Reviewerly/exHarmony huggingface dataset:
Global Solar Insolation Estimation and Investigation: A Case Study of Various Nations and Cities

Solar insolation is sun radiation converted to electrical energy and injected into the utility network using grid-connected solar power plants. Recently, in many nations’ electricity grids, the share of power generated using solar photovoltaic (PV) power plants is rapidly increasing. In this case study, solar insolation levels on different latitudes with fix & two seasonal tilt angles have been measured using Meteonorm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> software. For these latitudes, fourteen airport locations have been chosen across the globe. From the case study, it has been evident that a double seasonal tilt angle provides more global insolation as compared to fix tilt angle with a suitable azimuth angle. There is also a reduction in insolation level at fixed and two seasonal tilt angles if going away from the equator. Further, up to three seasonal tilt angles, there is sufficient enhancement has been observed in solar insolation. It is also analyzed that for multiple seasonal tilt angle based solar PV Plant, month-wise tilt angle selection is more important. Further Average, Rectangular, and Trapezoidal methods have been used for estimation of cumulative inclined insolation for a specific period and compared. From the comparison, the trapezoidal method has been found optimal for accurate estimation of cumulative inclined insolation.
Deep Face Age Progression: A Survey

Face Age Progression (FAP) refers to synthesizing face images while simulating ageing effects, thus enabling predicting the future appearance of an individual.The generation of age-progressed face images brings benefits for various applications, ranging from face recognition systems to forensic investigations and digital entertainment.In particular, the recent success achieved with deep generative networks significantly leveraged the quality of age-synthesized face images in terms of visual fidelity, ageing accuracy and identity preservation.However, the high number of contributions in recent years requires systematically structuring new findings and ideas to identify a common taxonomy, accelerate future research and reduce redundancy.Therefore, we present a comparative analysis of recent deep learning based face age progression methods for both adult and child-based face ageing, broken down into three high-level concepts: translation-based, condition-based, and sequence-based FAP.Further, we offer a comprehensive summary of the most common performance evaluation techniques, cross-age datasets, and open challenges to steer future research in the right direction.
Heimdall: an AI-based infrastructure for traffic monitoring and anomalies detection

Since their appearance, Smart Cities have aimed at improving the daily life of people, helping to make public services smarter and more efficient. Several of these services are often intended to provide better security conditions for citizens and drivers. In this vein, we present Heimdall, an AI-based video surveillance system for traffic monitoring and anomalies detection. The proposed system features three main tiers: a ground level, consisting of a set of smart lampposts equipped with cameras and sensors, and an advanced AI unit for detecting accidents and traffic anomalies in real time; a territorial level, which integrates and combines the information collected from the different lampposts, and cross-correlates it with external data sources, in order to coordinate and handle warnings and alerts; a training level, in charge of continuously improving the accuracy of the modules that have to sense the environment. Finally, we propose and discuss an early experimental approach for the detection of anomalies, based on a Faster R-CNN, and adopted in the proposed infrastructure.
An Experimental Study of Quantitative Evaluations on Saliency Methods

It has been long debated that eXplainable AI (XAI) is an important technology for model and data exploration, validation, and debugging. To deploy XAI into actual systems, an executable and comprehensive evaluation of the quality of generated explanation is highly in demand. In this paper, we briefly summarize the status quo of the quantitative metrics of different properties of XAI including evaluation on faithfulness, localization, sensitivity check, and stability. With an exhaustive experimental study based on them, we conclude that among all the typical methods we compare, no single explanation method dominates others in all metrics. Nonetheless, Gradient-weighted Class Activation Mapping (Grad-CAM) and Randomly Input Sampling for Explanation (RISE) perform fairly well in most of the metrics. We further present a novel utilization of the evaluation results to diagnose the classification bases for models. Hopefully, this valuable work could serve as a guide for future research.
Financial Feature Embedding with Knowledge Representation Learning for Financial Statement Fraud Detection

Identifying fraudulent financial statement is critical for capital market regulation and is generally formulated as a classification problem. Feature selection in traditional machine learning methods does not consider correlation information among financial features which may influence performance of classifiers. To explore correlation information on conducting financial statement fraud detection (FSFD), we combine traditional features with knowledge graph models, and learn new representations enriched with feature embedding of various financial categories. These feature relations defined by correlation types may form knowledge graphs with features as nodes and correlation relations as edges. Experimentations demonstrate that financial feature representations with correlation information significantly improve classification performances for SVM and K-NN, marginally better than decision trees and logistic regression, but not outperforming naive Bayes (Kernel).
Decentralised Peer-to-Peer Crop Insurance

Agriculture is the lifeline of Indian economy which contributes to 22% of the GDP, provides 58% employment, and sustains 69% of the population. However, agricultural production comes with various risk factors beyond human control. Farmers are highly exposed to risk and it is aggravated by the limited span of risk mitigation instruments such as insurance where there is a lack of participation from the private sector and the claim payout procedure is very lengthy, due to which the farmer cannot initiate new cropping activity or it is delayed. There is no proper and adequate provision of security for crops grown by Indian farmers and despite increasing vulnerability, they have not yet fully developed the capacity to mitigate risk effectively. There is a need to design innovative insurance products to balance the interests of the insurer as well as the insured. With the Prime Minister's call to be "self sufficient" or "aatmanirbhar", in this paper we present a decentralised peer-to-peer crop insurance framework which enables to overcome the current disadvantages by securing farmers' interests, eliminating intermediaries and providing a secure, standardized, and transparent system that does not hinder any information from stakeholders. Through blockchain technology, the system promotes trust in a trust less environment wherein the farmers and the private investors go into a contract. The investors pool their investments and provide cover for the farmers, thus help the nation become self sufficient and help the farmers prosper.
A Framework for Evaluating Agricultural Ontologies

An ontology is a formal representation of domain knowledge, which can be interpreted by machines. In recent years, ontologies have become a major tool for domain knowledge representation and a core component of many knowledge management systems, decision-support systems and other intelligent systems, inter alia, in the context of agriculture. A review of the existing literature on agricultural ontologies, however, reveals that most of the studies, which propose agricultural ontologies, are lacking an explicit evaluation procedure. This is undesired because without well-structured evaluation processes, it is difficult to consider the value of ontologies to research and practice. Moreover, it is difficult to rely on such ontologies and share them on the Semantic Web or between semantic-aware applications. With the growing number of ontology-based agricultural systems and the increasing popularity of the Semantic Web, it becomes essential that such evaluation methods are applied during the ontology development process. Our work contributes to the literature on agricultural ontologies by presenting a framework that guides the selection of suitable evaluation methods, which seems to be missing from most existing studies on agricultural ontologies. The framework supports the matching of appropriate evaluation methods for a given ontology based on the ontology’s purpose.
On the Modelling of Tethered Mobile Robots as Redundant Manipulators

Controlling a chain of tethered mobile robots (TMRs) can be a challenging task. This kind of system can be considered kinematically as an open-chain robotic arm where the mobile robots are considered as a revolute joint and the tether is considered as a variable length link, using a prismatic joint. Thus, the TMRs problem is decoupled into two parallel problems: the equivalent robotic manipulator control and the tether shape computation. Kinematic redundancy is exploited in order to coordinate the motion of all mobile robots forming the chain, expressing the constraints acting on the mobile robots as secondary tasks for the equivalent robotic arm. Implementation in the Gazebo simulation environment shows that the methodology is capable of controlling the chain of TMRs in cluttered environments.
SID: Incremental learning for anchor-free object detection via Selective and Inter-related Distillation

Incremental learning requires a model to continually learn new tasks from streaming data. However, traditional fine-tuning of a well-trained deep neural network on a new task will dramatically degrade performance on the old task — a problem known as catastrophic forgetting. In this paper, we address this issue in the context of anchor-free object detection, which is a new trend in computer vision as it is simple, fast, and flexible. Simply adapting current incremental learning strategies fails on these anchor-free detectors due to lack of consideration of their specific model structures. To deal with the challenges of incremental learning on anchor-free object detectors, we propose a novel incremental learning paradigm called Selective and Inter-related Distillation (SID). In addition, a novel evaluation metric is proposed to better assess the performance of detectors under incremental learning conditions. By selective distilling at the proper locations and further transferring additional instance relation knowledge, our method demonstrates significant advantages on the benchmark datasets PASCAL VOC and COCO. • We explore incremental detection on anchor-free fully convolutional object detectors. • A selective and inter-related distillation strategy is proposed. • A new evaluation metric is proposed to better evaluate incremental detection results. • We demonstrate the superior performance of our method on benchmark datasets.
Flood Detection using Semantic Segmentation and Multimodal Data Fusion

Real-time flood detection and notifying the citizens about its risk is of utmost importance. This work discusses the real-time deployment of one such notification system called Flood-Bot. FloodBot is a vision-powered flood detection and notification prototype deployed in a flash flood-prone Ellicott City, Maryland. We discuss the real-time deployment of FloodBot and our approach in detecting the flood event using semantic segmentation and multimodal data fusion. We implement the state-of-the-art semantic segmentation model U-Net and its modified version to track landmass with an accuracy of above 80%. We augment the parsed scene data with actual flood level sensor readings and ambient weather data for better scene representation. We validate the deep learning model's outcome using the flood sensor before posting risk message into social media. We then articulate the learning and challenges around our deployment from June - November 2020.
Isolation forests and landmarking-based representations for clustering algorithm recommendation using meta-learning

The data clustering problem can be described as the task of organizing data into groups, where in each group the objects share some similar attributes. Most of the problems clustering algorithms address do not have a prior solution. This paper addresses the algorithm selection challenge for data clustering, while taking the difficulty in evaluating clustering solutions into account. We present a new meta-learning method for recommending the most suitable clustering algorithm for a dataset. Based on concepts from the isolation forest algorithm, we propose a new similarity measure between datasets. Our proposed dataset characterization methods generate an embedding for a dataset using this similarity measure, which is then used to improve the quality of the problem’s characterization. The method utilizes landmarking concepts to characterize the dataset and then, inspired by the DeepFM algorithm, applies meta-learning to rank the candidate algorithms that are expected to perform the best for the current dataset. This ranking could, among other things, support AutoML systems. Our approach is evaluated on a corpus of 100 publicly available benchmark datasets. We compare our method’s ranking performance to that of existing meta-learning methods and show the dominance of our method in terms of predictive performance and computational complexity.
The values of web archives

This article considers how the development, promotion and adoption of a set of core values for web archives, linked to principles of "good governance", will help them to tackle the challenges of sustainability, accountability and inclusiveness that are central to their long-term societal and cultural worth. It outlines the work that has already been done to address these questions, as web archiving begins to move out of its establishment phase, and then discusses seven key principles of good governance that might be adapted by and embedded within web archives: participation, consensus, accountability, transparency, effectiveness and efficiency, inclusivity and legality. The article concludes with a call to action for researchers and archivists to co-create the core values for web archives that will be required if they are to remain a vital part of our cultural heritage infrastructure.
Online Arabic Handwriting Recognition Using Graphemes Segmentation and Deep Learning Recurrent Neural Networks

Currently, many research initiatives in online handwriting recognition field have been investigated. The challenge is more earnest for Arabic scripts due to some constraints such as the existence of similar shapes of characters and their inherent cursiveness, etc. The present study highlights a new framework for online Arabic handwriting recognition based on graphemes segmentation and two existing models of recurrent neural network (RNN)—long short-term memory (LSTM) and Bidirectional LSTM (BLSTM). After handwriting signal preprocessing, the developed algorithm proceeds by the detection of the script baseline considering the accordance between the alignment of its trajectory points and their tangent directions. Then, the handwritten words or pseudo-words are segmented in continuous part called graphemes delimited by the points of ligature bottoms neighboring the baseline. Next, each grapheme is described by a set of relevant features combining static and dynamic features obtained by employing beta-elliptic model, geometric features that contain Fourier descriptors for trajectory shape modeling and other normalized parameters modeling the grapheme dimensions, positions with respect to the baseline and the assignment diacritics codes. The extracted sequences of features vectors are subsequently used as input for the recognition module employing both LSTM and BLSTM version of RNN. Experimental results using the benchmarking ADAB database of online Arabic handwriting show the performance of the proposed system which exceeds the best result of other advanced states of-the-art works.
HUMANNET—A Two-Tiered Deep Neural Network Architecture for Self-Occluding Humanoid Pose Reconstruction

Majority of current research focuses on a single static object reconstruction from a given pointcloud. However, the existing approaches are not applicable to real world applications such as dynamic and morphing scene reconstruction. To solve this, we propose a novel two-tiered deep neural network architecture, which is capable of reconstructing self-obstructed human-like morphing shapes from a depth frame in conjunction with cameras intrinsic parameters. The tests were performed using on custom dataset generated using a combination of AMASS and MoVi datasets. The proposed network achieved Jaccards' Index of 0.7907 for the first tier, which is used to extract region of interest from the point cloud. The second tier of the network has achieved Earth Mover's distance of 0.0256 and Chamfer distance of 0.276, indicating good experimental results. Further, subjective reconstruction results inspection shows strong predictive capabilities of the network, with the solution being able to reconstruct limb positions from very few object details.
Golden Grain: Building a Secure and Decentralized Model Marketplace for MLaaS

ML-as-a-service (MLaaS) becomes increasingly popular and revolutionizes the lives of people. A natural requirement for MLaaS is, however, to provide highly accurate prediction services. To achieve this, current MLaaS systems integrate and combine multiple well-trained models in their services. Yet, in reality, there is no easy way for MLaaS providers, especially for startups, to collect sufficiently well-trained models from individual developers, due to the lack of incentives. In this article, we aim to fill this gap by building up a model marketplace, called as Golden Grain, to facilitate model sharing, which enforces the fair model-money swapping process between individual developers and MLaaS providers. Specifically, we deploy the swapping process on the blockchain, and further introduce a blockchain-empowered model benchmarking process for transparently determining the model prices according to their authentic performances, so as to motivate the faithful contributions of well-trained models. Especially, to ease the blockchain overhead for model benchmarking, our marketplace carefully offloads the heavy computation and designs a secure off-chain on-chain interaction protocol based on a trusted execution environment (TEE), for ensuring both the integrity and authenticity of benchmarking. We implement a prototype of our Golden Grain on the Ethereum blockchain, and conduct extensive experiments using standard benchmark datasets to demonstrate the practically affordable performance of our design.
Enhancing Robustness Verification for Deep Neural Networks via Symbolic Propagation

Abstract Deep neural networks (DNNs) have been shown lack of robustness, as they are vulnerable to small perturbations on the inputs. This has led to safety concerns on applying DNNs to safety-critical domains. Several verification approaches based on constraint solving have been developed to automatically prove or disprove safety properties for DNNs. However, these approaches suffer from the scalability problem, i.e., only small DNNs can be handled. To deal with this, abstraction based approaches have been proposed, but are unfortunately facing the precision problem, i.e., the obtained bounds are often loose. In this paper, we focus on a variety of local robustness properties and a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mi>δ</mml:mi><mml:mo>,</mml:mo><mml:mi>ε</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:math> -global robustness property of DNNs, and investigate novel strategies to combine the constraint solving and abstraction-based approaches to work with these properties: We propose a method to verify local robustness, which improves a recent proposal of analyzing DNNs through the classic abstract interpretation technique, by a novel symbolic propagation technique. Specifically, the values of neurons are represented symbolically and propagated from the input layer to the output layer, on top of the underlying abstract domains. It achieves significantly higher precision and thus can prove more properties. We propose a Lipschitz constant based verification framework. By utilising Lipschitz constants solved by semidefinite programming, we can prove global robustness of DNNs. We show how the Lipschitz constant can be tightened if it is restricted to small regions. A tightened Lipschitz constantcan be helpful in proving local robustness properties. Furthermore, a global Lipschitz constant can be used to accelerate batch local robustness verification, and thus support the verification of global robustness. We show how the proposed abstract interpretation and Lipschitz constant based approaches can benefit from each other to obtain more precise results. Moreover, they can be also exploited and combined to improve constraints based approach. We implement our methods in the tool PRODeep, and conduct detailed experimental results on several benchmarks
QuantumFed: A Federated Learning Framework for Collaborative Quantum Training

With the fast development of quantum computing and deep learning, quantum neural networks have attracted great attention recently. By leveraging the power of quantum computing, deep neural networks can potentially overcome computational power limitations in classic machine learning. However, when multiple quantum machines wish to train a global model using the local data on each machine, it may be very difficult to copy the data into one machine and train the model. Therefore, a collaborative quantum neural network framework is necessary. In this article, we borrow the core idea of federated learning to propose QuantumFed, a quantum federated learning framework to have multiple quantum nodes with local quantum data train a mode together. Our experiments show the feasibility and robustness of our framework.
Path planning for autonomous underwater vehicles based on interval optimization in uncertain flow fields

The predictive uncertainty of ocean currents needs to be considered in the path planning of autonomous underwater vehicles (AUV) in oceanic environments. An interval optimization (IO) scheme is presented in this paper to plan time-optimal paths for AUVs operating in oceanic environments with dynamic and uncertain flow fields; interval currents are established to process the uncertainty of forecasted ocean flows, and the IO scheme is used to search for time-optimal paths in the interval current fields. The IO scheme is a two-layer framework: In the outer layer, quantum-behaved particle swarm optimization is used to generate and optimize candidate paths. In the inner layer, the interval response of the candidate path from the outer layer is calculated under interval current fields, and they are converted into a time-optimal fitness value in accordance with the possibility degree of interval numbers. The influence of predictive uncertainty is analyzed via simulation, and the robustness of the IO scheme is verified.