Reviewerly/exHarmony huggingface dataset:
A compositional approach for modelling SDG7 indicators: Case study applied to electricity access

Monitoring energy indicators has acquired a renewed interest with the 2030 Agenda for Sustainable Development, and specifically with goal 7 (SDG7), which seeks to guarantee universal access to energy. The predominant criteria to monitor SDG7 are given in a set of individual indicators. Along this line, the UN indicators proposed in the 47th session of the UN Statistical commission are a practical starting point. A relevant characteristic of these indicators is that they can be expressed as proportions from a whole, i.e., they are compositions. Notably, directly implementing traditional multivariate models onto indicators that are proportions without an intermediate process can lead to spurious analysis. Here, we aim to assess the application of compositional data analysis(CoDa) to follow up on the temporal trend indicators of the energy sector in the context of SDG7, with a case study for the most affected areas addressing the problem of electricity access. Following CoDa methodology, we first use a log-ratio transformation to bring compositions to real space and then apply three multivariate methods: linear regression, generalized additive models and support vector machine. We also address other characteristic problems of the electricity access indicators, such as data quality, which was treated by considering models with interactions. In sum, CoDa facilitates a controlled management of the parts that make up population based indicators, suggesting that modelling evolution of compositions as individual components – even the standard splitting of country data into rural and urban “access to” indicator – should be avoided.
Using Collection Shards to Study Retrieval Performance Effect Sizes

Despite the bulk of research studying how to more accurately compare the performance of IR systems, less attention is devoted to better understanding the different factors that play a role in such performance and how they interact. This is the case of shards, i.e., partitioning a document collection into sub-parts, which are used for many different purposes, ranging from efficiency to selective search or making test collection evaluation more accurate. In all these cases, there is empirical knowledge supporting the importance of shards, but we lack actual models that allow us to measure the impact of shards on system performance and how they interact with topics and systems. We use the general linear mixed model framework and present a model that encompasses the experimental factors of system, topic, shard, and their interaction effects. This detailed model allows us to more accurately estimate differences between the effect of various factors. We study shards created by a range of methods used in prior work and better explain observations noted in prior work in a principled setting and offer new insights. Notably, we discover that the topic*shard interaction effect, in particular, is a large effect almost globally across all datasets, an observation that, to our knowledge, has not been measured before.
Currency stability using blockchain technology

To date, cryptocurrency prices are volatile and many cryptocurrency developers have adopted ad hoc approaches to stabilize their cryptocurrency price. When these currencies are not 100% backed by other valued assets, part of their price volatility may arise from self-fulfilling expectations of a speculative attack (as in Obstfeld (1996)). We show that an exchange rate policy, which is less than 100% backed and dynamically adjusts in response to traders’ conversion demand eliminates speculative attacks while, under some conditions, preserving much of the desired exchange rate stability. This dynamic exchange rate policy admits a great deal of discretion to and requires commitment by the party implementing the policy. We demonstrate how to implement this policy using the Ethereum network—a smart contract blockchain environment—and how this implementation yields commitment to the policy.
Spatiotemporal Prediction of Tidal Currents Using Gaussian Processes

Abstract Predicting fast tidal currents can be a challenging task. Unlike tidal water levels, currents can vary sharply over short distances. The classical approach of harmonic analysis can analyze data at point locations and there is a need for a method that can handle spatiotemporal data, as well as be robust to the uncertainty and noise inevitable in real‐world measurements. In this work, we present a Bayesian machine learning (ML) approach to tackle the problem. The method is based on Gaussian processes, a nonparametric ML technique that uses a kernel function to capture structures in the data. A case study is performed using data from a validated numerical model simulating the tidal dynamics in the Pentland Firth region, UK. Several sampling strategies are explored and the case where measurement location is changed after every sampling is found to produce the lowest average error in the predictions. We show that the presented methodology using data from just a single moving data source can provide a better spatiotemporal description than traditional techniques using continuous data from a large number of locations. The work can be useful to developers of tidal energy farms, navigation, and other purposes.
Ontology Supporting Model-Based Systems Engineering Based on a GOPPRR Approach

Model-based systems engineering (MBSE) has become a new trend in systems engineering. The core of MBSE is the use of models to formalize product development and the product itself (requirement, architecture, etc.) using different languages throughout the entire lifecycle. However, different tools based on different modeling languages in each development phase lead to considerable deviations during information sharing between the models. In this paper, a GOPPRR approach is proposed to describe system characteristics and system development based on meta-models for different architecture descriptions using meta-meta models including Graph, Object, Point, Property, Role, and Relationship. Moreover, ontology based on W3C Web Ontology Language (OWL) is designed to support the GOPPRR approach for unified model representations and further to implement model integration in the entire lifecycle. Finally, the feasibility of the GOPPRR approach and the ontology formalism supporting MBSE is verified through an auto-braking case of an autonomous driving system.
Feature augmentation for imbalanced classification with conditional mixture WGANs

Heterogeneity of class distribution is an intrinsic property of a real-world dataset. Therefore, imbalanced classification is a popular but challenging task. Several methods exist to address this problem. Notably, the adversarial-based data augmentation method, which aims to directly learn the distribution of minority classes unlike simple data modification, has been applied to the challenging task. While effective, the method focuses on a certain domain and lacks universality, and the generated samples lack diversity due to the mode collapse of Generative Adversarial Networks (GANs). In this paper, we propose a general framework of data augmentation using GANs in feature space for imbalanced classification. The core of the framework comprises conditional mixture WGANs (cMWGANs), which are used to approximate true feature distribution and generate label preserved and diverse features for the minority class of various datasets. We conduct three experiments on SVHN, FER2013, and Amazon Review of Instant Video to demonstrate the versatility of the framework and better performance of our cMWGANs in single feature learning. The results show significant improvement with feature augmentation of cMWGANs.
Pedestrian and cyclist detection based on deep neural network fast R-CNN

In this article, a unified joint detection framework for pedestrian and cyclist is established to realize the joint detection of pedestrian and cyclist targets. Based on the target detection of fast regional convolution neural network, a deep neural network model suitable for pedestrian and cyclist detection is established. Experiments for poor detection results for small-sized targets and complex and changeable background environment; various network improvement schemes such as difficult case extraction, multilayer feature fusion, and multitarget candidate region input were designed to improve detection and to solve the problems of frequent false detections and missed detections in pedestrian and cyclist target detection. Results of experimental verification of the pedestrian and cyclist database established in Beijing’s urban traffic environment showed that the proposed joint detection method for pedestrians and cyclists can realize the stable tracking of joint detection and clearly distinguish different target categories. Therefore, an important basis for the behavior decision of intelligent vehicles is provided.
Assessing color performance of whole‐slide imaging scanners for digital pathology

Abstract The color performance of two commercial whole‐slide imaging (WSI) scanners was compared against the ground truth and a hypothetical monochrome scanner. Three biological tissue slides were used to test the WSI scanners. A multispectral imaging system was developed to obtain the color truth of the biological tissue slides at the pixel level. The hypothetical monochrome scanner was derived from the color truth as a lower bound for comparison. The CIEDE2000 formula was used to measure color errors. Results show that color errors generated by the modern commercial WSI scanner, the legacy commercial WSI scanner, and the monochrome WSI scanner are in the range of [8.4, 13.0], [18.0, 26.33], and [17.4, 17.6] Δ E 00 , respectively. The legacy commercial WSI scanner was outperformed by not only the modern commercial WSI scanner but also by the hypothetical monochrome scanner.
Review of Quantum navigation

With the continuous improvement of the demand for navigation and positioning services, the accuracy and security of traditional navigation systems have become the limits of future applications. The development of quantum technology has become the hope of a new generation of navigation systems. This paper summarizes and analyzes the research status of quantum active positioning system and passive positioning system and their core technologies, and compares their advantages and disadvantages, and the future development trend of the corresponding technology. Finally, based on the analysis of the problems of quantum navigation system, this paper proposes a solution that may be applied in the future, and puts forward the prospect of the development of quantum navigation system.
Convolutional neural networks-based stereo image reversible data hiding method

For copyright and integrity protection of stereo images, a stereo image reversible data hiding (SIRDH) method based on the convolutional neural network (CNN) is presented. To increase the pixel prediction, a CNN-based predictor is designed. Firstly, pixels are divided into two types. For predicting one type of pixels in one view, the other type of pixels in the same view is considered as the low-resolution (LR) image. Secondly, a LR difference view between the LR image and the other view is computed, since the difference view consists of the texture and depth information. Then, CNN is trained to recover the high-precision difference view from the LR difference view, and the high-resolution image is computed to predict pixels accurately. Moreover, the prediction error expansion (PEE) is employed to embed data. Experimental results show that the proposed method is superior to existing SIRDH methods.
A new class of continuous Bayesian networks

In this paper, we introduce a new family of continuous Bayesian networks whose conditional distributions belong to the Tweedie class. This family of distributions is very important in statistical modeling as it includes a variety of density shapes and is widely used in many real-world applications in various fields. It also includes many well-known probability distributions such as Gaussian, Gamma and Inverse-Gaussian. In this context, the constructed Tweedie Bayesian network (TBN), which is based on Tweedie regression models, constitutes a wide and flexible class of Bayesian networks. A full procedure is provided for the learning of the TBN structure and parameters. We also introduce a sensitivity measure relying on the Kullback-Leibler divergence in order to perform a sensitivity analysis and validate the model output. The proposed graphical model is illustrated through both an application to a loan subscribers data set and a simulation study.
Supply Chain Trust

Reports on supply chains as major sources of cybersecurity threats. Companies and organizations increasingly share data, credentials, software code, applications, networks, and infrastructures with “trusted” supply chain partners. Supply chains can be sources of cybervulnerabilities. One estimate has suggested that supply chains account for 80% of all cyber breaches. Insecure supply chains have fostered well-known cyberattacks. In a quest to break large organizations’ networks, cyber-criminals may look beyond the first-tier supply chain partners.
Cloud detection in satellite images using multi-objective social spider optimization

Cloud detection algorithms have emerged to automate image data analysis because of its prime influential factor in remote sensing image quality. Cloud detection algorithm still needs domain-expert intervention and large number of training examples to ensure good performance whose acquirement becomes difficult due to unavailability of labeled data as well as the time and process heads involved. The paper puts forward multi-objective social spider optimization (MOSSO) based efficient clustering technique to detect clouds in the visible range. This paper explains the proposed MOSSO algorithm along-with the analysis carried on 14 benchmark two-objective test problems against MOEA/D, MODE, MOPSO and SPEA2 multi-objective algorithms. Further, the strengths and weaknesses of the proposed algorithm are analyzed and have been used for the implementation of an efficient clustering technique named as MOSSO-C. Optimal centroid matrix for clustering is attained in MOSSO-C through environmental selection whose performance evaluation has been done on six synthetic databases and are compared with above mentioned conventional multi-objective algorithms. The obtained results encourage the use of MOSSO-C technique to get labeled data for training process of neural network classifier. This approach efficiently classifies the cloudy pixels against various Earth’s surfaces (water, vegetation and land). The paper also discusses the performance evaluation of proposed technique on four Landsat 8 data which shows on an average 96.37% performance accuracy in detecting cloudy pixels.
A Novel Broad Learning System Based Leakage Detection and Universal Localization Method for Pipeline Networks

The security of pipeline systems draws attention increasingly; therefore, a novel method based on neural network and graph theory is proposed for the detection and localization of pipeline networks in this paper. First, the detection algorithm based on the broad learning system (BLS) is used to distinguish abnormities under large-scale pipeline network environments. During the process, the varied BLS models result in indeterminate performance and fast ergodic structure search is executed via adaptive mutation particle swarm algorithm (APSO) to generate an appropriate structure, succinct parameters, speedability, and accuracy. And manual features are implanted into the BLS feature layer to targetedly improve performance for complex pipeline network signals. Second, based on the detection results, a universal Dijkstra-based applicable localization method is proposed for diverse topological pipeline structures, including mesh-form networks, which have fewer sensors than anchors. The synchronous approximation is adopted to shun local minimum, and the shrinkage of search domain economizes time. Revised BLS was contrasted with several networks trained by real pipeline data and the system was integrated into SCADA and applied on an operational large-scale pipeline network successfully.
Robust Multi-Label Learning with PRO Loss

Multi-label learning methods assign multiple labels to one object. In practice, in addition to differentiating relevant labels from irrelevant ones, it is often desired to rank relevant labels for an object, whereas the ranking of irrelevant labels is not important. Thus, we require an algorithm to do classification and ranking of relevant labels simultaneously. Such a requirement, however, cannot be met because most existing methods were designed to optimize existing criteria, yet there is no criterion which encodes the aforementioned requirement. In this paper, we present a new criterion, PRO LOSS, concerning the prediction of all labels as well as the ranking of only relevant labels. We then propose ProSVM which optimizes PRO LOSS efficiently using alternating direction method of multipliers. We further improve its efficiency with an upper approximation that reduces the number of constraints from O(T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) to O(T), where T is the number of labels. We then notice that in real applications, it is difficult to get full supervised information for multi-label data. To make the proposed algorithm more robust to supervised information, we adapt ProSVM to deal with the multi-label learning with partial labels problem. Experiments show that our proposal is not only superior on PRO LOSS, but also highly competitive on existing evaluation criteria.
Designing Automated Facilitation for Design Thinking: A Chatbot for Supporting Teams in the Empathy Map Method

The Empathy Map Method (EMM) in the Design Thinking approach is a powerful tool for user centered design but relies on the methodological skills and experience of rare facilitation experts to guide the team.In a collaboration engineering effort, we aim to make this expertise available to teams without constant access to a professional facilitator by packaging facilitation knowledge into structured process support and stateof-the art technology.Based on requirements from scientific and practitioners' literature, we introduce the concept of a conversational agent in the form of a chatbot to take over the role of the facilitator of the EMM.We present an initial wizard of oz evaluation to derive insights and implications for improvements and the software implementation towards the ambitious goal of automated, non-human facilitation of EMM.
Theoretical Investigation of a Spectrally Pure-State Generation from Isomorphs of KDP Crystal at Near-Infrared and Telecom Wavelengths

Spectrally uncorrelated biphoton state generated from the spontaneous nonlinear optical process is an important resource for quantum information. Currently such spectrally uncorrelated biphoton state can only be prepared from limited kinds of nonlinear media, thus limiting their wavelengths. In order to explore wider wavelength range, here we theoretically study the generation of spectrally uncorrelated biphoton state from 14 isomorphs of potassium dihydrogen phosphate (KDP) crystal. We find that 11 crystals from the `KDP family' still maintain similar nonlinear optical properties of KDP, such as KDP, DKDP, ADP, DADP, ADA, DADA, RDA, DRDA, RDP, DRDP and KDA, which satisfy 3 kinds of the group-velocity matching conditions for spectrally uncorrelated biphoton state generation from near-infrared to telecom wavelengths. Based on the uncorrelated biphoton state, we investigate the generation of heralded pure-state single photon by detecting one member of the biphoton state to herald the output of the other. The purity of the heralded single photon is as high as 0.98 without using a narrow-band filter; the Hong-Ou-Mandel interference from independent sources can also achieve a visibility of 98%. This study may provide more and better single-photon sources for quantum information processing at near-infrared and telecom wavelengths.
Improving Failure Tolerance in Large-Scale Cloud Computing Systems

Large-scale cloud computing systems have served as the fundamental supporting platform for big data, Internet of Things, and artificial intelligence applications for the past decade. With the scale and complexity of these systems increasing dramatically, various hardware and software failures will inevitably occur and may not be detected and repaired in a timely manner. Besides, sophisticated architectural features of cloud computing may also have an adverse impact on system reliability. In response to these challenges, this paper proposes a simulation-driven framework based on real cloud computing system operation logs for improving failure tolerance in large-scale cloud computing systems. For a given cloud computing system, we first conduct a systematic analysis of its structure and operation characteristics. A Markov-based model is used to examine the system's potential failures, assess their severities, and suggest quick recoveries. During this process, the proposed reliability-aware resource scheduling algorithm is adopted to optimize resources so that the system's reliability can be improved cost-effectively. We also report a case study to demonstrate the application of our algorithm in improving failure tolerance of a large-scale cloud computing system.
Comprehensive survey on privacy-preserving protocols for sealed-bid auctions

The internet is a ubiquitous technology that has changed conventional human interactions. In our present time, it is not unusual for financial transactions to take place virtually with nothing more than a computer and a credit card; many times without ever having to engage face-to-face with another person. Part of these electronic transactions taking place day-to-day are not simple economic mechanisms, instead they are constructed in the form of auctions in which a key aspect is the protection of bidders. Ideally, electronic auction mechanisms should provide the following fundamental properties: privacy of the losing bids, robustness, verifiability, and non-repudiation. This can only be accomplished by proper sealed-bid auction protocols. Indeed, the main motivation for constructing sealed-bid auction protocols is the fact that auctioneers and/or sellers may use the past losing bids to maximize their revenues in future auctions and negotiations. In addition, private valuations can be used to disclose personal preferences and private information about the bidders. This paper therefore surveys existing cryptographic solutions that are used for electronic sealed-bid auction protocols, and provides the challenges that currently exist in this field. Overall, various technical approaches will be covered and the state-of-the-art will be reviewed thoroughly.
Zig-Zag Based Single-Pass Connected Components Analysis

Single-pass connected components analysis (CCA) algorithms suffer from a time overhead to resolve labels at the end of each image row. This work demonstrates how this overhead can be eliminated by replacing the conventional raster scan by a zig-zag scan. This enables chains of labels to be correctly resolved while processing the next image row. The effect is faster processing in the worst case with no end of row overheads. CCA hardware architectures using the novel algorithm proposed in this paper are, therefore, able to process images at higher throughput than other state-of-the-art methods while reducing the hardware requirements. The latency introduced by the conversion from raster scan to zig-zag scan is compensated for by a new method of detecting object completion, which enables the feature vector for completed connected components to be output at the earliest possible opportunity.