Reviewerly/exHarmony huggingface dataset:
Thanka Mural Inpainting Based on Multi-Scale Adaptive Partial Convolution and Stroke-Like Mask

Thanka murals are important cultural heritages of Tibet, but many precious murals were damaged during history. Thanka mural restoration is very important for the protection of Tibetan cultural heritage. Partial convolution has great potential for Thanka mural restoration due to its outstanding performance for inpainting irregular holes. However, three challenges prevent the existing partial convolution-based methods from solving Thanka restoration problems: 1) the features of multi-scale objects in Thanka murals cannot be extracted correctly because of single-scale partial convolution; 2) the stroke-like Thanka inpainting mode cannot be effectively simulated and learned by existing rectangular or arbitrary masks; and 3) the original content of damaged Thanka murals cannot be restored. To resolve these problems, we propose a Thanka mural inpainting method based on multi-scale adaptive partial convolution and stroke-like masks. The proposed method consists of three parts: 1) a kernel-level multi-scale adaptive partial convolution (MAPConv) to accurately discriminate valid pixels from invalid pixels, and to extract the features of multi-scale objects; 2) a parameter-configurable stroke-like mask generation method to simulate and learn the stroke-like Thanka inpainting mode; and 3) a 2-phase learning framework based on MAPConv Unet and different loss functions to restore the original content of Thanka murals. Experiments on both simulated and real damages of Thanka murals demonstrated that our approach works well on a small dataset (N=2780), generates realistic mural content, and restores the damaged Thanka murals with high speed (600 ms for multiple holes in 512×512 images). The proposed end-to-end method can be applied to other small datasets-based inpainting tasks.
Building blocks of sharding blockchain systems: Concepts, approaches, and open problems

Sharding is the prevalent approach to breaking the trilemma of simultaneously achieving decentralization, security, and scalability in traditional blockchain systems, which are implemented as replicated state machines relying on atomic broadcast for consensus on an immutable chain of valid transactions. Sharding is to be understood broadly as techniques for dynamically partitioning nodes in a blockchain system into subsets (shards) that perform storage, communication, and computation tasks without fine-grained synchronization with each other. Despite much recent research on sharding blockchains, much remains to be explored in the design space of these systems. Towards that aim, we conduct a systematic analysis of existing sharding blockchain systems and derive a conceptual decomposition of their architecture into functional components and the underlying assumptions about system models and attackers they are built on. The functional components identified are node selection, epoch randomness, node assignment, intra-shard consensus, cross-shard transaction processing, shard reconfiguration, and motivation mechanism. We describe interfaces, functionality, and properties of each component and show how they compose into a sharding blockchain system. For each component, we systematically review existing approaches, identify potential and open problems, and propose future research directions. We focus on potential security attacks and performance problems, including system throughput and latency concerns such as confirmation delays. We believe our modular architectural decomposition and in-depth analysis of each component, based on a comprehensive literature study, provides a systematic basis for conceptualizing state-of-the-art sharding blockchain systems, proving or improving security and performance properties of components, and developing new sharding blockchain system designs.
Edge Artificial Intelligence for Industrial Internet of Things Applications: An Industrial Edge Intelligence Solution

In this article, we study edge artificial intelligence (AI) for industrial Internet of Things (IIoT) applications. We discuss edge AI technology, which is considered the combination of AI with edge computing, and provide an overview of edge AI applications for IIoT networks, where the following three challenges are important to address: 1) personalization, 2) responsiveness, and 3) privacy preservation. To this end, we propose a federated active transfer learning (FATL) model, which through training and testing is able to address those open challenges. Details about the training and testing of the proposed FATL global model are given, including the corresponding simulation setup. This work concludes with a discussion and comparison of the obtained simulation results with existing edge AI training solutions, which provide useful insights about the proposed FATL model. The simulation results highlight how the FATL global model can efficiently address the open challenges of edge AI for future IIoT applications.
Accelerated gradient algorithm for RBF neural network

Gradient-based algorithms are commonly used for training radial basis function neural network (RBFNN). However, one of the challenges in the training process is determining how to avoid vanishing gradient. To solve this problem, an accelerated gradient algorithm (AGA) is designed to improve the learning performance of RBFNN in this paper. First, an indirect detection mechanism, based on the instantaneous gradient decay rate (IGDR) and instantaneous convergence rate (ICR), is developed to identify the vanishing gradient in learning process. Second, an amplification gradient strategy (AGS), which can increase the gradient value of learning parameters, is designed to accelerate the learning speed of RBFNN. Third, the analysis of AGA-based RBFNN (AGA-RBFNN) is given to guarantee the successful application. Finally, some benchmark and real problems are used to illustrate the effectiveness of AGA-RBFNN. The results demonstrate the effectiveness of AGA-RBFNN.
Deep Echo State Network With Multiple Adaptive Reservoirs for Time Series Prediction

In this article, considering the plasticity of reservoir, a deep echo state network with multiple adaptive reservoirs in series configuration, called MAR-DESN, is proposed for time series prediction. First, according to the characteristics of input signals and reservoir states, the number of reservoirs of MAR-DESN and each reservoir size can be automatically determined by using the principal component analysis. Second, a parameter optimization method based on Broyden–Fletcher–Goldfarb–Shanno quasi-Newton algorithm is given to optimize the reservoir parameters of MAR-DESN. Third, a sufficient condition for the uniform echo state property of MAR-DESN is given, such that the MAR-DESN can be stably applied in different applications. Finally, three examples are used to verify the effectiveness of MAR-DESN.
Diagnostic Performance of an Artificial Intelligence System in Breast Ultrasound

We study the performance of an artificial intelligence (AI) program designed to assist radiologists in the diagnosis of breast cancer, relative to measures obtained from conventional readings by radiologists.A total of 10 radiologists read a curated, anonymized group of 299 breast ultrasound images that contained at least one suspicious lesion and for which a final diagnosis was independently determined. Separately, the AI program was initialized by a lead radiologist and the computed results compared against those of the radiologists.The AI program's diagnoses of breast lesions had concordance with the 10 radiologists' readings across a number of BI-RADS descriptors. The sensitivity, specificity, and accuracy of the AI program's diagnosis of benign versus malignant was above 0.8, in agreement with the highest performing radiologists and commensurate with recent studies.The trained AI program can contribute to accuracy of breast cancer diagnoses with ultrasound.
FeARH: Federated machine learning with anonymous random hybridization on electronic medical records

• A new algorithm is proposed, federated machine learning with anonymous random hybridization (FeARH), to deal with untrustworthy situations. • Hybridizing parameters to degenrate connection between machine learning models’parameters and private medical information before sharing with other institutions. • Reaching comparable result as centralized machine learning and original federated machine learning. Electrical medical records are restricted and difficult to centralize for machine learning model training due to privacy and regulatory issues. One solution is to train models in a distributed manner that involves many parties in the process. However, sometimes certain parties are not trustable, and in this project, we aim to propose an alternative method to traditional federated learning with central analyzer in order to conduct training in a situation without a trustable central analyzer. The proposed algorithm is called “federated machine learning with anonymous random hybridization (abbreviated as ‘FeARH’)”, using mainly hybridization algorithm to degenerate the integration of connections between medical record data and models’ parameters by adding randomization into the parameter sets shared to other parties. Based on our experiment, our new algorithm has similar AUCROC and AUCPR results compared with machine learning in a centralized manner and original federated machine learning.
Methodological performance analysis applied to a novel IIoT access control system based on permissioned blockchain

Considering that RFID technology presents a significant growth in IIoT environments, industrial manufacturing is being one of the most benefited by this growth. As growth implies increased security risks, access control systems have emerged as an essential solution for IIoT environments and particularly in RFID systems. Considering Hyperledger Fabric Blockchain as a modular project oriented to environments with high level of performance in terms of speed and scalability, our manuscript proposes a performance analysis based on a methodological framework to demonstrate the viability of a comprehensive access control system which includes Identification, Authentication, Authorization and Accountability/Auditing based on the permissioned blockchain Hyperledger Fabric Blockchain. Our proposal promotes a novel approach to reliable data privacy, based on private data collection solution promoted by Hyperledger Fabric to implement the registration phase of our access control system. In this regard, the feasibility of using private data collection with respect to a private data local management solution is demonstrated. Finally, thanks to the modularity promoted by Hyperledger Fabric Blockchain, we define the optimal network model for our use case. To demonstrate these approaches, several experiments are conducted, based on a proposed methodological performance framework.
Adaptive and extendable control of unmanned surface vehicle formations using distributed deep reinforcement learning

Future ocean exploration will be dominated by a large-scale deployment of marine robots such as unmanned surface vehicles (USVs). Without the involvement of human operators, USVs exploit oceans, especially the complex marine environments, in an unprecedented way with an increased mission efficiency. However, current autonomy level of USVs is still limited, and the majority of vessels are being remotely controlled. To address such an issue, artificial intelligence (AI) such as reinforcement learning can effectively equip USVs with high-level intelligence and consequently achieve full autonomous operation. Also, by adopting the concept of multi-agent intelligence, future trend of USV operations is to use them as a formation fleet. Current researches in USV formation control are largely based upon classical control theories such as PID, backstepping and model predictive control methods with the impact by using advanced AI technologies unclear. This paper, therefore, paves the way in this area by proposing a distributed deep reinforcement learning algorithm for USV formations. More importantly, using the proposed algorithm USV formations can learn two critical abilities, i.e. adaptability and extendibility that enable formations to arbitrarily increase the number of USVs or change formation shapes. The effectiveness of algorithms has been verified and validated through a number of computer-based simulations.
VeriML: Enabling Integrity Assurances and Fair Payments for Machine Learning as a Service

Machine Learning as a Service (MLaaS) allows clients with limited resources to outsource their expensive ML tasks to powerful servers. Despite the huge benefits, current MLaaS solutions still lack strong assurances on: 1) service correctness (i.e., whether the MLaaS works as expected); 2) trustworthy accounting (i.e., whether the bill for the MLaaS resource consumption is correctly accounted); 3) fair payment (i.e., whether a client gets the entire MLaaS result before making the payment). Without these assurances, unfaithful service providers can return improperly-executed ML task results or partially-trained ML models while asking for over-claimed rewards. Moreover, it is hard to argue for wide adoption of MLaaS to both the client and the service provider, especially in the open market without a trusted third party. In this article, we present VeriML, a novel and efficient framework to bring integrity assurances and fair payments to MLaaS. With VeriML, clients can be assured that ML tasks are correctly executed on an untrusted server, and the resource consumption claimed by the service provider equals to the actual workload. We strategically use succinct non-interactive arguments of knowledge (SNARK) on randomly-selected iterations during the ML training phase for efficiency with tunable probabilistic assurance. We also develop multiple ML-specific optimizations to the arithmetic circuit required by SNARK. Our system implements six common algorithms: linear regression, logistic regression, neural network, support vector machine, K-means and decision tree. The experimental results have validated the practical performance of VeriML.
PECAM

As Video Streaming and Analytics (VSA) systems become increasingly popular, serious privacy concerns have risen on exposing too much unnecessary private information to the VSA providers. Yet, it is challenging to protect privacy while still preserving desired VSA features, i.e., effective analytics, forensic support, resource efficiency, and real-time execution. In this paper, we present a VSA privacy enhancement system (PECAM), which addresses the above challenge with no change in the VSA back-end. PECAM leverages a novel Generative Adversarial Network to perform the privacy-enhanced securely-reversible video transformation. PECAM also incorporates a couple of system optimizations into its VSA workflow to reduce network bandwidth usage and enable real-time processing on cameras. We implement our PECAM prototype on commodity hardware and evaluate its performance via both security study and extensive experiments. Results demonstrate that PECAM can effectively enhance the visual privacy of VSA in the presence of an adversary, and its transformed videos, when taken as input for various VSA back-end tasks, maintain a 96% accuracy of corresponding original videos. Additionally, it performs 12.3× and 1.8× better than baseline methods in terms of the computing cost and network bandwidth usage, respectively.
Pattern-enhanced Named Entity Recognition with Distant Supervision

Supervised deep learning methods have achieved state-of-the-art performance on the task of named entity recognition (NER). However, such methods suffer from high cost and low efficiency in training data annotation, leading to highly specialized NER models that cannot be easily adapted to new domains. Recently, distant supervision has been applied to replace human annotation, thanks to the fast development of domain-specific knowledge bases. However, the generated noisy labels pose significant challenges in learning effective neural models with distant supervision. We propose PatNER, a distantly supervised NER model that effectively deals with noisy distant supervision from domain-specific dictionaries. PatNER does not require human-annotated training data but only relies on unlabeled data and incomplete domain-specific dictionaries for distant supervision. It incorporates the distant labeling uncertainty into the neural model training to enhance distant supervision. We go beyond the traditional sequence labeling framework and propose a more effective fuzzy neural model using the tie-or-break tagging scheme for the NER task. Extensive experiments on three benchmark datasets in two domains demonstrate the power of PatNER. Case studies on two additional real-world datasets demonstrate that PatNER improves the distant NER performance in both entity boundary detection and entity type recognition. The results show a great promise in supporting high quality named entity recognition with domain-specific dictionaries on a wide variety of entity types.
Designing a long short-term network for short-term forecasting of global horizontal irradiance

Abstract Long short-term memory (LSTM) models based on specialized deep neural network-based architecture have emerged as an important model for forecasting time-series. However, the literature does not provide clear guidelines for design choices, which affect forecasting performance. Such choices include the need for pre-processing techniques such as deseasonalization, ordering of the input data, network size, batch size, and forecasting horizon. We detail this in the context of short-term forecasting of global horizontal irradiance, an accepted proxy for solar energy. Particularly, short-term forecasting is critical because the cloud conditions change at a sub-hourly having large impacts on incident solar radiation. We conduct an empirical investigation based on data from three solar stations from two climatic zones of India over two seasons. From an application perspective, it may be noted that despite the thrust given to solar energy generation in India, the literature contains few instances of robust studies across climatic zones and seasons. The model thus obtained subsequently outperformed three recent benchmark methods based on random forest, recurrent neural network, and LSTM, respectively, in terms of forecasting accuracy. Our findings underscore the importance of considering the temporal order of the data, lack of any discernible benefit from data pre-processing, the effect of making the LSTM model stateful. It is also found that the number of nodes in an LSTM network, as well as batch size, is influenced by the variability of the input data.
FastUAV-NET: A Multi-UAV Detection Algorithm for Embedded Platforms

In this paper, a real-time deep learning-based framework for detecting and tracking Unmanned Aerial Vehicles (UAVs) in video streams captured by a fixed-wing UAV is proposed. The proposed framework consists of two steps, namely intra-frame multi-UAV detection and the inter-frame multi-UAV tracking. In the detection step, a new multi-scale UAV detection Convolutional Neural Network (CNN) architecture based on a shallow version of You Only Look Once version 3 (YOLOv3-tiny) widened by Inception blocks is designed to extract local and global features from input video streams. Here, the widened multi-UAV detection network architecture is termed as FastUAV-NET and aims to improve UAV detection accuracy while preserving computing time of one-step deep detection algorithms in the context of UAV-UAV tracking. To detect UAVs, the FastUAV-NET architecture uses five inception units and adopts a feature pyramid network to detect UAVs. To obtain a high frame rate, the proposed method is applied to every nth frame and then the detected UAVs are tracked in intermediate frames using scalable Kernel Correlation Filter algorithm. The results on the generated UAV-UAV dataset illustrate that the proposed framework obtains 0.7916 average precision with 29 FPS performance on Jetson-TX2. The results imply that the widening of CNN network is a much more effective way than increasing the depth of CNN and leading to a good trade-off between accurate detection and real-time performance. The FastUAV-NET model will be publicly available to the research community to further advance multi-UAV-UAV detection algorithms.
Subjective databases

Online users are constantly seeking experiences, such as a hotel with clean rooms and a lively bar, or a restaurant for a romantic rendezvous. However, e-commerce search engines only support queries involving objective attributes such as location, price, and cuisine, and any experiential data is relegated to text reviews. In order to support experiential queries, a database system needs to model subjective data. Users should be able to pose queries that specify subjective experiences using their own words, in addition to conditions on the usual objective attributes. This paper introduces OpineDB, a subjective database system that addresses these challenges. We introduce a data model for subjective databases. We describe how OpineDB translates subjective queries against the subjective database schema, which is done by matching the user query phrases to the underlying schema. We also show how the experiential conditions specified by the user can be combined and the results aggregated and ranked. We demonstrate that subjective databases satisfy user needs more effectively and accurately than alternative techniques through experiments with real data of hotel and restaurant reviews.
A quantitative bow-tie cyber risk classification and assessment framework

Cyber-attacks pose a growing threat to global commerce that is increasingly reliant on digital technology to conduct business. Traditional risk assessment and underwriting practices face serious shortcomings when encountered with cyber threats. Conventional assessment frameworks rate risk based on historical frequency and severity of losses incurred, this method is effective for known risks; however, due to the absence of historical data, prove ineffective for assessing cyber risk. This paper proposes a conceptual cyber risk classification and assessment framework, designed to demonstrate the significance of proactive and reactive barriers in reducing companies’ exposure to cyber risk and quantify the risk. This method combines a bow-tie model with a risk matrix to produce a rating based on the likelihood of a cyber-threat occurring and the potential severity of the resulting consequences. The model can accommodate both historical data and expert opinion and previously known frameworks to score the Threats, Barriers and Escalators for the framework. The resultant framework is applied to a large city hospital in Europe. The results highlighted both cyber weaknesses and actions that should be taken to bolster cyber defences. The results provide a quick visual guide that is assessable to both experts and management. It also provides a practical framework that allows insurers to assess risks, visualise areas of concern and record the effectiveness of implementing control barriers.