Reviewerly/exHarmony huggingface dataset:
Realize your surroundings: Exploiting context information for small object detection

Small object detection is a highly challenging problem due to the limited resolution and information of small objects. Current state-of-the-art detectors only utilize the appearance feature to locate and classify objects. However, such detectors are prone to failure when detecting small objects, especially in the case of heavy appearance changes and background distractors, in which the appearance feature alone is not sufficient for robust detection. Exploiting context information in the surrounding scene can be highly beneficial in such cases. In this paper, we propose a novel detector, the Internal-External Network (IENet), which uses both the appearance and context information of the object for robust detection. In the proposed approach, small object detection is improved from feature extraction, proposal location, and classification. Specifically, three customized modules are designed, including the Bidirectional Feature Fusion Module (Bi-FFM), Context Reasoning Module (CRM), and Context Feature Augmentation Module (CFAM). Bi-FFM is designed to capture the internal feature of objects by transferring the semantic feature of deeper-level layers to lower-level layers and the detailed feature of lower-level layers to deeper-level layers in neural networks. The proposed approach not only utilizes the hierarchy of convolutional features but also improves its prediction via context relationships. CRM is designed to improve the quality of region proposals by context reasoning that uses easily detected objects to help understand hard ones. Furthermore, CFAM is designed to learn pair-wise relations between region proposals produced by CRM, and such relations are used to produce global feature information associated with the region proposals for accurate classification. Extensive experiments are conducted on the challenging COCO and WIDER FACE datasets to demonstrate the effectiveness of the proposed approach. Experimental results show that the detection performance of small objects is greatly improved over state-of-the-art detectors.
Quantum Algorithm for Simulating Real Time Evolution of Lattice Hamiltonians

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 10 December 2018Accepted: 05 October 2020Published online: 12 January 2021Keywordsquantum algorithm, local Hamiltonian, simulationAMS Subject Headings81-08, 65Z05, 68W25Publication DataISSN (print): 0097-5397ISSN (online): 1095-7111Publisher: Society for Industrial and Applied MathematicsCODEN: smjcat
Dimensional Expressivity Analysis of Parametric Quantum Circuits

Parametric quantum circuits play a crucial role in the performance of many variational quantum algorithms. To successfully implement such algorithms, one must design efficient quantum circuits that sufficiently approximate the solution space while maintaining a low parameter count and circuit depth. In this paper, develop a method to analyze the dimensional expressivity of parametric quantum circuits. Our technique allows for identifying superfluous parameters in the circuit layout and for obtaining a maximally expressive ansatz with a minimum number of parameters. Using a hybrid quantum-classical approach, we show how to efficiently implement the expressivity analysis using quantum hardware, and we provide a proof of principle demonstration of this procedure on IBM's quantum hardware. We also discuss the effect of symmetries and demonstrate how to incorporate or remove symmetries from the parametrized ansatz.
TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Tracking

Tracking multiple objects in videos relies on modeling the spatial-temporal interactions of the objects. In this paper, we propose a solution named TransMOT, which leverages powerful graph transformers to efficiently model the spatial and temporal interactions among the objects. TransMOT effectively models the interactions of a large number of objects by arranging the trajectories of the tracked objects as a set of sparse weighted graphs, and constructing a spatial graph transformer encoder layer, a temporal transformer encoder layer, and a spatial graph transformer decoder layer based on the graphs. TransMOT is not only more computationally efficient than the traditional Transformer, but it also achieves better tracking accuracy. To further improve the tracking speed and accuracy, we propose a cascade association framework to handle low-score detections and long-term occlusions that require large computational resources to model in TransMOT. The proposed method is evaluated on multiple benchmark datasets including MOT15, MOT16, MOT17, and MOT20, and it achieves state-of-the-art performance on all the datasets.
Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases

Existing studies on question answering on knowledge bases (KBQA) mainly operate with the standard i.i.d. assumption, i.e., training distribution over questions is the same as the test distribution. However, i.i.d. may be neither achievable nor desirable on large-scale KBs because 1) true user distribution is hard to capture and 2) randomly sampling training examples from the enormous space would be data-inefficient. Instead, we suggest that KBQA models should have three levels of built-in generalization: i.i.d., compositional, and zero-shot. To facilitate the development of KBQA models with stronger generalization, we construct and release a new large-scale, high-quality dataset with 64,331 questions, GrailQA, and provide evaluation settings for all three levels of generalization. In addition, we propose a novel BERT-based KBQA model. The combination of our dataset and model enables us to thoroughly examine and demonstrate, for the first time, the key role of pre-trained contextual embeddings like BERT in the generalization of KBQA.1
A survey: applications of blockchain in the Internet of Vehicles

Abstract Blockchain technology has completely changed the area of cryptocurrency with a Peer-to-Peer system named Bitcoin. It can provide a distributed, transparent and highly confidential database by recording immutable transactions. Currently, the technique has obtained great research interest on other areas, including the Internet of vehicles (IoVs). In order to solve some centralized problems and improve the architecture of the IoVs, the blockchain technology is utilized to build a decentralized and secure vehicular environment. In this survey, we aim to construct a comprehensive analysis on the applications of blockchain in the IoV. This paper starts with the introduction of the IoVs and the blockchain. Additionally, some existing surveys on the blockchain enabled IoVs are reviewed. Besides, the combination of the blockchain technology and the IoVs is analyzed from seven aspects to describe how the blockchain is implemented in the IoVs. Finally, the future research directions related to the integration are highlighted.
Mapping the Landscape of COVID-19 Crisis Visualizations

In response to COVID-19, a vast number of visualizations have been created to communicate information to the public. Information exposure in a public health crisis can impact people's attitudes towards and responses to the crisis and risks, and ultimately the trajectory of a pandemic. As such, there is a need for work that documents, organizes, and investigates what COVID-19 visualizations have been presented to the public. We address this gap through an analysis of 668 COVID-19 visualizations. We present our findings through a conceptual framework derived from our analysis, that examines who, (uses) what data, (to communicate) what messages, in what form, under what circumstances in the context of COVID-19 crisis visualizations. We provide a set of factors to be considered within each component of the framework. We conclude with directions for future crisis visualization research.
A Quantum Approach Towards the Adaptive Prediction of Cloud Workloads

This work presents a novel Evolutionary Quantum Neural Network (EQNN) based workload prediction model for Cloud datacenter. It exploits the computational efficiency of quantum computing by encoding workload information into qubits and propagating this information through the network to estimate the workload or resource demands with enhanced accuracy proactively. The rotation and reverse rotation effects of the Controlled-NOT (C-NOT) gate serve activation function at the hidden and output layers to adjust the qubit weights. In addition, a Self Balanced Adaptive Differential Evolution (SB-ADE) algorithm is developed to optimize qubit network weights. The accuracy of the EQNN prediction model is extensively evaluated and compared with seven state-of-the-art methods using eight real world benchmark datasets of three different categories. Experimental results reveal that the use of the quantum approach to evolutionary neural network substantially improves the prediction accuracy up to 91.6 percent over the existing approaches.
An Efficient Blockchain-Based Authentication Scheme for Energy-Trading in V2G Networks

Vehicle-to-grid (V2G) networks have been emerged as a new technology in the smart grid (SG). These networks allow a two-way flow of energy-trading between electric vehicles (EVs) and charging stations (CSs) in the SG. EVs are regarded as one of the most effective tools to reduce energy demands. It will bring a great impact on our society and human life. Thus, during energy trading between EVs and CSs, various security, and privacy challenges occur in V2G networks. Although several proposals have been proposed, still there are many issues like lack of integrity, mutual authentication, and identity privacy-preservation make the system more vulnerable. Researchers have used the centralized system in V2G networks which may act as a single point of failure. So, for deploying secure V2G networks in the SG, we propose an energy-trading scheme having blockchain between three communicating parties, i.e., EVs, CSs, utility center. The proposed system is divided into three phases, first, the registration process provides identity privacy-preservation to the EVs and CSs, second, the searching process makes the registration and key-generation steps faster, and third, the authentication process provides mutual authentication between them and a blockchain network is used to execute transactions using Merkle Root Hash. The security analysis result shows that the proposed scheme is secure for energy-trading in V2G networks. The performance evaluation results illustrate that our scheme has less communication cost and computation time as compared to the existing proposals.
Artificial Neural Network Training Criterion Formulation Using Error Continuous Domain

One of the trends in information technologies is implementing neural networks in modern software packages [1].The fact that neural networks cannot be directly programmed (but trained) is their distinctive feature.In this regard, the urgent task is to ensure sufficient speed and quality of neural network training procedures.The process of neural network training can differ significantly depending on the problem.There are verification methods that correspond to the task's constraints; they are used to assess the training results.Verification methods provide an estimate of the entire cardinal set of examples but do not allow to estimate which subset of those causes a significant error.This fact leads to neural networks' failure to perform with the given set of hyperparameters, making training a new one time-consuming.On the other hand, existing empirical assessment methods of neural networks training use discrete sets of examples.With this approach, it is impossible to say that the network is suitable for classification on the whole cardinal set of examples.This paper proposes a criterion for assessing the quality of classification results.The criterion is formed by describing the training states of the neural network.Each state is specified by the correspondence of the set of errors to the function range representing a cardinal set of test examples.The criterion usage allows tracking the network's classification defects and marking them as safe or unsafe.As a result, it is possible to formally assess how the training and validation data sets must be altered to improve the network's performance, while existing verification methods do not provide any information on which part of the dataset causes the network to underperform.
Triply Complementary Priors for Image Restoration

Recent works that utilized deep models have achieved superior results in various image restoration (IR) applications. Such approach is typically supervised, which requires a corpus of training images with distributions similar to the images to be recovered. On the other hand, the shallow methods, which are usually unsupervised remain promising performance in many inverse problems, e.g., image deblurring and image compressive sensing (CS), as they can effectively leverage nonlocal self-similarity priors of natural images. However, most of such methods are patch-based leading to the restored images with various artifacts due to naive patch aggregation in addition to the slow speed. Using either approach alone usually limits performance and generalizability in IR tasks. In this paper, we propose a joint low-rank and deep (LRD) image model, which contains a pair of triply complementary priors, namely, internal and external, shallow and deep, and non-local and local priors. We then propose a novel hybrid plug-and-play (H-PnP) framework based on the LRD model for IR. Following this, a simple yet effective algorithm is developed to solve the proposed H-PnP based IR problems. Extensive experimental results on several representative IR tasks, including image deblurring, image CS and image deblocking, demonstrate that the proposed H-PnP algorithm achieves favorable performance compared to many popular or state-of-the-art IR methods in terms of both objective and visual perception.
Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger

Backdoor attacks are a kind of insidious security threat against machine learning models.After being injected with a backdoor in training, the victim model will produce adversaryspecified outputs on the inputs embedded with predesigned triggers but behave properly on normal inputs during inference.As a sort of emergent attack, backdoor attacks in natural language processing (NLP) are investigated insufficiently.As far as we know, almost all existing textual backdoor attack methods insert additional contents into normal samples as triggers, which causes the trigger-embedded samples to be detected and the backdoor attacks to be blocked without much effort.In this paper, we propose to use the syntactic structure as the trigger in textual backdoor attacks.We conduct extensive experiments to demonstrate that the syntactic trigger-based attack method can achieve comparable attack performance (almost 100% success rate) to the insertionbased methods but possesses much higher invisibility and stronger resistance to defenses.These results also reveal the significant insidiousness and harmfulness of textual backdoor attacks.
The 51% Attack on Blockchains: A Mining Behavior Study

The applications that use blockchain are cryptocurrencies, decentralized finance applications, video games and many others. Most of these applications trust that the blockchain will prevent issues like fraud, thanks to the built-in cryptographic mechanisms provided by the data structure and the consensus protocol. However, blockchains suffers from what is called a 51% attack or majority attack, which is considered a high risk for the integrity of these blockchains, where if a miner, or a group of them, has more than half the computing capability of the network, it can rewrite the blockchain. Even though this attack is possible in theory, it is regarded as hard-achievable in practice, due to the assumption that, with enough active members, it is very complicated to have that much computing power; however, this assumption has not been studied with enough detail. In this work, a detailed characterization of the miners in the Bitcoin and Crypto Ethereum blockchains is presented, with the aim of proving the computing distribution assumption and to creating profiles that may allow the detection of anomalous behaviors and prevent 51% attacks. The results of the analysis show that, in the last years, there has been an increasing concentration of hash rate power in a very small set of miners, which generates a real risk for current blockchains. Also, that there is a pattern in mining among the main miners, which makes it possible to identify out-of-normal behavior.
Deep DIC: Deep learning-based digital image correlation for end-to-end displacement and strain measurement

Digital image correlation (DIC) has become an industry standard to retrieve accurate displacement and strain measurement in tensile testing and other material characterization. Though traditional DIC offers a high precision estimation of deformation for general tensile testing cases, the prediction becomes unstable at large deformation or when the speckle patterns start to tear. In addition, traditional DIC requires a long computation time and often produces a low spatial resolution output affected by filtering and speckle pattern quality. To address these challenges, we propose a new deep learning-based DIC approach – Deep DIC, in which two convolutional neural networks, DisplacementNet and StrainNet, are designed to work together for end-to-end prediction of displacements and strains. DisplacementNet predicts the displacement field and adaptively tracks a region of interest. StrainNet predicts the strain field directly from the image input without relying on the displacement prediction, which significantly improves the strain prediction accuracy. A new dataset generation method is developed to synthesize a realistic and comprehensive dataset, including the generation of speckle patterns and the deformation of the speckle image with synthetic displacement fields. Though trained on synthetic datasets only, Deep DIC gives highly consistent and comparable predictions of displacement and strain with those obtained from commercial DIC software for real experiments, while it outperforms commercial software with very robust strain prediction even at large and localized deformation and varied pattern qualities. In addition, Deep DIC is capable of real-time prediction of deformation with a calculation time down to milliseconds.
Systematic Literature Review on Application of Blockchain Technology in E-Finance and Financial Services

Blockchain is thought to be the most recent innovation in technology. Trust is especially important in areas like the banking sector. Blockchain technology is a decentralised, coded security system which permits the development of novel digital platforms and services using this developing technology. The current research focuses on the usage of blockchain technology in the financial and e-finance sectors. The study focuses of research questions such as the development of blockchain technology, the adoption & challenges in blockchain technology and the application of blockchain technology in the financial sector. This study conducted a systematic review of 76 scientific articles on blockchain technology, narrowing down the selection to 59 articles. As a result, the author created classification framework with three dimensions: blockchain development, challenges and applications in the financial sector. This study recognizes consequences for blockchain research and practice in the future. The study reveals that blockchain has various unexplored application in finance sector and also there is a scope of improvement in technology. Also, the technology is unregulated, thus, it is still in its nascent stage and a lot is to be done in the field.
Demonstration of a blockchain-based framework using smart contracts for supply chain collaboration

Blockchain technologies can support traceability, transparency and trust among participants. This has primarily been explored in established supply chains and not in the growing use of business networks or ecosystems, which is a notable limitation since supply chains typically are organised with a dominant actor that ensures common information systems and standards that negate blockchain benefits. Hence, this study explores the design of a blockchain-based collaborative framework for resource sharing using smart contracts. These are particularly well-suited for supporting operations in broader networks or ecosystems beyond supply chains with established collaborations and hierarchies. Based on a systematic literature review, a demonstrator framework was developed for stakeholder interactions through a procurement and distribution unit backed with blockchain technology. The framework consists of (a) network architecture to demonstrate partner interactions; (b) rules for network working principles based on supply collaboration requirements; (c) UML diagram to define smart contract interaction sequence; and (d) algorithm for smart contract network verification and validation. Applicability of these smart contracts was verified by deployment on an Ethereum blockchain. The demonstrator framework ensures quality and data authenticity in supply networks, so it is useful for effective resource utilisation in networks where outsourcing and production surpluses are major issues.
Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique

Variability in solar irradiance has an impact on the stability of solar systems and the grid&rsquo;s safety. With the decreasing cost of solar panels and recent advancements in energy conversion technology, precise solar energy forecasting is critical for energy system integration. Despite extensive research, there is still potential for advancement of solar irradiance prediction accuracy, especially global horizontal irradiance. Global Horizontal Irradiance (GHI) (unit: KWh/m2) and the Plane Of Array (POA) irradiance (unit: W/m2) were used as the forecasting objectives in this research, and a hybrid short-term solar irradiance prediction model called modified multi-step Convolutional Neural Network (CNN)-stacked Long-Short-Term-Memory network (LSTM) with drop-out was proposed. The real solar data from Sweihan Photovoltaic Independent Power Project in Abu Dhabi, UAE is preprocessed, and features were extracted using modified CNN layers. The output result from CNN is used to predict the targets using a stacked LSTM network and the efficiency is proved by comparing statistical performance measures in terms of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MAE), and R2 scores, with other contemporary machine learning and deep-learning-based models. The proposed model offered the best RMSE and R2 values of 0.36 and 0.98 for solar irradiance prediction and 61.24 with R2 0.96 for POA prediction, which also showed better performance as compared to the published works in the literature.