of a frame in which a face is frontal in order to allow a more accurate recognition. In this work the Web-Shaped Model algorithm is used for a coding of the pose of the face and then we apply regression algorithms to predict the pose of the face. The proposed approach stimulates the sensitivity of the regression methods to identify the head pose estimation. The goals is to predict the value of the dependent variable for the three angular values, for which some information relating to the explanatory variables is available, in order to estimate the effect on the dependent variable. The presented method is tested on some of the most well-known datasets for the head pose estimation as Biwi, AFLW2000 and Pointing’04 and compared with the various state of the art methods that use these datasets.
ASAPP-ASR: Multistream CNN and Self-Attentive SRU for SOTA Speech Recognition

In this paper we present state-of-the-art (SOTA) performance on the LibriSpeech corpus with two novel neural network architectures, a multistream CNN for acoustic modeling and a self-attentive simple recurrent unit (SRU) for language modeling. In the hybrid ASR framework, the multistream CNN acoustic model processes an input of speech frames in multiple parallel pipelines where each stream has a unique dilation rate for diversity. Trained with the SpecAugment data augmentation method, it achieves relative word error rate (WER) improvements of 4% on test-clean and 14% on test-other. We further improve the performance via N-best rescoring using a 24-layer self-attentive SRU language model, achieving WERs of 1.75% on test-clean and 4.46% on test-other.
Efficient intensity-based fringe projection profilometry method resistant to global illumination

Intensity-based fringe projection profilometry (IBFPP) is used widely because of its simple structure, high robustness, and noise resilience. Most IBFPP methods assume that any scene point is illuminated by direct illumination only, but global illumination effects introduce strong biases in the reconstruction result for many real-world scenes. To solve this problem, this paper describes an efficient IBFPP method for reconstructing three-dimensional geometry in the presence of global illumination. First, the average intensity of two sinusoidal patterns is used as a pixel-wise threshold to binarize the codeword patterns. The binarized template pattern is then used to convert other binarized fringe patterns into traditional Gray-code patterns. A proprietary compensation algorithm is then applied to eliminate fringe errors caused by environmental noise and lens defocusing. Finally, simple, efficient, and robust phase unwrapping can be achieved despite the effects of subsurface scattering and interreflection. Experimental results obtained in different environments show that the proposed method can obtain three-dimensional information reliably when influenced by global illumination.
Image Encryption Using Elliptic Curves and Rossby/Drift Wave Triads

We propose an image encryption scheme based on quasi-resonant Rossby/drift wave triads (related to elliptic surfaces) and Mordell elliptic curves (MECs). By defining a total order on quasi-resonant triads, at a first stage we construct quasi-resonant triads using auxiliary parameters of elliptic surfaces in order to generate pseudo-random numbers. At a second stage, we employ an MEC to construct a dynamic substitution box (S-box) for the plain image. The generated pseudo-random numbers and S-box are used to provide diffusion and confusion, respectively, in the tested image. We test the proposed scheme against well-known attacks by encrypting all gray images taken from the USC-SIPI image database. Our experimental results indicate the high security of the newly developed scheme. Finally, via extensive comparisons we show that the new scheme outperforms other popular schemes.
Plug and Play Autoencoders for Conditional Text Generation

Text autoencoders are commonly used for conditional generation tasks such as style transfer.We propose methods which are plug and play, where any pretrained autoencoder can be used, and only require learning a mapping within the autoencoder's embedding space, training embedding-to-embedding (Emb2Emb).This reduces the need for labeled training data for the task and makes the training procedure more efficient.Crucial to the success of this method is a loss term for keeping the mapped embedding on the manifold of the autoencoder and a mapping which is trained to navigate the manifold by learning offset vectors.Evaluations on style transfer tasks both with and without sequence-to-sequence supervision show that our method performs better than or comparable to strong baselines while being up to four times faster.
Machine Learning for Earth System Observation and Prediction

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).Corresponding author: Massimo Bonavita, massimo.bonavita@ecmwf.int
MZET: Memory Augmented Zero-Shot Fine-grained Named Entity Typing

Named entity typing (NET) is a classification task of assigning an entity mention in the context with given semantic types.However, with the growing size and granularity of the entity types, few previous researches concern with newly emerged entity types.In this paper, we propose MZET, a novel memory augmented FNET (Fine-grained NET) model, to tackle the unseen types in a zero-shot manner.MZET incorporates character-level, word-level, and contextural-level information to learn the entity mention representation.Besides, MZET considers the semantic meaning and the hierarchical structure into the entity type representation.Finally, through the memory component which models the relationship between the entity mention and the entity type, MZET transfers the knowledge from seen entity types to the zero-shot ones.Extensive experiments on three public datasets show the superior performance obtained by MZET, which surpasses the state-of-the-art FNET neural network models with up to 8% gain in Micro-F1 and Macro-F1 score.
SSGP: Sparse Spatial Guided Propagation for Robust and Generic Interpolation

Interpolation of sparse pixel information towards a dense target resolution finds its application across multiple disciplines in computer vision. State-of-the-art interpolation of motion fields applies model-based interpolation that makes use of edge information extracted from the target image. For depth completion, data-driven learning approaches are widespread. Our work is inspired by latest trends in depth completion that tackle the problem of dense guidance for sparse information. We extend these ideas and create a generic cross-domain architecture that can be applied for a multitude of interpolation problems like optical flow, scene flow, or depth completion. In our experiments, we show that our proposed concept of Sparse Spatial Guided Propagation (SSGP) achieves improvements to robustness, accuracy, or speed compared to specialized algorithms.
Optimizing a polynomial function on a quantum processor

Abstract The gradient descent method is central to numerical optimization and is the key ingredient in many machine learning algorithms. It promises to find a local minimum of a function by iteratively moving along the direction of the steepest descent. Since for high-dimensional problems the required computational resources can be prohibitive, it is desirable to investigate quantum versions of the gradient descent, such as the recently proposed (Rebentrost et al. 1 ). Here, we develop this protocol and implement it on a quantum processor with limited resources. A prototypical experiment is shown with a four-qubit nuclear magnetic resonance quantum processor, which demonstrates the iterative optimization process. Experimentally, the final point converged to the local minimum with a fidelity &gt;94%, quantified via full-state tomography. Moreover, our method can be employed to a multidimensional scaling problem, showing the potential to outperform its classical counterparts. Considering the ongoing efforts in quantum information and data science, our work may provide a faster approach to solving high-dimensional optimization problems and a subroutine for future practical quantum computers.
UBMTR: Unsupervised Boltzmann machine-based time-aware recommendation system

Visual media, in today’s world, has swept across most forms of day to day communication. In the paradigm of generative modelling, restricted Boltzmann machines (RBMs) are used to solve complex tasks such as feature extraction, neuroimaging, collaborative filtering, radar target cognition, etc. In this paper, we implement an unsupervised Boltzmann machine-based time-aware recommendation system (UBMTR) which detects underlying hidden features in user-movie ratings data in connection with the time at which each rating was made (temporal information). The model takes ratings and time as a dual-input and outputs binary values via the contrastive divergence algorithm which samples from a Monte Carlo Markov Chain. Arguably, there exists a correlation between the content requested and the temporal conditions, which is exactly what our model tries to exploit. There is seldom any work in the field of recommender systems built using Boltzmann machines that incorporate temporal information, which necessitates research in this domain. RBMs are adept at pattern completion to tackle missing values, and can deal with imbalanced datasets and unstructured data by encoding raw data into latent variables. Using RBM, the UBMTR outperforms many earlier made attempts made at recommendation systems done through CF and deep learning or their hybridized models.
Rating Prediction in Recommender Systems Based on User Behavior Probability and Complex Network Modeling

In recommender systems, measuring user similarity is essential for predicting a user's ratings on items. Most traditional works calculate the similarity based on historical ratings shared between two users, without considering the probability of users' different behaviors. To address this issue, our work designs a similarity measure based on the users' historical behavior probabilities. Based on the similarity method, a complex network of user relationships is modeled. The user degree and community information of the modeled network, as well as the number of shared ratings between users, are used with the proposed similarity measure to design a rating prediction algorithm for recommender systems. Experiments based on MovieLens and Netflix data sets show that this method is effective and can successfully improve the accuracy of rating predictions and reduce the number of neighbors required to achieve the optimal prediction accuracy. Our research shows that in a complex system, the relationship between users can be effectively measured by the users' historical behavior probability, providing a new perspective for future research on similarity measurement.
Information to Wisdom: Commonsense Knowledge Extraction and Compilation

Commonsense knowledge is a foundational cornerstone of artificial intelligence applications. Whereas information extraction and knowledge base construction for instance-oriented assertions, such as Brad Pitt's birth date, or Angelina Jolie's movie awards, has received much attention, commonsense knowledge on general concepts (politicians, bicycles, printers) and activities (eating pizza, fixing printers) has only been tackled recently. In this tutorial we present state-of-the-art methodologies towards the compilation and consolidation of such commonsense knowledge (CSK). We cover text-extraction-based, multi-modal and Transformer-based techniques, with special focus on the issues of web search and ranking, as of relevance to the WSDM community.
Vaping and Instagram: A Content Analysis of e-Cigarette Posts Using the Content Appealing to Youth (CAY) Index

Background The promotion of flavors, perceptions of “coolness,” and general curiosity are characteristics of electronic nicotine delivery systems (ENDS) that have appealed to young adults. However, little is known about the characteristics of popular social media posts related to ENDS on the social media network, Instagram. Methods: Content analysis was performed using the Content Appealing to Youth (CAY) index. Over 700 posts were collected from August 2019 – December 2019 by searching the Instagram hashtags, #vape and #vapelife. Frequencies and percentages were calculated for each of the six major categories and 35 sub-categories. Results: Nearly all of the images were color photographs and 84% featured an ENDS device (mod) as the focal point. The style of the device was often matte (75%) in only one or two main colors (55%). Warnings about age restrictions and nicotine were included in 28% of images, but commonly used promotional tactics, such as humor, presence of vapor puffs, and flavors were rarely utilized. Conclusions: Instagram posts featuring ENDS are visually appealing and like cigarette packaging, may have the capacity to influence perceptions about the product. Since it is culturally normative for appealing images to be shared on Instagram, greater attention should be placed on media literacy skills to educate young adults about ENDS viewed on social media.
Face mask detection based on Transfer learning and PP-YOLO

Taking into account the accuracy and reasoning speed of face mask detection tasks, this paper proposes a detection model PP-YOLO-Mask based on PP-YOLO through transfer learning, data augmentation and model compression methods. In a public scene, the mask detection mAP of this model is 86.69%, and the single picture recognition speed is 11.842ms. Experimental results show that compared with YOLOv3 and FasterRCNN, the model has faster accuracy and detection speed.
Multimodal Named Entity Recognition with Image Attributes and Image Knowledge

Multimodal named entity extraction is an emerging task which uses both textual and visual information to detect named entities and identify their entity types. The existing efforts are often flawed in two aspects. Firstly, they may easily ignore the natural prejudice of visual guidance brought by the image. Secondly, they do not further explore the knowledge contained in the image. In this paper, we novelly propose a novel neural network model which introduces both image attributes and image knowledge to help improve named entity extraction. While the image attributes are high-level abstract information of an image that could be labelled by a pre-trained model based on ImageNet, the image knowledge could be obtained from a general encyclopedia knowledge graph with multi-modal information such as DBPedia and Yago. Our emperical study conducted on real-world data collection demonstrates the effectiveness of our approach comparing with several state-of-the-art approaches.
Self-selective attention using correlation between instances for distant supervision relation extraction

Distant supervision relation extraction methods are widely used to extract relational facts in text. The traditional selective attention model regards instances in the bag as independent of each other, which makes insufficient use of correlation information between instances and supervision information of all correctly labeled instances, affecting the performance of relation extractor. Aiming at this problem, a distant supervision relation extraction method with self-selective attention is proposed. The method uses a layer of convolution and self-attention mechanism to encode instances to learn the better semantic vector representation of instances. The correlation between instances in the bag is used to assign a higher weight to all correctly labeled instances, and the weighted summation of instances in the bag is used to obtain a bag vector representation. Experiments on the NYT dataset show that the method can make full use of the information of all correctly labeled instances in the bag. The method can achieve better results as compared with baselines.