Reviewerly/exHarmony huggingface dataset:
Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information

We investigate the following question for machine translation (MT): can we develop a single universal MT model to serve as the common seed and obtain derivative and improved models on arbitrary language pairs?We propose mRASP, an approach to pre-train a universal multilingual neural machine translation model.Our key idea in mRASP is its novel technique of random aligned substitution, which brings words and phrases with similar meanings across multiple languages closer in the representation space.We pre-train a mRASP model on 32 language pairs jointly with only public datasets.The model is then fine-tuned on downstream language pairs to obtain specialized MT models.We carry out extensive experiments on 42 translation directions across a diverse settings, including low, medium, rich resource, and as well as transferring to exotic language pairs.Experimental results demonstrate that mRASP achieves significant performance improvement compared to directly training on those target pairs.It is the first time to verify that multiple lowresource language pairs can be utilized to improve rich resource MT.Surprisingly, mRASP is even able to improve the translation quality on exotic languages that never occur in the pretraining corpus.Code, data, and pre-trained models are available at https://github.com/linzehui/mRASP.
Deep graph similarity learning: a survey

Abstract In many domains where data are represented as graphs, learning a similarity metric among graphs is considered a key problem, which can further facilitate various learning tasks, such as classification, clustering, and similarity search. Recently, there has been an increasing interest in deep graph similarity learning, where the key idea is to learn a deep learning model that maps input graphs to a target space such that the distance in the target space approximates the structural distance in the input space. Here, we provide a comprehensive review of the existing literature of deep graph similarity learning. We propose a systematic taxonomy for the methods and applications. Finally, we discuss the challenges and future directions for this problem.
Disentangle domain features for cross-modality cardiac image segmentation

Unsupervised domain adaptation (UDA) generally learns a mapping to align the distribution of the source domain and target domain. The learned mapping can boost the performance of the model on the target data, of which the labels are unavailable for model training. Previous UDA methods mainly focus on domain-invariant features (DIFs) without considering the domain-specific features (DSFs), which could be used as complementary information to constrain the model. In this work, we propose a new UDA framework for cross-modality image segmentation. The framework first disentangles each domain into the DIFs and DSFs. To enhance the representation of DIFs, self-attention modules are used in the encoder which allows attention-driven, long-range dependency modeling for image generation tasks. Furthermore, a zero loss is minimized to enforce the information of target (source) DSFs, contained in the source (target) images, to be as close to zero as possible. These features are then iteratively decoded and encoded twice to maintain the consistency of the anatomical structure. To improve the quality of the generated images and segmentation results, several discriminators are introduced for adversarial learning. Finally, with the source data and their DIFs, we train a segmentation network, which can be applicable to target images. We validated the proposed framework for cross-modality cardiac segmentation using two public datasets, and the results showed our method delivered promising performance and compared favorably to state-of-the-art approaches in terms of segmentation accuracies. The source code of this work will be released via https://zmiclab.github.io/projects.html, once this manuscript is accepted for publication.
Data-Driven Resource Management for Ultra-Dense Small Cells: An Affinity Propagation Clustering Approach

Deploying dense small cells is the key to providing high capacity, but raise the serious issue of energy consumption and inter-cell interference.To understand the behaviors of ultra-dense small cells (UDSC) with dynamic interference and traffic patterns, this paper presents a data-driven resource management (DDRM) framework to implement power control and channel rearrangement in UDSC.We find that the inter-cell interference can be used to describe the affinity of cells.Thus, we propose an unsupervised learning algorithm for UDSC, called affinity propagation power control (APPC) mechanism.In principle, APPC first groups small cells into different clusters and identifies cluster centers.Next, the transmission power of a cluster center is decreased to reduce the interference to the neighboring cells' users in this cluster.Since lowering transmission power of a cluster center cell may cause the performance degradation to the users at the cell edge, a victim-aware channel rearrangement (VACR) mechanism is further designed to adjust the channel usage bandwidth of the neighboring cells in order to guarantee the quality of service of these victimized users.Our simulation results show that the DDRM framework can significantly improve energy efficiency and throughput in UDSC compared to the existing approaches.
Knowledge empowered prominent aspect extraction from product reviews

Many existing systems for analyzing and summarizing customer reviews about products or service are based on a number of prominent review aspects. Conventionally, the prominent review aspects of a product type are determined manually. This costly approach cannot scale to large and cross-domain services such as Amazon.com, Taobao.com or Yelp.com where there are a large number of product types and new products emerge almost everyday. In this paper, we propose a novel method empowered by knowledge sources such as Probase and WordNet, for extracting the most prominent aspects of a given product type from textual reviews. The proposed method, ExtRA (Extraction of Prominent Review Aspects), (i) extracts the aspect candidates from text reviews based on a data-driven approach, (ii) builds an aspect graph utilizing the Probase to narrow the aspect space, (iii) separates the space into reasonable aspect clusters by employing a set ofproposed algorithms and finally (iv) generates K most prominent aspect terms or phrases which do not overlap semantically automatically without supervision from those aspect clusters. ExtRA extracts high-quality prominent aspects as well as aspect clusters with little semantic overlap by exploring knowledge sources. ExtRA can extract not only words but also phrases as prominent aspects. Furthermore, it is general-purpose and can be applied to almost any type of product and service. Extensive experiments show that ExtRA is effective and achieves the state-of-the-art performance on a dataset consisting of different product types.
Spatial localization of EEG electrodes using 3D scanning

Objective. A reliable reconstruction of neural activity using high-density electroencephalography (EEG) requires an accurate spatial localization of EEG electrodes aligned to the structural magnetic resonance (MR) image of an individual's head. Current technologies for electrode positioning, such as electromagnetic digitization, are yet characterized by non-negligible localization and co-registration errors. In this study, we propose an automated method for spatial localization of EEG electrodes using 3D scanning, a non-invasive and easy-to-use technology with potential applications in clinical settings. Approach. Our method consists of three main steps: (1) the 3D scan is ambient light-corrected and spatially aligned to the head surface extracted from the anatomical MR image; (2) electrode positions are identified by segmenting the 3D scan based on predefined colour and topological properties; (3) electrode labelling is performed by aligning an EEG montage template to the electrode positions. The performance of the method was assessed on data collected in eight participants wearing high-density EEG caps with 128 sensors, from three different manufacturers. We estimated the co-registration error using the distance between the MR-based head shape and the closest 3D scan points. Also, we quantified the positioning error using the distance between the detected electrode positions and the corresponding locations manually selected on the 3D scan data. Main results. For all participants and EEG caps, we obtained a median error of co-registration below 3.0 mm and of spatial localization below 1.4 mm. The method based on 3D scanning data was significantly more precise compared to the electromagnetic digitization technique, and the total time required for obtaining electrode positions was reduced by about half. Significance. We have introduced a method to automatically detect EEG electrodes based on 3D scanning information. We believe that our work can contribute to a more effective, reliable and widespread use of high-density EEG as brain imaging tool.
(Re-)Framing Menopause Experiences for HCI and Design

Informed by considerations from medicine and wellness research, experience design, investigations of new and emerging technologies, and sociopolitical critique, HCI researchers have demonstrated that women's health is a complex and rich topic. Turning these research outputs into productive interventions, however, is difficult. We argue that design is well positioned to address such a challenge thanks to its methodological traditions of problem setting and framing situated in synthetic (rather than analytic) knowledge production. In this paper, we focus on designing for experiences of menopause. Building on our prior empirical work on menopause and our commitment to pursue design informed by women's lived experience, we iteratively generated dozens of design frames and accompanying design crits. We document the unfolding of our design reasoning, showing how good-seeming insights nonetheless often lead to bad designs, while working progressively towards stronger insights and design constructs. The latter we offer as a contribution to researchers and practitioners who work at the intersections of women's health and design.
GNER: A Generative Model for Geological Named Entity Recognition Without Labeled Data Using Deep Learning

Abstract A variety of detailed data about geological topics and geoscience knowledge are buried in the geoscience literature and rarely used. Named entity recognition (NER) provides both opportunities and challenges to leverage this wealth of data in the geoscience literature for data analysis and further information extraction. Existing NER models and techniques are mainly based on rule‐based and supervised approaches, and developing such systems requires a costly manual effort. In this paper, we first design a generic stepwise framework for domain‐specific NER. Following this framework, domain‐specific entities and domain‐general words are collected and selected as seed terms. Normalization and grouping processes are then applied to these seed terms for further analysis. A random extraction algorithm based on a unigram language model is used to generate a large‐scale training data set consisting of probabilistically labeled pseudosentences. Each generated sentence is then used as input to the self‐training and learning algorithm. Experimental results on two constructed data sets demonstrate that the proposed model effectively recognizes and identifies geological named entities.
Learning deep CNNs for impulse noise removal in images

Deep learning has been widely applied in image processing and computer vision due to its powerful learning capability. Although some learning models have been proposed to suppress noise in images, most of them are developed for Gaussian noise and few are for impulse noise. This paper proposes an image recovery method based on deep convolutional neural networks for impulse noise removal. The proposed framework falls into two components: a classifier network which divides image pixels into noisy and noise-free, and a regression network which is trained for image reconstruction. In the regression network, the noise-free pixels identified by the classifier network together with the original noisy image are used for recovery of the noisy image. Furthermore, batch normalization is embedded to the network to improve denoising performance. Experimental results show that the proposed method can excellently remove impulse noise, providing clear performance improvements over other state-of-the-art denoising methods.
Bayesian Hierarchical Dynamic Model for Human Action Recognition

Human action recognition remains as a challenging task partially due to the presence of large variations in the execution of action. To address this issue, we propose a probabilistic model called Hierarchical Dynamic Model (HDM). Leveraging on Bayesian framework, the model parameters are allowed to vary across different sequences of data, which increase the capacity of the model to adapt to intra-class variations on both spatial and temporal extent of actions. Meanwhile, the generative learning process allows the model to preserve the distinctive dynamic pattern for each action class. Through Bayesian inference, we are able to quantify the uncertainty of the classification, providing insight during the decision process. Compared to state-of-the-art methods, our method not only achieves competitive recognition performance within individual dataset but also shows better generalization capability across different datasets. Experiments conducted on data with missing values also show the robustness of the proposed method.
Women’s Syntactic Resilience and Men’s Grammatical Luck: Gender-Bias in Part-of-Speech Tagging and Dependency Parsing

Several linguistic studies have shown the prevalence of various lexical and grammatical patterns in texts authored by a person of a particular gender, but models for part-of-speech tagging and dependency parsing have still not adapted to account for these differences.To address this, we annotate the Wall Street Journal part of the Penn Treebank with the gender information of the articles' authors, and build taggers and parsers trained on this data that show performance differences in text written by men and women.Further analyses reveal numerous part-of-speech tags and syntactic relations whose prediction performances benefit from the prevalence of a specific gender in the training data.The results underscore the importance of accounting for gendered differences in syntactic tasks, and outline future venues for developing more accurate taggers and parsers.We release our data to the research community.
Quantized Minimum Error Entropy Criterion

Comparing with traditional learning criteria, such as mean square error, the minimum error entropy (MEE) criterion is superior in nonlinear and non-Gaussian signal processing and machine learning. The argument of the logarithm in Renyi's entropy estimator, called information potential (IP), is a popular MEE cost in information theoretic learning. The computational complexity of IP is, however, quadratic in terms of sample number due to double summation. This creates the computational bottlenecks, especially for large-scale data sets. To address this problem, in this paper, we propose an efficient quantization approach to reduce the computational burden of IP, which decreases the complexity from O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) to O(MN) with M ≪ N. The new learning criterion is called the quantized MEE (QMEE). Some basic properties of QMEE are presented. Illustrative examples with linear-in-parameter models are provided to verify the excellent performance of QMEE.
Automatic semantic style transfer using deep convolutional neural networks and soft masks

This paper presents an automatic image synthesis method to transfer the style of an example image to a content image. When standard neural style transfer approaches are used, the textures and colours in different semantic regions of the style image are often applied inappropriately to the content image, ignoring its semantic layout and ruining the transfer result. In order to reduce or avoid such effects, we propose a novel method based on automatically segmenting the objects and extracting their soft semantic masks from the style and content images, in order to preserve the structure of the content image while having the style transferred. Each soft mask of the style image represents a specific part of the style image, corresponding to the soft mask of the content image with the same semantics. Both the soft masks and source images are provided as multichannel input to an augmented deep CNN framework for style transfer which incorporates a generative Markov random field model. The results on various images show that our method outperforms the most recent techniques.
Deep Reinforcement Learning for Drone Delivery

Drones are expected to be used extensively for delivery tasks in the future. In the absence of obstacles, satellite based navigation from departure to the geo-located destination is a simple task. When obstacles are known to be in the path, pilots must build a flight plan to avoid them. However, when they are unknown, there are too many or they are in places that are not fixed positions, then to build a safe flight plan becomes very challenging. Moreover, in a weak satellite signal environment, such as indoors, under trees canopy or in urban canyons, the current drone navigation systems may fail. Artificial intelligence, a research area with increasing activity, can be used to overcome such challenges. Initially focused on robots and now mostly applied to ground vehicles, artificial intelligence begins to be used also to train drones. Reinforcement learning is the branch of artificial intelligence able to train machines. The application of reinforcement learning to drones will provide them with more intelligence, eventually converting drones in fully-autonomous machines. In this work, reinforcement learning is studied for drone delivery. As sensors, the drone only has a stereo-vision front camera, from which depth information is obtained. The drone is trained to fly to a destination in a neighborhood environment that has plenty of obstacles such as trees, cables, cars and houses. The flying area is also delimited by a geo-fence; this is a virtual (non-visible) fence that prevents the drone from entering or leaving a defined area. The drone has to avoid visible obstacles and has to reach a goal. Results show that, in comparison with the previous results, the new algorithms have better results, not only with a better reward, but also with a reduction of its variance. The second contribution is the checkpoints. They consist of saving a trained model every time a better reward is achieved. Results show how checkpoints improve the test results.
Efficacy of multi-season Sentinel-2 imagery for compositional vegetation classification

Vegetation maps are essential tools for the conservation and management of landscapes as they contain essential information for informing conservation decisions. Traditionally, maps have been created using field-based approaches which, due to limitations in costs and time, restrict the size of the area for which they can be created and frequency at which they can be updated. With the increasing availability of satellite sensors providing multi-spectral imagery with high temporal frequency, new methods for efficient and accurate vegetation mapping have been developed. The objective of this study was to investigate to what extent multi-seasonal Sentinel-2 imagery can assist in mapping complex compositional classifications at fine spatial scales. We deliberately chose a challenging case study, namely a visually and structurally homogenous scrub vegetation (known as kwongan) of Western Australia. The classification scheme consists of 24 target classes and a random 60/40 split was used for model building and validation. We compared several multi-temporal (seasonal) feature sets, consisting of numerous combinations of spectral bands, vegetation indices as well as principal component and tasselled cap transformations, as input to four machine learning classifiers (Support Vector Machines; SVM, Nearest Neighbour; NN, Random Forests; RF, and Classification Trees; CT) to separate target classes. The results show that a multi-temporal feature set combining autumn and spring images sufficiently captured the phenological differences between the classes and produced the best results, with SVM (74%) and NN (72%) classifiers returning statistically superior results compared to RF (65%) and CT (50%). The SWIR spectral bands captured during spring, the greenness indices captured during spring and the tasselled cap transformations derived from the autumn image emerged as most informative, which suggests that ecological factors (e.g. shared species, patch dynamics) occurring at a sub-pixel level likely had the biggest impact on class confusion. However, despite these challenges, the results are auspicious and suggest that seasonal Sentinel-2 imagery has the potential to predict compositional vegetation classes with high accuracy. Further work is needed to determine whether these results are replicable in other vegetation types and regions.
Comparison of Pre and Post-Action of a Finite Abelian Group Over Certain Nonlinear Schemes

This paper proposes to present a novel group theoretic approach of improvising the cryptographic features of substitution-boxes. The approach employs a proposed finite Abelian group of order 3720 with three generators and six relations. The pre and post-action of the new Abelian group on some nonlinear schemes is analyzed and investigated. It has been found that post-action is competent to construct substitution-boxes whose cryptographic strengths are quite better compared to them before the group action. The S-box strength improvisation has been perceived on multiple performance parameters including nonlinearity, differential uniformity, bits independent criteria, linear approximation probability, and auto-correlation functions along with the satisfaction of strict avalanche criteria. The suitability of proposed improved S-box is tested for image encryption applications under the majority logic criterions and differential analyses. The conducted statistical investigations demonstrated the proficiency of anticipated group action approach and its suitability for cryptographic usages.
A Secured and Robust Medical Image Watermarking Approach for Protecting Integrity of Medical Images

Abstract In this paper, a novel medical image watermarking (MIW) technique for tele ‐ medicine applications is proposed. In this approach homomorphic transform (HT), redundant discrete wavelet transform (RDWT) and singular value decomposition (SVD) transforms are successfully utilized for developing an effective watermarking technique. Reflectance component of medical host image is obtained using HT, on which RDWT decomposition and SVD are applied. Similarly medical watermark image is processed with RDWT and SVD. Singular values (Svs) were used for embedding the watermark image into host image. 2‐D chaotic Arnold transform (AT) is used for providing extra security to the watermark image by encrypting the watermarked image. Moreover, proposed technique is also tested for several wavelet families. Robustness and imperceptibility of the proposed technique is enhanced as depicted in experimental results under various attacks. Furthermore, better robustness is attained from proposed technique on comparing it with other formerly reported schemes.