Reviewerly/exHarmony huggingface dataset:
Predicting 2,4-dintroanisole (DNAN) sorption on various soil “types” using different compositional datasets

Soil heterogeneity is a major contributor to the uncertainty in predicting the environmental fate of data-scare contaminants. For this paper, we focused on research designed to predict the soil environmental fate of the new munition compound, called 2,4-dinitroanisole (DNAN) -a compound increasingly employed by the U.S. and international militaries in the next-generation, insensitive explosive formulations. Here, we employed multivariate statistical correlation models to predict DNAN sorption among different soil “types” seeking to reduce the uncertainty common in all contaminant sorption models by using soil taxonomic designation as a calibrant. We collected composite soils classified under the Ultisols taxonomic Order in the U.S. National Resource Conservation Service soil classification system and quantified their properties via physical and chemical characterizations. Using multivariate statistical modeling modified for compositional data analysis (CoDa), we developed quantitative analogies of the Ultisols by partitioning the characterization data up into four different compositions: Water-extracted, Mehlich-III (referring to the weak acid) extracted, particle-size distribution, and solid-phase carbon‑nitrogen‑sulfur compositions. DNAN sorption was measured in batch soil suspensions and distribution coefficients (KD) were calculated using linear regression modeling. Prediction models testing the correlation of the DNAN KD values to the centered logratio -transformed compositions were calculated using CoDa-modified multilinear regression. Results showed that DNAN sorption was only predictable by dissolved organic carbon, pH, and the exchangeable cations Ca and K within the water-extracted composition. Analogies for DNAN sorption were the most discriminating at the Suborder level because of the inherent ambiguity in the Hapludults class at the Great Group level.
Pannellum: a lightweight web-based panorama viewer

Pannellum is an interactive web browser-based panorama viewer written in JavaScript and primarily based on the WebGL web standard (Jackson, 2014) for graphics processing unit (GPU)-accelerated rendering to the HTML5 <canvas> element (Cabanier, Mann, Munro, Wiltzius, & Hickson, 2015).It supports the display of panoramic images that cover the full sphere, or only parts of it, in equirectangular format, in cube map format, or in a tiled format that encodes the panorama in multiple resolutions, which allows for parts of the panorama to be dynamically loaded, reducing data transfer requirements.In addition to single panoramas, multiple panoramas can be linked together into a virtual tour, with navigation enabled via "hot spots," which can also be used to add annotations.
Carried Object Recognition via Location Relation with Body Parts

We propose a robust carried object recognition method from surveillance video. Though tremendous strides have been made in the object recognition by using the appearance-based method such as Faster R-CNN, YOLO, etc., recognizing carried object classes is still a difficult task. It is because the carried objects in surveillance video are of small size, low-resolution, occluded by people. To address the problem, we introduce prior knowledge of the location relation between carried objects and human body parts to improve the performance of the appearance-based method. Specifically, for the extracted human region, we detect human body parts and use prior knowledge to estimate the probability distribution of the existence of each class of carried objects. We update the score results of the appearance-based method by applying the probability distribution. In this way, we can reduce false positives and enhance confidence score of true positives. We evaluate the proposed method on four datasets and show that the proposed method outperforms Faster R-CNN by 6% in terms of average F-measure.
Big Data Benchmarks of High-Performance Storage Systems on Commercial Bare Metal Clouds

Bare metal servers are widely available on public clouds to provide direct access to hardware and the system configuration with high performance storage and network devices are well suited for big data applications. Highly-optimized server with additional CPU core count and dense storage may lead to better performance in certain workloads and to ensure responsiveness of deployed services. Recent work on Hadoop ecosystems has addressed the performance improvement of scale-up machines configured with SSD storage and increased network bandwidth. The paper evaluates big data processing on dedicated clusters and provides the performance analysis of NVMe devices and SSD block storage options available on Amazon, Google, Microsoft, and Oracle Clouds. We show the benchmark results along with the system performance tests as we want to demonstrate the compute resource requirements for large-scale applications. The system capacity and limits for the underlying servers are described along with the cost analysis of scaling workloads on these platforms.
Feature Aggregation Convolution Network for Haze Removal

The Haze Removal technique refers to the process of reconstructing haze free images from inclement weather conditions in the same scene, which has a more extensive demand in practical application. At present, the model based on the deep convolution neural network has made significant progress in the field of haze removal, and its effect has greatly exceeded the traditional prior and constraint methods. In view of the current CNN based dehazing methiods only take one input image into consideration so that they cannot capture enough features for indicating the optimal transmission maps, we propose and design a Feature Aggregation Convolution Network (FACN), with the multi inputs and feature aggregated of CNN model and adversarial loss algorithm. A comparative experiment with a few previous methods shows improvement visual results.
Lossless Light Field Compression Using 4D Wavelet Transforms

This paper describes a lossless light field compression algorithm that exploits the redundancies present in all four dimensions of the light field using a 4D wavelet transform. We first group spatially collocated pixels from each view to form a hyper-pixel view. The redundancy present in each hyper-pixel is decorrelated using a 2D Haar wavelet transform. This process removes the angular correlations present in the light field. Nevertheless, each angular view is still spatially correlated. The 2D Cohen-Daubachies-Feaveau 5/3 wavelet transform is then employed to suppress the spatial redundacy of the light field. The resulting coefficients of the decorrelated light fields are then encoded using an arithmetic coding scheme. Simulation results demonstrate that the proposed lossless light field compression algorithm outperforms the lossless versions of JPEG, JPEG2000 and HEVC, and achieves a 47% bitrate reduction compared to the state of the art approach.
Using Structural Similarity to Predict Future Workload Behavior in the Cloud

Predicting workload behavior, in response to changes in allocated resources, is a critical part of effective resource management in the cloud. This paper presents a novel approach to predicting the future behavior of a reference workload based on a nearest neighbor similarity search in euclidean space. The proposed approach involves identifying a similar workload candidate, predicting the future behavior of the reference based on the candidate and then validating the prediction using a statistical hypothesis test. Finally, decision rules are generated that specify the required conditions for a successful prediction.
The Influence of Culture and Structure on Autonomous Teams in Established Companies

Motivation: Many companies aim to provide more autonomy to their development teams. While some teams report on successes, others still struggle with the agile adaption, e.g. due to the organisational environment. Objective: Our objective was to explore how organisational culture and structure influence team autonomy in bureaucratic companies. Method and Results: 30 qualitative interviews from different business divisions at a conglomerate revealed that organisational factors related to hierarchy, specialist culture and functionally departmentalised structure decreased agile team features and consequently resulted in a reduced speed of decision-making. We suggest the Agile Matching Theory which implies that prevalent organisational factors and desired agile team features need to match to allow team autonomy to occur. Conclusion: We therefore encourage managers to further work on a learning organisation and a supportive structure within which autonomous teams can grow.
Time-Sensitive Provisioning of Bare Metal Compute as a Cloud Service

A modern cloud service that is getting popular is the supply of bare metal servers on demand to consumers who need to run high-performance algorithms for short periods of time. However, provisioning-time latencies are unpredictable in bare metal commerce, primarily because suppliers cannot exploit virtualization levers to adjust and optimize capacity. This impacts bare metal service leverage because, in the face of indeterministic fulfillment times, consumers often pre-provision for peak demand, which goes against the fundamental tenets and advantages of cloud adoption. To address this, we advocate that a cloud service broker offers time-sensitive bare metal provisioning services by building and maintaining an inventory of bare metal servers. This, however, leads to inventory optimization and profit maximization problems that resemble what traditional supply chains face, but with characteristics unique to on-demand compute economics. In this paper, we build a brokered bare metal supply chain model that identifies impacting variables, their characteristics, and inter-relationships. We argue and demonstrate that, given complex inter-variable relationships and environmental uncertainties, simulation runs are needed to complete the model and yield recommendations to optimize inventory and maximize profits.
Relationships between Oculo-Motor Mesures as Task-evoked Mental Workloads During an Manipulation Task

The frequency of microsaccades is often used as a measurement of eye movement in order to estimate the level of a cognitive mental workload during an experimental task. When the experiment includes several levels of manipulation tasks, ocular information including microsaccades, saccades and pupil diameters can be measured and compared. The results show that the microsaccade rate reflects a participant's subjective impressions. In addition to measurements of oculomotors, microsaccade rates also reflect the level of effort required by a mental workload, and the mutual relationships between these rates are analyzed using causal analysis.
Research Hotspot Prediction and Regular Evolutionary Pattern Identification Based on NSFC Grants Using NMF and Semantic Retrieval

Analyzing the research hotspots and regular evolutionary patterns of R&D projects can help researchers find potential information. This paper considers the titles of the National Natural Science Foundation of China (NSFC) grants that have been awarded in the past 20 years as the research objects. First, we analyze the number and funding amounts of project grants for each department over the past 20 years. Second, we propose a topic discovery method that is based on nonnegative matrix factorization and further propose a keyword scoring method that is based on semantic retrieval, from which we can discover the regular evolutionary patterns of research hotspots. Finally, we conduct experiments on datasets on grants in the Department of Information Science. To identify research characteristics, trends and prospects, we explore the regular evolutionary patterns of research hotspots via experiments using three methods from multiple perspectives: word cloud, topic corresponded to keywords display and topic evolution display. The results of the experimental studies demonstrate that researchers can keep abreast of the main content and hotspots in the research field via hot spot discovery and the identification of regular evolutionary pattern of NSFC grants. This study can also help the government improve the allocation of scientific and technological resources and help decision makers make scientific decisions.
Evaluating Boundary Conditions and Hierarchical Visualization in CBIR

Multiple descriptors are employed to represent images in Content-Based Image Retrieval (CBIR) systems. Each descriptor consists of a feature extractor associated with a distance function. An extractor is generally suitable for representing a specific subset of images on a database. The boundary conditions are information used to detect this subset. The use of visualization in CBIR helps to represent the similarity relationship between images, improving the user's understanding of the CBIR system, allowing them to modify parameters to obtain better results. It is proven that the use of multiple descriptors with boundary conditions tends to improve the precision of CBIR queries, but there is no data on the impact that the technique generates on visualization. This paper uses multiple descriptors with boundary conditions to generate a Neighbor Joining similarity tree-based view. Tests have shown that the quality of the visualization may be related to the quality of the query result. In the context of similarity trees, the use of multiple descriptors contributed to a better-organized visualization.
A Signcryption Scheme Based Learning with Errors over Rings Without Trapdoor

Signcryption has been extensively studied based on bilinear pairings, but rarely on lattices. This paper constructed a new lattice-based signcryption scheme in the random oracle model by skillfully combining a learning with errors over ring (R-LWE) based signature scheme (using the Fiat-Shamir with aborts technique) and a R-LWE based key exchange scheme, and then given a tight security reduction of strong unforgeability against adaptive chosen messages attacks (SUF-CMA) and indistinguishability against adaptive chosen ciphertext attacks (IND-CCA2) from the R-LWE problem to proposed scheme. This construction removed trapdoor by using the Fiat-Shamir with aborts technique, thus has a high efficiency. It needn’t decrypt random coins for unsigncryption which may be used to construct multi-receiver signcryption.
Structural Texture Similarity for Material Recognition

We propose a new direct approach for material recognition under diverse illumination and viewing conditions based on visual texture. We apply K-means clustering to feature vectors that consist of steerable filter subband statistics and dominant colors of each texture image in order to obtain a small number of exemplars characterizing each material. We then use structural texture similarity metrics and color composition metrics to compare a query texture to the exemplars for material classification. Experimental results using the CUReT database establish the importance of color and demonstrate that five exemplars per texture provide performance comparable to the state of the art.
Opportunities and Challenges in Applying Distributed Ledger Technologies in Global Supply Chains for Social Good

OPINION article Front. Blockchain, 06 September 2019Sec. Blockchain for Good Volume 2 - 2019 | https://doi.org/10.3389/fbloc.2019.00011
Multi-Channel Structure of Multiscanning Observation Device Based on Hough Transform in Surveillance Radar

In this paper we consider a surveillance radar with an additional multiscanning observation device. Its functioning is based on coprocessing of the results of several scannings. In each multiscanning observation channel as part of the device the well-known procedure “track-before-detect” is implemented and it is supposed to process echo signals from a different number of space scannings stored in the buffer storage. The efficiency of the proposed multi-channel procedure is analysed.
Explanation of Recommenders Using Formal Concept Analysis

Formal Concept Analysis is a mathematical approach which enables formalisation of concepts as basic units of human thinking and analysing data in the object-attribute form. In this paper, we propose the use of FCA as a general resource for explanations and apply it to explain the results of recommender systems. Our method is reusable and applicable to different domains. We define different types of explanations by travelling the lattice structure and analyse how the lattice metrics can be used to characterise the different types of user profiles.
Redundancy Minimization and Cost Reduction for Workflows with Reliability Requirements in Cloud-Based Services

Reliability requirement assurance is an important quality of service (QoS) for workflow execution in cloud-based services. For a workflow with a reliability requirement, the enough replication for redundancy minimization (ERRM) and quantitative fault-tolerance with minimum execution cost + (QFEC+) algorithms are state-of-the-art algorithms to reduce the redundancy and cost, respectively. In this work, we define the reliability increment ratio (RIR) and propose the redundancy minimization using RIR (R_RIR) algorithm. In addition, we introduce the geometric mean and propose the cost reduction using geometric mean (C_GM) algorithm based on redundancy minimization. Experimental results show the proposed R_RIR and C_GM algorithms are superior to state-of-the-art algorithms: (1) although both R_RIR and ERRM show the same redundancy results, R_RIR is proven to generate minimal redundancy, whereas ERRM cannot; (2) R_RIR only consumes a few seconds to achieve minimal redundancy for large-scale workflows, and it has much higher time efficiency than ERRM; and (3) C_GM generates less cost than QFEC+ in a large part of cases.
DAnet: Depth-Aware Network for Crowd Counting

Image-based people counting is a challenging work due to the large scale variation problem caused by the diversity of distance between the camera and the person, especially in the congested scenes. To handle this problem, the previous methods focus on building complicated models and rely on labeling the sophisticated density maps to learn the scale variation implicitly. It is often time-consuming in data pre-processing and difficult to train these deep models due to the lack of training data. In this paper, we thus propose an alternative and novel way for crowd counting which handles the scale variation problem by leveraging the auxiliary depth estimation dataset. Using separated crowd and depth datasets, we train a unified network for two tasks- crowd density map estimation and depth estimation- at the same time. By introducing the auxiliary depth estimation task, we prove that the scale problem caused by distance can be well solved and the labeling cost can be reduced. The efficacy of our method is demonstrated in the extensive experiments by multiple evaluation criteria.
A Novel Embedded Platform for Secure and Privacy-Concerned Cross-Domain Service Access

Connected driving is a hot topic in the automotive industry and a leverage to push new Mobility as a Service (MaaS) methodologies, making vehicles an essential part of the Internet of Things (IoT). However, these new technologies often lead to security risks and privacy concerns, especially due to the increasing number of datasets exchanged between vehicles, drivers, and local infrastructure. Furthermore, the possibilities for vehicles to access heterogeneous services offered by different service providers are often limited due to rigid system boundaries. In this paper we present a novel federated service management concept for increased interoperability across distinct services in the field of Smart Mobility and Smart Cities. Our approach provides secure authentication and authorization between cars, their drivers, and other information systems, while retraining the level of privacy according to the users' preferences. The scalability and dynamic configurability of the solution and the elaborated proof-of-concept will set it apart from application-centered gateways to an embedded generic platform by virtue of its modular software design.
Error Back Propagation Algorithm with Adaptive Learning Rate

Error Back Propagation algorithm is one of the most popular method for training artificial neural networks. Unfortunately, this is also one of the slowest due to constant and small learning rate parameter used to update weights of neurons. There are therefore many methods for dynamic change this parameter in the adaptive manner. In this paper has been presented new method based on estimation of learning rate with respect of approximation for the value of output error.
Frame Stitching in Human Oral Cavity Environment Using Intraoral Camera

Intra-oral cameras are essentially enabling dentists to capture images of difficult-to-reach areas in the mouth. Oral dental applications based on visual data pose various challenges such as low lighting conditions and saliva. We introduce an approach to stitch images of human teeth that are captured by an intra-oral camera. In such monocular image matching, a low rate of features on teeth surfaces causes a problem leading to a mismatch between teeth images. In this paper, we propose an approach to improve the matching in these low-texture regions. First, normals of tooth surface is extracted using a shape from shading. Due to the oral environment, the surface normals impact many of imprecise values; hence we formulate an algorithm to rectify these values and generate normal maps. The normal maps reveals the impacted geometric properties of the images inside an area, boundary, and shape. Second, the normal maps are used to detect, extract and match the corresponding features. Finally, to enhance the stitching process for these unidealized data, normal maps are used to estimate as-projective-as-possible warps. The proposed approach outperforms the state-of-the-art auto-stitching approach and shows a better performance in such cases of low-texture regions.
orthoDr: Semiparametric Dimension Reduction via Orthogonality Constrained Optimization

orthoDr is a package in R that solves dimension reduction problems using orthogonality constrained optimization approach.The package serves as a unified framework for many regression and survival analysis dimension reduction models that utilize semiparametric estimating equations.The main computational machinery of orthoDr is a first-order algorithm developed by Wen and Yin (2012) for optimization within the Stiefel manifold.We implement the algorithm through Rcpp and OpenMP for fast computation.In addition, we developed a general-purpose solver for such constrained problems with user-specified objective functions, which works as a drop-in version of optim().The package also serves as a platform for future methodology developments along this line of work.