Reviewerly/exHarmony huggingface dataset:
S<sup>3</sup>Net:Graph Representational Network For Sketch Recognition

Sketches are distinctly different to photos. They are highly abstract and exhibit a severe lack of visual cues. Prior works have therefore explored additional traits unique to sketches to help recognition such as stroke ordering. In this paper, we pioneer in studying the role of structure in sketches, for the task of sketch recognition. In particular, we propose a novel graph representation specifically designed for sketches, which follows the inherent hierarchical relationship (segment-stroke-sketch”) of sketching elements. By conforming to this hierarchy, we also introduce ajoint network that encapsulates both the structural and temporal traits of sketches for sketch recognition, termed S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3Net.</sup> S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3Net</sup> employs a recurrent neural network (RNN) to extract segmentlevel features, followed by a graph convolutional network (GCN) to aggregate them into sketch-level features. The RNN first encodes temporal cues in sketches while its outputs are used as node embedding to construct a hierarchical sketch-graph. The GCN module then takes in this sketchgraph to produce a structure-aware embedding for sketches. Extensive experiments on the QuickDraw dataset, exhibit superior performance over state-of-the-arts, surpassing them by over 4%. Ablative studies further demonstrate the effectiveness of the proposed structural graph for both inter-class, and intra-class feature discrimination. Code is available at: https://github.com/yanglan0225/s3net;.
Advances in Computational Linguistics and Text Processing Frameworks

The chapter provides an eloquent account of the major methodologies and advances in the field of Natural Language Processing. The most popular models that have been used over time for the task of Natural Language Processing have been discussed along with their applications in their specific tasks. The chapter begins with the fundamental concepts of regex and tokenization. It provides an insight to text preprocessing and its methodologies such as Stemming and Lemmatization, Stop Word Removal, followed by Part-of-Speech tagging and Named Entity Recognition. Further, this chapter elaborates the concept of Word Embedding, its various types, and some common frameworks such as word2vec, GloVe, and fastText. A brief description of classification algorithms used in Natural Language Processing is provided next, followed by Neural Networks and its advanced forms such as Recursive Neural Networks and Seq2seq models that are used in Computational Linguistics. A brief description of chatbots and Memory Networks concludes the chapter.
A New Algorithm for Backlight Image Enhancement

Backlight images will have influences on many aspects of our lives, such as our shooting effect, the judgment of the images in the public security systems, etc. So it is very important to solve the backlight images for building a smart city. When processing the backlight images, we need to ensure that the backlight area of the images is effectively enhanced, and the image information of the non-backlight area cannot be lost. In this paper, we design a new function for backlight images. We first enhance the R, G and B channels of the backlight images locally to make the images more colorful. At the same time, we transform RGB brightness adjustment model into HSV brightness adjustment model and use the new function to adaptively adjust the V channel in the local enhanced images. The method of self-adaptive brightness adjustment can ensure that it can be applied to most backlight images. Finally, the traditional image enhancement methods are compared from the perspective of vision and information entropy to verify the effectiveness of this function.
Adversarial Distortion for Learned Video Compression

In this paper, we present a novel adversarial lossy video compression model. At extremely low bit-rates, standard video coding schemes suffer from unpleasant reconstruction artifacts such as blocking, ringing etc. Existing learned neural approaches to video compression have achieved reasonable success on reducing the bit-rate for efficient transmission and reduce the impact of artifacts to an extent. However, they still tend to produce blurred results under extreme compression. In this paper, we present a deep adversarial learned video compression model that minimizes an auxiliary adversarial distortion objective. We find this adversarial objective to correlate better with human perceptual quality judgement relative to traditional quality metrics such as MS-SSIM and PSNR. Our experiments using a state-of-the-art learned video compression system demonstrate a reduction of perceptual artifacts and reconstruction of detail lost especially under extremely high compression.
A Novel Convolutional Architecture For Video-Text Retrieval

The prevalent video-text retrieval methods usually use recurrent neural networks to encode sequences of frames in videos and sequences of words in text. In this paper, we introduce an encoding architecture based entirely on convolutional neural networks. Compared to recurrent models, the complexity is smaller, and computations over all elements can be fully parallelized during training to better exploit the GPU. We use the stacking of convolution kernels of different scales to realize the encoding of local and long-term features of video and text. Experiments validate that our method achieves a new state-of-the-art for the video-text retrieval on MSR-VTT and MSVD datasets with less training time.
Towards Better Graph Representation: Two-Branch Collaborative Graph Neural Networks For Multimodal Marketing Intention Detection

Inspired by the fact that spreading and collecting information through the Internet becomes the norm, more and more people choose to post for-profit contents (images and texts) in social networks. Due to the difficulty of network censors, malicious marketing may be capable of harming the society. Therefore, it is meaningful to detect marketing intentions online automatically. However, gaps between multimodal data make it difficult to fuse images and texts for content marketing detection. To this end, this paper proposes Two-Branch Collaborative Graph Neural Networks to collaboratively represent multimodal data by Graph Convolution Networks (GCNs) in an end-to-end fashion. We first separately embed groups of images and texts by GCNs layers from two views and further adopt the proposed multimodal fusion strategy to learn the graph representation collaboratively. Experimental results demonstrate that our proposed method achieves superior graph classification performance for marketing intention detection.
Can We Learn Heuristics for Graphical Model Inference Using Reinforcement Learning?

Combinatorial optimization is frequently used in computer vision. For instance, in applications like semantic segmentation, human pose estimation and action recognition, programs are formulated for solving inference in Conditional Random Fields (CRFs) to produce a structured output that is consistent with visual features of the image. However, solving inference in CRFs is in general intractable, and approximation methods are computationally demanding and limited to unary, pairwise and hand-crafted forms of higher order potentials. In this paper, we show that we can learn program heuristics, i.e., policies, for solving inference in higher order CRFs for the task of semantic segmentation, using reinforcement learning. Our method solves inference tasks efficiently without imposing any constraints on the form of the potentials. We show compelling results on the Pascal VOC and MOTS datasets.
Development of augmented reality systems displaying three-dimensional dynamic motion in real time

We developed a three-dimensional motion tracking system that displays trajectories as augmented reality in real time on a tablet device. The features of this system are as follows: (1) the three-dimensional trajectory can be observed from any direction. (2) Velocity and acceleration can be visualized as augmented reality.
End-To-End Deep Multimodal Clustering

Deep multimodal clustering is challenging, since it needs to learn appropriate features for different modalities and find correct clusters by using consistency among the modalities. Existing methods treat the two problems separately, nevertheless, the optimization of one does not guarantee the optimization of the other. In this paper, we propose an end-to-end Deep Multimodal Clustering (DMMC) framework, which achieves a joint optimization of feature learning and multimodal clustering. We encourage the learned features of each modality to be cluster-oriented by autoencoder with Frobenius norm loss. Then, DMMC integrates features of different modalities by clustering integration layer and learns consensus feature by a consensus loss. The clustering result can be discovered in the consensus feature. Compared with existing clustering methods, our framework achieves superior performances measured by the standard clustering evaluation metrics.
What Is It Like to Be a Game?—Object Oriented Inquiry for Games Research, Design, and Evaluation

Human-Computer Interaction (HCI) researchers more and more challenge the notion of technologies as objects and humans as subjects. This conceptualisation has led to various approaches inquiring into object perspectives within HCI. Even though the development and analysis of games and players is filled with notions of intersubjectivity, games research has yet to embrace an object-oriented perspective. Through an analysis of existing methods, we show how Object-Oriented Inquiry offers a useful, playful and speculative lens to proactively engage with and reflect on how we might know what it is like to be a game. We illustrate how to actively attend to a game’s perspective as a valid position. This has the potential to not only sharpen our understanding of implicit affordances but, in turn, about our assumptions regarding play and games more generally.In a series of case studies, we apply several object-oriented methods across three methodological explorations on becoming, being and acting as a game, and illustrate their usefulness for generating meaningful insights for game design and evaluation. Our work contributes to emerging object-oriented practices that acknowledge the agency of technologies within HCI at large and its games-oriented strand in particular.
A Comparative Analysis on Various Extreme Multi-Label Classification Algorithms

In the field of machine learning, the boom in big data has opened a variety of new research problems due to the availability of the extremely huge online data. Extreme Multi-Label Learning (XML) is the most challenging and popular among them. XML addresses the problem of learning a classifier that can automatically tag a data sample with the most relevant subset of labels from a given large label set. For instance, there are more than a million labels (i.e. categories) on Wikipedia and one may wish to build a classifier that can annotate a new article or web page with a subset of relevant Wikipedia categories. Extreme Multi-Label Learning or specifically classification is a very challenging research problem for the need to simultaneously dealing with massive labels, dimensions, and training points. In this paper, we review various approaches such as Embedding, Tree and One-vs-All methods to handle Extreme Multi-Label classification problems and have compared their performance in extreme settings.
High-Throughput DNN Inference with LogicNets

Deep Neural Networks (DNNs) have a wide application scope beyond computer vision tasks, promising to replace manual algorithmic implementations in applications ranging from large-scale physics experiments to next-generation network security. Such applications may require data processing rates in the millions of samples per second and sub-microsecond latency, which is possible with customized FPGA or ASIC implementations. We present a novel method called LogicNets for co-design of DNN topologies and hardware circuits that maps to a very efficient FPGA implementation to address the needs of such applications.
CANOPIC: Pre-Digital Privacy-Enhancing Encodings for Computer Vision

The standard pipeline for many vision tasks uses a conventional camera to capture an image that is then passed to a digital processor for information extraction. In some deployments, such as private locations, the captured digital imagery contains sensitive information exposed to digital vulnerabilities such as spyware, Trojans, etc. However, in many applications, the full imagery is unnecessary for the vision task at hand. In this paper we propose an optical and analog system that preprocesses the light from the scene before it reaches the digital imager to destroy sensitive information. We explore analog and optical encodings consisting of easily implementable operations such as convolution, pooling, and quantization. We perform a case study to evaluate how such encodings can destroy face identity information while preserving enough information for face detection. The encoding parameters are learned via an alternating optimization scheme based on adversarial learning with deep neural networks. We name our system CAnOPIC (Camera with Analog and Optical Privacy-Integrating Computations) and show that it has better performance in terms of both privacy and utility than conventional optical privacy-enhancing methods such as blurring and pixelation.
Analysis and Design of a Context-Aware Smart Home System

In order to provide a more ideal and friendly environment in smart homes. The design of a smart home based on context-awareness is studies in this paper. Firstly, it introduces the research status of smart home industry based on context-awareness home and abroad, then after analysis of the design methods of smart home system done by previous researchers and practitioners, a five-layer model based on context-awareness is proposed and the design of smart home system from low to high information processing level requirements is formulated, finally, some solutions to refer a new software architecture for smart homes are discussed. Testing of the proposed system shows that the novel method for the context information modeling and processing and the use of sensor technologies could greatly enhance home automation.
Medical Image Super-Resolution Via Granular Multi-Scale Network In Nsct Domain

High-resolution (HR) medical magnetic resonance images or computer tomography (CT) images can provide clearer anatomical details of human body, which facilitates early diagnosis of the diseases. However, due to the limitations of imaging environments, imaging systems and human factors, it is not easy to obtain clear HR medical images. In this paper, we propose a novel medical image super-resolution (SR) network, namely granular multi-scale network (GMSN), in the non-subsampled contourlet transform (NSCT) domain. GMSN mainly consists of a series of cascaded Res2Net blocks to exploit the multi-scale potential features at a granular level from medical images and increase the range of receptive fields for each network layer. The architecture possesses stronger multi-scale feature extraction ability, while maintaining a low computational load. In addition, most previous methods predict the HR images in the spatial domain, producing over-smoothed outputs while losing texture details. Thus, we formulate the medical image SR problem as the prediction of NSCT coefficients, which is able to further GMSN preserve richer structure details than that in spatial domain. The experimental results on our constructed medical image database show that our proposed method is capable of obtaining higher PSNR/SSIM values and preserving global topological structure and local texture detail better than other state-of-the-art methods.
Representations of Syntax [MASK] Useful: Effects of Constituency and Dependency Structure in Recursive LSTMs

Sequence-based neural networks show significant sensitivity to syntactic structure, but they still perform less well on syntactic tasks than tree-based networks.Such tree-based networks can be provided with a constituency parse, a dependency parse, or both.We evaluate which of these two representational schemes more effectively introduces biases for syntactic structure that increase performance on the subject-verb agreement prediction task.We find that a constituency-based network generalizes more robustly than a dependencybased one, and that combining the two types of structure does not yield further improvement.Finally, we show that the syntactic robustness of sequential models can be substantially improved by fine-tuning on a small amount of constructed data, suggesting that data augmentation is a viable alternative to explicit constituency structure for imparting the syntactic biases that sequential models are lacking.
Determination of suitable TPH remediation approach via MANOVA and inferential statistics assessment

Abstract Soil contamination resulting from petroleum hydrocarbon contaminants poses a fairly substantial hazard to human health and the environment. Phytoremediation, land farming, and chemico–biological stabilization were used to treat total petroleum hydrocarbons (TPHs) and polycyclic aromatic hydrocarbons (PAHs) at a crude oil polluted soil site in Nigeria. A field pilot study was conducted by preparing nine cells with subcells attached to each serving as a control with an overall area of 1.53 m 2 . A complete block design method was used for the study. The prepared soil sample cells were divided into three groups with each group having approximately 300 kg of soil and delineated as low, medium, and high test plots. The low samples were spiked with 6.1 kg of crude oil, the medium samples were spiked with 12.2 kg of crudeoil, and the high samples were spiked with 18.3 kg of crude oil. Each row containing three cells with low, medium, and high concentrations were treated separately using the three treatment methods. The ratio of the soil sample to the organic amendment for the treatments was 2:1. The results showed over 90% degradation in the initial concentration of TPH and PAHs across different contaminant levels except in the control subcells where only 30% of degradation was recorded. Multivariate analysis of variance was employed to assess the significant difference in each treatment group while inferential statistics using a mean performance plot was used to ascertain the optimum treatment method. Land farming, chemico–biological stabilization, and phytoremediation ranked 1, 2, and 3, respectively. In conclusion, the three treatment methods employed all degraded the contaminants (TPH and PAHs) with land farming emerging as the best method.
A Vehicle Detection Approach Using Deep Learning Network

This paper presents a fully automatic detection approach of vehicles using a deep neural network. We propose to explore the use of recent techniques of object detection and recognition. Firstly, we improve the quality of an image by correction and elimination of irrelevant information. Secondly, we properly prepare the training and validation set through a waterfall method. Thirdly, we apply a Fourier's transformation to extract object descriptors for representing the feature of the vehicle object. Finally, we adopt deep learning method to localize and recognize the vehicle model objects. To do this, deep neural network is training several iterations by machine learning methods. The object of this phase is to recognize only the vehicle from among all the other objects. Experimental result proves that our approach can effectively detect a vehicle with a good accuracy and recognition rate.
Embedded Image Processing the European Way: A new platform for the future automotive market

Within the European Processor Initiative (EPI) an objective is build an embedded High-Performance processing platform for future automotive applications such as autonomous driving. An embedded Field-Programmable-Gate-Array (eFPGA) enables the platform to be extended for future needs and requirements by various stakeholders. In this paper we give an overview about the project and our contributions to define the architecture of the eFPGA, which is suitable for the automotive market.Therefore, we describe our concept to explore the eFPGA architecture. It is motivated by a sound use case that deals with face recognition based on current neural networks. During the scope of the work we describe how the application is carefully mapped on the different domains of the EPI platform to make it more safe and secure as well as performant. As a result, we will find an apt eFPGA configuration, which can host common but also future neural network applications and a mapping of common image processing tasks.
Joint Learned and Traditional Video Compression for P Frame

In this paper, we propose a joint learned and traditional video compression framework for the P frame track on learned image compression hosted at CVPR2020. The main difference between video compression and image compression is that the former has high degree of similarity between the successive frames which can be utilized to reduce the temporal redundancy. Therefore, we first introduce a decoder-side template-based inter prediction method as an efficient way to obtain reference blocks without the need to signal the motion vectors. Secondly, a CNN post filter is proposed to suppress visual artifacts and improve the decoded image quality. Specifically, the spatial and temporal information is jointly exploited by taking both the current block and similar block in reference frame into consideration. Furthermore, an advanced SSIM based rate-distortion optimization model is proposed to achieve best balance between the coding bits and the decoded image quality. Experimental results show that the proposed P frame compression scheme achieves higher reconstruction quality in terms of both PSNR and MS-SSIM.
Contrast of Contextual Fisher Vectors for Ship Detection in SAR Images

The problem of ship detection based on superpixels in synthetic aperture radar (SAR) images is considered. Existing superpixel-based detectors are often developed according to the simple contrast between the gray-levels of ship targets and the sea clutter surrounding it. These detectors are only based on first-order statistics of superpixels and may suffer from deteriorative performance in low signal-to-clutter ratio (SCR) scenarios because of the blurry SAR images. In this paper, we propose a new detector for ship targets in SAR images based on the contrast of contextual Fisher vectors (CCFVs). CCFV provides more abundant contrast between ship targets and their surrounding background clutter on multi-order information and multi-Gaussian components. Experimental results on the measured SAR images show that, compared with commonly used detectors, the proposed CCFV-based detector achieves better performance especially for weak target detection in low SCR scenarios.