Reviewerly/exHarmony huggingface dataset:
A database of marine and terrestrial radiogenic Nd and Sr isotopes for tracing earth-surface processes

Abstract. The database presented here contains radiogenic neodymium and strontium isotope ratios measured on both terrestrial and marine sediments. The main purpose of this dataset is to help assess sediment provenance and transport processes for various time intervals. This can be achieved by either mapping sediment isotopic signature and/or fingerprinting source areas using statistical tools. The database has been built by incorporating data from the literature and the SedDB database and harmonizing the metadata, especially units and geographical coordinates. The original data were processed in three steps. Firstly, specific attention has been devoted to providing geographical coordinates to each sample in order to be able to map the data. When available, the original geographical coordinates from the reference (generally DMS coordinates) were transferred into the decimal degrees system. When coordinates were not provided, an approximate location was derived from available information in the original publication. Secondly, all samples were assigned a set of standardized criteria that help split the dataset into specific categories. For instance, samples were distinguished according to their location (“Region”, “Sub-region” and “Location” that relate to locations at continental to city or river scale) or the sample type (terrestrial samples – “aerosols”, “soil sediments”, “river sediments”, “rocks” – or marine samples – “marine sediment” or “trap sample”). Finally, samples were distinguished according to their deposition age, which allowed us to compute average values for specific time intervals. Graphical examples illustrating the functionality of the database are presented and the validity of the process was tested by comparing the results with published data. The dataset will be updated bi-annually in order to add more data points to increase the sampling density or provide new types of samples (e.g. seawater signature) and/or integrate additional information regarding the samples. It is publicly available (under CC4.0-BY Licence) from the GFZ data management service at https://doi.org/10.5880/GFZ.4.3.2019.001.
Deep learning with differential Gaussian process flows

We propose a novel deep learning paradigm of differential flows that learn a stochastic differential equation transformations of inputs prior to a standard classification or regression function. The key property of differential Gaussian processes is the warping of inputs through infinitely deep, but infinitesimal, differential fields, that generalise discrete layers into a dynamical system. We demonstrate excellent results as compared to deep Gaussian processes and Bayesian neural networks.
THE ROLE OF PSYCHOLOGICAL SAFETY

Leader to LeaderVolume 2019, Issue 92 p. 13-19 HESSELBEIN & COMPANY THE ROLE OF PSYCHOLOGICAL SAFETY MAXIMIZING EMPLOYEE INPUT AND COMMITMENT Amy Edmondson, Search for more papers by this author Amy Edmondson, Search for more papers by this author First published: 03 January 2019 https://doi.org/10.1002/ltl.20419Citations: 4Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Citing Literature Volume2019, Issue92Spring 2019Pages 13-19 RelatedInformation
Probabilistic Modeling and Simulation of Vehicular Cyber Attacks: An Application of the Meta Attack Language

Attack simulations are a feasible means to assess the cyber security of systems. The simulations trace the steps taken by an attacker to compromise sensitive system assets. Moreover, they allow to ...
Navigating Virtual Environments Using Leg Poses and Smartphone Sensors

Realization of navigation in virtual environments remains a challenge as it involves complex operating conditions. Decomposition of such complexity is attainable by fusion of sensors and machine learning techniques. Identifying the right combination of sensory information and the appropriate machine learning technique is a vital ingredient for translating physical actions to virtual movements. The contributions of our work include: (i) Synchronization of actions and movements using suitable multiple sensor units, and (ii) selection of the significant features and an appropriate algorithm to process them. This work proposes an innovative approach that allows users to move in virtual environments by simply moving their legs towards the desired direction. The necessary hardware includes only a smartphone that is strapped to the subjects’ lower leg. Data from the gyroscope, accelerometer and campus sensors of the mobile device are transmitted to a PC where the movement is accurately identified using a combination of machine learning techniques. Once the desired movement is identified, the movement of the virtual avatar in the virtual environment is realized. After pre-processing the sensor data using the box plot outliers approach, it is observed that Artificial Neural Networks provided the highest movement identification accuracy of 84.2% on the training dataset and 84.1% on testing dataset.
Spell checker for consumer language (CSpell)

Abstract Objective Automated understanding of consumer health inquiries might be hindered by misspellings. To detect and correct various types of spelling errors in consumer health questions, we developed a distributable spell-checking tool, CSpell, that handles nonword errors, real-word errors, word boundary infractions, punctuation errors, and combinations of the above. Methods We developed a novel approach of using dual embedding within Word2vec for context-dependent corrections. This technique was used in combination with dictionary-based corrections in a 2-stage ranking system. We also developed various splitters and handlers to correct word boundary infractions. All correction approaches are integrated to handle errors in consumer health questions. Results Our approach achieves an F1 score of 80.93% and 69.17% for spelling error detection and correction, respectively. Discussion The dual-embedding model shows a significant improvement (9.13%) in F1 score compared with the general practice of using cosine similarity with word vectors in Word2vec for context ranking. Our 2-stage ranking system shows a 4.94% improvement in F1 score compared with the best 1-stage ranking system. Conclusion CSpell improves over the state of the art and provides near real-time automatic misspelling detection and correction in consumer health questions. The software and the CSpell test set are available at https://umlslex.nlm.nih.gov/cSpell.
The Extension of Semantic Formalization of Service Workflow Specification Language

Service-Oriented Computing (SOC) is changing the way modern information systems that are designed, operated, and evolved. SOC makes possible to aggregate distributed resources at the phases of decision-making support and system operations. When myriads of resources with similar functionalities are available, effective methodologies are demanded to select services and compose them as service workflows for the specified goals. The computation for workflow composition is very complex since it depends on the numbers of services and their dynamic characteristics. Therefore, composing optimized workflows in a timely manner poses a great challenge. We are highly motivated to reduce the complexity of service selection and composition. The formalized semantics in SWSpec is extended so that unqualified or inferior services can be eliminated directly from the scope of the design solution space. In this paper, a brief review of the proposed SWSpec language is given and the focus is on the sematic formalization. A new compositional proof-system is developed with a set of inference rules and the proven system properties. The proposed semantic formalization has its great significance in reducing the complexity of composing workflows and developing efficient algorithms for compliance checking.
Incorporating Negative Sample Training for Ship Detection Based on Deep Learning

While ship detection using high-resolution optical satellite images plays an important role in various civilian fields—including maritime traffic survey and maritime rescue—it is a difficult task due to influences of the complex background, especially when ships are near to land. In current literatures, land masking is generally required before ship detection to avoid many false alarms on land. However, sea–land segmentation not only has the risk of segmentation errors, but also requires expertise to adjust parameters. In this study, Faster Region-based Convolutional Neural Network (Faster R-CNN) is applied to detect ships without the need for land masking. We propose an effective training strategy for the Faster R-CNN by incorporating a large number of images containing only terrestrial regions as negative samples without any manual marking, which is different from the selection of negative samples by targeted way in other detection methods. The experiments using Gaofen-1 satellite (GF-1), Gaofen-2 satellite (GF-2), and Jilin-1 satellite (JL-1) images as testing datasets under different ship detection conditions were carried out to evaluate the effectiveness of the proposed strategy in the avoidance of false alarms on land. The results show that the method incorporating negative sample training can largely reduce false alarms in terrestrial areas, and is superior in detection performance, algorithm complexity, and time consumption. Compared with the method based on sea–land segmentation, the proposed method achieves the absolute increment of 70% of the F1-measure, when the image contains large land area such as the GF-1 image, and achieves the absolute increment of 42.5% for images with complex harbors and many coastal ships, such as the JL-1 images.
On the Impact of Globalization on Financial Development: A Multi-country Panel Study

In this paper, we examine whether globalization effects financial development by generating incentives for institutional reforms, and therefore, enhance economic growth due to increased financial development in addition other channels.Specifically, we examine the relationship between globalization and financial development for a panel data of 36 countries over the period 1996-2016, using panel data estimation methods.In addition to overall globalization, we also consider economic, political and social subdimensions of globalization.Moreover, not only the financial institutional development but also its access, depth and efficiency dimensions are considered.The study also controls for several other covariates affecting financial development.The shows that all measures of globalization strongly enhances financial institutional development and its subdimensions except the efficiency for which the evidence is weaker.Thus, globalization has generated incentives for institutional reforms, and therefore contributed positively to financial institutional development and economic growth.
Artificial intelligence to support human instruction

The popular media’s recent interest in artificial intelligence (AI) has focused on autonomous systems that might ultimately replace people in fields as diverse as medicine, customer service, and transportation and logistics. Often neglected is a subfield of AI that focuses on empowering people by improving how we learn, remember, perceive, and make decisions. This human-centered focus relies on interdisciplinary research from cognitive neuroscience, psychology, and theoretical computer science.The synergy among these fields promises to improve the microorganization of human instruction: picking the next exercise for a student to attempt, choosing what sort of hints and feedback to provide, determining when material should be reviewed, and selecting among teaching activities. These are decisions which occur on a granularity that human instructors are typically unable to monitor and individualize, and for which students have poor metacognitive strategies (1). Such microinstruction complements the strengths of human teachers and can yield significant benefits. For example, in a semester-long experiment integrated into a middle-school foreign language course, setting aside roughly 30 min per week for AI-guided personalized review of previously introduced material led to a 16.5% improvement in overall course retention on a cumulative examination administered a month after the end of the semester, relative to a time-matched control condition that reflects current educational practice (2).In PNAS, Tabibian et al. (3) address the learning and retention of factual material such as foreign language vocabulary. They present an adaptive, data-driven method with theoretical guarantees for scheduling retrieval practice. Their work contributes to a growing body of results in algorithmic-education theory that proves properties of idealized mathematical models of educational scenarios (4⇓–6).Like every proposal for AI-based instruction, the approach of Tabibian et al. (3) is formalized by two models: student and teacher. The student model quantifies an individual’s current … [↵][1]1To whom correspondence should be addressed. Email: mozer{at}colorado.edu. [1]: #xref-corresp-1-1
In-situ non-contact 3D optical deformation measurement of large capacity composite tank based on close-range photogrammetry

Recently, fiber-reinforced composites are widely used in manufacturing of large storage tanks for transportation purposes, thanks to their excellent mechanical and chemical performance, and their good applicability to the shaping of particular geometries. In this contribution, a non-contact 3D optical deformation measurement technique is proposed for monitoring the deformation of carbon-fiber reinforced composite tank subjected to different loading scenarios, which could not be achieved by traditional displacement sensors and resistance strain gauges due to large measuring ranges. This technique is based on the close-range photogrammetry technology, wherein 3D information is extracted from two-dimensional photographs. This is done by positioning artificial reference point markers on the measuring tank before loading, and calculate the 3D coordinates of these markers through analyzing the group photos captured in each loading stage. By tracking and comparing the 3D coordinates of the point markers among different loading stages, the 3D deformation of the tank is obtained. The loading tests comprise both hydrostatic pressure and water-pressurizing tests. The performed in-situ measurements show that the proposed technique can fulfill the efficiency and accuracy requirement of deformation measurement in tank loading tests. This in turn significantly contributes to better understanding the mechanical response at the macroscopic scale, thus improving safety performance.
Adaptive deduplication of virtual machine images using AKKA stream to accelerate live migration process in cloud environment

Cloud Computing is a paradigm which provides resources to users from its pool based on demand to satisfy their requirements. During this process, many servers are overloaded and underloaded in the cloud environment. Thus, power consumption and load balancing are the major problems and are resolved by live virtual machine (VM) migration. Load balancing is addressed by moving virtual machines from overloaded host to under loaded host and from under loaded host to any other host which is not overloaded called VM migration. If this process is done without power off (Live) the virtual machines then it is called live VM migration. By this process, the issue of power consumption by physical hosts is also resolved. Migrating virtual machines involves virtualized components like storage disks, memory, CPU and networking, the entire state of VM is captured as a collection of data files. These data files are virtual disk files, configuration files, and log files. The virtual disk files take larger memory and take more migration time and hence the downtime. These disk files include many zero pages, similar and redundant pages. Many techniques such as compression, deduplication is used reduce the size of VM disk image file. Compression techniques are not widely used, due to the disadvantage of compression ratio and decompression time. Many researchers hence used deduplication techniques for reducing the VM disk image file in the live migration process. The significance of the research work is to design an adaptive deduplication mechanism for reducing VM disk image file size by performing fixed length and variable length block-level deduplication processes. The Rabin-Karp rolling hash algorithm is used in variable length block-level deduplication. Akka stream is used for streaming the VM disk image files as it is the bulk volume of live data transfer. To reduce the time of the deduplication process, many researchers used multithreading and multi-core technologies. We use multithreading in Akka framework to run the deduplication process concurrently without OutofMemory errors. The experiment results show that we achieved a maximum of 83% overall reduction in image storage space and 89.76% reduction in total migration time are achieved by adaptive deduplication method. 3% improvement in deduplication rate when compared with the existing image management system. The results are significant because when we apply this in the storage of data centres, there are much space savings. The reduction in size is dependent on the dataset was taken and the applications running on the VM.
Foreground Fisher Vector: Encoding Class-Relevant Foreground to Improve Image Classification

Image classification is an essential and challenging task in computer vision. Despite its prevalence, the combination of the deep convolutional neural network (DCNN) and the Fisher vector (FV) encoding method has limited performance since the class-irrelevant background used in the traditional FV encoding may result in less discriminative image features. In this paper, we propose the foreground FV (fgFV) encoding algorithm and its fast approximation for image classification. We try to separate implicitly the class-relevant foreground from the class-irrelevant background during the encoding process via tuning the weights of the partial gradients corresponding to each Gaussian component under the supervision of image labels and, then, use only those local descriptors extracted from the class-relevant foreground to estimate FVs. We have evaluated our fgFV against the widely used FV and improved FV (iFV) under the combined DCNN-FV framework and also compared them to several state-of-the-art image classification approaches on ten benchmark image datasets for the recognition of fine-grained natural species and artificial manufactures, categorization of course objects, and classification of scenes. Our results indicate that the proposed fgFV encoding algorithm can construct more discriminative image presentations from local descriptors than FV and iFV, and the combined DCNN-fgFV algorithm can improve the performance of image classification.
Language Person Search with Mutually Connected Classification Loss

In this work, we develop an effective person search algorithm with natural language descriptions. The contributions of this work mainly include two aspects. First, we design a baseline language person search framework including three basic components: a deep CNN model to extract visual features, a bi-directional LSTM to encode language descriptions and the triplet loss to conduct cross-modal feature embedding. Second, we propose a novel mutually connected classification loss to fully exploit the identity-level information, which not only introduces the identification information into both image and language descriptions but also encourages the cross-modal classification probabilities of the same identity to be more similar. The experimental results on the CUHK-PEDES dataset demonstrate that our method achieves significantly better performance than other state-of-the-art algorithms.
Part-Based Attribute-Aware Network for Person Re-Identification

Despite the rapid progress over the past decade, person re-identification (reID) remains a challenging task due to the fact that discriminative features underlying different granularities are easily affected by illumination and camera-view variation.Most deep learning-based algorithms for reID extract global embedding as the representation of the pedestrian from the convolutional neural network.Considering that person attributes are robust and informative to identify pedestrians.This paper proposes a multi-branch model, namely part-based attribute-aware network (PAAN), to leverage both person reID and attribute performance, which not only utilizes ID label visible to the whole image but also utilizes attribute information.In order to learn discriminative and robust global representation which is invariant to the fact mentioned above, we resort to global and local person attributes to build global and local representation, respectively, utilizing our proposed layered partition strategy.Our goal is to exploit global or local semantic information to guide the optimization of global representation.Besides, in order to enhance the global representation, we design a semantic bridge replenishing mid-level semantic information for the final representation, which contains high-level semantic information.The extensive experiments are conducted to demonstrate the effectiveness of our proposed approach on two large-scale person re-identification datasets including Market-1501 and DukeMTMC-reID, and our approach achieves rank-1 of 92.40% on Market-1501 and 82.59% on DukeMTMC-reID showing strong competitiveness among the start of the art.
Effects of music, video, and 360‐degree video on cycle ergometer exercise at the ventilatory threshold

Despite the seemingly ubiquitous presence of audiovisual stimuli in modern exercise facilities, there is a dearth of research examining the effects of audiovisual stimuli in combination during exercise. Accordingly, we examined the influence of a range of audiovisual stimuli on the improvement of affective, perceptual, and enjoyment responses to cycle ergometer exercise at the ventilatory threshold (VT), an intensity that is associated with the most affect‐related interindividual variability. A within‐subject design was employed, and participants (N = 18) completed a 25‐minute protocol that consisted of 2 minutes of seated rest, 5 minutes of warm‐up, 10 minutes of exercise at VT, 5 minutes of cooldown, and 3 minutes of seated rest. Participants exercised at VT under music, video, music‐video, 360‐degree video, 360‐degree video with music, and control conditions. The results revealed a condition × time interaction for perceived activation and a main effect of condition for state attention and perceived enjoyment. The 360‐degree video with music condition elicited the most positive affective valence, greatest perceived activation, most dissociative thoughts, and highest ratings of perceived enjoyment. The present findings indicate that audiovisual stimuli can influence affective, perceptual, and enjoyment responses to cycle ergometer exercise at the VT. Given the emerging support pertaining to a positive relationship between affective responses and exercise adherence, audiovisual stimuli, such as 360‐degree video with music, should be considered as a means by which to promote an enjoyable exercise experience.