Reviewerly/exHarmony huggingface dataset:
Accurate 3D Reconstruction from Circular Light Field Using CNN-LSTM

A light field is formed by densely capturing images on a regular sub-aperture grid. Geometry information endowed in the epipolar plane images(EPI) can only lead to a 2. 5D reconstruction. In order to obtain a full 360°view of an object, we focus on light fields captured by a circularly moving camera, resulting in circular light fields (or Cir-LFs in short). Compared with traditional EPIs, Circular EPIs(CEPIs) provide unique advantages, such as that corresponding points forming a 3D sinusoid like curve instead of a 2D straight line and geometry information encoded sequentially in multiple adjacent views along the curve. However, current reconstruction methods only focus on the 2D projection of 3D curve, leading to distortions in the reconstructed upper and lower surfaces. We propose to analyze 3D features contained in the 3D CEPI volume and we develop a deep CNN-LSTM network to model the gradient map in the CEPI volume. Additionally, a large scale Cir-LF dataset is constructed for research purpose. Experiments on both synthetic and real scenes demonstrate the effectiveness and generaliability of the proposed method.
PA-GGAN: Session-Based Recommendation with Position-Aware Gated Graph Attention Network

Session-based recommendation aims to predict user behaviors based on anonymous sessions. Recently, session sequences are modeled as graph-structured data. Based on the session graphs, Graph Neural Networks (GNNs) can capture complex transitions of items, compared with previous conventional sequential methods. However, the existing graph-construction approaches have limited power in capturing the position information of items in the session sequences. In addition, GNNs employed in the existing session-based recommendation are not capable to attend over their neighborhoods' features in feature aggregation phase. In this paper, we propose a Position-Aware Gated Graph Attention Network (PAGGAN). Specifically, a reverse-position mechanism is proposed to assign position embeddings to nodes in the session graphs based on the order of items in each session sequence. And we enhance Gated Graph Neural Network (GGNN) by introducing self-attention mechanism when aggregating features from nodes. Experimental results on two real-world datasets show that the PA-GGAN outperforms state-of-the-art methods.
Cloud Based Supply Chain Management System Using Blockchain

Within a few years, many companies are adopted by the new Supply Chain Management (SCM) system for offering various services for customers. The lack of services makes a supply chain (SC) to degrade in operational efficiency, reduction in transaction cost, quality and customer satisfaction. To take over this aforementioned problems cloud computing (CC) is one of the best choices in SC by providing a platform, infrastructure, faster deployment, secure access and software solution for the entire SC. According to a recent survey, the storage of data in cloud-based SCM is also a risky due to loss of stored data, credentials, etc., so we mentioned trust as the main problem for financial and operational benefits. To provide trust to the SCM, blockchain is introduced to record transactions efficiently, providing trust between business partners, information security, extended visibility, and product traceability. Furthermore, the determination of this review is, to improve a deep understanding among the current research state and on that identifying the future challenges. Thus an investigation is presented for trust and security approach using blockchain to develop a new Supply Chain framework in a cloud environment.
Image Processing based Vehicle Identification and Speed Measurement

Traffic Collisions are a major source of deaths, injuries and property damage every year. In many of the cases, the reason for the death is over speeding. The rapid recent advancements in the computation ability of everyday computers have made it possible to widely apply deep learning methods to the analysis of traffic surveillance videos. The vehicle speed detection system developed by using video processing techniques. In this, a video is captured through a fixed camera with various parameters noted. The video from the camera is processed frame by frame, in which the 1) Each frame is processed with Image Enhancement to improve the characteristics of the image 2) Vehicle is identified with a bounding box applied to the vehicle, to note the movement in each frame 3) Movement of the vehicle is noted with change in pixel 4) Calculating the speed using distance formula considering PPM.
Fast Texture Synthesis via Pseudo Optimizer

Texture synthesis using deep neural networks can generate high quality and diversified textures. However, it usually requires a heavy optimization process. The following works accelerate the process by using feed-forward networks, but at the cost of scalability. diversity or quality. We propose a new efficient method that aims to simulate the optimization process while retains most of the properties. Our method takes a noise image and the gradients from a descriptor network as inputs, and synthesize a refined image with respect to the target image. The proposed method can synthesize images with better quality and diversity than the other fast synthesis methods do. Moreover, our method trained on a large scale dataset can generalize to synthesize unseen textures.
Face Recognition using Deep Neural Network with "LivenessNet"

There is a continuous increase in the amount of population over the globe, and this, in turn, increases the number of complex datasets over a period. This necessitates improving artificial intelligence algorithms for better and accurate categorization of data. The most defining characteristic of the human body of the face. Every person’s face is unique, although have the same structure such as noise, eyes, lips, etc. but it can vary strikingly. It’s within this variance which lies the distinguishing characteristics that can be used to identify one person from another. Face recognition is a popular concept which is commonly used in surveillance cameras at public places for security purposes. The "Face Recognition using DNN with LivenessNet" presents a face recognition method based on deep neural networks for liveness. Any algorithm is considered to be efficient only if it is robust and accurate. It provides accurate results with face spoofing quickly and efficiently. The main advantage of using this technique is identifying the uniqueness in the datasets by capturing the real-time face data through different modes & jitter. Also providing accurate face recognition model which can be used for safety and security purpose.
A Lightweight Approach to Extract Interschema Properties from Structured, Semi-Structured and Unstructured Sources in a Big Data Scenario

The knowledge of interschema properties (e.g., synonymies, homonymies, hyponymies and subschema similarities) plays a key role for allowing decision-making in sources characterized by disparate formats. In the past, wide amount and variety of approaches to derive interschema properties from structured and semi-structured data have been proposed. However, currently, it is esteemed that more than 80% of data sources are unstructured. Furthermore, the number of sources generally involved in an interaction is much higher than in the past. As a consequence, the necessity arises of new approaches to address the interschema property derivation issue in this new scenario. In this paper, we aim at providing a contribution in this setting by proposing an approach capable of uniformly extracting interschema properties from a huge number of structured, semi-structured and unstructured sources.
Flexible Bilevel Image Layer Modeling For Robust Deraining

Visual quality degradation by rain streaks in images/videos is a significant factor that makes many computer vision systems fail to function properly. However, existing rain removal methods tend to remove a specific type of rain streaks while cannot deal with diverse real rainy images. In this paper, we formulate a novel rain model collectively with two contrasting rain streaks and a weighting map. To self-adaptively handle the rain removal problem in the presence of various types of rain streaks, we further propose a bilevel optimization learning framework. Then, we synthesize a new dataset to evaluate the ability of our method to deal with diverse rain streaks. Extensive experiments show that our method can make better performance on both synthesized and real rainy images.
Transfer Learning with L2 Norm Regularization for classifying static Two Hand Hindi Sign Language Gestures

Vision Based hand sign language recognition has gained wide attention among the researchers [1], [2], [3]. This technique serves as a medium of communication for hearing-disabled and vocally-impaired people. In the domain of sign language recognition, some authors used hard code algorithms like edge detection[4] and some investigated the use of AI-based algorithms to improve performance of existing implementations.
Vehicle Re-Identification Based on Complementary Features

In this work, we present our solution to the vehicle reidentification (vehicle Re-ID) track in AI City Challenge 2020 (AIC2020). The purpose of vehicle Re-ID is to retrieve the same vehicle appeared across multiple cameras, and it could make a great contribution to the Intelligent Traffic System(ITS) and smart city. Due to the vehicle's orientation, lighting and inter-class similarity, it is difficult to achieve robust and discriminative representation feature. For the vehicle Re-ID track in AIC2020, our method is to fuse features extracted from different networks in order to take advantages of these networks and achieve complementary features. For each single model, several methods such as multi-loss, filter grafting, semi-supervised are used to increase the representation ability as better as possible. Top performance in City-Scale Multi-Camera Vehicle ReIdentification demonstrated the advantage of our methods, we got 5-th place in the vehicle Re-ID track of AIC2020.
Attentive Semantic Preservation Network for Zero-Shot Learning

While promising progress has been achieved in the Zero-Shot Learning (ZSL) task , the existing generated approaches still suffer from overly plain pseudo features, resulting in poor discrimination of the generated visual features. To improve the quality of the generated features, we propose a novel Attentive Semantic Preservation Network (ASPN) to encode more discriminative as well as semantic-related information into the generated features with the category self-attention cues. Specifically, the feature generation and the semantic inference modules are formulated into a unified process to promote each other, which can effectively align the cross-modality semantic relation. The category attentive strategy encourages model to focus more on intrinsic information of the noisy generated features to alleviate the confusion of generated features. Moreover, prototype-based classification mechanism is introduced in an efficient way of leveraging known semantic information to further boost discriminative of the generated features. Experiments on four popular benchmarks, i.e., AWA1, AWA2, CUB, and FLO verify that our proposed approach outperforms state-of-the-art methods with obvious improvements under both the Traditional ZSL (TZSL) and the Generalized ZSL (GZSL) settings.
Mipso: Multi-Period Per-Scene Optimization For Http Adaptive Streaming

Video delivery over the Internet has become more and more established in recent years due to the widespread use of Dynamic Adaptive Streaming over HTTP (DASH). The current DASH specification defines a hierarchical data model for Media Presentation Descriptions (MPDs) in terms of periods <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , adaptation sets, representations, and segments. Although multi-period MPDs are widely used in live streaming scenarios, they are not fully utilized in Video-on-Demand (VoD) HTTP adaptive streaming (HAS) scenarios. In this paper, we introduce MiPSO, a framework for Mulri-Period per-Scene optimization, to examine multiple periods in VoD HAS scenarios. MiPSO provides different encoded representations of a video at either (i) maximum possible quality or (ii) minimum possible bitrate, beneficial to both service providers and subscribers. In each period, the proposed framework adjusts the video representations (resolution-bitrate pairs) by taking into account the complexities of the video content, with the aim of achieving streams at either higher qualities or lower bitrates. The experimental evaluation with a test video data set shows that MiPSO reduces the average bitrate of streams with the same visual quality by approximately 10% or increases the visual quality of streams by at least 1 dB in terms of Peak Signal-to-Noise (PSNR) at the same bitrate compared to conventional approaches.
Lightweight Prediction and Boundary Attention-Based Semantic Segmentation for Road Scene Understanding

Semantic segmentation is one of the most commonly used techniques for road scene understanding. Recently developed deep learning-based semantic segmentation networks are typically based on the encoder-decoder structure and have made great progress in road scene understanding. However, these conventional networks still encounter difficulties in recovering spatial details. To overcome this problem, we introduce a lightweight prediction and boundary-aware refinement module that can hierarchically refine the segmentation results with spatial details. The proposed refinement module has two attention units called the upper-level prediction attention unit and the upper-level boundary attention unit. The upper-level prediction attention unit emphasizes the features in the regions that need to be refined by using predicted class probability from the upper-level, whereas the upper-level boundary attention unit focuses on the features near the semantic boundary of the upper-level segmentation result. By using the proposed prediction and boundary-aware refinement module in the decoder network, the segmentation result can gradually be improved in a top-down manner to a finer and more complete one. Experimental results on the Cityscapes and CamVid datasets demonstrate that the proposed prediction and boundary attention-based refinement module can achieve considerable performance improvement in segmentation accuracy with a marginal increase in computational complexity.
Identification of Network Dynamics and Disturbance for a Multizone Building

We propose a method that simultaneously identifies a sparse transfer matrix and a disturbance signal for a multizone building's temperature dynamics from the measurements of inputs and outputs. The proposed method is based on solving a convex optimization problem whose cost function involves an ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -penalty to promote a sparse solution. The method ensures that the transfer matrix is sparse, so that only dominant interactions among zones are retained in the model. The disturbance, which is mostly occupant-induced, is assumed to be a piecewise-constant signal, which aids in identification, since the derivative of a piecewise-constant signal is a sparse signal. We test our method on data from a virtual building (a simulation model) and a real building. Results from the virtual building show that the proposed method can accurately identify a sparse network model and a transformed disturbance. Results from the real building data-that does not have a ground truth-show that the method produces sensible results.
Knowledge-Based Fine-Grained Classification For Few-Shot Learning

The small inter-class variance and the large intra-class variance make the few-shot and fine-grained image classification more difficult because the machine cannot obtain enough information from only a few images. The external knowledge contains more semantics and can support the model to extract important features, while most of existing few-shot learning algorithms only focus on leveraging the visual features from images, little attention has been paid to the cross-modal external knowledge. In this paper, we propose a knowledge-based fine-grained classification mechanism for few-shot learning, which can overcome the difficulty of only obtaining limited and discriminative features from unimodal samples. We extract the visual features and the knowledge features from textual descriptions and a domain-specific knowledge graph at global and local levels to build the semantic space. To tackle the gap between multimodal features, we propose a mirror framework, named Mirror Mapping Network (MMN), to map the multimodal features into the same semantic space with two directions. Extensive experimental results show that our method outperforms the state-of-the-art.
Scorpion: A Modular Sensor Fusion Approach for Complementary Navigation Sensors

There is a great need to decrease our reliance on GPS by utilizing novel complementary navigation sensors. While a number of complementary navigation sensors have been studied, each one has trade-offs in availability, reliability, accuracy and applicability in various environments. The development of a robust estimator therefore requires the integration of many diverse sensors into a sensor fusion platform. Unfortunately, as the number of sensors added to the system grows larger, so does the difficulty of developing a sensor fusion solution that optimally integrates them all into a single navigation estimate. In addition, a sensor fusion solution with many sensors is susceptible to sensor failures, modeling errors, and other phenomena which can cause degradation of the fusion solution. In this paper, we propose an open architecture for sensor fusion that allows for the development of modular navigation filters, sensor integration strategies, and integrity algorithms. The primary goal of this architecture is to allow for the rapid development of a novel complementary PNT sensor, fusion strategy, or integrity algorithm without modification of any other part of the system. In the future, this architecture will enable the community to develop a repository of well-tested software modules for sensor fusion which will in turn allow for the iterative development of robust estimators, where users may pick and choose the components that they wish to use from the repository and build an estimator that fits their application. In addition, domain experts in the community on a particular sensor phenomenology may contribute modules to the repository without needing to be experts in all aspects of sensor fusion. To facilitate this community engagement, we have developed an open source implementation of the architecture, which will be made available as a reference implementation of the architecture and approach. This paper details the design and overall approach to the open architecture, as well as shows some experimental results that were obtained by running flight data through the reference implementation.
Active Inference of GAN for No-Reference Image Quality Assessment

No-reference image quality assessment (NR-IQA) is a challenging task. It is a promising idea to design NR-IQA algorithms by mimicking how human visual system (HVS) works. The internal generative mechanism (IGM) indicates that HVS actively infers the primary content of an image for better understanding. Inspired by that, a novel NR-IQA method with active inference is proposed in this paper. First, a generative adversarial network (GAN) is proposed to predict the primary content of a distorted image, in which two IGM-inspired constraints are considered during the optimization. Next, based on the correlation between the distorted image and its primary content, different degradations (i.e., the content/distortion-/structure-dependency degradation) are measured simultaneously with a multi-stream convolutional neural network (CNN) for NR-IQA. Benefit from the primary content obtained from GAN and the multiple degradations measurement of CNN, our method achieves the state-of-the-art on five public IQA databases.
Match²

Community Question Answering (CQA) has become a primary means for people to acquire knowledge, where people are free to ask questions or submit answers. To enhance the efficiency of the service, similar question identification becomes a core task in CQA which aims to find a similar question from the archived repository whenever a new question is asked. However, it has long been a challenge to properly measure the similarity between two questions due to the inherent variation of natural language, i.e., there could be different ways to ask a same question or different questions sharing similar expressions. To alleviate this problem, it is natural to involve the existing answers for the enrichment of the archived questions. Traditional methods typically take aone-side usage, which leverages the answer as some expanded representation of the corresponding question. Unfortunately, this may introduce unexpected noises into the similarity computation since answers are often long and diverse, leading to inferior performance. In this work, we propose atwo-side usage, which leverages the answer as a bridge of the two questions. The key idea is based on our observation that similar questions could be addressed by similar parts of the answer while different questions may not. In other words, we can compare the matching patterns of the two questions over the same answer to measure their similarity. In this way, we propose a novel matching over matching model, namely Match2, which compares the matching patterns between two question-answer pairs for similar question identification. Empirical experiments on two benchmark datasets demonstrate that our model can significantly outperform previous state-of-the-art methods on the similar question identification task.
Human-Object Relation Network For Action Recognition In Still Images

Surrounding object information has been widely used for action recognition. However, the relation between human and object, as an important cue, is usually ignored in the still image action recognition field. In this paper, we propose a novel approach for action recognition. The key to ours is a human-object relation module. By using the appearance as well as the spatial location of human and object, the module can compute (b) the pair-wise relation information between human and object to enhance features for action classification and can be trained jointly with our action recognition network. Experimental results on two popular datasets demonstrate the effectiveness of the proposed approach. Moreover, our method yields the new state-of-the-art results of 92.8% and 94.6% mAP on the PASCAL VOC 2012 Action and Stanford 40 Actions datasets respectively. Ablation study and visualization confirm the proposed method can model and utilize the human-object relation for action recognition.