Reviewerly/exHarmony huggingface dataset:
Can we justify producing univariate machine-learning forecasts with satellite-derived solar irradiance?

Ground-based radiometry is the most reliable method for obtaining data for solar energy applications. However, due to potential calibration and data quality-control issues, as well as the high cost associated with instrumentation, reliable and long-term ground-based measurements are scarce. For this reason, irradiance data from alternative sources, such as modeled or remote-sensed data, are of interest. Nonetheless, data coming from these sources are known to have systematic errors that may deteriorate the performance of subsequent applications. This study aims to evaluate the quality of solar forecasts generated using two types of irradiance data, namely, ground-based and bias-corrected satellite-derived global horizontal irradiance using error decomposition frameworks. Hourly forecasts are generated using 5 high-performance machine-learning models, with ground-based data from 15 research-grade monitoring stations in Europe, South America, and Africa, and satellite-derived data at the collocated pixels, separately. On top of these component forecasts, ensemble forecasts are also evaluated. Instead of limiting the forecast verification to a set of accuracy measures (e.g., mean bias error or root mean square error), the Murphy–Winkler forecast verification framework is considered, in which the joint, conditional, and marginal distributions of observations and forecasts are examined. The framework describes the qualities of forecast, such as calibration, conditional bias, or discrimination, with quantitative measures. Hence, it provides additional insights to the traditional measure-oriented approach. The main results show that the forecasts generated using bias-corrected satellite-derived data have comparable quality as compared to those generated using ground-based data.
A description logic framework for commonsense conceptual combination integrating typicality, probabilities and cognitive heuristics

We propose a nonmonotonic Description Logic of typicality able to account for the phenomenon of the combination of prototypical concepts. The proposed logic relies on the logic of typicality ALC+TR, whose semantics is based on the notion of rational closure, as well as on the distributed semantics of probabilistic Description Logics, and is equipped with a cognitive heuristic used by humans for concept composition.We first extend the logic of typicality ALC+TR by typicality inclusions of the form p::T(C)⊑D, whose intuitive meaning is that ‘we believe with degree p about the fact that typical Cs are Ds’. As in the distributed semantics, we define different scenarios containing only some typicality inclusions, each one having a suitable probability. We then exploit such scenarios in order to ascribe typical properties to a concept C obtained as the combination of two prototypical concepts. We also show that reasoning in the proposed Description Logic is ExpTime-complete as for the underlying standard Description Logic ALC.
Learning From Synthetic Photorealistic Raindrop for Single Image Raindrop Removal

Raindrops adhered to camera lens or windshield are inevitable in rainy scenes and can become an issue for many computer vision systems such as autonomous driving. Because raindrop appearance is affected by too many parameters, therefore it is unlikely to find an effective model based solution. Learning based methods are also problematic, because traditional learning method cannot properly model the complex appearance. Whereas deep learning method lacks sufficiently large and realistic training data. To solve it, in our work, we propose the first photo-realistic dataset of synthetic adherent raindrops for training. The rendering is physics based with consideration of the water dynamic, geometric and photometry. The dataset contains various types of rainy scenes and particularly the rainy driving scenes. Based on the modeling of raindrop imagery, we introduce a detection network which has the awareness of the raindrop refraction as well as its blurring. Based on that, we propose the removal network that can well recover the image structure. Rigorous experiments demonstrate the state-of-the-art performance of our proposed framework.
Fingerspelling Recognition in the Wild With Iterative Visual Attention

Sign language recognition is a challenging gesture sequence recognition problem, characterized by quick and highly coarticulated motion. In this paper we focus on recognition of fingerspelling sequences in American Sign Language (ASL) videos collected in the wild, mainly from YouTube and Deaf social media. Most previous work on sign language recognition has focused on controlled settings where the data is recorded in a studio environment and the number of signers is limited. Our work aims to address the challenges of real-life data, reducing the need for detection or segmentation modules commonly used in this domain. We propose an end-to-end model based on an iterative attention mechanism, without explicit hand detection or segmentation. Our approach dynamically focuses on increasingly high-resolution regions of interest. It out-performs prior work by a large margin. We also introduce a newly collected data set of crowdsourced annotations of fingerspelling in the wild, and show that performance can be further improved with this additional data set.
Human Pose Estimation for Real-World Crowded Scenarios

Human pose estimation has recently made significant progress with the adoption of deep convolutional neural networks and many applications have attracted tremendous interest in recent years. However, many of these applications require pose estimation for human crowds, which still is a rarely addressed problem. For this purpose this work explores methods to optimize pose estimation for human crowds, focusing on challenges introduced with larger scale crowds like people in close proximity to each other, mutual occlusions, and partial visibility of people due to the environment. In order to address these challenges, multiple approaches are evaluated including: the explicit detection of occluded body parts, a data augmentation method to generate occlusions and the use of the synthetic generated dataset JTA [3]. In order to overcome the transfer gap of JTA originating from a low pose variety and less dense crowds, an extension dataset is created to ease the use for real-world applications.
IATE 2

Abstract The redevelopment of the European Union’s interinstitutional terminology database IATE (InterActive Terminology for Europe) has been an opportunity to rethink the technologies, architecture and data structure of the system in order to prepare it for future challenges, including interoperability, modularity, scalability and data exchange, among other things. This article describes which strategies are being put in place to allow IATE data – one of the largest multilingual terminology databases in the world – to be consumed by third-party tools, particularly computer-assisted translation environments (CATEs). The modernisation of the application, aligning it with the latest software and systems engineering standards and technologies for the benefit of all users and for improved data management by EU linguists, is also described.
The HealthChain Blockchain for Electronic Health Records: Development Study

Background Health care professionals are required to maintain accurate health records of patients. Furthermore, these records should be shared across different health care organizations for professionals to have a complete review of medical history and avoid missing important information. Nowadays, health care providers use electronic health records (EHRs) as a key to the implementation of these goals and delivery of quality care. However, there are technical and legal hurdles that prevent the adoption of these systems, such as concerns about performance and privacy issues. Objective This study aimed to build and evaluate an experimental blockchain for EHRs, named HealthChain, which overcomes the disadvantages of traditional EHR systems. Methods HealthChain is built based on consortium blockchain technology. Specifically, three organizations, namely hospitals, insurance providers, and governmental agencies, form a consortium that operates under a governance model, which enforces the business logic agreed by all participants. Every peer node hosts an instance of the distributed ledger consisting of EHRs and an instance of chaincode regulating the permissions of participants. Designated orderers establish consensus on the order of EHRs and then disseminate blocks to peers. Results HealthChain achieves functional and nonfunctional requirements. It can store EHRs in a distributed ledger and share them among different participants. Moreover, it demonstrates superior features, such as privacy preservation, security, and high throughput. These are the main reasons why HealthChain is proposed. Conclusions Consortium blockchain technology can help to build new EHR systems and solve the problems that prevent the adoption of traditional systems.
An Adaptive Outlier Detection and Processing Approach Towards Time Series Sensor Data

The intelligent environment monitoring network, as the foundation of ecosystem research, has rapidly developed with the ever-growing Internet of Things (IoT). IoT-networked sensors deployed to monitor ecosystems generate copious sensor data characterized by nonstationarity and nonlinearity such that outlier detection remains a source of concern. Most outlier detection models involve hypothesis tests based on setting outlier threshold values. However, signal decomposition describes stationary and nonstationary relationships sensor data. Therefore, this paper proposes a three-level hybrid model based on the median filter (MF), empirical mode decomposition (EMD), classification and regression tree (CART), autoregression (AR) and exponential weighted moving average (EWMA) methods called MF-EMD-CART-AR-EWMA to detect outliers in sensor data. The first-level performance is compared to that of the Butterworth filter, FIR filter, moving average filter, wavelet filter and Wiener filter. The second-level prediction performance is compared to support vector regression (SVR), K-nearest neighbor (KNN), CART, complementary ensemble EEMD with CART and AR (EEMD-CART-AR) and ensemble CEEMD with CART and AR (CEEMD-CART-AR) methods. Finally, EWMA is compared to Cumulative Sum Control Chart (CUSUM) and Shewhart control charts. The proposed hybrid model was evaluated with a real dataset from the hydrometeorological observation network in the Heihe River Basin, yielding experimental results with better generalization ability and higher accuracy than the compared models, and providing extremely effective detection of minor outliers in predicted values. This paper provides valuable insight and a promising reference for outlier detection involving sensor data and presents a new perspective for detecting outliers.
A Survey on Deep Learning-Based Person Re-Identification Systems

Person re-identification systems (person Re-ID) have recently gained more attention between computer vision researchers. They are playing a key role in intelligent visual surveillance systems and have widespread applications like applications for public security. The person Re-ID systems can identify if a person has been seen by a non-overlapping camera over large camera network in an unconstrained environment. It is a challenging issue since a person appears differently under different camera views and faces many challenges such as pose variation, occlusion and illumination changes. Many methods had been introduced for generating handcrafted features aimed to handle the person Re-ID problem. In recent years, many studies have started to apply deep learning methods to enhance the person Re-ID performance due the deep learning yielded significant results in computer vision issues. Therefore, this paper is a survey of the recent studies that proposed to improve the person Re-ID systems using deep learning. The public datasets that are used for evaluating these systems are discussed. Finally, the paper addresses future directions and current issues that must be considered toward improving the person Re-ID systems.
Collaborative Trust Blockchain Based Unbiased Control Transfer Mechanism for Industrial Automation

In industrial automation, numerous devices are interconnected in smart factories for further monitor and control. Various infrastructure devices in industrial automation are usually used for control instruction distribution, data collection, and collaboration of the industrial applications. Recent security threats on industrial automation are more frequent and the industrial control systems lack trust mechanism. Blockchain has been introduced due to its decentralization and security promise, but the election results in the original designs could be biased without collaboration trust, which leads the blockchain-based industry applications invalid. In addition, in existing solutions, neither supernodes nor normal nodes in blockchain can transfer their control authorities for disaster backup. To address the aforementioned challenges, this article proposes a collaborative trust based unbiased control transfer mechanism (CTM), which realizes a dynamic assignment of industrial control. First, a collaborative trust based delegated proof of stake consensus is proposed for determining the authorities of control dynamically and unbiasedly, by designing a lightweight trust propagation protocol. Second, a CTM for checking, alarming, and restarting CTM is devised for the disaster backup. The simulation results demonstrate the CTM, which is feasible and effective for industrial automation security.
Fuzzy test model for performance evaluation matrix of service operating systems

The performance evaluation matrix is a useful and convenient tool for collecting user opinions and identifying service items that are in need of improvement in operating systems. It has been used in many studies looking for ways to increase the response willingness of customers and solve the problem of fuzziness in user opinions. Likert scales are simple and can increase the response willingness of customers and the efficiency of data collection. In contrast, fuzzy linguistic scales can more accurately capture the voice of the customer, but responding to them is a more complex matter and lowers the response willingness of customers. Confidence intervals can lower the risk of misjudgment caused by sampling errors in user opinion collection; the smaller the confidence intervals are, the lower the risk of misjudgment is. We therefore developed the Buckley fuzzy test methods that maintain the simplicity offered by Likert scales in collecting data. We used the indices confidence intervals derived from the data to construct fuzzy membership functions and develop our fuzzy test methods. Clearly, those approaches solve the problem of fuzziness in customer opinions without affecting the response willingness of customers. Those methods are grounded in confidence intervals, which reduce the risk of misjudgment caused by sampling errors. Furthermore, when confidence intervals are relatively wide due to cost and time considerations, the Buckley fuzzy test methods overcome this limitation by swiftly identifying service items that are in need of improvement in an operating system, as well as prioritizing service items using pair-wise comparisons of the influence indices of the service items. This enables the identification of the service items that should take priority when resources are limited. Finally, we used a case study involving a computer-assisted language learning system to demonstrate the applicability of the proposed approach.
Sentiment analysis of tweets using refined neutrosophic sets

In the last decade, opinion mining and sentiment analysis have been the subject of fascinating interdisciplinary research. Alongside the evolution of social media networks, the sheer volume of social media text available for sentiment analysis has increased multi-fold, leading to a formidable corpus. Sentiment analysis of tweets have been carried out to gauge public opinion on breaking news, various policies, legislations, personalities and social movements. Fuzzy logic has been used in the sentiment analysis of twitter data, whereas neutrosophy which factors in the concept of indeterminacy has not been used to analyse tweets. In this paper, the concept of multi refined neutrosophic set (MRNS) with two positive, three indeterminate and two negative memberships is proposed. Single valued neutrosophic set (SVNS), triple refined indeterminate neutrosophic set (TRINS) and MRNS have been used in the sentiment analysis of tweets on ten different topics. Eight of these topics chosen for sentiment analysis are related to Indian scenario and two topics to international scenario. A comparative analysis of the methods show that the approach with MRNS provides better refinement to the indeterminacy present in the data.
Multitask Learning to Improve Egocentric Action Recognition

In this work we employ multitask learning to capitalize on the structure that exists in related supervised tasks to train complex neural networks.It allows training a network for multiple objectives in parallel, in order to improve performance on at least one of them by capitalizing on a shared representation that is developed to accommodate more information than it otherwise would for a single task.We employ this idea to tackle action recognition in egocentric videos by introducing additional supervised tasks.We consider learning the verbs and nouns from which action labels consist of and predict coordinates that capture the hand locations and the gaze-based visual saliency for all the frames of the input video segments.This forces the network to explicitly focus on cues from secondary tasks that it might otherwise have missed resulting in improved inference.Our experiments on EPIC-Kitchens and EGTEA Gaze+ show consistent improvements when training with multiple tasks over the single-task baseline.Furthermore, in EGTEA Gaze+ we outperform the state-of-the-art in action recognition by 3.84%.Apart from actions, our method produces accurate hand and gaze estimations as side tasks, without requiring any additional input at test time other than the RGB video clips.
IoT type-of-traffic forecasting method based on gradient boosting neural networks

Network traffic classification is an important task for any current data network. There any many possible classification targets for the traffic, but we have considered as especially important the activity state of a connection and the identification of elephant flows (few connections carrying most of the traffic). With these detection targets, this work presents a modification of the gaNet architecture for classification. gaNet is an additive network model formed by ‘learning blocks’ that are stacked iteratively following the principles of boosting models. The original gaNet model is intended for regression, being the purpose of this work to show that it can be extended to classification under several adaptations. The resulting architecture is a generic additive network applicable to any supervised classification problem (gaNet-C). To obtain experimental results, the model is applied to a type-of-traffic forecast problem using real IoT traffic from a mobile operator. The paper presents a comprehensive comparison of results between the proposed new model and many alternative algorithms in terms of classification and performance metrics. The proposed classifier can perform a k-step ahead detection forecast based exclusively on a limited time-series of previous values for each network connection. The results include two very different challenges: detection forecast of active connections and elephant flows; showing that, in both cases, the proposed algorithm presents state of the art results.
DistLog: A distributed logging scheme for IoT forensics

Digital forensics are vital in the Internet of Things (IoT) domain. This is due to the enormous growth of cyber attacks and their widespread use against IoT devices. While IoT forensics do not prevent IoT attacks, they help in reducing their occurrence by tracing their source, tracking their root causes and designing the corresponding countermeasures. However, modern IoT attacks use anti-forensics techniques to destroy or modify any important digital evidence including log files. Anti-forensics techniques complicate the task for forensic investigators in tracking the attack source. Thus, countermeasures are required to defend against anti-forensics techniques. In this paper, we aim at securing the IoT log files to prevent anti-forensics techniques that target the logs’ availability and integrity such as wiping and injecting attacks. In the proposed solution, and at regular intervals of time, the logs generated by IoT devices are aggregated, compressed and encrypted. Afterwards, the encrypted logs are fragmented, authenticated and distributed over n storage nodes, based on the proposed Modified Information Dispersal Algorithm (MIDA) that can ensure log files availability with a degree of (n−t). For data dispersal, two cases are considered: the case where the fog nodes are interconnected and the case where they are not. For the former case, the n obtained fragments are transmitted to n neighboring IoT devices (aggregation nodes). However, for the latter one, the output is transmitted to the corresponding fog and then, dispersed over the n neighboring fog nodes. A set of security and performance tests were performed showing the effectiveness and robustness of the proposed solution in thwarting well-known security attacks.
Unsupervised Deep Cross-modal Hashing with Virtual Label Regression

Unsupervised cross-modal hashing learns hash codes without dependence on the semantic labels. It has the desirable advantage of well scalability and thus can effectively support large-scale cross-media retrieval. However, how to directly learn discriminative discrete hash codes under the unsupervised learning paradigm is still an open challenging problem. In this paper, we aim to attack this problem by proposing an Unsupervised Deep Cross-modal Hashing with Virtual Label Regression (UDCH-VLR) method. We propose a novel unified learning framework to jointly perform deep hash function training, virtual label learning and regression. Specifically, we learn unified hash codes via collaborative matrix factorization on the multi-modal deep representations to preserve the multi-modal shared semantics. Further, we incorporate the virtual label learning into the objective functions and simultaneously regress the learned virtual labels to the hash codes. Finally, instead of simply exploiting the existing shallow features and relaxing the binary constraints, we devise an alternative optimization strategy to directly update the deep hash functions and discrete binary codes. Under such circumstance, the discriminative capability of hash codes can be progressively enhanced with iterative learning. Extensive experiments on three publicly available cross-media retrieval datasets demonstrate that our approach outperforms state-of-the-art methods.