Reviewerly/exHarmony huggingface dataset:
MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection

Weakly supervised detection of anomalies in surveillance videos is a challenging task. Going beyond existing works that have deficient capabilities to localize anomalies in long videos, we propose a novel glance and focus network to effectively integrate spatial-temporal information for accurate anomaly detection. In addition, we empirically found that existing approaches that use feature magnitudes to represent the degree of anomalies typically ignore the effects of scene variations, and hence result in sub-optimal performance due to the inconsistency of feature magnitudes across scenes. To address this issue, we propose the Feature Amplification Mechanism and a Magnitude Contrastive Loss to enhance the discriminativeness of feature magnitudes for detecting anomalies. Experimental results on two large-scale benchmarks UCF-Crime and XD-Violence manifest that our method outperforms state-of-the-art approaches.
SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation

Generating talking head videos through a face image and a piece of speech audio still contains many challenges. i.e., unnatural head movement, distorted expression, and identity modification. We argue that these issues are mainly caused by learning from the coupled 2D motion fields. On the other hand, explicitly using 3D information also suffers problems of stiff expression and incoherent video. We present SadTalker, which generates 3D motion coefficients (head pose, expression) of the 3DMM from audio and implicitly modulates a novel 3D-aware face render for talking head generation. To learn the realistic motion coefficients, we explicitly model the connections between audio and different types of motion coefficients individually. Precisely, we present ExpNet to learn the accurate facial expression from audio by distilling both coefficients and 3D-rendered faces. As for the head pose, we design PoseVAE via a conditional VAE to synthesize head motion in different styles. Finally, the generated 3D motion coefficients are mapped to the unsupervised 3D keypoints space of the proposed face render to synthesize the final video. We conducted extensive experiments to demonstrate the superiority of our method in terms of motion and video quality. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The code and demo videos are available at https://sadtalker.github.io.
REWOrD: Semantic Relatedness in the Web of Data

This paper presents REWOrD, an approach to compute semantic relatedness between entities in the Web of Data representing real word concepts. REWOrD exploits the graph nature of RDF data and the SPARQL query language to access this data. Through simple queries, REWOrD constructs weighted vectors keeping the informativeness of RDF predicates used to make statements about the entities being compared. The most informative path is also considered to further refine informativeness. Relatedness is then computed by the cosine of the weighted vectors. Differently from previous approaches based on Wikipedia, REWOrD does not require any prepro- cessing or custom data transformation. Indeed, it can lever- age whatever RDF knowledge base as a source of background knowledge. We evaluated REWOrD in different settings by using a new dataset of real word entities and investigate its flexibility. As compared to related work on classical datasets, REWOrD obtains comparable results while, on one side, it avoids the burden of preprocessing and data transformation and, on the other side, it provides more flexibility and applicability in a broad range of domains.
Constraint Propagation in Propositional Planning

Planning as Satisfiability is a most successful approach to optimal propositional planning. It draws its strength from the efficiency of state-of-the-art propositional satisfiability solvers, combined with the utilization of constraints that are inferred from the problem planning graph. One of the recent improvements of the framework is the addition of long-distance mutual exclusion (londex) constraints that relate facts and actions which refer to different time steps. In this paper we compare different encodings of planning as satisfiability wrt the constraint propagation they achieve in a modern SAT solver. This analysis explains some of the differences observed in the performance of different encodings, and leads to some interesting conclusions. For instance, the Blackbox encoding achieves more propagation than the one of Satplan06, and therefore is a stronger formulation of planning as satisfiability. Moreover, our investigation suggests a new more compact and stronger model for the problem. We prove that in this new formulation many of the londex constraints are redundant in the sense that they do not add anything to the constraint propagation achieved by the model. Experimental results suggest that the theoretical results obtained are practically relevant.
Content-Insensitive Blind Image Blurriness Assessment Using Weibull Statistics and Sparse Extreme Learning Machine

Most of the existing image blurriness assessment algorithms are proposed based on measuring image edge width, gradient, high-frequency energy, or pixel intensity variation. However, these methods are content sensitive with little consideration of image content variations, which causes variant estimations for images with different contents but same blurriness degrees. In this paper, a content-insensitive blind image blurriness assessment metric is developed utilizing Weibull statistics. Inspired by the property that the statistics of image gradient magnitude (GM) follows Weibull distribution, we parameterize the GM using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta$ </tex-math></inline-formula> (scale parameter) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\gamma$ </tex-math></inline-formula> (shape parameter) of Weibull distribution. We also adopt skewness ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\eta$ </tex-math></inline-formula> ) to measure the asymmetry of the GM distribution. In order to reduce the influence of image content and achieve more robust performance, divisive normalization is then incorporated to moderate the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\gamma$ </tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\eta$ </tex-math></inline-formula> . The final image quality is predicted using a sparse extreme learning machine. Performances evaluation on the blur image subsets in LIVE, CSIQ, TID2008, and TID2013 databases demonstrate that the proposed method is highly correlated with human perception and robust with image contents. In addition, our method has low computational complexity which is suitable for online applications.
An integrated approach for intrinsic plagiarism detection

Employing effective plagiarism detection methods are seen to be essential in the next generation web. In this paper, we present a novel approach for plagiarism detection without reference collections. The proposed approach relies on using some statistical properties of the most common words, and the Latent Semantic Analysis that is applied to extract the most common words usage patterns. This method aims to generate a model of author's "style" by revealing a set of certain features of authorship. The model generation procedure focuses on just one author, as an attempt to summarise the aspects of an author's style in a definitive and clear-cut manner. The feature set of the intrinsic model were based on the frequency of the most common words, their relative frequencies in the book series, and the deviation of these frequencies across all books for a particular author. The approach has been evaluated using the leave-one-out-cross-validation method on the CEN (Corpus of English Novel) data set. Results have indicated that, by integrating deep latent semantic and stylometric analyses, hidden changes can be identified when a reference collection does not exist. The results have also shown that our Multi-Layer Perceptron based approach statistically outperforms Bayesian Network, Support Vector Machine and Random Forest models, by accurately predicting the author classes with an overall accuracy of 97%.
Test without Trust: Optimal Locally Private Distribution Testing

We study the problem of distribution testing when the samples can only be accessed using a locally differentially private mechanism and focus on two representative testing questions of identity (goodness-of-fit) and independence testing for discrete distributions. First, we construct tests that use existing, general-purpose locally differentially private mechanisms such as the popular RAPPOR or the recently introduced Hadamard Response for collecting data and show that our proposed tests are sample optimal, when we insist on using these mechanisms. Next, we allow bespoke mechanisms designed specifically for testing and introduce the Randomized Aggregated Private Testing Optimal Response (RAPTOR) mechanism which is remarkably simple and requires only one bit of communication per sample. We show that our proposed mechanism yields sample-optimal tests, and in particular outperforms any test based on RAPPOR or Hadamard response. A distinguishing feature of our optimal mechanism is that, in contrast to existing mechanisms, it uses public randomness.
Bio-Inspired Deep Attribute Learning Towards Facial Aesthetic Prediction

Computational prediction of facial aesthetics has attracted ever-increasing research focus, which has wide range of prospects in multimedia applications. The key challenge lies in extracting discriminative and perception-aware features to characterize the facial beautifulness. To this end, the existing schemes simply adopt a direct feature mapping, which relies on handcraft-designed low-level features that cannot reflect human-level aesthetic perception. In this paper, we present a systematic framework towards designing biology-inspired, discriminative representation for facial aesthetic prediction. First, we design a group of biological experiments that adopt eye tracker to identify spatial regions of interest during the facial aesthetic judgments of subjects, which forms a Bio-inspired Facial Aesthetic Ontology (Bio-FAO) and is made public available. Second, we adopt the cutting-edge convolutional neural network to train a set of Bio-inspired Attribute features, termed Bio-AttriBank, which forms a mid-level interpretable representation corresponding to the aforementioned Bio-FAO. For a given image, the facial aesthetic prediction is then formulated as a classification problem over the Bio-AttriBank descriptor responses, which well bridges the affective gap, and provides explainable evidences on why/how a face is beautiful or not. We have carried out extensive experiments on both JAFFE and FaceWarehouse datasets, with comparisons to a set of state-of-the-art and alternative approaches. Superior performance gains in the experiments have demonstrated the merits of the proposed scheme.
Difficulty-Aware Attention Network with Confidence Learning for Medical Image Segmentation

Medical image segmentation is a key step for various applications, such as image-guided radiation therapy and diagnosis. Recently, deep neural networks provided promising solutions for automatic image segmentation; however, they often perform good on regular samples (i.e., easy-to-segment samples), since the datasets are dominated by easy and regular samples. For medical images, due to huge inter-subject variations or disease-specific effects on subjects, there exist several difficult-to-segment cases that are often overlooked by the previous works. To address this challenge, we propose a difficulty-aware deep segmentation network with confidence learning for end-to-end segmentation. The proposed framework has two main contributions: 1) Besides the segmentation network, we also propose a fully convolutional adversarial network for confidence learning to provide voxel-wise and region-wise confidence information for the segmentation network. We relax the adversarial learning to confidence learning by decreasing the priority of adversarial learning, so that we can avoid the training imbalance between generator and discriminator. 2) We propose a difficulty-aware attention mechanism to properly handle hard samples or hard regions considering structural information, which may go beyond the shortcomings of focal loss. We further propose a fusion module to selectively fuse the concatenated feature maps in encoder-decoder architectures. Experimental results on clinical and challenge datasets show that our proposed network can achieve state-of-the-art segmentation accuracy. Further analysis also indicates that each individual component of our proposed network contributes to the overall performance improvement.
Robust Interval Type-2 Fuzzy Observer for Fault Detection of Networked Control Systems Subject to Immeasurable Premise Variables

In this paper, a novel observer design has been suggested for fault detection of networked control systems under random measurement delays. A randomly varying communication delay structure has been assumed in data transmission between the physical system and the observer. In order to increase the applicability of the proposed Takagi-Sugeno fuzzy observer, it has been contemplated to be under immeasurable premise variables. In addition, time-varying parameter uncertainties are added to the system, which contribute to the system uncertainties. Theoretical materials related to the system and the observer design with a predetermined H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> index are provided resulting in less conservative constraints in the proof of asymptotical mean-square stability of the closed-loop system. Simulation results, which have been applied to a real-world system of tunnel diode circuit, have shown how the proposed observer would benefit from the use of interval type-2 fuzzy sets in dealing with uncertainties of the system.
A Semi-Automatic Annotation Approach for Human Activity Recognition

Modern smartphones and wearables often contain multiple embedded sensors which generate significant amounts of data. This information can be used for body monitoring-based areas such as healthcare, indoor location, user-adaptive recommendations and transportation. The development of Human Activity Recognition (HAR) algorithms involves the collection of a large amount of labelled data which should be annotated by an expert. However, the data annotation process on large datasets is expensive, time consuming and difficult to obtain. The development of a HAR approach which requires low annotation effort and still maintains adequate performance is a relevant challenge. We introduce a Semi-Supervised Active Learning (SSAL) based on Self-Training (ST) approach for Human Activity Recognition to partially automate the annotation process, reducing the annotation effort and the required volume of annotated data to obtain a high performance classifier. Our approach uses a criterion to select the most relevant samples for annotation by the expert and propagate their label to the most confident samples. We present a comprehensive study comparing supervised and unsupervised methods with our approach on two datasets composed of daily living activities. The results showed that it is possible to reduce the required annotated data by more than 89% while still maintaining an accurate model performance.
Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human Needs

Debjit Paul, Anette Frank. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Point Cloud Saliency Detection by Local and Global Feature Fusion

Inspired by the characteristics of the human visual system, a novel method is proposed for detecting the visually salient regions on 3D point clouds. First, the local distinctness of each point is evaluated based on the difference with its local surroundings. Then, the point cloud is decomposed into small clusters, and the initial global rarity value of each cluster is calculated; a random walk ranking method is then used to introduce cluster-level global rarity refinement to each point in all the clusters. Finally, an optimization framework is proposed to integrate both the local distinctness and the global rarity values to obtain the final saliency detection result of the point cloud. We compare the proposed method with several relevant algorithms and apply it to some computer graphics applications, such as interest point detection, viewpoint selection, and mesh simplification. The experimental results demonstrate the superior performance of the proposed method.
Contagion Effect in Cryptocurrency Market

The rapid development of cryptocurrencies has drawn attention to this particular market, with investors trying to understand its behaviour and researchers trying to explain it. The evolution of cryptocurrencies’ prices showed a kind of bubble and a crash at the end of 2017. Based on this event, and on the fact that Bitcoin is the most recognized cryptocurrency, we propose to evaluate the contagion effect between Bitcoin and other major cryptocurrencies. Using the Detrended Cross-Correlation Analysis correlation coefficient (ΔρDCCA) and comparing the period after and before the crash, we found evidence of a contagion effect, with this particular market being more integrated now than in the past—something that should be taken into account by current and potential investors.
Chittron: An Automatic Bangla Image Captioning System

Automatic image caption generation aims to produce an accurate description of an image in natural language automatically. How- ever, Bangla, the fifth most widely spoken language in the world, is lagging considerably in the research and development of such domain. Besides, while there are many established data sets related to image annotation in English, no such resource exists for Bangla yet. Hence, this paper outlines the development of “Chittron”, an automatic image captioning system in Bangla. To address the data set availability issue, a collection of 16, 000 Bangladeshi contextual images has been accumulated and manually annotated in Bangla. This data set is then used to train a model that integrates a pre-trained VGG16 image embedding model with stacked LSTM layers. The model is trained to predict the caption when the input is an image, one word at a time. The results show that the model has successfully been able to learn a working language model and to generate captions of images quite accurately in many cases. The results are evaluated mainly qualitatively. However, BLEU scores are also reported. It is expected that a better result can be obtained with a bigger and more varied data set.
Adaptive Residual Compensation Ensemble Models for Improving Solar Energy Generation Forecasting

This letter presents an improved ensemble learning framework for forecasting of solar power generation. A modified ensemble model based on a novel adaptive residual compensation (ARC) algorithm and an evolutionary optimization technique is proposed to improve the forecast accuracy. It is also applied to probabilistic solar power forecasting by using a nonparametric estimator. Extensive experimental results are provided, demonstrating the superiority of the proposed ARC ensemble model over traditional ensemble models, in terms of both deterministic and probabilistic forecasts.
Joint Online Coflow Routing and Scheduling in Data Center Networks

A coflow is a collection of related parallel flows that occur typically between two stages of a multi-stage computing task in a network, such as shuffle flows in MapReduce. The coflow abstraction allows applications to convey their semantics to the network so that application-level requirements can be better satisfied. In this paper, we study the routing and scheduling of multiple coflows to minimize the total weighted coflow completion time (CCT). We first propose a rounding-based randomized approximation algorithm, called OneCoflow, for single coflow routing and scheduling. The multiple coflow problem is more challenging as coexisting coflows will compete for the same network resources, such as link bandwidth. To minimize the total weighted CCT, we derive an online multiple coflow routing and scheduling algorithm, called OMCoflow. We then derive a competitive ratio bound of our problem and prove that the competitive ratio of OMCoflow is nearly tight. To the best of our knowledge, this is the first online algorithm with theoretical performance guarantees which considers routing and scheduling simultaneously for multi-coflows. Compared with existing methods, OMCoflow runs more efficiently and avoids frequently rerouting the flows. Extensive simulations on a Facebook data trace show that OMCoflow outperforms the state-of-the-art heuristic schemes significantly (e.g., reducing the total weighted CCT by up to 41.8% and the execution time by up to 99.2% against RAPIER).
A new family of maximum scattered linear sets in PG(1, q^6)

We generalize the example of linear set presented by the last two authors in "Vertex properties of maximum scattered linear sets of PG(1, qn)" (2019) to a more general family, proving that such linear sets are maximum scattered when q is odd and, apart from a special case, they are new. This solves an open problem posed in "Vertex properties of maximum scattered linear sets of PG(1, qn)" (2019). As a consequence of Sheekey's results in "A new family of linear maximum rank distance codes" (2016), this family yields to new MRD-codes with parameters (6, 6, q; 5).
Unsupervised Evaluation Metrics and Learning Criteria for Non-Parallel Textual Transfer

We consider the problem of automatically generating textual paraphrases with modified attributes or properties, focusing on the setting without parallel data (Hu et al., 2017;Shen et al., 2017).This setting poses challenges for evaluation.We show that the metric of post-transfer classification accuracy is insufficient on its own, and propose additional metrics based on semantic preservation and fluency as well as a way to combine them into a single overall score.We contribute new loss functions and training strategies to address the different metrics.Semantic preservation is addressed by adding a cyclic consistency loss and a loss based on paraphrase pairs, while fluency is improved by integrating losses based on style-specific language models.We experiment with a Yelp sentiment dataset and a new literature dataset that we propose, using multiple models that extend prior work (Shen et al., 2017).We demonstrate that our metrics correlate well with human judgments, at both the sentence-level and system-level.Automatic and manual evaluation also show large improvements over the baseline method of Shen et al. (2017).We hope that our proposed metrics can speed up system development for new textual transfer tasks while also encouraging the community to address our three complementary aspects of transfer quality.