Reviewerly/exHarmony huggingface dataset:
Towards Automatic Cinematography and Annotation for 360° Video

Omnidirectional (360°) video is a novel media format, rapidly becoming adopted in media production and consumption as part of today’s ongoing virtual reality revolution. Due to its novelty, there is a lack of tools for producing highly engaging 360° video for consumption on a multitude of platforms (VR headsets, smartphones or conventional TV sets). In this work, we describe our preliminary work on tools for automating several tasks in the production of 360° video, which are tedious and time consuming when done manually. We propose tools for automated cinematography (generate a lean-back experience without user interaction for conventional TV sets) and automated annotation of 360° video for simplifying linking to other resources like text or 2D images/videos. Both tools employ deep learning based methods for extracting the information about the objects in the scene. We will discuss the current state of these tools and ways how to improve the tools in the future.
Measuring ICO Performance Indicators: An Empirical Study Via White Papers

ICOs (Initial Coin Offering) have recently become the favorite fund raising method for entrepreneurs and startups. Research indicates that a successful ICO could provide a helpful precedence for those who are in need of raising funds in the near future. In addition, the results could help the regulatory authorities identify the elements and factors which should be taken into consideration if ICOs were subject to regulation. The purpose of this study is to discover the factors which may affect the success of an ICO from 533 ICO samples which successfully raised over 5 million US dollars during the past two-year period. In general, we found that the following elements of ICOs have significant positive correlation to the final amount of fund raised, including, a higher amount of total token supply, acceptance of fiat as consideration in exchange for tokens, using the USA as an issue country, a higher percentage of token allocation to subscribers, a higher percentage of token allocation to advisors, a higher percentage of token allocation to ecosystem development, and introducing a post-ICO bonus scheme. On the contrary, we found that the following have a negative correlation to the final amount of the fund raised in an ICO: making use of bonus schemes in the ICO process, and applying longer lock-up periods in an ICO. We hope the research result will provide a new perspective for fund raisers and the government authorities.
When Relational-Based Applications Go to NoSQL Databases: A Survey

Several data-centric applications today produce and manipulate a large volume of data, the so-called Big Data. Traditional databases, in particular, relational databases, are not suitable for Big Data management. As a consequence, some approaches that allow the definition and manipulation of large relational data sets stored in NoSQL databases through an SQL interface have been proposed, focusing on scalability and availability. This paper presents a comparative analysis of these approaches based on an architectural classification that organizes them according to their system architectures. Our motivation is that wrapping is a relevant strategy for relational-based applications that intend to move relational data to NoSQL databases (usually maintained in the cloud). We also claim that this research area has some open issues, given that most approaches deal with only a subset of SQL operations or give support to specific target NoSQL databases. Our intention with this survey is, therefore, to contribute to the state-of-art in this research area and also provide a basis for choosing or even designing a relational-to-NoSQL data wrapping solution.
Personalized Recommendation via Trust-Based Diffusion

The diffusion-based algorithm is a promising member of the family of recommendation algorithms. It makes recommendations through the diffusion process on user-object bipartite graphs. However, a user’s taste is often influenced by his/her trusted friends in social networks. In this paper, we propose a new trust-based diffusion on tripartite graphs, which integrates explicit trust relations and implicit trust relations into the diffusion process. Explicit trust relations are obtained from the social networks while implicit trust relations are inferred from implicit feedback. The experimental results indicate that our proposed method has a remarkable improvement in accuracy, and even only implicit trust relations employed, that is, diffusion on user-object bipartite graphs, the recommendation accuracy is still enhanced. We further present a general framework of applying implicit trust relations and explicit trust relations into basic network-based diffusion method, which is more general and flexible, and specific parameters can be selected to meet actual requirements for different datasets and real-world online platforms.
Joint learning of multiple image restoration tasks

Convolutional neural networks have recently been successfully applied to the problems of restoring clean images from their degraded versions. Most studies have designed and trained a dedicated network for each of many image restoration tasks, such as motion blur removal, rain-streak removal, haze removal, etc. In this paper, we show that a single network having a single input and multiple output branches can solve multiple image restoration tasks. This is made possible by improving the attention mechanism and an internal structure of the basic blocks used in the dual residual networks, which was recently proposed and shown to work well for a number of image restoration tasks by Liu et al. Experimental results show that the proposed approach achieves a new state-of-the-art performance on haze removal (both in PSNR/SSIM) and JPEG artifact removal (in SSIM). To the authors' knowledge, this is the first report of successful multi-task learning on diverse image restoration tasks.
Extreme Speech| Defining Online Hate and Its “Public Lives”: What is the Place for “Extreme Speech”?

Following Sahana Udupa and Matti Pohjonen's invitation to move the debate beyond a normative understanding of hate speech, this article seeks to build a foundation for conceptual and empirical inquiry of speech commonly considered deviant and disturbing. It develops in three stages. It first maps the public lives of terms that refer to online vitriol and how they have been used by different communities of researchers, politicians, advocacy groups, and national organizations. Second, it shows how different types of “haters” have been interpreted as parts of “swarms” or “armies,” depending on whether their violent potential emerges around critical incidents or whether they respond to longer-term strategies through which communities and their leaders tie their speech acts to explicit narratives. The article concludes by locating “extreme speech” within this broader conceptual tapestry, arguing that the paternalistic gaze that characterizes a lot of research on online hate speech is tied to what Chantal Mouffe has referred to as the “moralization of politics,” a phenomenon that cannot be matched by responses that are themselves moral.
The joint weight enumerator of an LCD code and its dual

A binary linear code is called LCD if it intersects its dual trivially. We show that the coefficients of the joint weight enumerator of such a code with its dual satisfy linear constraints, leading to a new linear programming bound on the size of an LCD code of given length and minimum distance. In addition, we show that this polynomial is, in general, an invariant of a matrix group of dimension 4 and order 12. Also, we sketch a Gleason formula for this weight enumerator.
Open-World Reasoning for Service Robots

A service robot accepting verbal commands from a human operator is likely to encounter requests that reference objects not currently represented in its knowledge base. In domestic or office settings, the construction of a complete knowledge base would be cumbersome and unlikely to succeed in most real-world deployments. The world that such a robot operates in is thus “open” in the sense that some objects that it must act on in the real world are not described in its internal representation. However, when an operator gives a command referencing an object that the robot has not yet observed (and thus not incorporated into its knowledge base), we can think of the object as being hypothetical to the robot. This paper presents a novel method for closing the robot’s world model for planning purposes by introducing hypothetical objects into the robot’s knowledge base, reasoning about these hypothetical objects, and acting on these hypotheses in the real world. We use our implementation of this method on a domestic service robot as an illustrative demonstration to explore how it works in practice.
Learning Belief Representations for Imitation Learning in POMDPs

We consider the problem of imitation learning from expert demonstrations in partially observable Markov decision processes (POMDPs). Belief representations, which characterize the distribution over the latent states in a POMDP, have been modeled using recurrent neural networks and probabilistic latent variable models, and shown to be effective for reinforcement learning in POMDPs. In this work, we investigate the belief representation learning problem for generative adversarial imitation learning in POMDPs. Instead of training the belief module and the policy separately as suggested in prior work, we learn the belief module jointly with the policy, using a task-aware imitation loss to ensure that the representation is more aligned with the policy's objective. To improve robustness of representation, we introduce several informative belief regularization techniques, including multi-step prediction of dynamics and action-sequences. Evaluated on various partially observable continuous-control locomotion tasks, our belief-module imitation learning approach (BMIL) substantially outperforms several baselines, including the original GAIL algorithm and the task-agnostic belief learning algorithm. Extensive ablation analysis indicates the effectiveness of task-aware belief learning and belief regularization.
Generation of Gaussian-modulated entangled states for continuous variable quantum communication

We present and demonstrate a high-efficiency and compact scheme for generating Gaussian-modulated Einstein–Podolsky–Rosen (EPR) entangled optical fields by injecting a modulated signal field into a non-degenerate optical parametric amplifier (NOPA). We perform a quantum analysis of the scheme and derive the variance of the output signal (idler) mode and the quantum entanglement between the signal and idler modes from the NOPA. An experimental study is presented with different Gaussian modulation depths, showing that the modulation of the injected signal field successfully enlarges the distribution of the field quadratures of the EPR source in phase space and has a negligible effect on the entanglement quality. The experimental observations have good agreement with the theoretical analysis. Our scheme can be used readily in continuous variable quantum key distribution protocol.
Research on Intelligence Computing Models of Fine-Grained Opinion Mining in Online Reviews

In order to obtain evaluation information about the various aspects of products or services, the Fine-grained Topic Sentiment Unification (FG-TSU) model is proposed based on the improvement of LDA (Latent Dirichlet Allocation) model. Firstly, the topics are divided into local and global topics. And the sliding window is introduced to lower co-occurrence information from document to sentence level, to implement fine-grained extraction of local topics. Secondly, the indicator variables are used to distinguish aspects and opinions. Finally, we incorporate the sentiment layer into LDA model to obtain the sentiment polarity of the whole review and specific aspects. The datasets of hotel and mobile phone are selected to verify the domain adaptability of this model. The experimental results verified the feasibility of FG-TSU model in the realization of opinion mining.
Robust Image Completion via Deep Feature Transformations

For many practical applications, it is essential to address both geometric corrections and missing information reconstruction of face images and natural images. However, it is unfavorable to separate the problem into two sub-tasks due to error accumulations of sequential tasks. In this paper, we propose a novel robust missing information reconstruction framework via deep feature transformations to simultaneously address both geometric corrections and image completion. Specifically, our proposed framework realizes multiple channel spatial transformations to tackle geometric corrections, and address image completion through non-linear features projections. The flow of our framework includes deep feature extraction, feature enhancement, feature projection, and feature refinement, where deep features are extracted and learnt to achieve robust image completion. Experimental results show the superior performance of our framework for both face images and natural images in various databases. Compared with the conventional approaches approach to split the problem into two sub-tasks, including image inpainting and spatial transformation, our proposed framework achieves a number of advantages, including i) an unified framework to automatically correct the geometric distortions and to reconstruct the missing information simultaneously and ii) achieving much better visual quality for those recovered images.
Augmented Reality for the Disabled: Review Articles

Augmented Reality or "AR" has been recognized for its significant importance in different areas of technology, not to mention its significant presence in the education sector. This research aimed to map the relevant literature on AR usage with the aim of rehabilitating people with disabilities. The search was performed on three databases: Science Direct, IEEE Explore and Web of Science from 2013 to 2018. . Based on our inclusion and exclusion criteria, (n=13) articles were selected. Seven of the studies (n=7/13) intellectual disability. Six of the studies (n=6/6) physical disability. The basic idea of this article is to measure the size of the virtual environment utilization in order to rehabilitate the disabled, in addition to the ability of modern technology to restore people with disabilities to normal life and integration into society. This research is expected to display the current literature in this area and to increase the understanding of this research field.
Locality Sensitive Discriminative Unsupervised Dimensionality Reduction

Graph-based embedding methods receive much attention due to the use of graph and manifold information. However, conventional graph-based embedding methods may not always be effective if the data have high dimensions and have complex distributions. First, the similarity matrix only considers local distance measurement in the original space, which cannot reflect a wide variety of data structures. Second, separation of graph construction and dimensionality reduction leads to the similarity matrix not being fully relied on because the original data usually contain lots of noise samples and features. In this paper, we address these problems by constructing two adjacency graphs to stand for the original structure featuring similarity and diversity of the data, and then impose a rank constraint on the corresponding Laplacian matrix to build a novel adaptive graph learning method, namely locality sensitive discriminative unsupervised dimensionality reduction (LSDUDR). As a result, the learned graph shows a clear block diagonal structure so that the clustering structure of data can be preserved. Experimental results on synthetic datasets and real-world benchmark data sets demonstrate the effectiveness of our approach.
An Augmented Reality Motion Planning Interface for Robotics

With recent advances in hardware technology, autonomous robots are increasingly present in research activities outside of robotics, performing a multitude of tasks such as image capture and sample collection. However, user interfaces for task-oriented robots have not kept pace with hardware breakthroughs. Current planning and control interfaces for robots are not intuitive and often place a large cognitive burden on those who are not highly trained in their use. Augmented reality (AR) has also seen major advances in recent years. This demonstration illustrates an initial system design for an AR user interface for path planning with robotics.
Neural Diffusion Distance for Image Segmentation

Diffusion distance is a spectral method for measuring distance among nodes on graph considering global data structure. In this work, we propose a spec-diff-net for computing diffusion distance on graph based on approximate spectral decomposition. The network is a differentiable deep architecture consisting of feature extraction and diffusion distance modules for computing diffusion distance on image by end-to-end training. We design low resolution kernel matching loss and high resolution segment matching loss to enforce the network's output to be consistent with human-labeled image segments. To compute high-resolution diffusion distance or segmentation mask, we design an up-sampling strategy by feature-attentional interpolation which can be learned when training spec-diff-net. With the learned diffusion distance, we propose a hierarchical image segmentation method outperforming previous segmentation methods. Moreover, a weakly supervised semantic segmentation network is designed using diffusion distance and achieved promising results on PASCAL VOC 2012 segmentation dataset.
Extended Global–Local Representation Learning for Video Person Re-Identification

Recently, person re-identification has become one of the research hotspots in the field of computer vision and has received extensive attention in the academic community. Inspired by the part-based research of image ReID, this paper presents a novel feature learning and extraction framework for video-based person re-identification, namely, the extended global-local representation learning network (E-GLRN). Given a video sequence of a pedestrian, the holistic and local features are simultaneously extracted using the E-GLRN network. Specifically, for the global feature learning, we adopt the channel attention convolutional neural network (CNN) and the bidirectional long short-term memory (Bi-LSTM) networks, which are responsible for introducing a CNN-LSTM module to learn the features of consecutive frames. The local feature learning module relies on the key local information extraction, which is based on the Bi-LSTM networks. In order to obtain the local feature more effectively, our work defines a concept of “the main image group” by selecting three representative frames. The local feature representation of a video is obtained by exploiting the spatial contextual and appearance information of this group. The local and global features extracted in this paper are complementary and further combined into a discriminative and robust feature representation of the video sequence. Extensive experiments are conducted on three video-based ReID datasets, including the iLIDS-VID, PRID2011 and MARS datasets. The experimental results demonstrate that the proposed method outperforms state-of-the-art video-based re-identification approaches.
Anomaly Detection in Power Quality Measurements Using Proximity-Based Unsupervised Machine Learning Techniques

Power quality (PQ) data analysis has become an issue of great interest in past few years. Due to numerous impacts, PQ can exhibit rare and significant deviations of a time series from its typical characteristic. Such unusual deviations are known as anomalies and the process of identifying such changes is referred to anomaly detection. In this paper, four proximity-based machine learning (ML) techniques are applied to original (nontransformed) PQ data for automatic anomaly detection. In contrast to standard classification tasks, these techniques are applied on unlabeled data, taking only the characteristics of the PQ data into account. The process of parameters tuning and their influence on methods performance are described. The methods performance has been evaluated on different time series, which represent the typical variation in PQ data. Finally, the ability to detect different types of anomalies is analyzed.
Solving Interpretable Kernel Dimensionality Reduction

Kernel dimensionality reduction (KDR) algorithms find a low dimensional representation of the original data by optimizing kernel dependency measures that are capable of capturing nonlinear relationships. The standard strategy is to first map the data into a high dimensional feature space using kernels prior to a projection onto a low dimensional space. While KDR methods can be easily solved by keeping the most dominant eigenvectors of the kernel matrix, its features are no longer easy to interpret. Alternatively, Interpretable KDR (IKDR) is different in that it projects onto a subspace \textit{before} the kernel feature mapping, therefore, the projection matrix can indicate how the original features linearly combine to form the new features. Unfortunately, the IKDR objective requires a non-convex manifold optimization that is difficult to solve and can no longer be solved by eigendecomposition. Recently, an efficient iterative spectral (eigendecomposition) method (ISM) has been proposed for this objective in the context of alternative clustering. However, ISM only provides theoretical guarantees for the Gaussian kernel. This greatly constrains ISM's usage since any kernel method using ISM is now limited to a single kernel. This work extends the theoretical guarantees of ISM to an entire family of kernels, thereby empowering ISM to solve any kernel method of the same objective. In identifying this family, we prove that each kernel within the family has a surrogate $\Phi$ matrix and the optimal projection is formed by its most dominant eigenvectors. With this extension, we establish how a wide range of IKDR applications across different learning paradigms can be solved by ISM. To support reproducible results, the source code is made publicly available on \url{https://github.com/ANONYMIZED}.
Cubic-Spline Flows

A normalizing flow models a complex probability density as an invertible transformation of a simple density. The invertibility means that we can evaluate densities and generate samples from a flow. In practice, autoregressive flow-based models are slow to invert, making either density estimation or sample generation slow. Flows based on coupling transforms are fast for both tasks, but have previously performed less well at density estimation than autoregressive flows. We stack a new coupling transform, based on monotonic cubic splines, with LU-decomposed linear layers. The resulting cubic-spline flow retains an exact one-pass inverse, can be used to generate high-quality images, and closes the gap with autoregressive flows on a suite of density-estimation tasks.
Factorization of Frieze patterns

In 2017, Michael Cuntz gave a definition of reducibility of a quiddity cycle of a frieze pattern: It is reducible if it can be written as a sum of two other quiddity cycles.We discuss the commutativity and associativity of this sum operator for quiddity cycles and its equivalence classes, respectively.We show that the sum is neither commutative nor associative, but we may circumvent this issue by passing to equivalence classes.We also address the question whether a decomposition of quiddity cycles into irreducible factors is unique and we answer it in the negative by giving counterexamples.We conclude that even under stronger assumptions, there is no canonical decomposition.