Reviewerly/exHarmony huggingface dataset:
A New Online Learned Interval Type-3 Fuzzy Control System for Solar Energy Management Systems

In this article, a novel method based on interval type-3 fuzzy logic systems (IT3-FLSs) and an online learning approach is designed for power control and battery charge planing for photovoltaic (PV)/battery hybrid systems. Unlike the other methods, the dynamics of battery, PV and boost converters are considered to be fully unknown. Also, the effects of variation of temperature, radiation, and output load are taken into account. The robustness and the asymptotic stability of the proposed method is analyzed by the Lyapunov/LaSalle's invariant set theorems, and the tuning rules are extracted for IT3-FLS. Also, the upper bound of approximation error (AE) is approximated, and then a new compensator is designed to deal with the effects of dynamic AEs. The superiority of the proposed method is examined in several conditions and is compared with some other well-known methods. It is shown that the schemed method results in high performance under difficult conditions such as variation of temperature and radiation and abruptly changing in the output load.
Efficient Identity-Based Provable Multi-Copy Data Possession in Multi-Cloud Storage

To increase the availability and durability of the outsourced data, many customers store multiple copies on multiple cloud servers. To guarantee the integrity of multi-copies, some provable data possession (PDP) protocols for multi-copy are presented. However, most of previous PDP protocols consider all copies to be stored on only one cloud storage server. In some degree, multi-copy makes little sense in such circumstance. Furthermore, many PDP protocols depend on the technique of public key infrastructure (PKI), which suffers many types of security vulnerabilities and also brings heavy communicational and computational cost. To increase the security and efficiency, we provide a novel identity-based PDP scheme of multi-copy on multiple cloud storage servers. In our scheme, all copies are delivered to different cloud storage servers, which work cooperatively to store the customer's data. By the homomorphic verifiable tags, the integrity of all copies can be checked simultaneously. The system model and security model of our scheme are provided in the paper. The security for our scheme is proved based on the computation Diffie-Hellman (CDH) hard problem. Analysis and experimental evaluation show that our scheme is efficient and practical. The proposed scheme is the first identity-based PDP scheme for multi-copy and multi-cloud servers.
FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow

Most sequence-to-sequence (seq2seq) models are autoregressive; they generate each token by conditioning on previously generated tokens.In contrast, non-autoregressive seq2seq models generate all tokens in one pass, which leads to increased efficiency through parallel processing on hardware such as GPUs.However, directly modeling the joint distribution of all tokens simultaneously is challenging, and even with increasingly complex model structures accuracy lags significantly behind autoregressive models.In this paper, we propose a simple, efficient, and effective model for non-autoregressive sequence generation using latent variable models.Specifically, we turn to generative flow, an elegant technique to model complex distributions using neural networks, and design several layers of flow tailored for modeling the conditional density of sequential latent variables.We evaluate this model on three neural machine translation (NMT) benchmark datasets, achieving comparable performance with state-of-the-art nonautoregressive NMT models and almost constant decoding time w.r.t the sequence length.
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization

This paper studies learning the representations of whole graphs in both unsupervised and semi-supervised scenarios. Graph-level representations are critical in a variety of real-world applications such as predicting the properties of molecules and community analysis in social networks. Traditional graph kernel based methods are simple, yet effective for obtaining fixed-length representations for graphs but they suffer from poor generalization due to hand-crafted designs. There are also some recent methods based on language models (e.g. graph2vec) but they tend to only consider certain substructures (e.g. subtrees) as graph representatives. Inspired by recent progress of unsupervised representation learning, in this paper we proposed a novel method called InfoGraph for learning graph-level representations. We maximize the mutual information between the graph-level representation and the representations of substructures of different scales (e.g., nodes, edges, triangles). By doing so, the graph-level representations encode aspects of the data that are shared across different scales of substructures. Furthermore, we further propose InfoGraph*, an extension of InfoGraph for semisupervised scenarios. InfoGraph* maximizes the mutual information between unsupervised graph representations learned by InfoGraph and the representations learned by existing supervised methods. As a result, the supervised encoder learns from unlabeled data while preserving the latent semantic space favored by the current supervised task. Experimental results on the tasks of graph classification and molecular property prediction show that InfoGraph is superior to state-of-the-art baselines and InfoGraph* can achieve performance competitive with state-of-the-art semi-supervised models.
Visualization and Visual Analysis of Ensemble Data: A Survey

Over the last decade, ensemble visualization has witnessed a significant development due to the wide availability of ensemble data, and the increasing visualization needs from a variety of disciplines. From the data analysis point of view, it can be observed that many ensemble visualization works focus on the same facet of ensemble data, use similar data aggregation or uncertainty modeling methods. However, the lack of reflections on those essential commonalities and a systematic overview of those works prevents visualization researchers from effectively identifying new or unsolved problems and planning for further developments. In this paper, we take a holistic perspective and provide a survey of ensemble visualization. Specifically, we study ensemble visualization works in the recent decade, and categorize them from two perspectives: (1) their proposed visualization techniques; and (2) their involved analytic tasks. For the first perspective, we focus on elaborating how conventional visualization techniques (e.g., surface, volume visualization techniques) have been adapted to ensemble data; for the second perspective, we emphasize how analytic tasks (e.g., comparison, clustering) have been performed differently for ensemble data. From the study of ensemble visualization literature, we have also identified several research trends, as well as some future research opportunities.
Multifactorial Genetic Programming for Symbolic Regression Problems

Genetic programming (GP) is a powerful evolutionary algorithm that has been widely used for solving many real-world optimization problems. However, traditional GP can only solve a single task in one independent run, which is inefficient in cases where multiple tasks need to be solved at the same time. Recently, multifactorial optimization (MFO) has been proposed as a new evolutionary paradigm toward evolutionary multitasking. It intends to conduct evolutionary search on multiple tasks in one independent run. To enable multitasking GP, in this paper, we propose a novel multifactorial GP (MFGP) algorithm. To the best of our knowledge, this is the first attempt in the literature to conduct multitasking GP using a single population. The proposed MFGP consists of a novel scalable chromosome encoding scheme which is capable of representing multiple solutions simultaneously, and new evolutionary mechanisms for MFO based on self-learning gene expression programming. Further, comprehensive experimental studies are conducted on multitask scenarios consisting of commonly used GP benchmark problems and real world applications. The obtained empirical results confirmed the efficacy of the proposed MFGP.
A Comprehensive Survey on Blockchain in Industrial Internet of Things: Motivations, Research Progresses, and Future Challenges

With rapid development of enabling technologies such as Internet of Things, robotics, and big data, the fourth industrial revolution known as “Industry 4.0” (I4.0) has become a great opportunity for productivity innovation around the world. Industrial Internet of Things (IIoT), as a typical implication of Cyber-Physical Systems (CPS) in I4.0 era, has attracted significant attention. It aims at optimizing resources and improving production efficiency for manufacturing industry through deep interconnection of physical equipment, wide collection of multi-dimensional data, intelligent awareness of environment and automatic control. Blockchain is a kind of distributed ledger technology and has features including decentralization, anti-tamper, transparency, anonymity, and contract-autonomy. These features are helpful to improve the services and promote the development of IIoT. In this paper, we provide a comprehensive survey on the literatures applying blockchain technology into IIoT. We firstly introduce the related works and background knowledge. And then, the motivations and benefits for applying blockchain into IIoT are discussed in detail. Furthermore, based on the layered architecture of blockchain we summarized in the previous work, we outline the research framework of blockchain in IIoT. Meanwhile, the related application research is presented from the perspectives of devices security, data collection and sharing, and industrial application. Moreover, we explore the technical requirements of blockchain platforms in IIoT applications. Finally, some challenges and future directions on blockchain in IIoT, as well as broader perspectives, are concluded for further study.
Inference with Arbitrary Clustering

Analyses of spatial or network data are now very common. Yet statistical inference is challenging since unobserved heterogeneity can be correlated across neighboring observational units. We develop an estimator for the variance-covariance matrix (VCV) of OLS and 2SLS that allows for arbitrary dependence of the errors across observations in space or network structure, and across time periods. As a proof of concept, we conduct Monte Carlo simulations in a geospatial setting based on US Metropolitan areas; tests based on our estimator of the VCV asymptotically correctly reject the null hypothesis where conventional inference methods, e.g. those without clusters, or with clusters based on administrative units, reject the null hypothesis too often. We also provide simulations in a network setting based on the IDEAS structure of co-authorship and real life data on scientific performance; the Monte Carlo results again show that our estimator yields inference at the right significance level already in moderately sized samples, and it dominates other commonly used approaches to inference in networks. We provide guidance to the applied researcher with respect to (i) including or not potentially correlated regressors and (ii) choice of cluster bandwidth. Finally we provide a companion statistical package (acreg) enabling users to adjust OLS and 2SLS coefficient's standard errors, accounting for arbitrary dependence.
AUNet: attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms

Mammography is one of the most commonly applied tools for early breast cancer screening. Automatic segmentation of breast masses in mammograms is essential but challenging due to the low signal-to-noise ratio and the wide variety of mass shapes and sizes. Existing methods deal with these challenges mainly by extracting mass-centered image patches manually or automatically. However, manual patch extraction is time-consuming and automatic patch extraction brings errors that could not be compensated in the following segmentation step. In this study, we propose a novel attention-guided dense-upsampling network (AUNet) for accurate breast mass segmentation in whole mammograms directly. In AUNet, we employ an asymmetrical encoder-decoder structure and propose an effective upsampling block, attention-guided dense-upsampling block (AU block). Especially, the AU block is designed to have three merits. Firstly, it compensates the information loss of bilinear upsampling by dense upsampling. Secondly, it designs a more effective method to fuse high- and low-level features. Thirdly, it includes a channel-attention function to highlight rich-information channels. We evaluated the proposed method on two publicly available datasets, CBIS-DDSM and INbreast. Compared to three state-of-the-art fully convolutional networks, AUNet achieved the best performances with an average Dice similarity coefficient of 81.8% for CBIS-DDSM and 79.1% for INbreast.
NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo

In this work, we present a new multi-view depth estimation method that utilizes both conventional SfM reconstruction and learning-based priors over the recently proposed neural radiance fields (NeRF). Unlike existing neural network based optimization method that relies on estimated correspondences, our method directly optimizes over implicit volumes, eliminating the challenging step of matching pixels in indoor scenes. The key to our approach is to utilize the learning-based priors to guide the optimization process of NeRF. Our system firstly adapts a monocular depth network over the target scene by finetuning on its sparse SfM reconstruction. Then, we show that the shape-radiance ambiguity of NeRF still exists in indoor environments and propose to address the issue by employing the adapted depth priors to monitor the sampling process of volume rendering. Finally, a per-pixel confidence map acquired by error computation on the rendered image can be used to fur ther improve the depth quality. Experiments show that our proposed framework significantly outperforms state-of-the-art methods on indoor scenes, with surprising findings presented on the effectiveness of correspondence-based opti-mization and NeRF-based optimization over the adapted depth priors. In addition, we show that the guided opti-mization scheme does not sacrifice the original synthesis capability of neural radiance fields, improving the rendering quality on both seen and novel views. Code is available at https://github.com/weiyithu/NerfingMVS.
Ultimate transformation: How will automation technologies disrupt the travel, tourism and hospitality industries?

Abstract Companies from the travel, tourism and hospitality industry have started adopting robots, artificial intelligence and service automation technologies (RAISA) in their operations. Self-check-in kiosks, robotic pool cleaners, delivery robots, robot concierges, chatbots, etc., are used increasingly by tourism companies and transform the ways they create and deliver services. This paper investigates the impact of RAISA technologies on travel, tourism and hospitality companies – their operations, facilities design, marketing, supply chain management, human resource management, and financial management. The paper emphasises that in the future companies will divide into two large segments – high-tech tourism companies offering standardized cheap robot-delivered services, and high-touch companies, which rely on human employees.
Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron

Modern machine learning focuses on highly expressive models that are able to fit or interpolate the data completely, resulting in zero training loss. For such models, we show that the stochastic gradients of common loss functions satisfy a strong growth condition. Under this condition, we prove that constant step-size stochastic gradient descent (SGD) with Nesterov acceleration matches the convergence rate of the deterministic accelerated method for both convex and strongly-convex functions. We also show that this condition implies that SGD can find a first-order stationary point as efficiently as full gradient descent in non-convex settings. Under interpolation, we further show that all smooth loss functions with a finite-sum structure satisfy a weaker growth condition. Given this weaker condition, we prove that SGD with a constant step-size attains the deterministic convergence rate in both the strongly-convex and convex settings. Under additional assumptions, the above results enable us to prove an O(1/k^2) mistake bound for k iterations of a stochastic perceptron algorithm using the squared-hinge loss. Finally, we validate our theoretical findings with experiments on synthetic and real datasets.
Prediction short-term photovoltaic power using improved chicken swarm optimizer - Extreme learning machine model

Photovoltaic power generation is greatly affected by weather conditions while the photovoltaic power has a certain negative impact on the power grid. The power sector takes certain measures to abandon photovoltaic power generation, thus limiting the development of clean energy power generation. This study is to propose an accurate short-term photovoltaic power prediction method. A new short-term photovoltaic power output prediction model is proposed Based on extreme learning machine and intelligent optimizer. Firstly, the input of the model is determined by correlation coefficient method. Then the chicken swarm optimizer is improved to strengthen the convergence. Secondly, the improved chicken swarm optimizer is used to optimize the weights and the extreme learning machine thresholds to improve the prediction effect. Finally, the improved chicken swarm optimizer extreme learning machine model is used to predict the photovoltaic power under different weather conditions. The testing results show that the average mean absolute percentage error and root mean square error of improved chicken swarm optimizer - extreme learning machine model are 5.54% and 3.08%. The proposed method is of great significance for the economic dispatch of power systems and the development of clean energy.
Hybrid query expansion using lexical resources and word embeddings for sentence retrieval in question answering

Question Answering (QA) systems based on Information Retrieval return precise answers to natural language questions, extracting relevant sentences from document collections. However, questions and sentences cannot be aligned terminologically, generating errors in the sentence retrieval. In order to augment the effectiveness in retrieving relevant sentences from documents, this paper proposes a hybrid Query Expansion (QE) approach, based on lexical resources and word embeddings, for QA systems. In detail, synonyms and hypernyms of relevant terms occurring in the question are first extracted from MultiWordNet and, then, contextualized to the document collection used in the QA system. Finally, the resulting set is ranked and filtered on the basis of wording and sense of the question, by employing a semantic similarity metric built on the top of a Word2Vec model. This latter is locally trained on an extended corpus pertaining the same topic of the documents used in the QA system. This QE approach is implemented into an existing QA system and experimentally evaluated, with respect to different possible configurations and selected baselines, for the Italian language and in the Cultural Heritage domain, assessing its effectiveness in retrieving sentences containing proper answers to questions belonging to four different categories.
When Does Self-supervision Improve Few-Shot Learning?

We investigate the role of self-supervised learning (SSL) in the context of few-shot learning. Although recent research has shown the benefits of SSL on large unlabeled datasets, its utility on small datasets is relatively unexplored. We find that SSL reduces the relative error rate of few-shot meta-learners by 4%–27%, even when the datasets are small and only utilizing images within the datasets. The improvements are greater when the training set is smaller or the task is more challenging. Although the benefits of SSL may increase with larger training sets, we observe that SSL can hurt the performance when the distributions of images used for meta-learning and SSL are different. We conduct a systematic study by varying the degree of domain shift and analyzing the performance of several meta-learners on a multitude of domains. Based on this analysis we present a technique that automatically selects images for SSL from a large, generic pool of unlabeled images for a given dataset that provides further improvements.
Reasoning Over Semantic-Level Graph for Fact Checking

Fact checking is a challenging task because verifying the truthfulness of a claim requires reasoning about multiple retrievable evidence.In this work, we present a method suitable for reasoning about the semantic-level structure of evidence.Unlike most previous works, which typically represent evidence sentences with either string concatenation or fusing the features of isolated evidence sentences, our approach operates on rich semantic structures of evidence obtained by semantic role labeling.We propose two mechanisms to exploit the structure of evidence while leveraging the advances of pre-trained models like BERT, GPT or XLNet.Specifically, using XLNet as the backbone, we first utilize the graph structure to re-define the relative distances of words, with the intuition that semantically related words should have short distances.Then, we adopt graph convolutional network and graph attention network to propagate and aggregate information from neighboring nodes on the graph.We evaluate our system on FEVER, a benchmark dataset for fact checking, and find that rich structural information is helpful and both our graph-based mechanisms improve the accuracy.Our model is the state-of-the-art system in terms of both official evaluation metrics, namely claim verification accuracy and FEVER score.
B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets

Advancements in medical technology have created numerous large datasets including many features. Usually, all captured features are not necessary, and there are redundant and irrelevant features, which reduce the performance of algorithms. To tackle this challenge, many metaheuristic algorithms are used to select effective features. However, most of them are not effective and scalable enough to select effective features from large medical datasets as well as small ones. Therefore, in this paper, a binary moth-flame optimization (B-MFO) is proposed to select effective features from small and large medical datasets. Three categories of B-MFO were developed using S-shaped, V-shaped, and U-shaped transfer functions to convert the canonical MFO from continuous to binary. These categories of B-MFO were evaluated on seven medical datasets and the results were compared with four well-known binary metaheuristic optimization algorithms: BPSO, bGWO, BDA, and BSSA. In addition, the convergence behavior of the B-MFO and comparative algorithms were assessed, and the results were statistically analyzed using the Friedman test. The experimental results demonstrate a superior performance of B-MFO in solving the feature selection problem for different medical datasets compared to other comparative algorithms.