Reviewerly/exHarmony huggingface dataset:
Urban Radiance Fields

The goal of this work is to perform 3D reconstruction and novel view synthesis from data captured by scanning platforms commonly deployed for world mapping in urban outdoor environments (e.g., Street View). Given a sequence of posed RGB images and lidar sweeps acquired by cameras and scanners moving through an outdoor scene, we produce a model from which 3D surfaces can be extracted and novel RGB images can be synthesized. Our approach extends Neural Radiance Fields, which has been demonstrated to synthesize realistic novel images for small scenes in controlled settings, with new methods for leveraging asynchronously captured lidar data, for addressing exposure variation between captured images, and for leveraging predicted image segmentations to supervise densities on rays pointing at the sky. Each of these three extensions provides significant performance improvements in experiments on Street View data. Our system produces state-of-the-art 3D surface reconstructions and synthesizes higher quality novel views in comparison to both traditional methods (e.g. COLMAP) and recent neural representations (e.g. Mip-NeRF).
Efficiently embedding QUBO problems on adiabatic quantum computers

Adiabatic quantum computers like the D-Wave 2000Q can approximately solve the QUBO problem, which is an NP-hard problem, and have been shown to outperform classical computers on several instances. Solving the QUBO problem literally means solving virtually any NP-hard problem like the traveling salesman problem, airline scheduling problem, protein folding problem, genotype imputation problem, thereby enabling significant scientific progress, and potentially saving millions/billions of dollars in logistics, airlines, healthcare and many other industries. However, before QUBO problems are solved on quantum computers, they must be embedded (or compiled) onto the hardware of quantum computers, which in itself is a very hard problem. In this work, we propose an efficient embedding algorithm, that lets us embed QUBO problems fast, uses less qubits and gets the objective function value close to the global minimum value. We then compare the performance of our embedding algorithm to that of D-Wave’s embedding algorithm, which is the current state of the art, and show that our embedding algorithm convincingly outperforms D-Wave’s embedding algorithm. Our embedding approach works with perfect Chimera graphs, i.e., Chimera graphs with no missing qubits.
SDL: Spectrum-Disentangled Representation Learning for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (RGB-IR ReID) is extremely important for the surveillance applications under poor illumination conditions. Since the difference in the feature representations not only lies in the person' pose, viewpoint or illumination variations, but also comes from huge spectrum discrepancy, the task becomes practically very challenging. Existing RGB-IR ReID models focus on bridging the gap between RGB and IR images through shared feature embedding, subspace learning or via adversarial learning. However, these methods do not explicitly disregard the spectrum information which is otherwise irrelevant for ReID. Further, adversarial learning methods has less promising convergence. This motivates us to design a non-adversarial and fast disentanglement method to disentangle the spectrum information while learning the identity discriminative features. To extract these features, we propose a novel network with disentanglement loss which can distill identity features and dispel spectrum features. Our network has two branches, spectrum dispelling and spectrum distilling branch. On spectrum dispelling branch, we apply identification loss to learn the identity related and spectrum disentangled features. On spectrum distilling branch, we apply an identity-dispeller loss to fool the identity classifier so that it primarily learns spectrum related information. The entire network is trained in an end-to-end manner, which minimizes spectrum information and maximizes invariant identity relevant information at spectrum dispelling branch. Extensive experiments on existing datasets demonstrate the superior performance of our approach compared to the state-of-the-art.
Day-Ahead Solar Irradiation Forecasting Utilizing Gramian Angular Field and Convolutional Long Short-Term Memory

The operations of power systems are becoming more challenging on account of the high penetration of renewable power generation, including photovoltaic systems. One method for improving the power system operation involves making accurate forecasts of day-ahead solar irradiation, enabling operators to minimize uncertainty in managing the balance between generation and load. To overcome the limitations of Long Short-term Memory (LSTM) in a one-dimensional forecasting problem, this work proposes a novel method in forecasting solar irradiation by encoding time-series data into images using the Gramian Angular Field and the Convolutional LSTM (ConvLSTM) network. The pre-processed data become a five-dimensional input tensor that is perfectly suitable for ConvLSTM. The ConvLSTM network uses convolution operations in its input-to-state transition and state-to-state transition. The network thus enables time-series forecasting by a feature-rich approach, which ultimately provides competitive forecasting performance despite the use of a small dataset. The proposed method was evaluated in day-ahead solar irradiation forecasting using a univariate dataset of Global Horizontal Irradiation (GHI) data from Fuhai in Taiwan. The proposed method was resampled using 5×2-fold cross-validation, and assessed using the Wilcoxon signed-rank test to determine the statistical significance of the result. It outperformed benchmark methods such as Autoregressive Integrated Moving Average (ARIMA), Convolutional Neural Network cascaded with Long Short-term Memory (CNN-LSTM), and LSTM cascaded with a fully-connected (FC) network.
Deep Crisp Boundaries: From Boundaries to Higher-Level Tasks

Edge detection has made significant progress with the help of deep Convolutional Networks (ConvNet). These ConvNet based edge detectors have approached human level performance on standard benchmarks. We provide a systematical study of these detectors' outputs. We show that the detection results did not accurately localize edge pixels, which can be adversarial for tasks that require crisp edge inputs. As a remedy, we propose a novel refinement architecture to address the challenging problem of learning a crisp edge detector using ConvNet. Our method leverages a top-down backward refinement pathway, and progressively increases the resolution of feature maps to generate crisp edges. Our results achieve superior performance, surpassing human accuracy when using standard criteria on BSDS500, and largely outperforming state-of-the-art methods when using more strict criteria. More importantly, we demonstrate the benefit of crisp edge maps for several important applications in computer vision, including optical flow estimation, object proposal generation and semantic segmentation.
Blockchain Based Data and Energy Trading in Internet of Electric Vehicles

The drastic increase in real-time vehicle generated data of various types has imparted a great concept of data trading in vehicular networks.Whereas immense usage of Electric Vehicles (EVs) as mobile energy carriers have supported distributed energy trading due to their bidirectional charging and discharging capabilities.The trustless environment of Internet of Electric Vehicles (IoEV), including fuel vehicles and EVs, encounters trading disputes and conflicting interests among trading parties.To address these challenges, we exploit consortium blockchain to maintain transparency and trust in trading activities.Smart contracts are used to tackle trading disputes and illegal actions.Data duplication problem occurs when a dishonest user sell previously traded data multiple times for financial gain.Therefore, data duplication validation is done through previously stored hash-list at roadside units (RSUs) employed with bloom filters for efficient data lookup.Removing data duplication at an earlier stage reduces storage cost.Moreover, an elliptic curve bilinear pairing based digital signature scheme is used to ensure the reliability and integrity of traded data.To ensure persistent availability of traded data, InterPlanetary File System (IPFS) is used, which provides fault-tolerant and a reliable data storage without any single point of failure.On the other hand, the energy trading transactions among EVs face some security and privacy protection challenges.An adversary can infer the energy trading records of EVs, and launch the data linkage attacks.To address this issue, an account generation technique is used that hides the energy trading trends.The new account generation for an EV depends upon its traded volume of energy.The experimental results verify the efficiency of the proposed data and energy trading scheme in IoEV with the reliable and secure data storage.
An analysis on the role of blockchain-based platforms in agricultural supply chains

The traditional agricultural supply chain (ASC) has been overwhelmed by several challenges, including financing risk, counterparty risk, and lack of consumer trust. Platforms based on blockchain technology combined with Internet-of-Things technology have emerged to address these challenges by improving supply chain visibility, guaranteeing the execution of contracts, and increasing the authenticity of products’ provenance information in the ASC. This study analyzes how the adoption of a blockchain-based platform can affect the decisions of ASC participants and identifies how the platform creates value for the supply chain by addressing these three challenges. We consider a two-level supply chain featuring a typical cooperative and a buyer and establish stylized game models with and without the blockchain-based platform. By comparing equilibrium outcomes with and without the blockchain-based platform, we show that the involvement of the blockchain-based platform can lead to increased production quantity and total surplus of the supply chain. This can also motivate more sustainability/green investment to produce greener products. Interestingly, we show that the value of the blockchain-based platform decreases in the credibility of the business environment in which the supply chain operates. Furthermore, the buyer will always benefit from the established blockchain-based platform, whereas the cooperative can benefit in most cases but could be worse off under certain conditions. The adoption and operational costs could outweigh the benefits caused by the addition of the blockchain-based platform.
Robust and adaptable quantum key distribution network without trusted nodes

Quantum key distribution (QKD) networks are promising to serve large numbers of users with information-theoretic secure communication. In QKD networks, the detection-safe protocol, termed measurement-device-independent (MDI) QKD, can naturally enhance realistic security by supporting untrusted measurement nodes. However, the environmental disturbances to quantum states degrade the performance of multi-user communication. Here we propose an MDI-QKD networking scheme with robustness against environmental disturbance and adaptability to multi-user access, where more than two users can generate keys simultaneously regardless of aligning reference frames and compensating channel disturbance on polarization. To achieve this, we introduce the reference-frame-independent protocol as well as a polarization-compensation-free method, design a multi-user measurement unit, and combine it with original two-user units. The scheme is experimentally demonstrated for the improvement of network robustness and adaptability in multi-user scenarios, and the time and device costs of disturbance compensation can be saved from <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>O</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi>N</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:math> to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>O</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mn>1</mml:mn> <mml:mo stretchy="false">)</mml:mo> </mml:math> for an N-user network.
Building a Smart System for Preservation of Government Records in Digital Form

In this research paper, we concentrated on the development of a smart system for the preservation of Government of Iraq records in digital format. In literature, many existing preservation techniques and models have been discussed and presented with their detailed comparison as preservation rate of digital data growth has increased. Many western countries have already upgraded their paper-based systems to the smart system for preservation of government records including Turkey. In methodology, we used python language for the implementation of smart system application with OAIS framework to preserve archived paper-based records, It also serves as a blueprint for the new digital ecology. It provides a framework for identifying roles and responsibilities in a constrained system. Iraq's major ministries (such as finance, power, and science and technology) are working on guidelines for preserving digital artifacts and data. During the preservation phase, records were also subjected to accession, normalization, and transformation in order to clean them up and convert them to a new format. In results, we achieved good preservation of most of the records from 2015 to 2019, the goal is to preserve which most of the records it belongs to digital format. The distribution of records preservation for major ministries of Iraq government in term of paper based and digitally preserved records from year 2015 to 2019. The preservation of digital records was recorded very low in 2015 as much of the record archival were paper based in all major ministries of the government. However, year-by-year the smart system tends preserve the records in digital format from old paper-based format and till 2019 large number of records are being converted to the digital format. To have such smart system can be very helpful for preserving the records of the government, and useful for understanding how records are being preserved and their functionality.
Progressive Distributed and Parallel Similarity Retrieval of Large CT Image Sequences in Mobile Telemedicine Networks

Computed tomography image (CTI) sequence is essentially a time-series data that typically consists of a large amount of nearby and similar CTIs. Due to the high communication and computational costs, it is difficult to perform a progressive distributed similarity retrieval of the large CTI sequence (CTIS)s, particularly in resource-constraint mobile telemedicine network (MTN)s. In this paper, we present a Dprs method—progressive distributed and parallel similarity retrieval scheme for the CTISs in the MTN. To the best of our knowledge, there is little research on the Dprs processing, especially in the MTN. Four supporting techniques (i.e., (1) PCTI-based similarity measurement, (2) lightweight privacy-preserving strategy, (3) SSL-based data distribution scheme, and (4) the UDI framework) are developed. The experimental evaluation indicates that our proposed Dprs method is more progressive than the state of the art, with a significant reduction in response time.
Membership Inference Attacks From First Principles

A membership inference attack allows an adversary to query a trained machine learning model to predict whether or not a particular example was contained in the model’s training dataset. These attacks are currently evaluated using average-case “accuracy” metrics that fail to characterize whether the attack can confidently identify any members of the training set. We argue that attacks should instead be evaluated by computing their true-positive rate at low (e.g., ≤ 0.1%) false-positive rates, and find most prior attacks perform poorly when evaluated in this way. To address this we develop a Likelihood Ratio Attack (LiRA) that carefully combines multiple ideas from the literature. Our attack is $10\times$ more powerful at low false-positive rates, and also strictly dominates prior attacks on existing metrics.
Pseudo RGB-D Face Recognition

In the last decade, advances and popularity of low-cost RGB-D sensors have enabled us to acquire depth information of objects. Consequently, researchers began to solve face recognition problems by capturing RGB-D face images using these sensors. Until now, it is not easy to acquire the depth of human faces because of limitations imposed by privacy policies, and RGB face images are still more common. Therefore, obtaining the depth map directly from the corresponding RGB image could be helpful to improve the performance of subsequent face processing tasks, such as face recognition. Intelligent creatures can use a large amount of experience to obtain 3D spatial information only from 2D plane scenes. It is machine learning methodology, which is to solve such problems, that can teach computers to generate correct answers by training. To replace the depth sensors by generated pseudo-depth maps, in this article, we propose a pseudo RGB-D face recognition framework and provide data-driven ways to generate the depth maps from 2D face images. Specially we design and implement a generative adversarial network model named “D+GAN” to perform the multiconditional image-to-image translation with face attributes. By this means, we validate the pseudo RGB-D face recognition with experiments on various datasets. With the cooperation of image fusion technologies, especially non-subsampled shearlet transform (NSST), the accuracy of face recognition has been significantly improved.
P2T: Pyramid Pooling Transformer for Scene Understanding

Recently, the vision transformer has achieved great success by pushing the state-of-the-art of various vision tasks. One of the most challenging problems in the vision transformer is that the large sequence length of image tokens leads to high computational cost (quadratic complexity). A popular solution to this problem is to use a single pooling operation to reduce the sequence length. This paper considers how to improve existing vision transformers, where the pooled feature extracted by a single pooling operation seems less powerful. To this end, we note that pyramid pooling has been demonstrated to be effective in various vision tasks owing to its powerful ability in context abstraction. However, pyramid pooling has not been explored in backbone network design. To bridge this gap, we propose to adapt pyramid pooling to Multi-Head Self-Attention (MHSA) in the vision transformer, simultaneously reducing the sequence length and capturing powerful contextual features. Plugged with our pooling-based MHSA, we build a universal vision transformer backbone, dubbed Pyramid Pooling Transformer (P2T). Extensive experiments demonstrate that, when applied P2T as the backbone network, it shows substantial superiority in various vision tasks such as image classification, semantic segmentation, object detection, and instance segmentation, compared to previous CNN- and transformer-based networks. The code will be released at https://github.com/yuhuan-wu/P2T.
Breast cancer detection using deep learning: Datasets, methods, and challenges ahead

Breast Cancer (BC) is the most commonly diagnosed cancer and second leading cause of mortality among women. About 1 in 8 US women (about 13%) will develop invasive BC throughout their lifetime. Early detection of this life-threatening disease not only increases the survival rate but also reduces the treatment cost. Fortunately, advancements in radiographic imaging like "Mammograms", "Computed Tomography (CT)", "Magnetic Resonance Imaging (MRI)", "3D Mammography", and "Histopathological Imaging (HI)" have made it feasible to diagnose this life-taking disease at an early stage. However, the analysis of radiographic images and Histopathological images is done by experienced radiologists and pathologists, respectively. The process is not only costly but also error-prone. Over the last ten years, Computer Vision and Machine Learning (ML) have transformed the world in every way possible. Deep learning (DL), a subfield of ML has shown outstanding results in a variety of fields, particularly in the biomedical industry, because of its ability to handle large amounts of data. DL techniques automatically extract the features by analyzing the high dimensional and correlated data efficiently. The potential and ability of DL models have also been utilized and evaluated in the identification and prognosis of BC, utilizing radiographic and Histopathological images, and have performed admirably. However, AI has shown good claims in retrospective studies only. External validations are needed for translating these cutting-edge AI tools as a clinical decision maker. The main aim of this research work is to present the critical analysis of the research and findings already done to detect and classify BC using various imaging modalities including "Mammography", "Histopathology", "Ultrasound", "PET/CT", "MRI", and "Thermography". At first, a detailed review of the past research papers using Machine Learning, Deep Learning and Deep Reinforcement Learning for BC classification and detection is carried out. We also review the publicly available datasets for the above-mentioned imaging modalities to make future research more accessible. Finally, a critical discussion section has been included to elaborate open research difficulties and prospects for future study in this emerging area, demonstrating the limitations of Deep Learning approaches.
Design and realization of discrete memristive hyperchaotic map with application in image encryption

The investigation of discrete memristive chaotic systems with complex dynamics has been an interesting and meaningful research work. This paper is devoted to constructing a hyperchaotic system with no fixed point and infinitely many coexisting attractors. The memristive Gaussian map (MGM) is coupled by a sinusoidal discrete memristor with the Gaussian map and induces extreme multistability associated with the initial state due to the import of sinusoidal nonlinearity. Creatively, the iterative number is imported into the system as a variable, which effectively boosts the complexity of its export. The most remarkable feature of the system is a large range of chaos and hyperchaos captured under multiple sets of parameters, and with various coexisting attractors. In addition, digital experiments are designed to verify the feasibility of the hardware implementation, an image encryption algorithm and PRNG are proposed based on the MGM chaotic sequence. • New memristive system with infinite hidden coexisting hyperchaos is presented. • Complex dynamics and digital circuit implementation of the system is given. • New chaos-based image encryption algorithm with high security is designed.