Reviewerly/exHarmony huggingface dataset:
Syntactically Guided Generative Embeddings for Zero-Shot Skeleton Action Recognition

We introduce SynSE (Syntactically guided Skeleton Embeddings), a novel syntactically guided generative approach for Zero-Shot Learning (ZSL). Our end-to-end approach learns progressively refined generative embedding spaces constrained within and across the involved modalities (visual, language). The inter-modal constraints are defined between action sequence embedding and embeddings of Parts of Speech (PoS) tagged words in the corresponding action description. We deploy SynSE for the task of skeleton-based action sequence recognition. Our design choices enable SynSE to generalize compositionally, i.e., recognize sequences whose action descriptions contain words not encountered during training. We also extend our approach to the more challenging Generalized Zero-Shot Learning (GZSL) problem via a confidence-based gating mechanism. We are the first to present zero-shot skeleton action recognition results on the large-scale NTU-60 and NTU-120 skeleton action datasets with multiple splits. Our results demonstrate SynSE’s state-of-the-art performance in both ZSL and GZSL settings compared to strong baselines on the NTU-60 and NTU-120 datasets.
Obstacle Avoidance Path Planning Based on Improved APF and RRT

In recent years, collaborative robots have been widely used in the field of intelligent manufacturing. Obstacle avoidance path planning, as a key technology of collaborative robots in motion planning, is becoming more and more important. In order to solve the shortcomings of APF easily falling into local minimums and the shortcomings of RRT's strong randomness, this paper improves and combines these two algorithms. Firstly, artificial potential field algorithm is used in local obstacle avoidance path planning. When falling into local minimum, the improved RRT algorithm can adaptively select the temporary target point and make the search process jump out of the local minimum point. In addition, artificial potential field algorithm will be applied when the robot arm leaves the local minimum. Finally, we use a greedy algorithm to remove redundant nodes in the path. Experiments show that this algorithm can greatly improve the success rate of planning and reduce planning time.
Deep Light Field Super-Resolution Using Frequency Domain Analysis and Semantic Prior

Light field (LF) camera can simultaneously capture the intensity and direction information of light rays, which has been widely concerned. However, limited by the size of the imaging sensor, the captured LF image (LFI) has a trade-off between spatial and angular resolutions. To this end, this paper proposes a new LF super-resolution method using frequency domain analysis and semantic prior, which designs a two-stage learning framework to enhance the spatial and angular resolutions of LFI. Specifically, the proposed method first decomposes the spatial and angular information to explore the 4D structure of LFI by using frequency domain transformation, and formulates the LF super-resolution as a frequency restoration process. Then, the decomposed frequency components are recovered in a progressive restoration manner, with new cascaded 2D and 3D convolutional neural networks. To further improve the quality of the reconstructed LFI, especially at the object boundary, the semantic prior is incorporated into the designed network to enhance its representation ability. Finally, the super-resolved LFI is reconstructed by inverse frequency domain transformation. Experimental results show that the proposed method can effectively generate high-resolution LFI, and outperforms other state-of-the-art methods in terms of both subjective visual perception and objective quality evaluation. Moreover, the proposed method can enhance the performance of LF applications such as depth estimation.
Extremely randomized neural networks for constructing prediction intervals

The aim of this paper is to propose a novel prediction model based on an ensemble of deep neural networks adapting the extremely randomized trees method originally developed for random forests. The extra-randomness introduced in the ensemble reduces the variance of the predictions and improves out-of-sample accuracy. As a byproduct, we are able to compute the uncertainty about our model predictions and construct interval forecasts. Some of the limitations associated with bootstrap-based algorithms can be overcome by not performing data resampling and thus, by ensuring the suitability of the methodology in low and mid-dimensional settings, or when the i.i.d. assumption does not hold. An extensive Monte Carlo simulation exercise shows the good performance of this novel prediction method in terms of mean square prediction error and the accuracy of the prediction intervals in terms of out-of-sample prediction interval coverage probabilities. The advanced approach delivers better out-of-sample accuracy in experimental settings, improving upon state-of-the-art methods like MC dropout and bootstrap procedures.
Exploring Data Visualisations: An Analytical Framework Based on Dimensional Components of Data Artefacts in Journalism

This study introduces a synthesised framework for the analysis of data visualisations in the news. Through a close examination of seminal content analyses, their methodologies and findings, this article proposes a framework that consolidates dimensional components of data visualisations previously scattered across this body of research. To transition from incidental and essentialist examinations of visual data artefacts towards a systematic and theory-informed exploration, we consider the diagrammatic dimensions of data visualisations. The offered synthesized framework can serve as a starting point for both theory-infused descriptive purposes as well as more theory-guided explorations. The framework is put to the test by analysing 185 visualisations drawn from award-winning data stories. Findings generated through the application of the framework highlight the varied composition of components of data visualisations, though certain combinations of components are prevalent, leading to static categorical comparisons or interactive spatial localization. After all, data artefacts can be understood as problem-posing elements that are the outcome of diagrammatic thinking that journalists employ to communicate claims.
Omnidirectional stereo depth estimation based on spherical deep network

Omnidirectional depth estimation is an emerging research topic and has received significant attention in recent years. However, the existing methods were developed based on the theory of planar stereo matching; and introduce the nonlinear epipolar constraint and significant distortions of re-projections. In this paper, we propose a novel approach that use spherical CNNs and the epipolar constraint on sphere for omnidirectional depth estimation. We discuss the epipolar constraint for spherical stereo imaging and convert the nonlinear constraint on a planar projection to the linear constraint on a sphere. We then propose a Spherical Convolution Residual Network (SCRN) for omnidirectional depth estimation via the spherical linear epipolar constraint. The input equirectangular projection (ERP) images are sampled to spherical meshes and fed into SCRN to calculate spherical depth maps. For 2D visualization, we design a Planar Refinement Network (PRN) and adopt the cascade learning scheme to improve the accuracy of depth maps. This scheme reduces the errors caused by projection, interpolation, and the limitation of spherical representation. The experiment shows that our full scheme Cascade Spherical Depth Network (CSDNet) results in more accurate and detailed depth maps with lower errors, as compared to recent seminal works. Our approach yields the comparable performance to the other state-of-the-art works on the omnidirectional stereo datasets with less number of parameters. The effectiveness of the spherical network and the cascade learning scheme is validated, and the influence of spherical sampling density is also discussed.
The role of relevance for scalar diversity: a usage-based approach

abstract Scalar inferences occur when a weaker statement like It’s warm is used when a stronger one like It’s hot could have been used instead, resulting in the inference that whoever produced the weaker statement believes that the stronger statement does not hold. The rate at which this inference is drawn varies across scalar words, a result termed ‘scalar diversity’. Here, we study scalar diversity in adjectival scalar words from a usage-based perspective. We introduce novel operationalisations of several previously observed predictors of scalar diversity using computational tools based on usage data, allowing us to move away from existing judgment-based methods. In addition, we show in two experiments that, above and beyond these previously observed predictors, scalar diversity is predicted in part by the relevance of the scalar inference at hand. We introduce a corpus-based measure of relevance based on the idea that scalar inferences that are more relevant are more likely to occur in scalar constructions that draw an explicit contrast between scalar words (e.g., It’s warm but not hot ). We conclude that usage has an important role to play in the establishment of common ground, a requirement for pragmatic inferencing.
DisP+V: A Unified Framework for Disentangling Prototype and Variation From Single Sample per Person

Single sample per person face recognition (SSPP FR) is one of the most challenging problems in FR due to the extreme lack of enrolment data. To date, the most popular SSPP FR methods are the generic learning methods, which recognize query face images based on the so-called prototype plus variation (i.e., P+V) model. However, the classic P+V model suffers from two major limitations: 1) it linearly combines the prototype and variation images in the observational pixel-spatial space and cannot generalize to multiple nonlinear variations, e.g., poses, which are common in face images and 2) it would be severely impaired once the enrolment face images are contaminated by nuisance variations. To address the two limitations, it is desirable to disentangle the prototype and variation in a latent feature space and to manipulate the images in a semantic manner. To this end, we propose a novel disentangled prototype plus variation model, dubbed DisP+V, which consists of an encoder-decoder generator and two discriminators. The generator and discriminators play two adversarial games such that the generator nonlinearly encodes the images into a latent semantic space, where the more discriminative prototype feature and the less discriminative variation feature are disentangled. Meanwhile, the prototype and variation features can guide the generator to generate an identity-preserved prototype and the corresponding variation, respectively. Experiments on various real-world face datasets demonstrate the superiority of our DisP+V model over the classic P+V model for SSPP FR. Furthermore, DisP+V demonstrates its unique characteristics in both prototype recovery and face editing/interpolation.
Introduction to Sentiment Analysis Covering Basics, Tools, Evaluation Metrics, Challenges, and Applications

Sentiment analysis has been applied to the datasets collected from social networking websites to get valuable insights. The exemplary growth of social networking has attracted researchers, and there has been a vast contribution in this area. In this chapter, we discuss the basics of sentiment analysis and its methodology, including data collection, data pre-processing, and feature extraction methods. Next, we discuss enhancement techniques for sentiment analysis, including text categorization, feature selection, data integration, ontology-based approaches, and so on. The chapter further provides information about available sentiment analysis tools, challenges, and evaluation metrics. We also discuss sentiment analysis applications and insights for further attention.
Hate Speech in Political Discourse: A Case Study of UK MPs on Twitter

Online presence is becoming unavoidable for politicians worldwide. In countries such as the UK, Twitter has become the platform of choice, with over 85% (553 of 650) of the Members of Parliament (MPs) having an active online presence. Whereas this has allowed ordinary citizens unprecedented and immediate access to their elected representatives, it has also led to serious concerns about online hate towards MPs. This work attempts to shed light on the problem using a dataset of conversations between MPs and non-MPs over a two month period. Deviating from other approaches in the literature, our data captures entire threads of conversations between Twitter handles of MPs and citizens in order to provide a full context for content that may be flagged as 'hate'. By combining widely-used hate speech detection tools trained on several widely available datasets, we analyse 2.5 million tweets to identify hate speech against MPs and we characterise hate across multiple dimensions of time, topics and MPs' demographics. We find that MPs are subject to intense 'pile on' hate by citizens whereby they get more hate when they are already busy with a high volume of mentions regarding some event or situation. We also show that hate is more dense with regard to certain topics and that MPs who have an ethnic minority background and those holding positions in Government receive more hate than other MPs. We find evidence of citizens expressing negative sentiments while engaging in cross-party conversations, with supporters of one party (e.g. Labour) directing hate against MPs of another party (e.g. Conservative).
Label Distribution Learning by Exploiting Label Distribution Manifold

Label correlation is helpful to alleviate the overwhelming output space of label distribution learning (LDL). However, existing studies either only consider one of global and local label correlations or exploit label correlation by some prior knowledge (e.g., low-rank assumption, which may not hold sometimes). To efficiently exploit both global and local label correlations in a data-driven way, we propose in this article a new LDL method called label distribution learning by exploiting label distribution manifold (LDL-LDM). Our basic idea is that the underlying manifold structure of label distribution may encode the correlations among labels. LDL-LDM works as follows. First, to exploit global label correlation, we learn the label distribution manifold and encourage the outputs of our model to lie in the same manifold. Second, we learn the label distribution manifold of different clusters of samples to consider local label correlations. Third, to handle incomplete label distribution learning (incomplete LDL), we jointly learn label distribution and label distribution manifold. Theoretical analysis demonstrates the generalization of our method. Finally, experimental results validate the effectiveness of LDL-LDM in both full and incomplete LDL cases.
High capacity steganography based on IWT using eight-way CVD and<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e916" altimg="si5.svg"><mml:mi>n</mml:mi></mml:math>-LSB ensuring secure communication

The state-of-the-art integer wavelet transform (IWT) based steganographic techniques suffer from low embedding capacity. To overcome this limitation, an IWT based secure high capacity steganographic technique is proposed. The aim of this work is to develop and evaluate new technique to offer performance improvements over the existing approaches. The vertical and horizontal subbands are utilized by eight-way coefficient value differencing (CVD) technique. These subbands are fragmented into non-overlapping 3 × 3 blocks. The n -LSB method ( n is the number of bits) is used in the diagonal subband. The distortion due to n -LSB method is reduced using 2 n correction technique. The proposed technique accomplishes an average embedding capacity of 2.63 bpp which is better than the recent state-of-the-art works. To enhance the security of the system, secret data are embedded in a non-sequential manner in the horizontal and vertical subbands. The proposed technique withstands the steganalysis test. It also resists the series of tests provided by StirMark Benchmark 4.0.
Thousand to One: Semantic Prior Modeling for Conceptual Coding

Conceptual coding has been an emerging research topic recently, which encodes natural images into disentangled conceptual representations for compression. However, the compression performance of the existing methods is still suboptimal due to the lack of comprehensive consideration of rate constraint and reconstruction quality. To this end, we propose a novel end-to-end semantic prior modeling based conceptual coding scheme towards extremely low bitrate image compression, which leverages semantic-wise deep representations as a unified prior for entropy estimation and texture synthesis. Specifically, we employ semantic segmentation maps as structural guidance for extracting deep semantic prior, which provides fine-grained texture distribution modeling for better detail construction and higher flexibility in subsequent high-level vision tasks. Moreover, a cross-channel entropy model is proposed to further exploit the inter-channel correlation of the spatially independent semantic prior, leading to more accurate entropy estimation for rate-constrained training. The proposed scheme achieves an ultra-high 1000× compression ratio, while still enjoying high visual reconstruction quality and versatility towards visual processing and analysis tasks.
FSNet: A Failure Detection Framework for Semantic Segmentation

Semantic segmentation is an important task that helps autonomous vehicles understand their surroundings and navigate safely. However, during deployment, even the most mature segmentation models are vulnerable to various external factors that can degrade the segmentation performance with potentially catastrophic consequences for the vehicle and its surroundings. To address this issue, we propose a failure detection framework to identify pixel-level misclassification. We do so by exploiting internal features of the segmentation model and training it simultaneously with a failure detection network. During deployment, the failure detector flags areas in the image where the segmentation model has failed to segment correctly. We evaluate the proposed approach against state-of-the-art methods and achieve 12.30%, 9.46%, and 9.65% performance improvement in the AUPR-Error metric for Cityscapes, BDD100k, and Mapillary semantic segmentation datasets.
SmartCommit: a graph-based interactive assistant for activity-oriented commits

In collaborative software development, it is considered to be a best practice to submit code changes as a sequence of cohesive commits, each of which records the work result of a specific development activity, such as adding a new feature, bug fixing, and refactoring. However, rather than following this best practice, developers often submit a set of loosely-related changes serving for different development activities as a composite commit, due to the tedious manual work and lack of effective tool support to decompose such a tangled changeset. Composite commits often obfuscate the change history of software artifacts and bring challenges to efficient collaboration among developers. To encourage activity-oriented commits, we propose SmartCommit, a graph-partitioning-based interactive approach to tangled changeset decomposition that leverages not only the efficiency of algorithms but also the knowledge of developers. To evaluate the effectiveness of our approach, we (1) deployed SmartCommit in an international IT company, and analyzed usage data collected from a field study with 83 engineers over 9 months; and (2) conducted a controlled experiment on 3,000 synthetic composite commits from 10 diverse open-source projects. Results show that SmartCommit achieves a median accuracy between 71–84% when decomposing composite commits without developer involvement, and significantly helps developers follow the best practice of submitting activity-oriented commits with acceptable interaction effort and time cost in real collaborative software development.
Text Classification Using Neural Network Language Model (NNLM) and BERT: An Empirical Comparison

Text Classification is one of the most cited applications of Natural Language Processing. Classification can save the cost of manual efforts and at the same time increase the accuracy of a task. With multiple advancements in language modeling techniques over the last two decades, a number of word embedding models have been proposed. In this study, we discuss and compare two of the most recent models for the task text classification and present a technical comparison.
Literature Review : A Comparative Study of Real Time Streaming Technologies and Apache Kafka

For data ware housing projects, there are multiple licensed ETL (Extraction Transformation & Load) tools available in the market. To process and pass the data between two applications industry is using ETL tools like IBM Info-sphere Data Stage, Informatica, Ab Initio etc. These tools are exceptionally costly and has recurring enterprise licensees, and processed data is not available in real time, data is getting processed in batches and is available during pre-defined time intervals or on demand. Industry has started adopting the Open Source technologies to avoid the huge licensing cost and that also includes the complete end to end IT infrastructure cost. Open Source technologies and frameworks enables users to run projects with best in class performance and within the budget.In this literature survey paper, all possible technologies have been studied and evaluated, available in the market capable of real/ “near-real-time” streaming. All licensed and open source products which are utilized and evaluated by various IT organizations and which are also evaluated by researchers have been included in this survey. There is a need of a distributed scalable technology that enables the users to ensure availability of data from one end point to another in real time with good throughput, performance and low latency. To study this, a detailed comparative survey of an open source technology Apache Kafka has been done and it compared with the other available technologies capable of doing real time streaming.
Graph-based Normalizing Flow for Human Motion Generation and Reconstruction

Data-driven approaches for modeling human skeletal motion have found various applications in interactive media and social robotics. Challenges remain in these fields for generating high-fidelity samples and robustly reconstructing motion from imperfect input data, due to e.g. missed marker detection. In this paper, we propose a probabilistic generative model to synthesize and reconstruct long horizon motion sequences conditioned on past information and control signals, such as the path along which an individual is moving. Our method adapts the existing work MoGlow by introducing a new graph-based model. The model leverages the spatial-temporal graph convolutional network (ST-GCN) to effectively capture the spatial structure and temporal correlation of skeletal motion data at multiple scales. We evaluate the models on a mixture of motion capture datasets of human locomotion with foot-step and bone-length analysis. The results demonstrate the advantages of our model in reconstructing missing markers and achieving comparable results on generating realistic future poses. When the inputs are imperfect, our model shows improvements on robustness of generation.