Reviewerly/exHarmony huggingface dataset:
Evolutionary Equilibrium Analysis for Decision on Block Size in Blockchain Systems

In a PoW-based blockchain network, mining pools (the solo miner could be regarded as a mining pool containing one miner) compete to successfully mine blocks to pursue rewards. Generally, the rewards include the fixed block subsidies and time-varying transaction fees. The transaction fees are offered by the senders whose transactions are packaged into blocks and is positively correlated with the block size. However, the larger size of a block brings the longer latency, resulting in a smaller probability of successfully mining. Therefore, finding the optimal block size to trade off these two factors is a complex and crucial problem for the mining pools. In this paper, we model a repeated mining competition dynamics in blockchain system as an evolutionary game to study the interactions among mining pools. In this game, each pool has two strategies: to follow the default size \(\bar{B}\), i.e., the upper bound of a block size, or not follow. Because of the bounded rationality, each mining pool pursues its evolutionary stable block size (ESS) according to the mining pools' computing power and other factors by continuous learning and adjustments during the whole mining process. A study framework is built for the general evolutionary game, based on which we then theoretically explore the existence and stability of the ESSs for a case of two mining pools. Numerical experiments with real Bitcoin data are conducted to show the evolutionary decisions of mining pools and to demonstrate the theoretical findings in this paper.
Windowing Decomposition Convolutional Neural Network for Image Enhancement

Image enhancement aims to improve the aesthetic quality of images. Most enhancement methods are based on image decomposition techniques. For example, an entire image can be decomposed into a smooth base layer and a residual detail layer. Applying appropriate algorithms to different layers can solve most enhancement problems. Besides decomposing the entire image, the local decomposition approach in local Laplacian filter can also achieve satisfied enhancement results. As a standard convolution is also a local operator that the output values is determined by neighborhood pixels, we observe that the standard convolution can be improved by integrating the local decomposition method for better solving image enhancement problems. Based on this analysis, we propose Windowing Decomposition Convolution (WDC) that decomposes the content of each convolution window by a windowing basic value before applying convolution operation. Using different windowing basic values, the WDC can gather global information and locally separate the processing of different components of images. Moreover, combined with WDC, a new Windowing Decomposition Convolutional Neural Network (WDCNN) is presented. The experimental results show that our WDCNN achieves superior enhancement performance on the MIT-Adobe FiveK and sRGB-SID datasets for noise-free image retouching and low-light noisy image enhancement compared with state-of-the-art techniques.
Situational Anomaly Detection in Multimedia Data under Concept Drift

Anomaly detection has been a very challenging and active area of research for decades, particularly for video surveillance. However, most of the works detect predefined anomaly classes using static models. These frameworks have limited applicability for real-life surveillance where the data have concept drift. Under concept drift, the distribution of both normal and anomaly classes changes over time. An event may change its class from anomaly to normal or vice-versa. The non-adaptive frameworks do not handle this drift. Additionally, the focus has been on detecting local anomalies, such as a region of an image. In contrast, in CCTV-based monitoring, flagging unseen anomalous situations can be of greater interest. Utilizing multiple sensory information for anomaly detection has also received less attention. This extended abstract discusses these gaps and possible solutions.
SymbolFinder: Brainstorming Diverse Symbols Using Local Semantic Networks

Visual symbols are the building blocks for visual communication. They convey abstract concepts like reform and participation quickly and effectively. When creating graphics with symbols, novice designers often struggle to brainstorm multiple, diverse symbols because they fixate on a few associations instead of broadly exploring different aspects of the concept. We present SymbolFinder, an interactive tool for finding visual symbols for abstract concepts. SymbolFinder molds symbol-finding into a recognition rather than recall task by introducing the user to diverse clusters of words associated with the concept. Users can dive into these clusters to find related, concrete objects that symbolize the concept. We evaluate SymbolFinder with two studies: a comparative user study, demonstrating that SymbolFinder helps novices find more unique symbols for abstract concepts with significantly less effort than a popular image database and a case study demonstrating how SymbolFinder helped design students create visual metaphors for three cover illustrations of news articles.
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning

High-level representation-guided pixel denoising and adversarial training are independent solutions to enhance the robustness of CNNs against adversarial attacks by pre-processing input data and re-training models, respectively. Most recently, adversarial training techniques have been widely studied and improved while the pixel denoising-based method is getting less attractive. However, it is still questionable whether there exists a more advanced pixel denoising-based method and whether the combination of the two solutions benefits each other. To this end, we first comprehensively investigate two kinds of pixel denoising methods for adversarial robustness enhancement (i.e., existing additive-based and unexplored filtering-based methods) under the loss functions of image-level and semantic-level, respectively, showing that pixel-wise filtering can obtain much higher image quality (e.g., higher PSNR) as well as higher robustness (e.g., higher accuracy on adversarial examples) than existing pixel-wise additive-based method. However, we also observe that the robustness results of the filtering-based method rely on the perturbation amplitude of adversarial examples used for training. To address this problem, we propose predictive perturbation-aware & pixel-wise filtering, where dual-perturbation filtering and an uncertainty-aware fusion module are designed and employed to automatically perceive the perturbation amplitude during the training and testing process. The method is termed as AdvFilter. Moreover, we combine adversarial pixel denoising methods with three adversarial training-based methods, hinting that considering data and models jointly is able to achieve more robust CNNs. The experiments conduct on NeurIPS-2017DEV, SVHN and CIFAR10 datasets and show advantages over enhancing CNNs' robustness, high generalization to different models and noise levels.
SciA11y: Converting Scientific Papers to Accessible HTML

We present SciA11y, a system that renders inaccessible scientific paper PDFs into HTML. SciA11y uses machine learning models to extract and understand the content of scientific PDFs, and reorganizes the resulting paper components into a form that better supports skimming and scanning for blind and low vision (BLV) readers. SciA11y adds navigation features such as tagged headings, a table of contents, and bidirectional links between inline citations and references, which allow readers to resolve citations without losing their context. A set of 1.5 million open access papers are processed and available at https://scia11y.org/. This system is a first step in addressing scientific PDF accessibility, and may significantly improve the experience of paper reading for BLV users.
Accountability in AI: From principles to industry-specific accreditation

Recent AI-related scandals have shed a spotlight on accountability in AI, with increasing public interest and concern. This paper draws on literature from public policy and governance to make two contributions. First, we propose an AI accountability ecosystem as a useful lens on the system, with different stakeholders requiring and contributing to specific accountability mechanisms. We argue that the present ecosystem is unbalanced, with a need for improved transparency via AI explainability and adequate documentation and process formalisation to support internal audit, leading up eventually to external accreditation processes. Second, we use a case study in the gambling sector to illustrate in a subset of the overall ecosystem the need for industry-specific accountability principles and processes. We define and evaluate critically the implementation of key accountability principles in the gambling industry, namely addressing algorithmic bias and model explainability, before concluding and discussing directions for future work based on our findings.
Toward reliability in the NISQ era: Robust interval guarantee for quantum measurements on approximate states

Near-term quantum computation holds potential across multiple application domains. However, imperfect preparation and evolution of states due to algorithmic and experimental shortcomings, characteristic in the near-term implementation, would typically result in measurement outcomes deviating from the ideal setting. It is thus crucial for any near-term application to quantify and bound these output errors. We address this need by deriving robustness intervals which are guaranteed to contain the output in the ideal setting. The first type of interval is based on formulating robustness bounds as semidefinite programs, and uses only the first moment and the fidelity to the ideal state. Furthermore, we consider higher statistical moments of the observable and generalize bounds for pure states based on the non-negativity of Gram matrices to mixed states, thus enabling their applicability in the NISQ era where noisy scenarios are prevalent. Finally, we demonstrate our results in the context of the variational quantum eigensolver (VQE) on noisy and noiseless simulations.
Artificial Intelligence Trends: Insights for Digital Economy Policymakers

Artificial intelligence (AI) is reshaping the economy, redefining the industry and the services sector. It is a disruptive technology, contributing to the development of different practices, new business models, and more efficient industrial processes. The literature highlights that AI will be included in all areas of organizations and the personal lives of citizens, and such dynamics are still significantly unstudied. The purpose of this research is to highlight the latest research about AI through a bibliometric analysis, which will include an up-to-date overview of artificial intelligence research on the goals, results, methodologies, and geographic distribution of studies during 2015–2019. AI applications to the industry and services have been growing over the last 5 years, as the research results show. The results include that most studies have concentrated on the effect of artificial intelligence on industries’ productivity potential. Robotics was the area of subject matter most researched. The findings show that the most common AI technologies are predictive analysis, machine learning, and robotics.
Neural Architecture Search for Efficient Uncalibrated Deep Photometric Stereo

We present an automated machine learning approach for uncalibrated photometric stereo (PS). Our work aims at discovering lightweight and computationally efficient PS neural networks with excellent surface normal accuracy. Unlike previous uncalibrated deep PS networks, which are handcrafted and carefully tuned, we leverage differentiable neural architecture search (NAS) strategy to find uncalibrated PS architecture automatically. We begin by defining a discrete search space for a light calibration network and a normal estimation network, respectively. We then perform a continuous relaxation of this search space and present a gradient-based optimization strategy to find an efficient light calibration and normal estimation network. Directly applying the NAS methodology to uncalibrated PS is not straightforward as certain task-specific constraints must be satisfied, which we impose explicitly. Moreover, we search for and train the two networks separately to account for the Generalized Bas-Relief (GBR) ambiguity. Extensive experiments on the DiLiGenT dataset show that the automatically searched neural architectures performance compares favorably with the state-of-the-art uncalibrated PS methods while having a lower memory footprint.
Training Gaussian boson sampling by quantum machine learning

We use neural networks to represent the characteristic function of many-body Gaussian states in the quantum phase space. By a pullback mechanism, we model transformations due to unitary operators as linear layers that can be cascaded to simulate complex multi-particle processes. We use the layered neural networks for non-classical light propagation in random interferometers, and compute boson pattern probabilities by automatic differentiation. We also demonstrate that multi-particle events in Gaussian boson sampling can be optimized by a proper design and training of the neural network weights. The results are potentially useful to the creation of new sources and complex circuits for quantum technologies.
Adversarially-trained Hierarchical Feature Extractor for Vehicle Re-identification

Vehicle Re-identification (Re-ID) aims to retrieve all instances of query vehicle images present in an image pool. However viewpoint, illumination, and occlusion variations along with subtle differences between two unique images pose a significant challenge towards achieving an effective system. In this paper, we emphasize upon enhancing the performance of visual feature based ReID system by improving feature embedding quality and propose (1) an attention-guided hierarchical feature extractor (HFE) that leverages the structure of a backbone CNN to extract coarse and fine-grained features and (2) to train the proposed network within a hard negative adversarial framework that generates samples exhibiting extreme variations, encouraging the network to extract important distinguishing features across varying scales. To demonstrate the effectiveness of the proposed framework we use VERI-Wild, VRIC and Veri-776 datasets that exhibit extreme intra-class and minute inter-class differences and achieve state-of-the-art (SoTA) performance. Codes related to this paper are publicly available at https://github.com/PS06/VReID.
An End-to-End Data Pipeline for Managing Learning Analytics

Despite the importance of learning analytics in digital education, there is limited support for researchers in education to generate, access, and share experimental data while complying with ethical and privacy legislation. We propose a set of related tools that support researchers with these tasks and present a blueprint for how these tools can be integrated with existing platforms, enabling researchers to run studies within learning environments, adhere to legal and ethical privacy frameworks, and share their anonymous or anonymized data with a wider audience. We demonstrate the integration of these features into an existing online learning platform.
Precise Forecasting of Sky Images Using Spatial Warping

The intermittency of solar power, due to occlusion from cloud cover, is one of the key factors inhibiting its widespread use in both commercial and residential settings. Hence, real-time forecasting of solar irradiance for grid-connected photovoltaic systems is necessary to schedule and allocate resources across the grid. Ground-based imagers that capture wide field-of-view images of the sky are commonly used to monitor cloud movement around a particular site in an effort to forecast solar irradiance. However, these wide FOV imagers capture a distorted image of sky image, where regions near the horizon are heavily compressed. This hinders the ability to precisely predict cloud motion near the horizon which especially affects prediction over longer time horizons. In this work, we combat the aforementioned constraint by introducing a deep learning method to predict a future sky image frame with higher resolution than previous methods. Our main contribution is to derive an optimal warping method to counter the adverse affects of clouds at the horizon, and learn a framework for future sky image prediction which better determines cloud evolution for longer time horizons.
Improved Byzantine Fault-Tolerant Algorithm Based on Alliance Chain

Alliance chain is a typical multicenter block chain and is easily implemented, so it is supported by more and more enterprises and governments. This paper analyzes the advantages and disadvantages of the Practical Byzantine Fault Tolerance (PBFT) in the alliance chain application scene. Aiming at the low efficiency of multinode consensus of the PBFT algorithm, the C-Raft-PBFT consensus algorithm is proposed. By integrating the Raft algorithm and the PBFT algorithm with the credit mechanism, designing node credit evaluation and grading protocols, and increasing Byzantine node detection based on feedback mechanism and other methods, the system efficiency is improved. The experiment results show that the improved algorithm has better throughput and lower delay, and the system’s fault tolerance is also improved. Among them, the delay is reduced by 1.93 seconds on average; in the case of an increase in system nodes, the number of nodes in the experimental data is between 200 and 225, and the throughput is increased by 6.46% on average.
Multi-Layer Aggregate Verification for IoT Blockchain

We design a Multi-Layer Aggregate Verification (MLAV) solution to improve supply chain management with IoT Blockchain devices. We apply MLAV to IoT Blockchain applications in Agriculture 4.0 to demonstrate the feasibility of our solutions and models. In the current Agriculture 4.0 structure, large companies have successfully applied blockchain solutions and ecosystems for tracking and tracing agricultural produce, achieving transparency, traceability, and digitalization. However, these existing blockchain solutions are not comprehensive. First, the upstream nodes they serve are all large-scale production suppliers, and smallholders are not taken into consideration. In order to solve this problem, we use a multi-layer architecture that serves three purposes: facilitating smallholders in joining the agricultural blockchain as equal-opportunity nodes, uploading of production activity data, and reducing costs (ex. Ethereum gas fee). Second, the majority of IoT blockchains adopt an ID-based signature scheme in IoT devices, which frequently has lower efficiency. In applying aggregate verification, we may effectively increase ID-based verification efficiency while processing large clusters of data transferred by IoT devices. Finally, we design a blockchain management framework using smart contracts to facilitate the financing of upstream producers.
Cooperative coevolutionary algorithm with resource allocation strategies to minimize unnecessary computations

In this paper, we propose a new computational resource allocation (CRA)-based cooperative coevolutionary (CC) algorithm, called ECCRA. To effectively allocate the computational resources into the sub-problems, ECCRA compensatively utilizes various strategies: (i) evaluate a degree of contribution for each sub-problem; (ii) extricate the stagnant sub-problems from any local minimums; (iii) allocate individuals adaptively according to a size of each sub-problem and its contribution; (iv) prune the unpromising sub-problems from the evolution process; and (v) utilize the multi-armed bandit (MAB)-based selection method to choose various sub-problems extensively. In the experiments, ECCRA achieved best optimization results for the CEC'2010 benchmark problems. Moreover, ECCRA showed notable optimization performance for imbalanced problems in the CEC'2013 benchmark suite. Thus, we found that ECCRA could considerably outperform the existing CRA-based CC algorithms in terms of the optimization quality and convergence.
Fast k-NN Graph Construction by GPU based NN-Descent

NN-Descent is a classic k-NN graph construction approach. It is still widely employed in machine learning, computer vision, and information retrieval tasks due to its efficiency and genericness. However, the current design only works well on CPU. In this paper, NN-Descent has been redesigned to adapt to the GPU architecture. A new graph update strategy called selective update is proposed. It reduces the data exchange between GPU cores and GPU global memory significantly, which is the processing bottleneck under GPU computation architecture. This redesign leads to full exploitation of the parallelism of the GPU hardware. In the meantime, the genericness, as well as the simplicity of NN-Descent, are well-preserved. Moreover, a procedure that allows to k-NN graph to be merged efficiently on GPU is proposed. It makes the construction of high-quality k-NN graphs for out-of-GPU-memory datasets tractable. Our approach is 100-250× faster than the single-thread NN-Descent and is 2.5-5× faster than the existing GPU-based approaches as we tested on million as well as billion scale datasets.
Edge-Guided Near-Eye Image Analysis for Head Mounted Displays

Eye tracking provides an effective way for interaction in Augmented Reality (AR) Head Mounted Displays (HMDs). Current eye tracking techniques for AR HMDs require eye segmentation and ellipse fitting under near-infrared illumination. However, due to the low contrast between sclera and iris regions and unpredictable reflections, it is still challenging to accomplish accurate iris/pupil segmentation and the corresponding ellipse fitting tasks. In this paper, inspired by the fact that most essential information is encoded in the edge areas, we propose a novel near-eye image analysis method with edge maps as guidance. Specifically, we first utilize an Edge Extraction Network ($E^{2}-$Net) to predict high-quality edge maps, which only contain eyelids and iris/pupil contours without other undesired edges. Then we feed the edge maps into an Edge-Guided Segmentation and Fitting Network (ESF-Net) for accurate segmentation and ellipse fitting. Extensive experimental results demonstrate that our method outperforms current state-of-the-art methods in near-eye image segmentation and ellipse fitting tasks, based on which we present applications of eye tracking with AR HMD.
Text Detection and Recognition by using CNNs in the Austro-Hungarian Historical Military Mapping Survey

Historical maps include precious data about historical, geographical and economic perspectives of a period. However, several unique challenges and opportunities accompany historical maps compared to modern maps, such as low-quality images, degraded manuscripts and the huge quantity of non-annotated digital map collections. In the recent decade, Convolutional Neural Networks (CNNs) are applied to solve various image processing problems, but they need enormous annotated data to have accurate results. In this work, we annotated text regions of the Third Military Mapping Survey of Austria-Hungary historical map series conducted between 1884 and 1918 manually and made them accessible for researchers. Then, we detected the pixel-wise positions of text regions by employing the deep neural network architecture and recognized them with encouraging error rates.