Reviewerly/exHarmony huggingface dataset:
Composition and diagenesis of Neoproterozoic Guaritas Group sandstones in the Minas do Camaquã fault zone, Camaquã Basin

Geological structures influence the formation and evolution of sedimentary rocks. The Minas do Camaquã fault zone is a primary structure of the Camaquã Basin, controlling the uplift of the ore-bearing units of the Santa Bárbara Group. To the south of the Camaquã River, the fault zone deforms alluvial and eolian sequences attributed either to the Santa Bárbara or Guaritas groups. In this study, a facies and petrographic composition and diagenetic analysis are presented to understand the evolution of the fault zone. Facies analysis was accomplished using high-resolution orthophoto mosaics and field surveys. Seven sedimentary facies were defined, grouped into three facies associations. Facies associations correspond to a succession of climate-influenced depositional environments. The transition from humid to dry conditions occurs from a fluvial (facies association 1) to eolian environments (facies association 2). These deposits are overlaid at the top by a high energy environment deposits characterized by amalgamated gravelly and sandy bodies, corresponding to an alluvial environment (facies association 3). Despite a small compositional variation, sandstones present a continental block provenance which may be related to mixed anorogenic or orogenic provenance. Diagenetic features are similar in the three facies associations, suggesting the same burial history for the sedimentary deposits separated by the fault zone. The Minas do Camaquã fault zone in the study area is an intraformational structure, as analyzed sequences are attributed to the Guaritas Group, implying a relatively high degree of deformation late after its deposition.
Low power &amp; mobile hardware accelerators for deep convolutional neural networks

This article provides a comprehensive review of recent developments in the field of computational hardware for mobile low power machine learning hardware accelerators. The article provides an introduction to neural networks, convolutional neural networks and details recent developments in state of the art deep convolutional neural networks. The key considerations in the design of low power hardware accelerators are discussed with reference to a conceptual system. Strategies for reducing the energy cost of memory access and computation in state of the art hardware accelerators are detailed. This includes techniques such as dataflow, reduced precision, model compression and sparsity. Recent reported digital mobile accelerators for deep convolutional neural networks with power consumptions of less than 3.3 W are observed to have 4x-20x better efficiency than the reference GPU accelerator at 16-bit precision, and can achieve 20x-1171x better efficiency at less than 4-bit precision. Efficiency improvements of 20x-1171x over a GPU is observed for reported mobile accelerators with reduced precision.
Resource Management and Scheduling for Big Data Applications in Cloud Computing Environments

This chapter presents software architectures of the big data processing platforms. It also provides in-depth knowledge on resource management techniques involved while deploying big data processing systems in the cloud environment. It starts from the very basics and gradually introduce the core components of resource management which are divided into multiple layers. It covers the state-of-art practices and researches done in SLA-based resource management with a specific focus on the job scheduling mechanisms.
Improved appearance loss for deep estimation of image depth

Several recent deep network architectures tried to handle the depth estimation process as an image reconstruction problem, in order to overcome the shortfall that the ground truth depth data is not sufficiently available. The authors introduce and validate an efficient deeper network architecture for unsupervised depth estimation with an automated parameter optimisation. In addition, a hybrid appearance loss function is also proposed to improve the depth estimation accuracy and effectiveness. The authors' proposed model achieves the advantage that individual element of the loss function is weighted using normal distribution characteristics of a Gaussian model. The proposed ideas are validated on KITTI dataset achieving best reported results among recent state-of-the-art methods.
The role of cognitive biases in anticipating and responding to cyberattacks

Purpose The purpose of this paper is to explore the role of four cognitive biases, namely, selective perception, exposure to limited alternatives, adjustment and anchoring, and illusion of control in anticipating and responding to Distributed-Denial-of-Service (DDoS) attacks. Design/methodology/approach The paper is based on exploratory case study research and secondary data on decision making in the Australian Bureau of Statistics (ABS) in regards to planning and managing DDoS attacks on Census day in 2016. Findings Cognitive biases limited the ABS’s awareness of the eCensus system’s vulnerabilities, preparation for and management of DDoS attacks. Cyberattacks are on the increase, and managers should expect and be prepared to deal with them. Research limitations/implications Due to the sensitivity of the topic, it was not possible to interview relevant stakeholders. Analysis is based on high-quality secondary data that includes comprehensive government reports investigating the events on Census day. Practical implications Cyberattacks are inevitable and not an aberration. A checklist of actions is identified to help organisations avoid the failures revealed in the case study. Managers need to increase their awareness of cyberattacks, develop clear processes for dealing with them and increase the robustness of their decision-making processes relating to cybersecurity. Originality/value This the authors believe that it is the first major study of the DDoS attacks on the Australian census. DDoS is a security reality of the twenty-first century and this case study illustrates the significance of cognitive biases and their impact on developing effective decisions and conducting regular risk assessments in managing cyberattacks.
Theoretical study on optical response in nanostructures in the Born–Markov regime: The role of spontaneous emission and dephasing

We theoretically study the linear and nonlinear optical susceptibility in a nanostructure that interacts with a classical monochromatic optical field, by means of the solution of a Born–Markov master equation in the Lindblad form, considering as environment for the system the quantum vacuum of the continuum of modes of the external electromagnetic field. The processes of spontaneous emission and dephasing between two levels of the nanostructured quantum system are studied formally, within the framework of the quantum theory of open systems. It was found that the phenomenological master equation corresponds to a Born–Markov master equation in the Lindblad form that considers only the dissipative terms of spontaneous emission and dephasing, provided that the transverse coherence time T2 is smaller than spontaneous emission time T1. Finally, comparative results are presented between the proposed methodology and the conventional method for: optical rectification, optical absorption, generation of second harmonic and generation of third harmonic in a cylindrical quantum dot (CQD) of AsGa∕AsGaAl with a hydrogen-like impurity inside it and under the action of a uniform magnetic field.
Domain Attentive Fusion for End-to-end Dialect Identification with Unknown Target Domain

End-to-end deep learning language or dialect identification systems operate on the spectrogram or other acoustic feature and directly generate identification scores for each class. An important issue for end-to-end systems is to have some knowledge of the application domain, because the system can be vulnerable to use cases that were not seen in the training phase; such a scenario is often referred to as a domain mismatched condition. In general, we assume that there is enough variation in the training dataset to expose the system to multiple domains. In this work, we study how to best make use a training dataset in order to have maximum effectiveness on unknown target domains. Our goal is to process the input without any knowledge of the target domain while preserving robust performance on other domains as well. To accomplish this objective, we propose a domain attentive fusion approach for end-to-end dialect/language identification systems. To help with experimentation, we collect a dataset from three different domains, and create experimental protocols for a domain mismatched condition. The results of our proposed approach, which were tested on a variety of broadcast and YouTube data, shows significant performance gain compared to traditional approaches, even without any prior target domain information.
Shifted powers in Lucas–Lehmer sequences

We develop a general framework for finding all perfect powers in sequences derived via shifting non-degenerate quadratic Lucas–Lehmer binary recurrence sequences by a fixed integer. By combining this setup with bounds for linear forms in logarithms and results based upon the modularity of elliptic curves defined over totally real fields, we are able to answer a question of Bugeaud, Luca, Mignotte and the third author by explicitly finding all perfect powers of the shape $$F_k \pm 2 $$ where $$F_k$$ is the k-th term in the Fibonacci sequence.
Health literacy as knowledge construction: Learning about health by expanding objects and crossing boundaries in networked activities

In this paper we examine health literacy as a set of practices that unfold in networked activity systems. Focusing on the literacy practices of pregnant couples and parents of children with heart defect, we show that they participate in multiple activities with the object of constructing knowledge about the child's condition. The contexts for these activities are doctor-patient consultations and the parents' online searching and sharing. The study builds on ethnographic interviews, recorded medical consultations and collection of texts from online forums, blogs and social media. An analysis based on literacy practices and activity theory shows that these activities enable parents' learning, but they can also be restricting as to the mediating tools they provide and the rules that dictate the tools. Additionally, the object of learning about heart defect is not always clearly formulated and stable but it keeps alternating and expanding. As a result, the parents cross boundaries between activities with different mediating tools, rules and communities and thereby different possibilities for learning. We show that doing health literacy is comprised by a set of recontextualised practices of looking for medical and experiential knowledge and it is by a combination of the two that meaningful learning is achieved.
Introduction to special issue on geologic remote sensing

Herein we provide an overview of science and technology involved in remote sensing, and outlines some practical constraints in applications to geological problems. We further summarize diagnostic spectral features of important geological material that can be detected using satellite- and air-borne remote sensing. Finally, the papers contained in the special issue are briefly introduced.
The optical signal analysis (OSA) method to process fringe patterns containing displacement information

In preceding publications, the authors have analyzed different aspects of the recovery of displacement information encoded in scalar fields of gray levels. The signals that contain displacement information can be generated by different optical methods or can be computer generated, for example, in the images of the magnetic resonance method (MRI). This paper merges ideas of the authors’ previous developments, included in the manuscript references, with new developments. These developments simplify fundamental basic steps of the process of retrieving displacement information. They also extend the applicability of basic algorithms to a very diverse variety of patterns, including patterns that contain displacement singularities. Singularities in displacement fields indicate departures of the standard continuity conditions assumed in the mathematical model of Continuum Mechanics, these discontinuities can be local or can divide the field into piecewise continuous regions. To illustrate the practical aspect of these derivations the proposed methodology is applied to very complex patterns. The analysis of these patterns with standard techniques will require complex and time-consuming pattern processing procedures. In the approach presented in this paper, the same task can be accomplished by a much simpler and general scheme.
Study on the Modeling Method of Knowledge Base System in Web Environment

A knowledge model for knowledge base system in the Web environment is proposed, which includes problem description layer and knowledge layer. A knowledge base is made up of a set of knowledge models. Knowledge models are used to support knowledge representation and reasoning. Visual knowledge modeling tool and visual knowledge service tool based on Web environment realize the construction of knowledge base system in Web environment. The knowledge base system supports the construction of agricultural expert system. By comparison with CommonKADS, it is proved that this method can improve the efficiency of knowledge acquisition, management and maintenance in Web environment. This method also has the reference significance to the construction of the knowledge base system in the Web environment in other domains.
Multimodal clothing recognition for semantic search in unconstrained surveillance imagery

To date, surveillance based person search has focused on locating a person of interest from an image query, distinct from the law enforcement task of locating a person from a description. In this paper, we introduce a novel probabilistic framework that combines multiple traits whilst incorporating their uncertainty to tackle the emerging challenge: locating a person from a semantic query. In addressing this, we improve clothing texture recognition by leveraging Dempster-Shafer theory against an ensemble of support vector machines; achieving state-of-the-art performance for high and low resolution clothing textures. Our proposed person search framework combines information from clothing texture and colour in the torso and leg regions to produce a probabilistic match between unknown subjects and the designated target query. Results are presented on a newly created 520 subject surveillance dataset which is made available to researchers. This multi-modal person search technique achieves promising results for locating target subjects, without the requirement of pre-search target enrollment.
Generating fluent embedded domain-specific languages with subchaining

This paper presents Silverchain, a tool that generates a fluent embedded domain-specific language (EDSL) from a BNF-style grammar. A fluent EDSL is a class library that allows its users to embed domain-specific sentences into a program written in a general-purpose language by method chaining. A generated EDSL is designed so that its users can find syntactic errors from type errors and make effective use of method chaining in various situations. The first feature is realized by setting the return type of each method based on which methods may be invoked next, and the second feature is achieved by providing subchaining APIs besides regular chaining APIs. The contribution of this paper is the development of a translation method from a grammar to such an EDSL. Our translation method is modeled as the construction of a set of single-state real-time deterministic pushdown automata (RPAs). A fluent EDSL is obtained by encoding those RPAs into class definitions. In the construction of RPAs, Silverchain does not add or remove any non-terminal from the given grammar. This constraint is required to generate subchaining APIs as specified in the grammar.
Techniques for Privacy Preserving Data Publication in the Cloud for Smart City Applications

Numerous modern cities aim to integrate information technology into each appearance of city life to create so-called smart cities. A large number of application areas and technologies are utilized in smart cities to achieve complex interactions between citizens, city departments, and third parties. This great complexity is one reason why privacy protection only seldom comes into the picture. A violation of privacy can result in discrimination and social sorting. Moreover, recent advancements in information and communication technologies (ICT) have demanded much of cloud services in respect to sharing users’ private data. Data from various organizations are the vital information source for analysis and research. Generally, this sensitive or private data involves information related to medical records, census data, voter registration, social networks, and customer services. A primary concern of cloud service providers in data publishing is to hide the sensitive information of individuals. One of the cloud services that fulfils these confidentiality concerns is privacy preserving data mining (PPDM). The PPDM service in cloud computing (CC) enables data publishing with minimized distortion and absolute privacy. In this method, datasets are anonymized via generalization to meet privacy requirements. However, the well-known privacy-preserving data mining technique called K-anonymity suffers from several limitations. To surmount those shortcomings, we propose a new heuristic anonymization framework for preserving the privacy of sensitive datasets when publishing in the cloud for smart cities. The advantages of K-anonymity, L-diversity, and (α, k)-anonymity methods for efficient information utilization and privacy protection are emphasized. Experimental results have revealed the superior performance of these techniques.
Quantum Algorithm for Spectral Regression for Regularized Subspace Learning

In this paper, we propose an efficient quantum algorithm for spectral regression which is a dimensionality reduction framework based on the regression and spectral graph analysis. The quantum algorithm involves two core subroutines: the quantum principal eigenvectors analysis and the quantum ridge regression algorithm. The quantum principal eigenvectors analysis can be performed by an efficient sparse Hamiltonian simulation. For the ridge regression, we propose a quantum algorithm that is derived from the quantum singular value decomposition method. Our quantum ridge regression algorithm is more suitable for data matrices that are non-sparse and skewed. Our analysis demonstrates that the quantum subroutines can be implemented with an approximatively polynomial speedup on a quantum computer over their classical counterparts.
VA-Store: A Virtual Approximate Store Approach to Supporting Repetitive Big Data in Genome Sequence Analyses

In recent years, we have witnessed an increasing demand to process big data in numerous applications. It is observed that there often exist substantial amounts of repetitive data in different portions of a big data repository/dataset for applications such as genome sequence analyses. In this paper, we present a novel method, called the VA-Store, to reduce the large space requirement for repetitive data in prevailing genome sequence analysis tasks using k-mers (i.e., subsequences of length k) with multiple k values. The VA-Store maintains a physical store for one portion of the input dataset (i.e., k <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -mers) and supports multiple virtual stores for other portions of the dataset (i.e., k-mers with k ≠ k <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ). Utilizing important relationships among repetitive data, the VA-Store transforms a given query on a virtual store into one or more queries on the physical store for execution. Both precise and approximate transformations are considered. Accuracy estimation models for approximate solutions are derived. Query optimization strategies are suggested to improve query performance. Our experiments using real and synthetic datasets demonstrate that the VA-Store is quite promising in providing effective storage and efficient query processing for solving a kernel database problem on repetitive big data for genome sequence analysis applications.
Continuously analyzing finite, message-driven, time-synchronous component &amp; connector systems during architecture evolution

Understanding the semantic differences of continuously evolving system architectures by semantic analyses facilitates engineers during evolution analysis in understanding the impact of the syntactical changes between two architecture versions. To enable effective semantic differencing usable in practice, this requires means to fully automatically check whether one version of a system admits behaviors that are not possible in another version. Previous work produced very general system models for message-driven time-synchronous (MDTS) systems that impede fully automated semantic differencing but very adequately describe such systems from a black-box viewpoint abstracting from hidden internal component behavior. This paper presents a system model for MDTS systems from a white-box viewpoint (assuming component implementation availability) and presents a sound and complete method for semantic differencing of finite MDTS system architectures. This method relies on representing (sub-)architectures as channel automata and a reduction from the semantic differencing problem for such automata to the language inclusion problem for Büchi automata. The system model perfectly captures the logical basics of MDTS systems from a white-box viewpoint and the method enables to fully automatically calculate semantic differences between two finite MDTS systems on push-button basis, yields witnesses, and ultimately facilitates semantic evolution analysis of such systems.
A simple linear-space data structure for constant-time range minimum query

We revisit the range minimum query problem and present a new O(n)-space data structure that supports range minimum queries in O(1) time. The goal is to construct a static data structure that efficiently supports range minimum queries on a given list A[0:n−1] of n items drawn from a totally ordered set. Each range minimum query consists of an input pair of indices (i,j) for which the minimum element of the subarray A[i:j] must be returned. Although previous data structures exist whose asymptotic bounds match ours, our goal is to introduce a new solution that is simple, intuitive, and practical without increasing asymptotic costs for query time or space. We analyze our new data structure theoretically and practically, the latter through an evaluation of its performance relative to implementations of four of the top range minimum query data structures.
The Wigner flow on the sphere

We derive a continuity equation for the evolution of the SU(2) Wigner function under nonlinear Kerr evolution. We give explicit expressions for the resulting quantum Wigner current, and discuss the appearance of the classical limit. We show that the global structure of the quantum current significantly differs from the classical one, which is clearly reflected in the form of the corresponding stagnation lines.