Reviewerly/exHarmony huggingface dataset:
Densely Semantic Enhancement for Domain Adaptive Region-Free Detectors

Unsupervised domain adaptive object detection aims to adapt a well-trained detector from its original source domain with rich labeled data to a new target domain with unlabeled data. Previous works focus on improving the domain adaptability of region-based detectors, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , Faster-RCNN, through matching cross-domain instance-level features that are explicitly extracted from a region proposal network (RPN). However, this is unsuitable for region-free detectors such as single shot detector (SSD), which perform a dense prediction from all possible locations in an image and do not have the RPN to encode such instance-level features. As a result, they fail to align important image regions and crucial instance-level features between the domains of region-free detectors. In this work, we propose an adversarial module, namely, densely semantic enhancement module (DSEM), to strengthen the cross-domain matching of instance-level features for region-free detectors. Firstly, to emphasize the important regions of image, the DSEM learns to predict a transferable foreground enhancement mask that can be utilized to suppress the background disturbance in an image. Secondly, considering that region-free detectors recognize objects of different scales using multi-layer feature maps, the DSEM encodes multi-scale representations across different domains. Finally, the DSEM is pluggable into different region-free detectors, ultimately achieving the densely semantic feature matching via adversarial learning. Extensive experiments have been conducted on PASCAL VOC, Clipart, Comic, W atercolor, and FoggyCityscape benchmarks, and their results well demonstrate that the proposed approach not only improves the domain adaptability of region-free detectors but also outperforms existing domain adaptive region-based detectors under various domain shift settings.
Integrating Gate and Attention Modules for High-Resolution Image Semantic Segmentation

Semantic segmentation of high-resolution (HR) remote sensing images achieved great progress by utilizing deep convolutional neural networks (DCNNs) in recent years. However, the decrease of resolution in the feature map of DCNNs brings about the loss of spatial information and thus leads to the blurring of object boundary and misclassification of small objects. In addition, the class imbalance and the high diversity of geographic objects in HR images exacerbate the performance. To deal with the above problems, we proposed an end-to-end DCNN network named GAMNet to balance the contradiction between global semantic information and local details. An integration of attention and gate module (GAM) is specially designed to simultaneously realize multiscale feature extraction and boundary recovery. The integration module can be inserted in an encoder-decoder network with skip connection. Meanwhile, a composite loss function is designed to achieve deep supervision of GAM by adding an auxiliary loss, which can help improve the effectiveness of the integration module. The performance of GAMNet is quantitatively evaluated on the ISPRS 2-D semantic labeling datasets and achieves state-of-the-art performance in comparison with other representative methods.
ERBANet: Enhancing region and boundary awareness for salient object detection

Existing methods based on convolutional neural networks (CNNs) have progressed significantly in the salient object detection task. However, in some cases, the accurate detection of intact objects and maintenance of their original detailed structures, such as boundaries, was not possible. In this paper, we propose an Enhancing Region and Boundary Awareness Network (ERBANet), equipped with attentional feature enhancement (AFE) modules to improve the detection performance. The AFE modules act on high-level and low-level features to generate corresponding attentional features. High-level attentional features are used to highlight entire salient objects, while low-level attentional features help retain their boundaries. The proposed ERBANet aims to effectively aggregate high-level and low-level attentional features, fully utilizing their respective advantages. Furthermore, we propose a novel boundary maintenance loss (BML) for learning to preserve the original boundaries of salient objects. Meanwhile, dice loss is combined for learning to enhance the integrity of salient regions. The experimental results on five benchmark datasets demonstrate that our proposed method outperforms recent state-of-the-art methods and achieves better performance.
Provoking a Cultural Shift in Data Quality

Abstract Ecological studies require quality data to describe the nature of ecological processes and to advance understanding of ecosystem change. Increasing access to big data has magnified both the burden and the complexity of ensuring quality data. The costs of errors in ecology include low use of data, increased time spent cleaning data, and poor reproducibility that can result in a misunderstanding of ecosystem processes and dynamics, all of which can erode the efficacy of and trust in ecological research. Although conceptual and technological advances have improved ecological data access and management, a cultural shift is needed to embed data quality as a cultural practice. We present a comprehensive data quality framework to evoke this cultural shift. The data quality framework flexibly supports different collaboration models, supports all types of ecological data, and can be used to describe data quality within both short- and long-term ecological studies.
A Systematic Mapping with Bibliometric Analysis on Information Systems Using Ontology and Fuzzy Logic

The ontology-based information systems (IS) development is beneficial for analyzing, conceptual modeling, designing, and re-engineering complex IS to be semantically enriched and suitable for sophisticated reasoning on the IS content. On the other hand, fuzzy theory employment to handle uncertainty and fuzziness in IS becomes a hot topic in different practical domains, such as engineering, IS, computer sciences, etc. As such, ontology- and fuzzy-based IS are being developed. Consequently, there is a need to provide a comprehensive systematic mapping study (SMS) to build a structure on the ontology- and fuzzy-based IS field of interest and to grasp the main ideas. This paper presents findings of SMS, based on the papers extracted from Web of Science and Scopus and employing a bibliometric analysis tool to automate keyword mapping. We conclude this paper by summarizing the previous work and identifying possible research trends, which future investigations can extend. The main finding indicates that ontology and fuzzy logic contribute to ISs by expanding traditional IS to be intelligent IS, which is applicable for solving complex, fuzzy, and semantically rich (ontological) information collection, saving, processing, sharing, and reasoning in different application domains according to users’ needs in various countries.
Data‐driven generalized predictive control for car‐like mobile robots using interval type‐2 T‐S fuzzy neural network

Abstract In this paper, for the nonlinear influencing factors such as uncertainty and external disturbance existing in the actual driving process of the car‐like mobile robot (CLMR), a data‐driven generalized predictive control (GPC) method based on interval type‐2 T‐S fuzzy neural network (IT2TSFNN) is proposed for the trajectory tracking of CLMR. The controlled auto‐regressive integrated moving average (CARIMA) model of the mobile robot is established by analyzing data samples and using IT2TSFNN. Then, a generalized predictive controller is designed for the CARIMA model. Also, the global convergence of IT2TSFNN is verified by the Stone‐Weirstrass theorem. Unlike most previous results, the proposed method does not rely on the mathematical model of the mobile robot but only on the historical data of its operation. Finally, the simulation results show that the proposed method can avoid repeatedly debugging parameters, deal with the influence of uncertain factors, and obtain higher tracking accuracy.
On the confidence of stereo matching in a deep-learning era: a quantitative evaluation

Stereo matching is one of the most popular techniques to estimate dense depth maps by finding the disparity between matching pixels on two, synchronized and rectified images. Alongside with the development of more accurate algorithms, the research community focused on finding good strategies to estimate the reliability, i.e., the confidence, of estimated disparity maps. This information proves to be a powerful cue to naively find wrong matches as well as to improve the overall effectiveness of a variety of stereo algorithms according to different strategies. In this paper, we review more than ten years of developments in the field of confidence estimation for stereo matching. We extensively discuss and evaluate existing confidence measures and their variants, from hand-crafted ones to the most recent, state-of-the-art learning based methods. We study the different behaviors of each measure when applied to a pool of different stereo algorithms and, for the first time in literature, when paired with a state-of-the-art deep stereo network. Our experiments, carried out on five different standard datasets, provide a comprehensive overview of the field, highlighting in particular both strengths and limitations of learning-based strategies.
THE NORMS OF ALGORITHMIC CREDIT SCORING

Abstract This article examines the growth of algorithmic credit scoring and its implications for the regulation of consumer credit markets in the UK. It constructs a frame of analysis for the regulation of algorithmic credit scoring, bound by the core norms underpinning UK consumer credit and data protection regulation: allocative efficiency, distributional fairness and consumer privacy (as autonomy). Examining the normative trade-offs that arise within this frame, the article argues that existing data protection and consumer credit frameworks do not achieve an appropriate normative balance in the regulation of algorithmic credit scoring. In particular, the growing reliance on consumers’ personal data by lenders due to algorithmic credit scoring, coupled with the ineffectiveness of existing data protection remedies has created a data protection gap in consumer credit markets that presents a significant threat to consumer privacy and autonomy. The article makes recommendations for filling this gap through institutional and substantive regulatory reforms.
Corpus-based typology: applications, challenges and some solutions

Over the last few years, the number of corpora that can be used for language comparison has dramatically increased. The corpora are so diverse in their structure, size and annotation style, that a novice might not know where to start. The present paper charts this new and changing territory, providing a few landmarks, warning signs and safe paths. Although no corpus at present can replace the traditional type of typological data based on language description in reference grammars, corpora can help with diverse tasks, being particularly well suited for investigating probabilistic and gradient properties of languages and for discovering and interpreting cross-linguistic generalizations based on processing and communicative mechanisms. At the same time, the use of corpora for typological purposes has not only advantages and opportunities, but also numerous challenges. This paper also contains an empirical case study addressing two pertinent problems: the role of text types in language comparison and the problem of the word as a comparative concept.
Robustness analysis of classical and fuzzy decision trees under adversarial evasion attack

Although decision trees have been widely applied to different security related applications, their security has not been investigated extensively in an adversarial environment. This work aims to study the robustness of classical decision tree (DT) and Fuzzy decision tree (FDT) under evasion attack that manipulate the features in order to mislead the decision of a classifier. To the best of our knowledge, existing attack methods cannot be applied to DT due to non-differentiation of its decision function. This is the first attack model designed for both DT and FDT. Our model quantifies the influence of changing a feature on the decision. The effectiveness of our method is compared with Papernot (PPNT) and Robustness Verification of Tree-based Models (RVTM), which are state-of-the-art attack methods for DT, and the attack methods employing surrogate and Generative Adversarial Network (GAN) methods. The experimental results suggest that the fuzzifying process increases the robustness of DT. Moreover, FDT with more membership functions is more vulnerable since a smaller number of features is usually used. This study fills the gap of examining the security issue of fuzzy systems in an adversarial environment.
Manifold adversarial training for supervised and semi-supervised learning

We propose a new regularization method for deep learning based on the manifold adversarial training (MAT). Unlike previous regularization and adversarial training methods, MAT further considers the local manifold of latent representations. Specifically, MAT manages to build an adversarial framework based on how the worst perturbation could affect the statistical manifold in the latent space rather than the output space. Particularly, a latent feature space with the Gaussian Mixture Model (GMM) is first derived in a deep neural network. We then define the smoothness by the largest variation of Gaussian mixtures when a local perturbation is given around the input data point. On one hand, the perturbations are added in the way that would rough the statistical manifold of the latent space the worst. On the other hand, the model is trained to promote the manifold smoothness the most in the latent space. Importantly, since the latent space is more informative than the output space, the proposed MAT can learn a more robust and compact data representation, leading to further performance improvement. The proposed MAT is important in that it can be considered as a superset of one recently-proposed discriminative feature learning approach called center loss. We conduct a series of experiments in both supervised and semi-supervised learning on four benchmark data sets, showing that the proposed MAT can achieve remarkable performance, much better than those of the state-of-the-art approaches. In addition, we present a series of visualization which could generate further understanding or explanation on adversarial examples.
Discriminative Learning Approach Based on Flexible Mixture Model for Medical Data Categorization and Recognition

In this paper, we propose a novel hybrid discriminative learning approach based on shifted-scaled Dirichlet mixture model (SSDMM) and Support Vector Machines (SVMs) to address some challenging problems of medical data categorization and recognition. The main goal is to capture accurately the intrinsic nature of biomedical images by considering the desirable properties of both generative and discriminative models. To achieve this objective, we propose to derive new data-based SVM kernels generated from the developed mixture model SSDMM. The proposed approach includes the following steps: the extraction of robust local descriptors, the learning of the developed mixture model via the expectation-maximization (EM) algorithm, and finally the building of three SVM kernels for data categorization and classification. The potential of the implemented framework is illustrated through two challenging problems that concern the categorization of retinal images into normal or diabetic cases and the recognition of lung diseases in chest X-rays (CXR) images. The obtained results demonstrate the merits of our hybrid approach as compared to other methods.
Efficient homomorphic evaluation of <i>k</i>-NN classifiers

Abstract We design and implement an efficient, secure, homomorphic k-Nearest Neighbours determination algorithm, to be used for regression or classification over private data. Our algorithm runs in quadratic complexity with regard to the size of the database but is the only one in the literature to make the secure determination completely non-interactively. We show that our secure algorithm is both efficient and accurate when applied to classification problems requiring a small set of model vectors, and still scales to larger sets of model vectors with high accuracy yet at greater (sequential) computational costs.
Conversational End-to-End TTS for Voice Agents

End-to-end neural TTS has achieved excellent performance on reading style speech synthesis. However, it is still a challenge to build a high-quality conversational TTS due to the limitations of corpus and modeling capability. This study aims at building a conversational TTS for a voice agent under sequence to sequence modeling framework. We firstly construct a spontaneous conversational speech corpus well designed for the voice agent with a new recording scheme ensuring both recording quality and conversational speaking style. Secondly, we propose a conversation context-aware end-to-end TTS approach that employs an auxiliary encoder and a conversational context encoder to specifically reinforce the information about the current utterance and its context in a conversation as well. Experimental results show that the proposed approach produces more natural prosody in accordance with the conversational context, with significant preference gains at both utterance-level and conversation-level. Moreover, we find that the model has the ability to express some spontaneous behaviors like fillers and repeated words, which makes the conversational speaking style more realistic.
A pre-recognition SART algorithm for the volumetric reconstruction of the light field PIV

As a novel volumetric velocimetry technique, the light field particle image velocimetry (LF-PIV) based on a single light field camera benefits from its single perspective configuration and has great potential applications in the space-constraint flow measurements. However, the severe particle elongation effects and relatively heavy computational load in the volumetric reconstruction of the tracer particles limit the practical applications of the LF-PIV. To address these issues, a pre-recognition method for optimizing the weight matrix in the volumetric reconstruction of LF-PIV is proposed in this work. The crucial pre-recognition indices significantly influencing the reconstruction accuracy and efficiency are investigated using the synthetic light field images. The feasibility of the pre-recognition-based SART (PR-SART) algorithm is further experimentally validated through the reconstructions of a light source and the measurements of the horizontal laminar flow using an assembled cage-typed light field camera. The results indicated that the PR-SART algorithm is capable of mitigating the elongation effects of the reconstructed tracer particles at different depths and improving the measurement accuracy of the instantaneous velocity field. Additionally, the computational efficiency of the PR-SART algorithm is 4 times faster than the conventional SART algorithm, while the required memory is only 1/5 of the latter.
<i>Wordify:</i> A Tool for Discovering and Differentiating Consumer Vocabularies

Abstract This work describes and illustrates a free and easy-to-use online text-analysis tool for understanding how consumer word use varies across contexts. The tool, Wordify, uses randomized logistic regression (RLR) to identify the words that best discriminate texts drawn from different pre-classified corpora, such as posts written by men versus women, or texts containing mostly negative versus positive valence. We present illustrative examples to show how the tool can be used for such diverse purposes as (1) uncovering the distinctive vocabularies that consumers use when writing reviews on smartphones versus PCs, (2) discovering how the words used in Tweets differ between presumed supporters and opponents of a controversial ad, and (3) expanding the dictionaries of dictionary-based sentiment-measurement tools. We show empirically that Wordify’s RLR algorithm performs better at discriminating vocabularies than support vector machines and chi-square selectors, while offering significant advantages in computing time. A discussion is also provided on the use of Wordify in conjunction with other text-analysis tools, such as probabilistic topic modeling and sentiment analysis, to gain more profound knowledge of the role of language in consumer behavior.
Fine-Grained and Context-Aware Behavioral Biometrics for Pattern Lock on Smartphones

Pattern lock has been widely used in smartphones as a simple and effective authentication mechanism, which however is shown to be vulnerable to various attacks. In this paper, we design a novel authentication system for more secure pattern unlocking on smartphones. The basic idea is to utilize various behavior information of the user during pattern unlocking as additional authentication fingerprints, so that even if the pattern password is leaked to an attacker, the system remains safe and protected. To accommodate a variety of user contexts by our system, a context-aware module is proposed to distinguish any of such contexts (e.g., body postures when drawing the pattern) and use it to guide the authentication. Moreover, we design a polyline weighted strategy with overlapping based on the consistency of pattern lock, which analyzes the behavior information of the user during the unlock process in a fine-grained manner and takes an overall consideration the results of different polylines. Based on 14,850 samples collected from 77 participants, we have extensively evaluated the proposed system. The results demonstrate that it outperforms state-of-the-art implicit authentication based pattern lock approaches, and that each key module in our system is effective.
Towards optimal task positioning in multi-robot cells, using nested meta-heuristic swarm algorithms

While multi-robot cells are being used more often in industry, the problem of work-piece position optimization is still solved using heuristics and the human experience and, in most industrial cases, even a feasible solution takes a considerable amount of trials to be found. Indeed, the optimization of a generic performance index along a path is complex, due to the dimension of the feasible-configuration space. This work faces this challenge by proposing an iterative layered-optimization method that integrates a Whale Optimization and an Ant Colony Optimization algorithm, the method allows the optimization of a user-defined objective function, along a working path, in order to achieve a quasi-optimal, collision free solution in the feasible-configuration space.
Towards Robust State Estimation by Boosting the Maximum Correntropy Criterion Kalman Filter With Adaptive Behaviors

This work proposes a resilient and adaptive state estimation framework for robots operating in perceptually-degraded environments. The approach, called Adaptive Maximum Correntropy Criterion Kalman Filtering (AMCCKF), is inherently robust to corrupted measurements, such as those containing jumps or general non-Gaussian noise, and is able to modify filter parameters online to improve performance. Two separate methods are developed - the Variational Bayesian AMCCKF (VB-AMCCKF) and Residual AMCCKF (R-AMCCKF) - that modify the process and measurement noise models in addition to the bandwidth of the kernel function used in MCCKF based on the quality of measurements received. The two approaches differ in computational complexity and overall performance which is experimentally analyzed. The method is demonstrated in real experiments on both aerial and ground robots and is part of the solution used by the COSTAR team participating at the DARPA Subterranean Challenge.