Reviewerly/exHarmony huggingface dataset:
Applying big data beyond small problems in climate research

Commercial success of big data has led to speculation that big-data-like reasoning could partly replace theory-based approaches in science. Big data typically has been applied to ‘small problems’, which are well-structured cases characterized by repeated evaluation of predictions. Here, we show that in climate research, intermediate categories exist between classical domain science and big data, and that big-data elements have also been applied without the possibility of repeated evaluation. Big-data elements can be useful for climate research beyond small problems if combined with more traditional approaches based on domain-specific knowledge. The biggest potential for big-data elements, we argue, lies in socioeconomic climate research. Big data is increasingly popular in many research domains. This Perspective discusses where elements of big data approaches have been employed in climate research and where combining big data with theory-driven research can be most fruitful.
Europe’s Quantum Flagship initiative

As the first applications leap out of research laboratories toward commercialization, the global race for dominance in the maturing field of quantum technologies is becoming ever fiercer. To retain its historical lead and kick-start a continent-wide quantum-driven industry and accelerate market take-up, Europe has launched the Quantum Flagship, an ambitious €1 billion, 10 year endeavor. This article provides an overview of the underlying considerations and the current state of the initiative. Furthermore, it briefly presents the 20 projects selected to be at the core of the ramp-up phase of the initiative, which will address core applications of quantum technologies such as communications, computing, simulation, as well as sensing and metrology, all of which are supported by basic science. Finally, we present the broader ecosystem of European funding instruments and institutions which aim to create the next generation of disruptive technologies within quantum sciences, placing Europe as a worldwide knowledge-based industrial and technological leader in this innovative field.
FormFab

Several systems have illustrated the concept of interactive fabrication, i.e. rather than working through a digital editor, users make edits directly on the physical workpiece. However, so far the interaction has been limited to turn-taking, i.e., users first perform a command and then the system responds with physical feedback. In this paper, we present a first step towards interactive fabrication that changes the workpiece continuously while the user is manipulating it.
A Triaxial Acceleration-based Human Motion Detection for Ambient Smart Home System

Health industry off late has been driven heavily by sensors i.e. accelerometers, magnetometers etc. which has allowed instant medical response to any injurious activity in an indoor/outdoor environment. Among the medical applications of accelerometers, fitness systems have used this component extensively but it still holds prominent room for deployment in an ambient smart home system to monitor daily life. In this paper, a novel accelerometer-based motion recognition system using statistical features have been proposed. Axial components of accelerometer have been processed statistically to produce discriminating features values from each activity. The proposed system was validated against accelerometer dataset and achieved satisfactory accuracy of 79.58% with random forest. The proposed system can be applied to health monitoring systems, interactive games and for examination of behaviors in outdoor and indoor environments.
Future developments in standardisation of cyber risk in the Internet of Things (IoT)

Abstract In this research article, we explore the use of a design process for adapting existing cyber risk assessment standards to allow the calculation of economic impact from IoT cyber risk. The paper presents a new model that includes a design process with new risk assessment vectors, specific for IoT cyber risk. To design new risk assessment vectors for IoT, the study applied a range of methodologies, including literature review, empirical study and comparative study, followed by theoretical analysis and grounded theory. An epistemological framework emerges from applying the constructivist grounded theory methodology to draw on knowledge from existing cyber risk frameworks, models and methodologies. This framework presents the current gaps in cyber risk standards and policies, and defines the design principles of future cyber risk impact assessment. The core contribution of the article therefore, being the presentation of a new model for impact assessment of IoT cyber risk.
On greedy randomized coordinate descent methods for solving large linear least‐squares problems

Summary For solving large scale linear least‐squares problem by iteration methods, we introduce an effective probability criterion for selecting the working columns from the coefficient matrix and construct a greedy randomized coordinate descent method. It is proved that this method converges to the unique solution of the linear least‐squares problem when its coefficient matrix is of full rank, with the number of rows being no less than the number of columns. Numerical results show that the greedy randomized coordinate descent method is more efficient than the randomized coordinate descent method.
Semi-supervised deep learning of brain tissue segmentation

Brain image segmentation is of great importance not only for clinical use but also for neuroscience research. Recent developments in deep neural networks (DNNs) have led to the application of DNNs to brain image segmentation, which required extensive human annotations of whole brain images. Annotating three-dimensional brain images requires laborious efforts by expert anatomists because of the differences among images in terms of their dimensionality, noise, contrast, or ambiguous boundaries that even prevent these experts from necessarily attaining consistency. This paper proposes a semi-supervised learning framework to train a DNN based on a relatively small number of annotated (labeled) images, named atlases, but also a relatively large number of unlabeled images by leveraging image registration to attach pseudo-labels to images that were originally unlabeled. We applied our proposed method to two different datasets: open human brain images and our original marmoset brain images. When provided with the same number of atlases for training, we found our method achieved superior and more stable segmentation results than those by existing registration-based and DNN-based methods.
Coronary Arteries Segmentation Based on 3D FCN With Attention Gate and Level Set Function

Coronary heart disease is one of the most serious health problems in the world nowadays.By segmenting and examining the coronary arteries in medical images, we can find artery stenosis and plaque, which are the main causes of this certain disease.Segmenting coronary arteries manually is timeconsuming and subjective, and the traditional segmentation method requires a good initial point, which is also difficult to apply for the 3D coronary computed tomography angiography (CTA) data.In this paper, we propose a joint framework for coronary CTA segmentation based on deep learning and traditional level set method.A 3D fully convolutional network (FCN) is used to learn the 3D semantic features of coronary arteries, which provides an excellent initial point for the traditional level set.Moreover, an attention gate is added to the entire network, aiming to enhance the vessels and suppress irrelevant regions.The output of 3D FCN with attention gate is optimized by the level set to smooth the boundary to better fit the ground truth segmentation.The proposed algorithm is evaluated by the Jaccard index (JI) and the Dice similarity coefficient (DSC) scores.The experimental results show that the proposed framework provides significant better segmentation results than the vanilla 3D FCN intuitively and quantitively.
CoPart

Workload consolidation is a widely-used technique to maximize server resource utilization in cloud and datacenter computing. Recent commodity CPUs support last-level cache (LLC) and memory bandwidth partitioning functionalities that can be used to ensure the fairness of the consolidated workloads. While prior work has proposed a variety of resource partitioning techniques, it still remains unexplored to characterize the impact of LLC and memory bandwidth partitioning on the fairness of the consolidated workloads and investigate system software support to dynamically control LLC and memory bandwidth partitioning in a coordinated manner.
Integration of social and IoT technologies: architectural framework for digital transformation and cyber security challenges

Growing synergies between the Internet of Things (IoT) and Social technologies are contributing to the advances in Cyber Physical Social Systems. Integration of new technologies is facing key challenges related to information security and privacy. This paper explores the interplay and synergetic relationships among these technologies, identifying relevant interactions, key challenges primarily focusing on cybersecurity and privacy. Specific contributions include (1) concise description of digital transformation based on the literature and resulting potential business impact (2) identification of opportunities related to the integration of social and IoT technologies and (3) challenges in technology convergence with a focus on cybersecurity challenges.
A posthumanist critique of flexible online learning and its “anytime anyplace” claims

Abstract Flexible approaches to online learning are gaining renewed interest in some part due to their capacity to address emergent opportunities and concerns facing higher education. Importantly, flexible approaches to online learning are purported to be democratizing and liberatory, broadening access to higher education and enabling learners to participate in educational endeavours at “anytime” from “anyplace.” In this paper, we critique such narratives by showing that flexibility is neither universal nor neutral. Using critical theory, we demonstrate how flexibility assumes imagined autonomous learners that are self‐reliant and individualistic. Through relevant examples, we show how such a framing to flexibility is oppressive, and argue that a contextual, relative and relational understanding of flexibility may in fact be more liberatory. Such an approach to flexibility, for example, may involve contextual and relational efforts to relax prescribed curricula within courses or programmes of study.
Trade-off between exploration and exploitation with genetic algorithm using a novel selection operator

As an intelligent search optimization technique, genetic algorithm (GA) is an important approach for non-deterministic polynomial (NP-hard) and complex nature optimization problems. GA has some internal weakness such as premature convergence and low computation efficiency, etc. Improving the performance of GA is a vital topic for complex nature optimization problems. The selection operator is a crucial strategy in GA, because it has a vital role in exploring the new areas of the search space and converges the algorithm, as well. The fitness proportional selection scheme has essence exploitation and the linear rank selection is influenced by exploration. In this article, we proposed a new selection scheme which is the optimal combination of exploration and exploitation. This eliminates the fitness scaling issue and adjusts the selection pressure throughout the selection phase. The $$\chi ^2$$ goodness-of-fit test is used to measure the average accuracy, i.e., mean difference between the actual and expected number of offspring. A comparison of the performance of the proposed scheme along with some conventional selection procedures was made using TSPLIB instances. The application of this new operator gives much more effective results regarding the average and standard deviation values. In addition, a two-tailed t test is established and its values showed the significantly improved performance by the proposed scheme. Thus, the new operator is suitable and comparable to established selection for the problems related to traveling salesman problem using GA.
The Speechtransformer for Large-scale Mandarin Chinese Speech Recognition

Attention-based sequence-to-sequence architectures have made great progress in the speech recognition task. The SpeechTransformer, a no-recurrence encoder-decoder architecture, has shown promising results on small-scale speech recognition data sets in previous works. In this paper, we focus on a large-scale Mandarin Chinese speech recognition task and propose three optimization strategies to further improve the performance and efficiency of the SpeechTransformer. Our first improvement is to use a much lower frame rate, which is shown very beneficial to not only the computation efficiency but also the model performance. The other two strategies are scheduled sampling and focal loss, which are both very effective to reduce the character error rate (CER). On a 8,000 hours task, the proposed improvements yield 10.8%-26.1% relative gain in CER on four different test sets. Compared to a strong hybrid TDNN-LSTM system, which is trained with LF-MMI criterion and decoded with a large 4-gram LM, the final optimized Speech-Transformer gives 12.2%-19.1% relative CER reduction without any explicit language models.
CMU Wilderness Multilingual Speech Dataset

This paper describes the CMU Wilderness Multilingual Speech Dataset. A dataset of over 700 different languages providing audio, aligned text and word pronunciations. On average each language provides around 20 hours of sentence-lengthed transcriptions. We describe our multi-pass alignment techniques and evaluate the results by building speech synthesizers on the aligned data. Most of the resulting synthesizers are good enough for deployment and use. The tools to do this work are released as open source, and instructions on how to apply such alignment for novel languages are given.
The state-of-the-art in expert recommendation systems

The recent rapid growth of the Internet content has led to building recommendation systems that guide users to their needs through an information retrieving process. An expert recommendation system is an emerging area that attempts to detect the most knowledgeable people in some specific topics. This detection is based on both the extracted information from peoples’ activities and the content of the documents concerned with them. Moreover, an expert recommendation system takes a user topic or query and then provides a list of people sorted by the degree of their relevant expertise with the given topic or query. These systems can be modeled by information retrieval approaches, along with search engines or a combination of natural language processing systems. The following study provides a critical overview of existing expert recommendation systems and their advantages and disadvantages, considering as well different techniques employed by them.
K-Means Clustering With Incomplete Data

Clustering has been intensively studied in machine learning and data mining communities. Although demonstrating promising performance in various applications, most of the existing clustering algorithms cannot efficiently handle clustering tasks with incomplete features which is common in practical applications. To address this issue, we propose a novel K-means based clustering algorithm which unifies the clustering and imputation into one single objective function. It makes these two processes be negotiable with each other to achieve optimality. Furthermore, we design an alternate optimization algorithm to solve the resultant optimization problem and theoretically prove its convergence. The comprehensive experimental study has been conducted on nine UCI benchmark datasets and real-world applications to evaluate the performance of the proposed algorithm, and the experimental results have clearly demonstrated the effectiveness of our algorithm which outperforms several commonly-used methods for incomplete data clustering.
Concept Drift Detection and Adaption in Big Imbalance Industrial IoT Data Using an Ensemble Learning Method of Offline Classifiers

In a smart factory, thousands of industrial Internet of Things (IIoT) devices or sensors are installed in production machines to collect big data on machine conditions and transmit it to a cyber-physical system in the cloud center of the factory.Then, the system employs a variety of condition-based maintenance (CBM) methods to predict the time point when machines start to be operated abnormally and to maintain them or replace their components in advance so as to avoid manufacturing enormous detective products.CBM suffers from problems of concept drifts (i.e., the distribution of fault patterns may change over time) and imbalance data (i.e., the data with faults accounts for a minority of all data).Ensemble learning that integrates the diversity of multiple classifiers provides a high-performance solution to address these problems.In practice, most companies may not have a sufficient budget to establish a sound infrastructure to support real-time online classifiers, but may have off-the-shelf offline classifiers in their existing systems.However, most previous works on ensemble learning only focused on supporting online classifiers.Consequently, this work proposes an ensemble learning algorithm that supports offline classifiers to cope with three-stage CBM with concept drifts and imbalance data, in which Stages 1 (training an ensemble classifier) and 3 (creating a new ensemble) employ an improved Dynamic AdaBoost.NC classifier and the SMOTE method to address imbalance data; and Stage 2 (detecting concept drifts in imbalance data) employs an improved LFR (Linear Four Rates) method.The experimental results on datasets with different degrees of imbalance show that the proposed method can successfully detect all concept drifts, and has a high accuracy rate in detecting minority-class data, which is over 94%.
An Empirical Investigation of Incident Triage for Online Service Systems

Online service systems have become increasingly popular. During operation of an online service system, incidents (unplanned interruptions or outages of the service) are inevitable. As an initial step of incident management, it is important to be able to automatically assign an incident report to a suitable team. We call this step incident triage, which can significantly affect the efficiency and accuracy of overall incident management. To better understand the incident-triage practice in industry, we perform an empirical study of incident triage on 20 large-scale online service systems in Microsoft. We find that incorrect assignment of incident reports occurs frequently and incurs unnecessary cost, especially for the incidents with high severity. For example, about 4.11% to 91.58% of incident reports are reassigned at least once and the average increment in incident-triage time caused by the reassignments is up to 10.16X. Considering the similarity between bug triage (automatically assigning bug reports to software developers) and incident triage, we then explore the applicability of typical bug-triage techniques to incident triage for online service systems. The results demonstrate that these bug-triage techniques are able to correctly assign incident reports to a certain extent, but still need to be further improved, especially for the incident reports that are assigned incorrectly at the first time. We further discuss possible ways to improve the accuracy of incident triage based on the empirical study. To our best knowledge, we are the first to investigate incident triage in industrial practice. Our results are useful for both practitioners and researchers to develop methods and tools to improve the current incident-triage practice for online service systems.
Survey of agent-based cloud computing applications

In the state of the art, there are very few studies on agent-based Cloud Computing. Nevertheless, this is an emerging trend and the number of studies and applications in this field is beginning to increase. Cloud Computing and Agents are complementary technologies. The features of Cloud Computing can provide advanced computational characteristics to multi-agent systems. In turn, the inclusion of agent systems in the core of the Cloud platform makes it possible to incorporate different functionalities, such as reasoning and learning capabilities. This study analyzes the emerging relationship between both distributed systems. Specifically, this study proposes a new classification from the point of view of Cloud Computing, based on the reference architecture proposed by the National Institute of Standards and Technology and the different responsibilities of each of the roles that participate in the Cloud Computing paradigm as identified in the architecture: Provider, Consumer, Broker, Carrier and Auditor.
Structured general and specific multi-view subspace clustering

In this paper, we propose a structured general and specific multi-view subspace clustering method for image clustering. Unlike most existing multi-view subspace clustering methods which harness the shared cluster structure to preserve the consistence between different views or utilize the diversity regularization to exploit the complementary information from different views, our method learns the structured general and specific representation matrices to obtain the common and specific characteristics of different views with structure consistence and diversity regularization. The general representation matrix guarantees the consistence between different views and the specific representation matrices indicate the diversity among different views. Hence, our method can well exploit the common structure and diversity information of multi-view data. Specifically, the proposed framework can be applied into many existing multi-view subspace clustering methods. Moreover, we develop an efficient and effective optimization approach to solve the objective function of which the time and convergence analyses are also provided. Experimental results on four benchmark datasets are presented to show the effectiveness of proposed method.
Iterated maximum correntropy unscented Kalman filters for non-Gaussian systems

The maximum correntropy unscented Kalman filter (MCUKF) is a newly proposed non-linear state estimation algorithm that is robust to non-Gaussian noise. In this paper, we propose improved versions of MCUKF by introducing a new cost function for the filtering update step. To optimize this cost function, we derive two iterative procedures to compute the state and covariance updates, based on the Gauss-Newton method and the Levenberg-Marguardt method, respectively, leading to two iterated filters GN-IMCUKF and LM-IMCUKF. Simulation results on a target tracking example show that the proposed IMCUKFs have better estimation accuracy and numerical stability under non-Gaussian noise compared with existing related filters.