Reviewerly/exHarmony huggingface dataset:
Sparse Self-Attention LSTM for Sentiment Lexicon Construction

Sentiment lexicon is a very important resource for opinion mining. Recently, many state-of-the-art works employ deep learning techniques to construct sentiment lexicons. In general, they firstly learn sentiment-aware word embeddings, and then use it as word features to construct sentiment lexicons. However, these methods do not consider the importance of each word to the distinguish of documents' sentiment polarities. As we know, most words among a document do not contribute to understand documents' semantic or sentiment. For example, in the tweet It's a good day, but i can't feel it. I'm really unhappy. The words `unhappy', `feel' and `can't' are much more important than the words `good', `day' in predicting the sentiment polarity of this twitter. Meanwhile, many words, such as `the', `in', `it' and `I'm' are uninformative. In this paper, we propose a novel sparse self-attention LSTM (SSALSTM) to efficiently capture the above intuitive facts, and then construct a large scale sentiment lexicons in twitter. In SSALSTM, we use a novel self-attention mechanism to capture the importance of each words to the distinguish of documents' sentiment polarities. In addition, a $L_1$ regularize is applied in the attentions which can ensure the sparsity characters that most words in a document are semantic and sentiment indistinguishable. Once we learn an efficient sentiment-aware word embedding, we train a classifier which uses sentiment-aware word embedding as features to predict the sentiment polarities of words. Extensive experiments on four publicly available datasets, SemEval 2013-2016, indicate that the sentiment lexicon generated by our proposed model achieves state-of-the-art performance on both supervised and unsupervised sentiment classification tasks.
The Central Role of Weathering in the Geosciences

Research Article| August 01, 2019 The Central Role of Weathering in the Geosciences Patrick J. Frings; Patrick J. Frings Earth Surface Geochemistry, GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, GermanyDepartment of Geosciences, Swedish Museum of Natural History, Frescativägen 40, 10405 Stockholm, Sweden E-mail: patrick.frings@gfz-potsdam.de Search for other works by this author on: GSW Google Scholar Heather L. Buss Heather L. Buss School of Earth Sciences, University of Bristol, Wills Memorial Building, Queens Road, Clifton, BS8 1RJ Bristol, United Kingdom E-mail: H.Buss@bristol.ac.uk Search for other works by this author on: GSW Google Scholar Elements (2019) 15 (4): 229–234. https://doi.org/10.2138/gselements.15.4.229 Article history first online: 29 Jul 2019 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Patrick J. Frings, Heather L. Buss; The Central Role of Weathering in the Geosciences. Elements 2019;; 15 (4): 229–234. doi: https://doi.org/10.2138/gselements.15.4.229 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyElements Search Advanced Search Weathering is the chemical and physical alteration of rock at the surface of the Earth, but its importance is felt well beyond the rock itself. The repercussions of weathering echo throughout the Earth sciences, from ecology to climatology, from geomorphology to geochemistry. This article outlines how weathering interacts with various geoscience disciplines across a huge range of scales, both spatial and temporal. It traces the evolution of scientific thinking about weathering and man's impact on weathering itself—for better and for worse. Future computational, conceptual and methodological advances are set to cement weathering's status as a central process in the Earth sciences. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
Cytokeratin-Supervised Deep Learning for Automatic Recognition of Epithelial Cells in Breast Cancers Stained for ER, PR, and Ki-67

Immunohistochemistry (IHC) of ER, PR, and Ki-67 are routinely used assays in breast cancer diagnostics. Determination of the proportion of stained cells (labeling index) should be restricted on malignant epithelial cells, carefully avoiding tumor infiltrating stroma and inflammatory cells. Here, we developed a deep learning based digital mask for automated epithelial cell detection using fluoro-chromogenic cytokeratin-Ki-67 double staining and sequential hematoxylin-IHC staining as training material. A partially pre-trained deep convolutional neural network was fine-tuned using image batches from 152 patient samples of invasive breast tumors. Validity of the trained digital epithelial cell masks was studied with 366 images captured from 98 unseen samples, by comparing the epithelial cell masks to cytokeratin images and by visual evaluation of the brightfield images performed by two pathologists. A good discrimination of epithelial cells was achieved (AUC of mean ROC = 0.93; defined as the area under mean receiver operating characteristics), and well in concordance with pathologists' visual assessment (4.01/5 and 4.67/5). The effect of epithelial cell masking on the Ki-67 labeling index was substantial. 52 tumor images initially classified as low proliferation (Ki-67 <; 14%) without epithelial cell masking were re-classified as high proliferation (Ki-67 ≥ 14%) after applying the deep learning based epithelial cell mask. The digital epithelial cell masks were found applicable also to IHC of ER and PR. We conclude that deep learning can be applied to detect carcinoma cells in breast cancer samples stained with conventional brightfield IHC.
Financial news recommendation based on graph embeddings

Most of the existing methods are not enough for securities companies to make their decisions on recommending the most suitable financial news to a specific user. On the one hand, such news articles often contain external knowledge related to companies and stocks. On the other hand, it is important for financial news recommendations to dynamically measure users' interests since people are usually interested in multiple specific concepts, companies, stocks and industry categories. To address the above challenges, we start by building a heterogeneous graph consisting of users, news, companies, concepts, and industry categories. Then, the graph embeddings of the nodes are generated using node2vec, and user-news relatedness can be computed based on them. Since financial news articles are time-sensitive, we propose an incremental method for alleviating the computational efficiency problem. The combination of a node2vec-based recommendation method and the incremental method can achieve a good balance between time efficiency and recommendation accuracy in the financial news recommendation task. Our methods are evaluated on a real-world dataset from a Chinese securities company, are shown to outperform other commonly used baseline models, can provide decision support for companies choosing news to be recommended to target users and allow users to obtain personalized real-time news recommendations.
GM-PLL: Graph Matching Based Partial Label Learning

Partial Label Learning (PLL) aims to learn from the data where each training example is associated with a set of candidate labels, among which only one is correct. The key to deal with such problem is to disambiguate the candidate label sets and obtain the correct assignments between instances and their candidate labels. In this paper, we interpret such assignments as instance-to-label matchings, and reformulate the task of PLL as a matching selection problem. To model such problem, we propose a novel Graph Matching based Partial Label Learning (GM-PLL) framework, where Graph Matching (GM) scheme is incorporated owing to its excellent capability of exploiting the instance and label relationship. Meanwhile, since conventional one-to-one GM algorithm does not satisfy the constraint of PLL problem that multiple instances may correspond to the same label, we extend a traditional one-to-one probabilistic matching algorithm to the many-to-one constraint, and make the proposed framework accommodate to the PLL problem. Moreover, we also propose a relaxed matching prediction model, which can improve the prediction accuracy via GM strategy. Extensive experiments on both artificial and real-world data sets demonstrate that the proposed method can achieve superior or comparable performance against the state-of-the-art methods.
Fruit fly optimization algorithm based on a hybrid adaptive-cooperative learning and its application in multilevel image thresholding

Multilevel thresholding is widely exploited in image processing, however, most of the techniques are time-consuming. In this paper, we present a novel approach, multilevel thresholding with fruit fly optimization algorithm (FOA). As yet, FOA has not been applied to resolve the complex image processing problems. Nevertheless, the merits of FOA were validated in former research, which include few parameters, simple structure, easy to understand and implement. Here, we introduce it into the study of multi-threshold image processing area. Moreover, we incorporate a hybrid adaptive-cooperative learning strategy with the proposed method called HACLFOA. The fruit fly population is divided into two sub-populations and both of them have a different iteration step range. In addition, each dimension of the solution vector will be optimized during one search, and we also make the best of the temporary global optimum information. The results of computational experiments on 24 benchmark functions demonstrate that the proposed algorithm has superior global convergence ability against other algorithms. Most significantly, extensive results show that the proposed algorithm is time-saving in multilevel image thresholding, and that it has great potential in the image processing field.
Depth Completion with Deep Geometry and Context Guidance

In this paper, we present an end-to-end convolutional neural network (CNN) for depth completion. Our network consists of a geometry network and a context network. The geometry network, a single encoder-decoder network, learns to optimize a multi-task loss to generate an initial propagated depth map and a surface normal. The complementary outputs allow it to correctly propagate initial sparse depth points in slanted surfaces. The context network extracts a local and a global feature of an image to compute a bilateral weight, which enables it to preserve edges and fine details in the depth maps. At the end, a final output is produced by multiplying the initially propagated depth map with the bilateral weight. In order to validate the effectiveness and the robustness of our network, we performed extensive ablation studies and compared the results against state-of-the-art CNN-based depth completions, where we showed promising results on various scenes.
Visual Robot Task Planning

Prospection is key to solving challenging problems in new environments, but it has not been deeply explored as applied to task planning for perception-driven robotics. We propose visual robot task planning, where we take in an input image and must generate a sequence of high-level actions and associated observations that achieve some task. In this paper, we describe a neural network architecture and associated planning algorithm that (1) learns a representation of the world that can generate prospective futures, (2) uses this generative model to simulate the result of sequences of high-level actions in a variety of environments, and (3) evaluates these actions via a variant of Monte Carlo Tree Search to find a viable solution to a particular problem. Our approach allows us to visualize intermediate motion goals and learn to plan complex activity from visual information, and used this to generate and visualize task plans on held-out examples of a block-stacking simulation.
A Stacked BiLSTM Neural Network Based on Coattention Mechanism for Question Answering

Deep learning is the crucial technology in intelligent question answering research tasks. Nowadays, extensive studies on question answering have been conducted by adopting the methods of deep learning. The challenge is that it not only requires an effective semantic understanding model to generate a textual representation but also needs the consideration of semantic interaction between questions and answers simultaneously. In this paper, we propose a stacked Bidirectional Long Short-Term Memory (BiLSTM) neural network based on the coattention mechanism to extract the interaction between questions and answers, combining cosine similarity and Euclidean distance to score the question and answer sentences. Experiments are tested and evaluated on publicly available Text REtrieval Conference (TREC) 8-13 dataset and Wiki-QA dataset. Experimental results confirm that the proposed model is efficient and particularly it achieves a higher mean average precision (MAR) of 0.7613 and mean reciprocal rank (MRR) of 0.8401 on the TREC dataset.
Semi-Supervised Adversarial Monocular Depth Estimation

In this paper, we address the problem of monocular depth estimation when only a limited number of training image-depth pairs are available. To achieve a high regression accuracy, the state-of-the-art estimation methods rely on CNNs trained with a large number of image-depth pairs, which are prohibitively costly or even infeasible to acquire. Aiming to break the curse of such expensive data collections, we propose a semi-supervised adversarial learning framework that only utilizes a small number of image-depth pairs in conjunction with a large number of easily-available monocular images to achieve high performance. In particular, we use one generator to regress the depth and two discriminators to evaluate the predicted depth, i.e., one inspects the image-depth pair while the other inspects the depth channel alone. These two discriminators provide their feedbacks to the generator as the loss to generate more realistic and accurate depth predictions. Experiments show that the proposed approach can (1) improve most state-of-the-art models on the NYUD v2 dataset by effectively leveraging additional unlabeled data sources; (2) reach state-of-the-art accuracy when the training set is small, e.g., on the Make3D dataset; (3) adapt well to an unseen new dataset (Make3D in our case) after training on an annotated dataset (KITTI in our case).
A Cover Selection HEVC Video Steganography Based on Intra Prediction Mode

Existing video steganography puts much emphasis on the design of algorithms such as mapping rules or distortion functions, thereby ignoring the selection of cover to embed secret information. However, this is just one of the major differences between image steganography and video steganography. In addition, since HEVC is the latest standard in the video codec field, it is of important academic significance and applied value to study HEVC-based steganography. This paper proposes a novel video steganography in HEVC, based on intra-prediction mode (IPM). Firstly, this paper analyzes the probability distribution of 4 × 4 IPMs. Then a cover selection rule combined with the Coding Unit (CU) and Prediction Unit (PU) coding information is proposed, which can improve the security performance of a stego video stream. In addition, matrix coding is used as a coding example to implement the steganography on HEVC video streams. Experimental results show that the proposed algorithm can not only maintain the video quality and the security performance but is also easy to implement. Furthermore, the proposed cover selection rule can also be integrated into other HEVC IPM based steganography.
Video Action Recognition Via Neural Architecture Searching

Deep neural networks have achieved great success for video analysis and understanding. However, designing a high-performance neural architecture requires substantial efforts and expertise. In this paper, we make the first attempt to let algorithm automatically design neural networks for video action recognition tasks. Specifically, a spatio-temporal network is developed in a differentiable space modeled by a directed acyclic graph, thus a gradient-based strategy can be performed to search an optimal architecture. Nonetheless, it is computationally expensive, since the computational burden to evaluate each architecture candidate is still heavy. To alleviate this issue, we, for the video input, introduce a temporal segment approach to reduce the computational cost without losing global video information. For the architecture, we explore in an efficient search space by introducing pseudo 3D operators. Experiments show that, our architecture outperforms popular neural architectures, under the training from scratch protocol, on the challenging UCF101 dataset, surprisingly, with only around one percentage of parameters of its manual-design counterparts.
Gradient-Based Illumination Description for Image Forgery Detection

The goal of blind image forensics is to determine authenticity and origin of an image without using an explicitly embedded security scheme. Most existing forensic methods can roughly be grouped into statistical and physics-based approaches. Statistical methods can oftentimes be fully automated, and achieve impressive results on current state-of-the-art benchmarks. Physics-based methods explain image inconsistencies using an analytic model, and are more robust to common image processing operations such as resizing or recompression. In this work, we propose a physics-based forensic descriptor to characterize 2-D lighting environments of objects. The key idea is that the integral over a gradient field of an object indicates the direction of incident light in the image plane. In contrast to prior 2-D lighting methods, the proposed method is remarkably robust to changes in object color and variations in user input, as it operates on the whole object area instead of object contours. Furthermore, we show that the proposed method is unaffected by image resizing or compression, which makes it possible to analyze images that are impossible to analyze with current state-of-the-art statistical methods.
Learning Hierarchical Self-Attention for Video Summarization

Video summarization still remains a challenging task. Due to sufficient video data on the Internet, such task draws significant attention in the vision community and benefits a wide range of applications, e.g., video retrieval, search, etc. To effectively perform video summarization by deriving the keyframes which represent the given input video, we propose a novel framework named Hierarchical Multi-Attention Network (H-MAN) which comprises the shot-level reconstruction model and multi-head attention model. While our designed attention model is two-stage hierarchical structure for producing various attention maps, we are among the first to utilize the multi-attention mechanism in the video summarization task, which brings improved performance. The quantitative and qualitative results demonstrate the effectiveness of our model, which performs favorably against state-of-the-art approaches.
Face recognition: a novel multi‐level taxonomy based survey

In a world where security issues have been gaining growing importance, face recognition systems have attracted increasing attention in multiple application areas, ranging from forensics and surveillance to commerce and entertainment. To help to understand the landscape and abstraction levels relevant for face recognition systems, face recognition taxonomies allow a deeper dissection and comparison of the existing solutions. This study proposes a new, more encompassing and richer multi-level face recognition taxonomy, facilitating the organisation and categorisation of available and emerging face recognition solutions; this taxonomy may also guide researchers in the development of more efficient face recognition solutions. The proposed multi-level taxonomy considers levels related to the face structure, feature support, and feature extraction approach. Following the proposed taxonomy, a comprehensive survey of representative face recognition solutions is presented. This study concludes with a discussion on current algorithmic and application related challenges, which may define future research directions for face recognition.
A Speech Recognition System for Bengali Language using Recurrent Neural Network

Speech recognition is the most interactive technology between a human and a machine. Over the past 70 years, tremendous work has been accomplished in this fundamental area of speech communication. However, implementation of the Bengali language is unsubstantial in the field of Human-Computer Interaction. This research paper tried to implement convolution neural network technique for creating a speech recognition system in the Bengali language. We also implemented recurrent neural network to find the Bengali character probabilities which were then improved further by using CTC loss function and language model. This paper implemented Bengali language consisting of diacritic characters and such languages are very much difficult to train in a model.
A secure and auditable logging infrastructure based on a permissioned blockchain

Information systems in organizations are regularly subject to cyber attacks targeting confidential data or threatening the availability of the infrastructure. In case of a successful attack it is crucial to maintain integrity of the evidence for later use in court. Existing solutions to preserve integrity of log records remain cost-intensive or hard to implement in practice. In this work we present a new infrastructure for log integrity preservation which does not depend upon trusted third parties or specialized hardware. The system uses a blockchain to store non-repudiable proofs of existence for all generated log records. An open-source prototype of the resulting log auditing service is developed and deployed, followed by a security and performance evaluation. The infrastructure represents a novel software-based solution to the secure logging problem, which unlike existing approaches does not rely on specialized hardware, trusted third parties or modifications to the logging source.
Fourth paradigm GIScience? Prospects for automated discovery and explanation from data

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Agile Transformation at LEGO Group

Overview: LEGO Group launched an Agile transformation of its corporate digital departments in early 2018; this transformation included the introduction of a new digital operating model. One year into the transformation, the impact of the new model is beginning to show in a significant reduction in the time required to respond to change—from months to weeks—in the company’s core functions. This article describes how the Agile transformation was orchestrated and Agile values and principles integrated into the process to avoid implementing a set of Agile processes and tools without changing behavior and mind-set.