Reviewerly/exHarmony huggingface dataset:
Panchromatic Image Super-Resolution Via Self Attention-Augmented Wasserstein Generative Adversarial Network

Panchromatic (PAN) images contain abundant spatial information that is useful for earth observation, but always suffer from low-resolution ( LR) due to the sensor limitation and large-scale view field. The current super-resolution (SR) methods based on traditional attention mechanism have shown remarkable advantages but remain imperfect to reconstruct the edge details of SR images. To address this problem, an improved SR model which involves the self-attention augmented Wasserstein generative adversarial network ( SAA-WGAN) is designed to dig out the reference information among multiple features for detail enhancement. We use an encoder-decoder network followed by a fully convolutional network (FCN) as the backbone to extract multi-scale features and reconstruct the High-resolution (HR) results. To exploit the relevance between multi-layer feature maps, we first integrate a convolutional block attention module (CBAM) into each skip-connection of the encoder-decoder subnet, generating weighted maps to enhance both channel-wise and spatial-wise feature representation automatically. Besides, considering that the HR results and LR inputs are highly similar in structure, yet cannot be fully reflected in traditional attention mechanism, we, therefore, designed a self augmented attention (SAA) module, where the attention weights are produced dynamically via a similarity function between hidden features; this design allows the network to flexibly adjust the fraction relevance among multi-layer features and keep the long-range inter information, which is helpful to preserve details. In addition, the pixel-wise loss is combined with perceptual and gradient loss to achieve comprehensive supervision. Experiments on benchmark datasets demonstrate that the proposed method outperforms other SR methods in terms of both objective evaluation and visual effect.
Lexicon A Linguistic Approach for Sentiment Classification

It is known that social media has become a part and parcel in everyone's life. Human emotions are constantly being expressed in real time on various social networking sites. The availability of an enormous amount of opinion rich data from various social networking sites has fueled interest in opinion mining and sentiment analysis. There are mainly two approaches for performing sentiment analysis that is a lexicon-based approach and a machine learning based approach. In this paper, we chose to limit our study to just the lexicon-based approach of sentiment analysis. Lexicon based approach relies on the lexicons for classifying input data. Lexicon is a set of words, idioms, phrases, etc. having a semantic meaning. In this paper, prior research done in lexicon-based sentiment analysis has been studied; Also, a review of some state-of-the-art lexicon-based solutions have been presented for polarity classification of Sentiment Analysis. This paper is mainly oriented towards the various lexicons used for polarity classification.
Unions, intersections and a one-shot quantum joint typicality lemma

A fundamental tool to prove inner bounds in classical network information theory is the so-called ‘conditional joint typicality lemma’. In addition to the lemma, one often uses unions and intersections of typical sets in the inner bound arguments without so much as giving them a second thought. These arguments do not work in the quantum setting. This bottleneck shows up in the fact that so-called ‘simultaneous decoders’, as opposed to ‘successive cancellation decoders’, are known for very few channels in quantum network information theory. Another manifestation of this bottleneck is the lack of so-called ‘simultaneous smoothing’ theorems for quantum states. In this paper, we overcome the bottleneck by proving for the first time a one-shot quantum joint typicality lemma with robust union and intersection properties. To do so we develop two novel tools in quantum information theory, which may be of independent interest. The first tool is a simple geometric idea called tilting, which increases the angles between a family of subspaces in orthogonal directions. The second tool, called smoothing and augmentation, is a way of perturbing a multipartite quantum state such that the partial trace over any subset of registers does not increase the operator norm much. Our joint typicality lemma allows us to construct simultaneous quantum decoders for many multiterminal quantum channels. It provides a powerful tool to extend many results in classical network information theory to the one-shot quantum setting.
Modeling and optimization of watering robot optimal path for ornamental plant care

Watering ornamental plants is considered to be a time-consuming task with the increasing garden size. Employing robots to conduct the watering task is a feasible way to improve the watering efficiency. However, general path planning models for watering robots are faced with various difficulties, such as the curse of dimensionality caused by the excessively oversized irrigation area and insufficient irrigation due to weather conditions. To tackle these issues, this paper proposes two novel strategies for watering robot optimal path modeling. Firstly, for tackling the curse of dimensionality, a sliding window strategy is proposed. To be specific, the whole watering area is partitioned into many subareas using the sliding window, and each subarea is watered independently. Secondly, to overcome the problem that the soil moisture still can not reach the expectation due to various weather conditions, this paper proposes a placeholder strategy, which enables the watering robot to dynamically adjust the watering path. To test these strategies, a novel genetic algorithm with neighbor exchanging strategy is proposed. Extensive experiments demonstrate the effectiveness of watering robot path planning models based on the proposed strategies.
A Novel Unsupervised domain adaptation method for inertia-Trajectory translation of in-air handwriting

As a new method of human-computer interaction, inertial sensor-based in-air handwriting can provide natural and unconstrained interaction to express more complex and rich information in 3D space. However, most of the existing literature is mainly focused on in-air handwriting recognition (IAHR), which makes these works suffer from the poor readability of inertial signals and the lack of labeled samples. To address these two problems, we use an unsupervised domain adaptation method to recover the trajectory of inertial signals and generate inertial samples using handwritten trajectories. In this paper, we propose an Air-Writing Translator model to learn the bi-directional translation between trajectory domain and inertial domain in the absence of paired inertial and trajectory samples. Through latent-level adversarial learning and latent classification loss, the proposed model learns to extract domain-invariant features between the inertial signal and the trajectory while preserving semantic consistency during the translation across the two domains. In addition, the proposed framework can accept inputs of arbitrary length and translate between different sampling rates. Experiments on two public datasets, 6DMG (in-air handwriting dataset) and CT (handwritten trajectory dataset), are conducted and the results demonstrate that the proposed model can achieve reliable translation between inertial domain and trajectory domain. Empirically, our method also yields the best results in comparison to the state-of-the-art methods for IAHR.
Multi-label Detection and Classification of Red Blood Cells in Microscopic Images

Cell detection and cell type classification from biomedical images play an important role for high-throughput imaging and various clinical application. While classification of single cell sample can be performed with standard computer vision and machine learning methods, analysis of multi-label samples (region containing congregating cells) is more challenging, as separation of individual cells can be difficult (e.g. touching cells) or even impossible (e.g. overlapping cells). As multi-instance images are common in analyzing Red Blood Cell (RBC) for Sickle Cell Disease (SCD) diagnosis, we develop and implement a multi-instance cell detection and classification framework to address this challenge. The framework firstly trains a region proposal model based on Region-based Convolutional Network (RCNN) to obtain bounding-boxes of regions potentially containing single or multiple cells from input microscopic images, which are extracted as image patches. High-level image features are then calculated from image patches through a pre-trained Convolutional Neural Network (CNN) with ResNet-50 structure. Using these image features inputs, six networks are then trained to make multi-label prediction of whether a given patch contains cells belonging to a specific cell type. As the six networks are trained with image patches consisting of both individual cells and touching/overlapping cells, they can effectively recognize cell types that are presented in multi-instance image samples. Finally, for the purpose of SCD testing, we train another machine learning classifier to predict whether the given image patch contains abnormal cell type based on outputs from the six networks. Testing result of the proposed framework shows that it can achieve good performance in automatic cell detection and classification.
Switched Fuzzy Sampled-Data Control of Chaotic Systems With Input Constraints

This paper designs a sampled-data controller to exponentially stabilize a kind of chaotic systems (CSs) which are represented by T-S fuzzy model with nonuniform sampling.By employing a switched technology and Lyapunov functional dependent on fuzzy membership-functions (FMFs), a new control criterion for chaotic systems was established.Compared with the existing methods, less conservatism and a large sampling period can be obtained by above approaches.Consequently, the amount of transmitted information is greatly reduced and the efficiency of bandwidth is much improved.Finally, the proposed T-S fuzzy sampled-data (TSFSD) controller is applied to Lorenz system to prove the effectiveness of those methods.
Instant Panoramic Texture Mapping with Semantic Object Matching for Large-Scale Urban Scene Reproduction

This paper proposes a novel panoramic texture mapping-based rendering system for real-time, photorealistic reproduction of large-scale urban scenes at a street level. Various image-based rendering (IBR) methods have recently been employed to synthesize high-quality novel views, although they require an excessive number of adjacent input images or detailed geometry just to render local views. While the development of global data, such as Google Street View, has accelerated interactive IBR techniques for urban scenes, such methods have hardly been aimed at high-quality street-level rendering. To provide users with free walk-through experiences in global urban streets, our system effectively covers large-scale scenes by using sparsely sampled panoramic street-view images and simplified scene models, which are easily obtainable from open databases. Our key concept is to extract semantic information from the given street-view images and to deploy it in proper intermediate steps of the suggested pipeline, which results in enhanced rendering accuracy and performance time. Furthermore, our method supports real-time semantic 3D inpainting to handle occluded and untextured areas, which appear often when the user's viewpoint dynamically changes. Experimental results validate the effectiveness of this method in comparison with the state-of-the-art approaches. We also present real-time demos in various urban streets.
Topic-Centric Unsupervised Multi-Document Summarization of Scientific and News Articles

Recent advances in natural language processing have enabled automation of a wide range of tasks, including machine translation, named entity recognition, and sentiment analysis. Automated summarization of documents, or groups of documents, however, has remained elusive, with many efforts limited to extraction of keywords, key phrases, or key sentences. Accurate abstractive summarization has yet to be achieved due to the inherent difficulty of the problem, and limited availability of training data. In this paper, we propose a topic-centric unsupervised multi-document summarization framework to generate extractive and abstractive summaries for groups of scientific articles across 20 Fields of Study (FoS) in Microsoft Academic Graph (MAG) and news articles from DUC-2004 Task 2. The proposed algorithm generates an abstractive summary by developing salient language unit selection and text generation techniques. Our approach matches the state-of-the-art when evaluated on automated extractive evaluation metrics and performs better for abstractive summarization on five human evaluation metrics (entailment, coherence, conciseness, readability, and grammar). We achieve a kappa score of 0.68 between two co-author linguists who evaluated our results. We plan to publicly share MAG- 20, a human-validated gold standard dataset of topic-clustered research articles and their summaries to promote research in abstractive summarization.
Reminiscent Net: Conditional GAN-based Old Image De-Creasing

Documents are an essential source of valuable information and knowledge, and photographs are a great way of reminiscing old memories and past events. However, it becomes difficult to preserve the quality of such ancient documents and old photographs for an extremely long time, as these images usually get damaged or creased due to various extrinsic effects. Utilizing image editing software like Photoshop to manually reconstruct such old photographs and documents is a strenuous and an enduring process. This paper attempts to leverage the generative modeling capabilities of Conditional Generative Adversarial Networks by utilizing specialized architectures for the Generator and the Discriminator. The proposed Reminiscent Net has a U-Net-based Generator with numerous feature maps for complete information transfer with the incorporation of location and contextual details, and the absence of dense layers allows utilization of diverse sized images. Implementation of the PatchGAN-based Discriminator that penalizes the image at the scale of patches has been proposed. NADAM optimizer has been implemented to enable faster and better convergence of the loss function. The proposed method produces visually appealing de-creased images, and experiments indicate that the architecture performs better than various novel approaches, both qualitatively and quantitatively.
A one-parameter family of clear-sky solar irradiance models adapted for different aerosol types

In this paper, a procedure for generating a family of parametrical broadband clear-sky models is described. The key element is the conversion of one or more model input variables into tunable mathematical parameters. The approach is tested on the PS model [Paulescu, M. and Schlett, Z., Theor. Appl. Climatol. 75, 203 (2003)], with the free parameter being the Ångström exponent (α). This allows us to fine-tune for conditions dominated by desert dust, urban-industrial, and mixed aerosols. We find that for an arbitrary set of data, the optimal value of the free parameter is not the same as its actual measured value (inserting the measured value in the model would result in a lower performance). We attribute this fact to the inaccurate nature of the base model. The optimal α value varies with the considered solar irradiance component, aerosol type and loading, and the error measure(s) used for assessing model accuracy. A set of recommended models for each aerosol type and loading class is given. The tabled values for the aerosol transmittance coefficients are also listed. A preliminary validation shows that the newly developed models are very reliable. The optimal version generally falls within a few percent of the results of REST2v5, a benchmark model in clear-sky solar irradiance estimation. While some established models outperform REST2v5 for certain aerosol types and for only one solar irradiance component, the new models prove competitive under most scenarios. Beyond showing the performance of the developed model family, these results hint at great potential of our approach.
Cost-sensitive Multi-class AdaBoost for Understanding Driving Behavior with Telematics

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Explainable anomaly detection for procurement fraud identification—lessons from practical deployments

Abstract This article reports the results of our work to construct a system for the detection of fraudulent behavior in procurement transactions. To solve the problem, we model different types of fraud via separate statistical indicators. We propose a formalized framework to describe the severity of fraud in a unified way regardless of underlying fraud mechanics. Subsequently, we leverage this concept to build indicator ensembles that collect evidence from multiple indicators and deliver an interpretable per transaction score to the procurement audit officer. As a case study, we overview 48 such fraud indicators constructed for our client and describe two examples in detail showing how our formal definitions can be transformed into a practical implementation. The presented results include experiments with all indicators on data covering four years of procurement activity with approximately 216,000 transactions coming from a large government organization in Singapore. The final evaluation of our system shows 67.1% precision in detecting suspicious transactions. The article describes how outcome of our work helped to effectively cope with the problem of anomaly detection explainability and the lessons learned from integrating this solution to operational practices of a procurement department.
Complicating the Social Networks for Better Storytelling: An Empirical Study of Chinese Historical Text and Novel

Digital humanities is an important subject because it enables developments in history, literature, and films. In this article, we perform an empirical study of a Chinese historical text, Records of the Three Kingdoms (Records), and a historical novel of the same story, Romance of the Three Kingdoms (Romance). We employ deep-learning-based natural language processing (NLP) techniques to extract characters and their relationships. The adopted NLP approach can extract 93% and 91% characters that appeared in the two books, respectively. Then, we characterize the social networks and sentiments of the main characters in the historical text and the historical novel. We find that the social network in Romance is more complex and dynamic than that of Records, and the influence of the main characters differs. These findings shed light on the different styles of storytelling in the two literary genres and how the historical novel complicates the social networks of characters to enrich the literariness of the story.
MetaInsight: Automatic Discovery of Structured Knowledge for Exploratory Data Analysis

Automatic Exploratory Data Analysis (EDA) focuses on automatically discovering pieces of knowledge in the form of interesting data patterns. However, the conveyed knowledge by these suggested data patterns are disjointed or lack organization. Therefore, it is difficult for users to gain structured knowledge, and as the number of suggested patterns grows, these stand-alone patterns are less likely to motive users to conduct follow-up analysis, which hinders it from being effectively utilized to facilitate EDA. In this paper, we propose MetaInsight, a structured representation of knowledge extracted from multi-dimensional data aiming to facilitate EDA automatically and effectively. Specifically, we propose a novel formulation of basic data pattern to capture essential characteristics of raw data distribution to achieve knowledge extraction. Then based on the mined Homogeneous Data Patterns (HDP) and inter-pattern similarity, MetaInsight is identified by categorizing basic data patterns (within an HDP) into commonness(es) and exceptions thus achieving structured knowledge representation. The commonness(es) and exceptions concretize the knowledge obtained by induction and validation processes which are two typical analysis mechanisms conducted in EDA. We propose a novel scoring function to quantify the usefulness of MetaInsight, an effective and efficient mining procedure and a ranking algorithm to automatically discover high-quality MetaInsights from multi-dimensional data. We demonstrate the effectiveness and efficiency of MetaInsights (w.r.t. facilitating EDA) through evaluation on real-world datasets and user studies on both expert users and non-expert users.
Understanding and Detecting Convergence for Stochastic Gradient Descent with Momentum

Convergence detection of iterative stochastic optimization methods is of great practical interest. This paper considers stochastic gradient descent (SGD) with a constant learning rate and momentum. We show that there exists a transient phase in which iterates move towards a region of interest, and a stationary phase in which iterates remain bounded in that region around a minimum point. We construct a statistical diagnostic test for convergence to the stationary phase using the inner product between successive gradients and demonstrate that the proposed diagnostic works well. We theoretically and empirically characterize how momentum can affect the test statistic of the diagnostic, and how the test statistic captures a relatively sparse signal within the gradients in convergence. Finally, we demonstrate an application to automatically tune the learning rate by reducing it each time stationarity is detected, and show the procedure is robust to mis-specified initial rates.
Vpassport: A Digital Architecture to Support Social Restart during the SARS-CoV-2 Pandemic

As a consequence of the Sars-CoV-2 pandemic, the causative agent of the COVID-19 coronavirus, the world is currently witnessing profound changes in everyday life. The infection and the resulting death number forecasts generate an increasing threat to the lives of people and the economics of countries. As the acute phase of the pandemic ends, the greatest challenge that most governments are currently undergoing is the lack of tools to certify the immunity status of citizens and the related infection risk of the spread of the COVID-19 virus. To mitigate this challenge, this study proposes an innovative approach to implement a set of IT tools, here named VPassport, that assist large-scale test execution/result management in a distributed way and store the results of all tests made through all channels in a blockchain under country authority control. The proposed approach aims to produce an effective system able to support governments, health authorities, and citizens to take informed decisions on which services and social activities can be accessed respecting policies and rules set by the authorities. This aims to allow a controlled restart of the activities of the country, giving to all citizens the possibility to manage their immunity tests while allowing the authorities to manage the reopening of services and social activities. The proposed model helps in managing this phase and, therefore, the resulting outcome can be used to authorize possible behaviors (e.g., going to the office, production plants, public transportation, theaters, cinemas, etc.). The knowledge of being infected or not in a secure and not modifiable way that can be shown in a simple way, accessible to all, will be the real change in managing the coexistence with the virus until a vaccine will be available for all people.