Reviewerly/exHarmony huggingface dataset:
Saliency detection via multi-level integration and multi-scale fusion neural networks

The recent advance on saliency models has remarkably improved performance due to the pervasive application of deep convolutional neural networks. However, for more challenging images, it is worthwhile to explore in deep convolutional neural networks how to effectively exploit features at different levels and scales for saliency detection. In this paper, we propose an end-to-end multi-level feature integration and multi-scale feature fusion network to better predict salient objects in challenging images. Specifically, our network first integrates multi-level features from high-level to low-level features in the network based on ResNet. Then, the feature combined by the multi-level feature integration network is further refined by four parallel residual connected blocks with dilated convolution, in which each block has a specific dilation rate to capture multi-scale context information. Finally, we fuse the outputs of residual connected blocks with dilated convolution and obtain the saliency map by up-sampling operation. Extensive experimental results demonstrate that the proposed model outperforms the state-of-the-art saliency models on several challenging image datasets.
A real-time and unsupervised face re-identification system for human-robot interaction

In the context of Human-Robot Interaction (HRI), face Re-Identification (face Re-ID) aims to verify if certain detected faces have already been observed by robots. The ability of distinguishing between different users is crucial in social robots as it will enable the robot to tailor the interaction strategy toward the users' individual preferences. So far face recognition research has achieved great success, however little attention has been paid to the realistic applications of Face Re-ID in social robots. In this paper, we present an effective and unsupervised face Re-ID system which simultaneously re-identifies multiple faces for HRI. This Re-ID system employs Deep Convolutional Neural Networks to extract features, and an online clustering algorithm to determine the face's ID. Its performance is evaluated on two datasets: the TERESA video dataset collected by the TERESA robot, and the YouTube Face Dataset (YTF Dataset). We demonstrate that the optimised combination of techniques achieves an overall 93.55% accuracy on TERESA dataset and an overall 90.41% accuracy on YTF dataset. We have implemented the proposed method into a software module in the HCI^2 Framework for it to be further integrated into the TERESA robot, and has achieved real-time performance at 10~26 Frames per second.
Slanderous user detection with modified recurrent neural networks in recommender system

We focus on how to tackle a unique multi-view unsupervised issue: slanderous user detection, with recurrent neural networks to benefit recommender systems. In real-world recommender systems, some consumers always give fake reviews and low ratings to the items they bought on purpose. In order to ensure their profits, these slanderous users make a semantic gap between their ratings and reviews to avoid detection, which makes slanderous user detection a more difficult problem. On some occasions, they give a false low rating with a positive review which confuse recommender systems, and vice versa. To address the above problem, in this paper, we propose a novel recommendation framework: Slanderous user Detection Recommender System (SDRS). In SDRS, we design a Hierarchical Dual-Attention recurrent Neural network (HDAN) with a modified GRU (mGRU) to compute an opinion level for reviews. Then a joint filtering method is proposed to catch the gap between ratings and reviews. With joint filtering, slanderous users can be detected and omitted. Finally, a modified non-negative matrix factorization is proposed to make recommendations. Extensive experiments are conducted in four datasets: Amazon, Yelp, Taobao, and Jingdong, in which the results demonstrate that our proposed method can detect slanderous users and make accurate recommendations in a uniform framework. Also, with slanderous user detection, some state-of-the-art recommendation systems can be benefited.
Deconvolving Convolutional Neural Network for Cell Detection

Automatic cell detection in histology images is a challenging task due to varying size, shape and features of cells and stain variations across large cohorts. Conventional deep learning methods regress the probability of each pixel belonging to the centre of a cell followed by detection of local maxima. We propose a three stage method (MapDe) to improve cell detection. (a) The dot annotations are convolved with a mapping filter to generate artificial labels. (b) A convolutional neural network (CNN) is modified to convolve its output with the same mapping filter. The mapping filter is fixed during training forcing the network to generate better probability maps. (c) Output of the trained CNN is deconvolved to generate points as cell detection. The results show that (1) local maxima performs better cell detection with probability maps generated using fixed convolution filter, (2) the results can be further improved by deconvolving the output with fewer parameters to tune.
“That Looks Hard!”: Effects of Objective and Perceived Textual Complexity on Factual and Structural Political Knowledge

Communication of political information is vital for a well-functioning democratic system, and texts are one of the main mediums of politics. Most studies dealing with political text consider how such texts communicate content, rather than the structural characteristics of texts themselves. The current study focuses on complexity as one of the focal structural characteristics of political text. Previous research showed that different types of textual complexity affect learning processes. Such prior studies are, however, limited both conceptually and empirically. This study addresses these gaps by employing a large-scale experimental design (N= 822), investigating how different dimensions of textual complexity affect factual and structural political knowledge, and whether such relationships are mediated through perceived complexity. Results indicate that different levels (high vs. low) and different dimensions of textual complexity (semantic vs. syntactic) influence reader’s perception of text, as well as their factual and structural knowledge. Only semantic complexity has an effect on one’s perceived complexity, which in turn negatively affects factual and structural knowledge. Syntactic complexity directly lowered one’s factual knowledge acquisition, while there was no direct effect of syntactic complexity on structural knowledge. The results suggest that text complexity indeed plays an important role in political information acquisition, and our findings also highlight the importance of perception in mediating the structural effects of the text.
Nonlinear supervised dimensionality reduction via smooth regular embeddings

The recovery of the intrinsic geometric structures of data collections is an important problem in data analysis. Supervised extensions of several manifold learning approaches have been proposed in the recent years. Meanwhile, existing methods primarily focus on the embedding of the training data, and the generalization of the embedding to initially unseen test data is rather ignored. In this work, we build on recent theoretical results on the generalization performance of supervised manifold learning algorithms. Motivated by these performance bounds, we propose a supervised manifold learning method that computes a nonlinear embedding while constructing a smooth and regular interpolation function that extends the embedding to the whole data space in order to achieve satisfactory generalization. The embedding and the interpolator are jointly learnt such that the Lipschitz regularity of the interpolator is imposed while ensuring the separation between different classes. Experimental results on several image data sets show that the proposed method outperforms traditional classifiers and the supervised dimensionality reduction algorithms in comparison in terms of classification accuracy in most settings.
Strategy and organisational cybersecurity: a knowledge-problem perspective

Purpose The purpose of this paper is to frame organisational cybersecurity through a strategic lens, as a function of an interplay of pragmatism, inference, holism and adaptation. The authors address the hostile epistemic climate for intellectual capital management presented by the dynamics of cybersecurity as a phenomenon. The drivers of this hostility are identified and their implications for research and practice are discussed. Design/methodology/approach The philosophical foundations of cybersecurity in its relation with strategy, knowledge and intellectual capital are explored through a review of the literature as a mechanism to contribute to the emerging theoretical underpinnings of the cybersecurity domain. Findings This conceptual paper argues that a knowledge-based perspective can serve as the necessary platform for a phenomenon-based view of organisational cybersecurity, given its multi-disciplinary nature. Research limitations/implications By recognising the knowledge-related vectors, mechanisms and tendencies at play, a novel perspective on the topic can be developed: cybersecurity as a “knowledge problem”. In order to facilitate such a perspective, the paper proposes an emergent epistemology, rooted in systems thinking and pragmatism. Practical implications In practice, the knowledge-problem narrative can underpin the development of new organisational support constructs and systems. These can address the distinctiveness of the strategic challenges that cybersecurity poses for the growing operational reliance on intellectual capital. Originality/value The research narrative presents a novel knowledge-based analysis of organisational cybersecurity, with significant implications for both interdisciplinary research in the field, and practice.
Joint Stabilization and Direction of 360° Videos

Three-hundred-sixty-degree (360°) video provides an immersive experience for viewers, allowing them to freely explore the world by turning their head. However, creating high-quality 360° video content can be challenging, as viewers may miss important events by looking in the wrong direction, or they may see things that ruin the immersion, such as stitching artifacts and the film crew. We take advantage of the fact that not all directions are equally likely to be observed; most viewers are more likely to see content located at “true north,” i.e., in front of them, due to ergonomic constraints. We therefore propose 360° video direction, where the video is jointly optimized to orient important events to the front of the viewer and visual clutter behind them, while producing smooth camera motion. Unlike traditional video, viewers can still explore the space as desired, but with the knowledge that the most important content is likely to be in front of them. Constraints can be user guided, either added directly on the equirectangular projection or by recording “guidance” viewing directions while watching the video in a VR headset or automatically computed, such as via visual saliency or forward-motion direction. To accomplish this, we propose a new motion estimation technique specifically designed for 360° video that outperforms the commonly used five-point algorithm on wide-angle video. We additionally formulate the direction problem as an optimization where a novel parametrization of spherical warping allows us to correct for some degree of parallax effects. We compare our approach to recent methods that address stabilization-only and converting 360° video to narrow field-of-view video. Our pipeline can also enable the viewing of wide-angle non-360° footage in a spherical 360° space, giving an immersive “virtual cinema” experience for a wide range of existing content filmed with first-person cameras.
Correlation Filter Selection for Visual Tracking Using Reinforcement Learning

Correlation filter has been proven to be an effective tool for a number of approaches in visual tracking, particularly for seeking a good balance between tracking accuracy and speed. However, correlation filter-based models are susceptible to wrong updates stemming from inaccurate tracking results. To date, very little effort has been devoted towards handling the correlation filter update problem. In this paper, we propose a novel approach to address the correlation filter update problem. In our approach, we update and maintain multiple correlation filter models in parallel, and we use deep reinforcement learning for the selection of an optimal correlation filter model among them. To facilitate the decision process in an efficient manner, we propose a decision-net to deal with target appearance modeling, which is trained through hundreds of challenging videos using proximal policy optimization and a lightweight learning network. An exhaustive evaluation of the proposed approach on the OTB100 and OTB2013 benchmarks shows that the approach is effective enough to achieve the average success rate of 62.3% and the average precision score of 81.2%, both exceeding the performance of traditional correlation filter-based trackers.
Recognition of Handwritten Chinese Characters Based on Concept Learning

Many deep-learning character recognition methods have been developed over the past few years.Chinese characters are widely used in many countries; however, the deep-learning-based Chinese character recognition methods are faced with various problems, such as a large amount of data required for training, numerous parameters, and a large consumption of computing resources.Concept learning is a hominine learning approach.Unlike existing deep-learning models, conceptual model learning can be realized by using as little as one sample.This paper is the first to propose a handwritten Chinese character recognition method based on concept learning.Different from the existing image representation-based character recognition methods, the proposed method builds a meta stroke library with prior knowledge, and then, presents a Chinese character conceptual model based on stroke relationship learning using a character stroke extraction method and Bayesian program learning.During character recognition, Monte Carlo Markov chain sampling is utilized to obtain the character generation model for each character conceptual.This generation model can calculate the probability of the target and training characters being the same classification, and thereby determines the classification of the target character.The experimental results indicate that, with the proposed method, the conceptual model of each character can be built for character classification prediction using as few as one character sample.Our approach obtains better performance than the state-of-the-art methods on ICDAR-2013 competition dataset.
Learning to Weight for Text Classification

In information retrieval (IR) and related tasks, term weighting approaches typically consider the frequency of the term in the document and in the collection in order to compute a score reflecting the importance of the term for the document. In tasks characterized by the presence of training data (such as text classification) it seems logical that the term weighting function should take into account the distribution (as estimated from training data) of the term across the classes of interest. Although “supervised term weighting” approaches that use this intuition have been described before, they have failed to show consistent improvements. In this article, we analyze the possible reasons for this failure, and call consolidated assumptions into question. Following this criticism, we propose a novel supervised term weighting approach that, instead of relying on any predefined formula, learns a term weighting function optimized on the training set of interest; we dub this approach Learning to Weight (LTW). The experiments that we run on several well-known benchmarks, and using different learning methods, show that our method outperforms previous term weighting approaches in text classification.
Funnelling

Cross-lingual Text Classification (CLC) consists of automatically classifying, according to a common set C of classes, documents each written in one of a set of languages L , and doing so more accurately than when “naïvely” classifying each document via its corresponding language-specific classifier. To obtain an increase in the classification accuracy for a given language, the system thus needs to also leverage the training examples written in the other languages. We tackle “multilabel” CLC via funnelling , a new ensemble learning method that we propose here. Funnelling consists of generating a two-tier classification system where all documents, irrespective of language, are classified by the same (second-tier) classifier. For this classifier, all documents are represented in a common, language-independent feature space consisting of the posterior probabilities generated by first-tier, language-dependent classifiers. This allows the classification of all test documents, of any language, to benefit from the information present in all training documents, of any language. We present substantial experiments, run on publicly available multilingual text collections, in which funnelling is shown to significantly outperform a number of state-of-the-art baselines. All code and datasets (in vector form) are made publicly available.
External Labeling Techniques: A Taxonomy and Survey

Abstract External labeling is frequently used for annotating features in graphical displays and visualizations, such as technical illustrations, anatomical drawings, or maps, with textual information. Such a labeling connects features within an illustration by thin leader lines with their labels, which are placed in the empty space surrounding the image. Over the last twenty years, a large body of literature in diverse areas of computer science has been published that investigates many different aspects, models, and algorithms for automatically placing external labels for a given set of features. This state‐of‐the‐art report introduces a first unified taxonomy for categorizing the different results in the literature and then presents a comprehensive survey of the state of the art, a sketch of the most relevant algorithmic techniques for external labeling algorithms, as well as a list of open research challenges in this multidisciplinary research field.
Understanding Interaction Design Challenges in Mobile Extreme Citizen Science

Extreme citizen science is a bottom up practice used to empower people by supporting them, via processes and technological tools, to find solutions for local problems, but also to tackle major sustainability challenges of the 21st century. Methods and tools based on mobile computing have been utilized by communities in various parts of the world, from the Congo Basin through the Amazonian rain forest. However, extreme citizen science initiatives often face severe challenges as pre-designed technological solutions prove to be non-transferable to peculiar environments of rural developing regions. In this paper we collect and investigate evidence from the implementation of various extreme citizen science initiatives in the developing world. Our aim is to identify key obstacles towards their successful realization, mainly focusing on the problem of user interaction with mobile computing solutions. We conduct interviews with nine experienced researchers who all performed extensive fieldwork within these initiatives, and who reflect on the technology interaction, knowledge organization, inter-cultural, social and usability issues. Based on our analysis we report among others, symptomatic difficulties with abstractions, representational hierarchies, and navigation commands, as well as potential improvements that mobile technology developers can implement in order to create a more inclusive environment for extreme citizen science.
A logic of knowing why

When we say “I know why he was late”, we know not only the fact that he was late, but also an explanation of this fact. We propose a logical framework of “knowing why” inspired by the existing formal studies on why-questions, scientific explanation, and justification logic. We introduce the $${{\mathcal {K}}{}\textit{y}}_i$$ operator into the language of epistemic logic to express “agent i knows why $$\varphi $$ ” and propose a Kripke-style semantics of such expressions in terms of knowing an explanation of $$\varphi $$ . We obtain two sound and complete axiomatizations w.r.t. two different model classes depending on different assumptions about introspection. Finally we connect our logic with justification logic technically by providing an alternative semantics and an in-depth comparison on various design choices.
Estimating the Coefficients of a Mixture of Two Linear Regressions by Expectation Maximization

We give convergence guarantees for estimating the coefficients of a symmetric mixture of two linear regressions by expectation maximization (EM). In particular, we show that the empirical EM iterates converge to the target parameter vector at the parametric rate, provided the algorithm is initialized in an unbounded cone. In particular, if the initial guess has a sufficiently large cosine angle with the target parameter vector, a sample-splitting version of the EM algorithm converges to the true coefficient vector with high probability. Interestingly, our analysis borrows from tools used in the problem of estimating the centers of a symmetric mixture of two Gaussians by EM. We also show that the population of EM operator for mixtures of two regressions is anti-contractive from the target parameter vector if the cosine angle between the input vector and the target parameter vector is too small, thereby establishing the necessity of our conic condition. Finally, we give empirical evidence supporting this theoretical observation, which suggests that the sample-based EM algorithm may not converge to the target vector when initial guesses are drawn accordingly. Our simulation study also suggests that the EM algorithm performs well even under model misspecification (i.e., when the covariate and error distributions violate the model assumptions).
A Control Theoretic Approach to ABR Video Streaming: A Fresh Look at PID-Based Rate Adaptation

Adaptive bitrate streaming (ABR) has become the de facto technique for video streaming over the Internet. Despite a flurry of techniques, achieving high quality ABR streaming over cellular networks remains a tremendous challenge. ABR streaming can be naturally modeled as a control problem. There has been some initial work on using PID, a widely used feedback control technique, for ABR streaming. Existing studies, however, either use PID control directly without fully considering the special requirements of ABR streaming, leading to suboptimal results, or conclude that PID is not a suitable approach. In this paper, we take a fresh look at PID-based control for ABR streaming. We design a framework called PIA (PID-control based ABR streaming) that strategically leverages PID control concepts and incorporates several novel strategies to account for the various requirements of ABR streaming. We evaluate PIA using simulation based on real LTE network traces, as well as using real DASH implementation. The results demonstrate that PIA outperforms state-of-the-art schemes in providing high average bitrate with significantly lower bitrate changes (reduction up to 40 percent) and stalls (reduction up to 85 percent), while incurring very small runtime overhead. We further design PIA-E (PIA Enhanced), which improves the performance of PIA in the important initial playback phase.