Reviewerly/exHarmony huggingface dataset:
A loss-balanced multi-task model for simultaneous detection and segmentation

Scene understanding comes in many flavors, two of the most popular being object detection and semantic segmentation, which act as two important aspects for scene understanding, and are applied to many areas, such as autonomous driving and intelligent surveillance. Although much progress has already been made, the two tasks of object detection and semantic segmentation are often investigated independently. In practice, scene understanding is complicated, and comprises many sub-tasks, so that research of learning multiple tasks simultaneously with a single model is feasible. With the interrelated goals of these two tasks, there is a strong motivation to improve the object detection accuracy with the help of semantic segmentation, and vice versa. In this paper, we propose a loss-balanced multi-task model for simultaneous object detection and semantic segmentation. We explore multi-task learning with sharing parameters based on deep learning to realize improved object detection and segmentation, and propose a single-stage deep architecture based on multi-task learning, jointly performing object detection and semantic segmentation to boost each other. With no more computation load in the inference compared with the baselines of SSD and FCN, we show that these two tasks, object detection and semantic segmentation, benefit from each other. Experimental results on Pascal VOC and COCO show that our method improves much in object detection and semantic segmentation compared with the corresponding baselines of both tasks.
Bearing feature extraction using multi-structure locally linear embedding

The locally linear embedding aims to extract the significant features by only digging the individual geometric structure of original data set, for which the intrinsic features can not be completely expressed. In this study, two LLE-based multi-structure fusion methods are proposed. In the proposed methods, the least squares and sparse structures are first estimated in original space, and then the coefficient and function fusion approaches are introduced to integrate the least squares and sparse structures. Furthermore, the relationship between the two fusion methods are analyzed and we demonstrate that the solution of coefficient fusion is a subset of the one of the function fusion. Extensive experiments are carried out on benchmark fault data set and the bearing data set collected from our own laboratory, and experimental results indicate that the proposed multi-structure methods outperform the existing state-of-art related methods.
Mutual Information Driven Federated Learning

Federated Learning (FL) is an emerging research field that yields a global trained model from different local clients without violating data privacy. Existing FL techniques often ignore the effective distinction between local models and the aggregated global model when doing the client-side weight update, as well as the distinction of local models for the server-side aggregation. In this article, we propose a novel FL approach with resorting to mutual information (MI). Specifically, in client-side, the weight update is reformulated through minimizing the MI between local and aggregated models and employing Negative Correlation Learning (NCL) strategy. In server-side, we select top effective models for aggregation based on the MI between an individual local model and its previous aggregated model. We also theoretically prove the convergence of our algorithm. Experiments conducted on MNIST, CIFAR-10, ImageNet, and the clinical MIMIC-III datasets manifest that our method outperforms the state-of-the-art techniques in terms of both communication and testing performance.
Transformer Based Multi-Grained Attention Network for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis aims to predict sentiment polarity for every aspect in a sentence review.Most existing approaches are based on the sequence models, which may superimpose the emotional semantics of different tendencies and lack syntactic structure information.And most models adopt coarse-grained attention mechanism which still face the issues of weakness interaction between aspect and context.In this paper, we propose a transformer based multi-grained attention network (T-MGAN), which utilizes the Transformer module to learn the word-level representations of aspects and context respectively, and further utilizes the Tree Transformer module to obtain the phrase-level representations of contexts.It is capable of extracting the syntactic structure features and syntax information of aspect and context.In addition, we adopt dual-pooling method and multi-grained attention network to extract high quality aspect-context interactive representations.We evaluate the proposed model on three datasets and prove the effectiveness of the proposed model.
Leveraging Semantic Parsing for Relation Linking over Knowledge Bases

Knowledge base question answering systems are heavily dependent on relation extraction and linking modules. However, the task of extracting and linking relations from text to knowledge bases faces two primary challenges; the ambiguity of natural language and lack of training data. To overcome these challenges, we present SLING, a relation linking framework which leverages semantic parsing using Abstract Meaning Representation (AMR) and distant supervision. SLING integrates multiple approaches that capture complementary signals such as linguistic cues, rich semantic representation, and information from the knowledge base. The experiments on relation linking using three KBQA datasets, QALD-7, QALD-9, and LC-QuAD 1.0 demonstrate that the proposed approach achieves state-of-the-art performance on all benchmarks.
State of XR research in architecture with focus on professional practice – a systematic literature review

Immersive technologies are not only gaining popularity in various fields but are also heralded as the obvious next step in architectural practice. Now that almost five years have passed since the release of more accurate and affordable headsets, a review focusing on immersive technology applications in the architectural field is needed to reflect the current fields investigated. This systematic literature review discusses the sample used in the 201 selected studies about immersive technologies published from 2015 to 2019. The study identifies gaps in the current literature. The results highlight that professional architects are almost never queried in searches conducted over the past five years in the selected database. It unveils the necessity to take into consideration the context of studies in order to develop tools truly dedicated to the real practices of professional architects. This paper constitutes a reference for further researches by facilitating their contextualization within the research landscape.
Calibration method for the rotating axis in panoramic 3D shape measurement based on a turntable

Abstract In panoramic three-dimensional (3D) shape measurement based on a turntable, the point cloud registration accuracy is greatly affected by the calibration of the rotating axis. To improve the calibration accuracy of the rotating axis, a calibration method based on an auxiliary camera is proposed. The calibration procedure uses a checkerboard and an auxiliary camera. Firstly, the checkerboard is rotated on the turntable to determine the rotation axis, and is used to calibrate the relative orientation of the auxiliary camera and the measurement camera, so that the two cameras have the same world coordinate system. Then, the origin point of the coordinate of the rotation axis is obtained by the auxiliary camera, and the Z -axis direction is taken as the direction of the rotation axis. Finally, the 3D shape of the multi-view is measured by the fringe projection system, and the rotation registration is completed according to the rotation axis vector. The proposed method does not need to reconstruct the 3D coordinates of multiple points when the rotating axis is calibrated. The experimental results show that the proposed method effectively improves the calibration accuracy of the rotation axis vector, and reduces the complexity of the calibration process.
Tensor Network Quantum Simulator With Step-Dependent Parallelization

In this work, we present a new large-scale quantum circuit simulator. It is based on the tensor network contraction technique to represent quantum circuits. We propose a novel parallelization algorithm based on step-dependent slicing . In this paper, we push the requirement on the size of a quantum computer that will be needed to demonstrate the advantage of quantum computation with Quantum Approximate Optimization Algorithm (QAOA). We computed a single amplitude of QAOA ansatz state on 210 qubits. The simulation involved 1,785 gates on 1,024 nodes of the Cray XC 40 supercomputer Theta. To the best of our knowledge, this constitutes the largest simulation of QAOA ansatz simulations reported to this date.
Examining argumentative style

Abstract In this theoretical expose, it is argued that the notion of argumentative style is more encompassing and at the same time more specific than the more familiar notion of linguistic style. According to van Eemeren, argumentative styles always have three dimensions: the selection of standpoints, starting-points, arguments or other argumentative moves (topical choice dimension), the adjustment of argumentative moves to the frame of reference and preferences of the listeners or readers (audience demand dimension), and the choice of verbal or non-verbal means for advancing argumentative moves (presentational dimension). In argumentative discourse, the three dimensions of argumentative style manifest themselves in the argumentative moves made in trying to resolve a difference of opinion (analytic overview), the dialectical routes chosen in making these argumentative moves (argumentative pattern) and the strategic considerations brought to bear in this endeavour (strategic design). Van Eemeren explains what this means in practice by discussing the distinctive features of the three dimensions of two general categories of argumentative styles that can be regularly encountered, in one variant or other, in argumentative discourse: detached argumentative styles and engaged argumentative styles.
Longtime Effects of Videoquality, Videocanvases and Displays on Situation Awareness during Teleoperation of Automated Vehicles

Due to the challenges of autonomous driving, for the near future, automated vehicles will not be able to drive in all conditions without any human intervention. The challenge arises when no human driver is inside the vehicle to resolve the challenging situation. One solution for this might be teleoperation, here a remote operator takes control over the car and resolves the situation from a distance. But teleoperation technology itself comes with certain challenges, one of them being creating a good situational awareness at the operator site based on the sensor data transmitted from the automated vehicle. To understand this challenge better, in this paper a five-week long-time study is conducted with the goal of measuring the impact of different displays, video-canvases and -streaming quality on situation awareness, workload and decision making. The objective results show a significant impact of video streaming quality on various factors of situation awareness. On the other hand the subjective results such as workload, immersion, usability and presence indicate that video streaming quality only has an impact in situations with high contrasts and over all scenes the impact of video quality on subjective satisfaction is not significant. Between the three display modes no significance regarding quality was found. However, the participants preferred the head mounted display over the other options even though the results indicate that the head mounted display is most sensitive to changes in video streaming quality.
A Syntax-Augmented and Headline-Aware Neural Text Summarization Method

With the advent of the information age, excessive information collection leads to information overload.Automatic text summarization technology has become an effective way to solve information overload.This paper proposes an automatic text summarization model, which extends traditional sequenceto-sequence ( Seq2Seq) neural text summarization model by using a syntax-augmented encoder and a headline-aware decoder.The encoder encodes both syntactic structure and word information of a sentence in the sentence embedding.A hierarchical attention mechanism is proposed to pay attentions to syntactic units.The decoder is improved by a headline attention mechanism and a Dual-memory-cell LSTM network to achieve a higher quality of generated summaries.We designed experiments to compare the proposed method with baseline models on the CNN/DM datasets.The experiment results show that the proposed method is superior to abstractive baseline models in terms of the scores on ROUGE evaluation metrics, and achieve a summary generation performance comparable to the extractive baseline method.Though qualitative analysis, the summary quality of the propose method is more readable and less redundant, which agrees well with our intuition.
An Ensemble-Based Approach to Anomaly Detection in Marine Engine Sensor Streams for Efficient Condition Monitoring and Analysis

This study proposes an unsupervised anomaly detection method using sensor streams from the marine engine to detect the anomalous system behavior, which may be a possible sign of system failure. Previous works on marine engine anomaly detection proposed a clustering-based or statistical control chart-based approach that is unstable according to the choice of hyperparameters, or cannot fit well to the high-dimensional dataset. As a remedy to this limitation, this study adopts an ensemble-based approach to anomaly detection. The idea is to train several anomaly detectors with varying hyperparameters in parallel and then combine its result in the anomaly detection phase. Because the anomaly is detected by the combination of different detectors, it is robust to the choice of hyperparameters without loss of accuracy. To demonstrate our methodology, an actual dataset obtained from a 200,000-ton cargo vessel from a Korean shipping company that uses two-stroke diesel engine is analyzed. As a result, anomalies were successfully detected from the high-dimensional and large-scale dataset. After detecting the anomaly, clustering analysis was conducted to the anomalous observation to examine anomaly patterns. By investigating each cluster’s feature distribution, several common patterns of abnormal behavior were successfully visualized. Although we analyzed the data from two-stroke diesel engine, our method can be applied to various types of marine engine.
Co-Design of a Playful Mixed Reality Installation: An Interactive Crane in the Museum of Marble Crafts

Interactive installations in museums usually adopt hybrid technologies that combine physical elements with digital content, and studies so far show that this approach enhances the interest and engagement of visitors compared to non-interactive media or purely digital environments. However, the design of such systems is complicated, as it involves a large number of stakeholders and specialists. Additionally, the functional components need to be carefully orchestrated to deliver a rich user experience. Thus, there is a need for further research on tools and methods that facilitate the process. In this paper we present the design and development of a mixed reality installation for the Museum of Marble Crafts in Tinos island in Greece, which places visitors in the role of the crane operator and they have to complete challenges in a gamified version of the old quarry. The system lets users operate a tangible controller and their actions are executed by digital workers in a rich 3D environment. Our design approach involved iterative prototyping, research and co-design activities. The creative process has been supported by a series of organized workshops. The evaluation results indicate that mixed reality can be a promising medium for rich interactive experiences in museums that combine tangible and intangible heritage.
A Novel Approach for Detection of Counterfeit Indian Currency Notes Using Deep Convolutional Neural Network

Abstract In recent years, the Indian economy has shown rapid growth among all other major economies. India has been tragically reviled with issues like corruption and black currency, fake money notes is additionally major issues to it, in spite of a strong security feature are endorsed by RBI to print original currency. The advancement of color printing technology helped local racketeers and foreign racketeers to print a large amount of counterfeit Indian currency notes in the market. Albeit counterfeit money is being printed with accuracy, it likely is distinguished with some effort. In this paper, the proposed model efficiently detect the counterfeit Indian currency notes by adapting three-layered Deep Convolutional Neural Network (Deep ConvNet), and achieved an accuracy of 96.6%.
Bilingual Sentiment Analysis for a Code-mixed Punjabi English Social Media Text

India is a multilingual country and a large portion of its population speaks more than one language. It has been observed that such multilingual speakers switch between the languages while communicating informally. Such code-mixed sentences not only contain words from different languages but also different grammar rules in a single sentence. In this work, we have focused on one such coded-mixed language pair i.e. Punjabi-English. In informal communication and social media, the code-mixed language is very popular, and analyzing the sentiment of such mixed sentences is a challenging task. We have verified that the language models and sentiment analysis methods designed for a single language don't work well in such sentences. And by reviewing the drawbacks, we propose our novel model which is based on LSTM. The proposed model provides quite satisfactory results for the code-mixed data set.
A Regularized Attribute Weighting Framework for Naive Bayes

The Bayesian classification framework has been widely used in many fields, but the covariance matrix is usually difficult to estimate reliably. To alleviate the problem, many naive Bayes (NB) approaches with good performance have been developed. However, the assumption of conditional independence between attributes in NB rarely holds in reality. Various attribute-weighting schemes have been developed to address this problem. Among them, class-specific attribute weighted naive Bayes (CAWNB) has recently achieved good performance by using classification feedback to optimize the attribute weights of each class. However, the derived model may be over-fitted to the training dataset, especially when the dataset is insufficient to train a model with good generalization performance. This paper proposes a regularization technique to improve the generalization capability of CAWNB, which could well balance the trade-off between discrimination power and generalization capability. More specifically, by introducing the regularization term, the proposed method, namely regularized naive Bayes (RNB), could well capture the data characteristics when the dataset is large, and exhibit good generalization performance when the dataset is small. RNB is compared with the state-of-the-art naive Bayes methods. Experiments on 33 machine-learning benchmark datasets demonstrate that RNB outperforms the compared methods significantly.
Image Segmentation and Region Classification in Automotive High-Resolution Radar Imagery

Image segmentation and classification of surfaces and obstacles in automotive radar imagery are the key technologies to provide valuable information for path planning in autonomous driving. As opposed to traditional radar processing, where clutter is considered as an unwanted return and should be effectively removed, autonomous driving requires full scene characterization. Hence, clutter carries necessary information for situational awareness of the autonomous platform and needs to be fully assessed to find the passable areas. In this paper, we proposed a method of automatic segmentation of automotive radar images based on two main steps: unsupervised image pre-segmentation using marker-based watershed transformation, followed by the supervised segmentation and classification of regions containing objects and surfaces based on the use of statistical distribution parameters. Several distributions were considered to characterize returns from specific region types of interest within the scene (denoted as classes) in calibrated radar imagery-the extracted distribution parameters were assessed for their ability to distinguish each class. These parameters were then used as features in a multivariate Gaussian distribution model classifier. Both the performances of the proposed supervised classification algorithm and the automatically segmented results were investigated using F1-score and Jaccard similarity coefficients, respectively.
Building and Operating a Large-Scale Enterprise Data Analytics Platform

Over the last three years we have been running a large-scale data processing platform for applying analytics to corporate data at scale on an OpenStack private cloud instance. Our platform makes a wide variety of corporate data assets, such as sales, marketing, customer information, as well as data from less conventional sources such as weather, news and social media available for analytics purposes to hundreds of globally distributed teams across the company. We control every layer in the stack from the processing engines down to the hardware. Here we report our experiences in building and operating such a system. We describe our technical choices and describe how they evolved as we observed the actual workloads created by users.
A Human-Like Traffic Scene Understanding System: A Survey

Autonomous vehicles, also known as self-driving cars, have the capability to perceive the environment, locate its position, and safely drive to the destination without any human intervention. This field has made amazing improvements because of the advanced technologies and progress of the artificial intelligence (AI) field. While the existing surveys have addressed many topics, e.g., vehicle sensors, perception, and object detection, none of the existing works summarize the work studying the ability of human-like understanding, e.g., common sense reasoning. Therefore, in this article, we present a novel system flow for empowering autonomous vehicles to understand the traffic scene and summarize the state of the art research.
A plug-in attribute correction module for generalized zero-shot learning

While Zero Shot Learning models can recognize new classes without training examples, they often fails to incorporate both seen and unseen classes together at the test time, which is known as the Generalized Zero-shot Learning (GZSL) problem. This paper identifies a bottleneck issue when attributes are not well-defined, reliable, inaccurate in quantitative representations, or suffering from the visual-semantic discrepancy. We propose a Generic Plug-in Attribute Correction (GPAC) module which can effectively accommodate conventional ZSL in GZSL tasks. Different from existing embedding-based approaches which often lose the favor of transparency in attributes, our key challenge is to fully preserve the original meaning of the attributes and make it complementary and interpretable to upgrade existing ZSL models. To this end, we propose a novel nonnegative constraint with iterative Stochastic Gradient Descent toolbox to effectively fit our GPAC module into previous ZSL models. Extensive experiments on five popular datasets show that our method can effectively correct attributes and make conventional ZSL can achieve state-of-the-art performance on GZSL tasks. It is also a good practice for future models when incorporating prior human knowledge.