Reviewerly/exHarmony huggingface dataset:
Encrypted Keyword Search in Cloud Computing using Fuzzy Logic

Research and Development, and information management professionals routinely employ simple keyword searches or more complex Boolean queries when using databases such as PubMed and Ovid and search engines like Google to find the information they need. While satisfying the basic needs of the researcher, basic search is limited which can adversely affect both precision and recall, decreasing productivity and damaging the researchers' ability to discover new insights. The cloud service providers who store user's data may access sensitive information without any proper authority. A basic approach to save the data confidentiality is to encrypt the data. Data encryption also demands the protection of keyword privacy since those usually contain very vital information related to the files. Encryption of keywords protects keyword safety. Fuzzy keyword search enhances system usability by matching the files perfectly or to the nearest possible files against the keywords entered by the user based on similar semantics. Encrypted keyword search in cloud using this logic provides the user, on entering keywords, to receive best possible files in a more secured manner, by protecting the user's documents.
Conditional importance sampling for particle filters

In this paper, we present a new importance sampling method, namely the conditional importance sampling (CIS). This new method uses a conditional density as a proposal density and exploits rejection sampling, adaptively neglecting samples whose importance weights are relatively low. The CIS improves the efficiency of estimation without creating bias. We apply the CIS to the bootstrap filter to obtain a new algorithm, named the conditional bootstrap filter, which achieves higher estimation efficiency than the bootstrap filter and shows advantages over some other filters in our simulations.
Pseudodifferential Inpainting: The Missing Link Between PDE- and RBF-Based Interpolation

Inpainting with partial differential equations (PDEs) has been used successfully to reconstruct missing parts of an image, even for sparse data. On the other hand, sparse data interpolation is a rich field of its own with different methods such as scattered data interpolation with radial basis functions (RBFs). The goal of this paper is to establish connections between inpainting with linear shift- and rotation-invariant differential operators and interpolation with radial basis functions. The bridge between these two worlds is built by generalising inpainting methods to handle pseudodifferential operators and by considering their Green’s functions. In this way, we find novel relations of various multiquadrics to pseudodifferential operators. Moreover, we show that many popular radial basis functions are related to processes from the diffusion and scale-space literature. We present a single numerical algorithm for all methods. It combines conjugate gradients with pseudodifferential operator evaluations in the Fourier domain. Our experiments show that the linear PDE- and the RBF-based communities have developed concepts of comparable quality.
The Intangible Culture Heritage “New Ecology” Under Emotional Demand of Interactive Experience

The traditional Chinese flower arrangement as a national intangible cultural heritage has high historical value and artistic value, with the increasing demand of user's emotional needs for interactive experience, its popularity, and interaction are relatively limited. In the information age of the rapid development of new media technology, using digital technology to protect and inherit this excellent intangible cultural traditional art has far-reaching significance and it has high theoretical value for developing the history and innovation of traditional flower arranging art. Based on the augmented reality technology of intelligent mobile terminal, the article integrates the digital resources of flower arranging art from the perspective of satisfying the user's emotional needs, restore the art of flower arranging classic works, and enhance user interaction experience. Build an operable digital virtual flower arranging application system that transcends time and space, the combination of traditional flower arranging art and new media communication platform can form a three-dimensional digital "new ecosystem" for the dissemination and protection of intangible cultural heritage.
Modeling of Hidden Layer Architecture in Multilayer Artificial Neural Networks

A generated solution for an artificial neural network (ANN) may result in complex computations of neural networks, deployment, and usage of trained networks due to its inappropriate architecture. Therefore, modeling the hidden layer architecture of artificial neural networks remains as a research challenge. This paper presents a solution to achieve the hidden layer architecture of artificial neural networks which is inspired by some facts of neuroplasticity. The proposed method has two phases. First, it determines the number of hidden layers for the best architecture and then removes unnecessary hidden neurons from the network to enhance the performance. Experimental results in several benchmark problems show that the modified network shows better generalization than the original network.
Learning in Growing Robots: Knowledge Transfer from Tadpole to Frog Robot

Inspired by natural growing processes, we investigate how morphological changes can potentially help to lead and facilitate the task of learning to control a robot. We use the model of a tadpole that grows in four discrete stages into a frog. The control task to learn is to locomote to food positions that occur at random positions. We employ reinforcement learning, which is able to find a tail-driven swimming strategy for the tadpole stage that transitions into a leg-driven strategy for the frog. Furthermore, by using knowledge transferred from one growing stage to the next one, we were able to show that growing can benefit from guiding the controller optimization through morphological changes. The results suggest that learning time can be reduced compared to the cases when learning each stage individually from scratch.
Stuck? No worries!

Data analytics software applications have become an integral part of the decision-making process of analysts. Users of such a software face challenges due to insufficient product and domain knowledge, and find themselves in need of help. To alleviate this, we propose a task-aware command recommendation system, to guide the user on what commands could be executed next. We rely on topic modeling techniques to incorporate information about user's task into our models. We also present a help prediction model to detect if a user is in need of help, in which case the system proactively provides the aforementioned command recommendations. We leverage the log data of a web-based analytics software to quantify the superior performance of our neural models, in comparison to competitive baselines.
Best Feature Selection for Horizontally Distributed Private Biomedical Data Based on Genetic Algorithms

Due to the growing success of machine learning in the healthcare domain, medical institutions are striving to share their patients' data in the intention to build more accurate models which will be used to make better decisions. However, due to the privacy of the data, they are reluctant. To build the best models, they have to make the best feature selection for horizontally distributed private biomedical data. The previous proposed solutions are based on data perturbation techniques with the loss of performance. In this article, the researchers propose an original solution without perturbation. This is so the data utility is preserved and therefore the performance. The proposed solution uses a genetic algorithm, a distributed Naïve Bayes classifier, and a trusted third-party. The results obtained by the proposed approach surpass those obtained by other researchers, for the same problem.
Characterising renaming within OCaml’s module system: theory and implementation

We present an abstract, set-theoretic denotational semantics for a significant subset of OCaml and its module system, allowing to reason about the correctness of renaming value bindings. Our semantics captures information about the binding structure of programs, as well as about which declarations are related by the use of different language constructs (e.g. functors, module types and module constraints). Correct renamings are precisely those that preserve this structure. We show that our abstract semantics is sound with respect to a (domain-theoretic) denotational model of the operational behaviour of programs, and that it allows us to prove various high-level, intuitive properties of renamings. This formal framework has been implemented in a prototype refactoring tool for OCaml that performs renaming.
Phone Sensor Data in the Mathematics Classroom

Recently, a new source of data with great potential for use in the mathematics classroom has become available. The smartphones in our pockets are packed with sensors. Most phones have at a minimum global positioning, accelerometer, magnetometer, and gyroscope sensors. We give an example of the collection and analysis of global positioning sensor (GPS) data. Specifically, we find the fastest mile in a smartphone recording of an ordinary jogging workout. All of the analysis is done with the free Jupyter/Python computing environment. This example can be used by an instructor knowledgeable in the use of Jupyter/Python to scaffold a GPS project for students in a laboratory calculus classroom. More broadly, via this example, we provide a framework for instructors to incorporate these kinds of easily accessible data projects into the mathematics classroom. We also encourage the collaborative development of other phone sensor projects via a github repository.
Twitter Analyzer—How to Use Semantic Analysis to Retrieve an Atmospheric Image around Political Topics in Twitter

Social media are heavily used to shape political discussions. Thus, it is valuable for corporations and political parties to be able to analyze the content of those discussions. This is exemplified by the work of Cambridge Analytica, in support of the 2016 presidential campaign of Donald Trump. One of the most straightforward metrics is the sentiment of a message, whether it is considered as positive or negative. There are many commercial and/or closed-source tools available which make it possible to analyze social media data, including sentiment analysis (SA). However, to our knowledge, not many publicly available tools have been developed that allow for analyzing social media data and help researchers around the world to enter this quickly expanding field of study. In this paper, we provide a thorough description of implementing a tool that can be used for performing sentiment analysis on tweets. In an effort to underline the necessity for open tools and additional monitoring on the Twittersphere, we propose an implementation model based exclusively on publicly available open-source software. The resulting tool is capable of downloading Tweets in real-time based on hashtags or account names and stores the sentiment for replies to specific tweets. It is therefore capable of measuring the average reaction to one tweet by a person or a hashtag, which can be represented with graphs. Finally, we tested our open-source tool within a case study based on a data set of Twitter accounts and hashtags referring to the Syrian war, covering a short time window of one week in the spring of 2018. The results show that while high accuracy of commercial or other complicated tools may not be achieved, our proposed open source tool makes it possible to get a good overview of the overall replies to specific tweets, as well as a practical perception of tweets, related to specific hashtags, identifying them as positive or negative.
Data science applications in oil and gas exploration: an in-depth perspective

Low oil and gas prices have motivated petroleum executives to look into cost reduction in their supply chains more seriously. To this end, a technology considered in hydrocarbon exploration is data science. There are three exploration-related geoscientific domains in which data science is applied: surface geology, structural geology and reservoir property issues. This research provides an in-depth perspective on data science applications in these domains by considering a variety of work in each of them. The result is an understanding of the specific geoscientific problems studied in the literature along with the relative data science models. This way, this work tries to lay the ground for a mutual understanding on oil and gas exploration between the data scientists and geoscientists.
BNF-Style Notation as It Is Actually Used

The famous BNF grammar notation, as introduced and used in the Algol 60 report, was subsequently followed by numerous notational variants (EBNF, ABNF, RBNF, etc.), and later by a new formal “grammars” metalanguage used for discussing structured objects in Computer Science and Mathematical Logic. We refer to this latter offspring of BNF as MBNF (Math-BNF), and to aspects common to MBNF, BNF, and its notational variants as BNF-style. MBNF is sometimes called “abstract syntax”, but we avoid that name because MBNF is in fact a concrete form and there is a more abstract form. What all BNF-style notations share is the use of production rules like (P) below which state that “every instance of $$\circ _i$$ for $$i \in \{1,...,n\}$$ is also an instance of $$\bullet $$ ”. However, MBNF is distinct from all variants of BNF in the entities and operations it allows. Instead of strings, MBNF builds arrangements of symbols that we call math-text and allows “syntax” to be built by interleaving MBNF production rules and other mathematical definitions that can contain chunks of math-text. The differences between BNF (or its variant forms) and MBNF have not been clearly presented before. (There is also no clear definition of MBNF anywhere but this is beyond the scope of this paper.) This paper reviews BNF and some of its variant forms as well as MBNF, highlighting the differences between BNF (including its variant forms) and MBNF. We show via a series of detailed examples that MBNF, while superficially similar to BNF, differs substantially from BNF and its variants in how it is written, the operations it allows, and the sets of entities it defines. We also argue that the entities MBNF handles may extend far outside the scope of rewriting relations on strings and syntax trees derived from such rewriting sequences, which are often used to express the meaning of BNF and its notational variants.
An Analysis of Service Level Agreement Parameters and Scheduling in Multi-Tenant Cloud Systems

In Cloud Systems it usually the case that there exists a multi-tenancy of Cloud Service Customers meaning that some Cloud Service Customers applications share the same Cloud infrastructure. In this situation there must exist a Service Level Agreement (SLA) contract between the Multi-Tenant Cloud Service Provider (MTCSP) and the Cloud Service Customers (CSCs). In this article we discuss about the parameters of an SLA in the Cloud and we particularize it in the case of multi-tenancy. We analyze and implement two algorithms with the purpose of optimizing the scheduling of tasks from the tenants of the Multi-Tenant Cloud Service. The results show that the algorithms can be used in different situations with good results.
Distributed Ledger Witness Selection in Bounded Width Directed Acyclic Graphs

This work explores a new low-latency distributed ledger structure based on bounded width directed acyclic graphs (DAGs). In this design, each user has their own blockchain traceable back to a common root. These individual chains mutually witness each other to confirm transactions and form a global partially ordered set of events. The number of witness hashes required to establish new blocks is defined as the “DAG width”. This width has an upper bound which of the total number of users in the system, though the actual number of witnesses required is usually far less. In this paper we mathematically model the expected DAG width using analytical techniques from the near miss birthday problem and simulate expected widths for expected network latency and user activity.
Modified satellite remote sensing technique for hydrocarbon deposit detection

Recently, significant reserves of oil were discovered off the coast of Lagos State, southwest Nigeria. The lateral situation or compositions of the oil-bearing deposits is not clear because these findings are based on a particular position of a single well. The conventional methods of oil exploration have shown a fundamental theoretical shortcoming that may not be resolved, hence scientists or professionals may have to keep modifying the theories to explore different geological terrain. In this study, the remote sensing technique was adopted. The dataset were adopted from MERRA, Landsat 8 OLI and ETM imagery. The temperature distributions (soil and geothermal temperature) over the research area were calculated using existing algorithms to compliment the satellite remote sensing results. A prospective hydrocarbon deposits was suggested for further research work.
Exploratory Stage Lighting Design using Visual Objectives

Abstract Lighting is a critical element of theater. A lighting designer is responsible for drawing the audience's attention to a specific part of the stage, setting time of day, creating a mood, and conveying emotions. Designers often begin the lighting design process by collecting reference visual imagery that captures different aspects of their artistic intent. Then, they experiment with various lighting options to determine which ideas work best on stage. However, modern stages contain tens to hundreds of lights, and setting each light source's parameters individually to realize an idea is both tedious and requires expert skill. In this paper, we describe an exploratory lighting design tool based on feedback from professional designers. The system extracts abstract visual objectives from reference imagery and applies them to target regions of the stage. Our system can rapidly generate plausible design candidates that embody the visual objectives through a Gibbs sampling method, and present them as a design gallery for rapid exploration and iterative refinement. We demonstrate that the resulting system allows lighting designers of all skill levels to quickly create and communicate complex designs, even for scenes containing many color‐changing lights.
A Confidentiality Scheme for Storing Encrypted Data through Cloud

The advantages of Cloud computing constitute the substantial motivation for user to store and access their data. The cloud storage capacity reduces the data owner's overhead. In spite of several techniques were proposed for storing data on cloud, the data owner still suffers from a lack of confidentiality and time delay in storing their data on cloud. In this paper, a new scheme is proposed for storing data which achieved data confidentiality and time efficiency. A Middleware Management Control (MWM) is built which is an-intermediary layer between customer and cloud providers. Several text documents sizes are prepared for downloading and uploading process to measure time performance. MWM rotates each document rows to columns; then, it divides document into sub-files vertically. Finally, it encrypts each sub-file and stores it into different cloud storage. The divided sub files are mapping with cloud providers through location table. The proposed scheme is evaluated to measure confidentiality and time performance for the upload and downloads process. In spite of the proposed scheme uses extra operations (rotation, dividing and encryption) to enhance confidentiality, the experimental results show that significantly the same time performance has been achieved for uploading and downloading process compared with other works.
Vibration Reduction for Camera Systems Onboard Small Unmanned Aircraft

The performance of camera vibration isolation systems used on a popular small unmanned aircraft system (sUAS) is presented. The use of sUAS or drones for image collection is becoming ever more popular for hobbyists, as well as in commercial and military operations. Many types and methods for vibration isolation and absorption are used to create a more stable environment to acquire images or video. While many systems promise vibration reduction, few studies have been conducted to measure and evaluate their performance. Therefore, this review will provide data obtained by experimentally measuring the vibration levels of the camera and host aircraft. Using this data the transmissibility is determined and the effectiveness assessed. Along with the experimental data, analytic models of the systems will be generated to allow for the integration into future modeling efforts. The analysis utilized a common airframe used in the UAS community along with frequently used camera mounting systems. The data is obtained from aircraft fully powered and airborne in a hovering or level flight configuration. This study will provide sUAS operators the information required for choosing the most effective camera vibration reduction system and/or method for the system of interest.
Towards a More Reliable Interpretation of Defect Models

Software Quality Assurance (SQA) activities are exercised to ensure high-quality software systems. Defect models help developers identify the most risky modules to prioritise their limited SQA resources. The interpretation of defect models also helps managers understand what factors impact software quality to chart quality improvement plans. Unfortunately, the commonly-used interpretation techniques (e.g., ANOVA for logistic regression and variable importance for random forests) only explain defect models at the high level (e.g., what factors impact software quality). Researchers and practitioners also raise concerns about a lack of explainability of defect models that hinders the adoption in practice. This thesis hypothesises that: A lack of explainability poses a critical challenge when adopting defect models in practice. To validate the hypothesis, we formulate 3 research questions, i.e., (1) what is the best defect modelling workflow that produces the most accurate and reliable interpretation of defect models?, (2) what is the best technique for explaining the predictions of defect models?, and (3) how do practitioners perceive when adopting explainable defect models? Through case studies of publicly-available open-source and industrial software systems, the results show that correlated variables impact the interpretation of defect models and must be mitigated; our proposed feature selection technique, AutoSpearman, is the only studied feature selection technique that can automatically mitigate correlated variables with a little impact on model performance; and the instance-level interpretation of defect models is needed to derive actionable insights to guide operational and technical decisions in SQA efforts.
Spherediar: An Effective Speaker Diarization System for Meeting Data

In this paper, we present SphereDiar, a speaker diarization system composed of three novel subsystems: the Sphere-Speaker (SS) neural network, designed for speaker embedding extraction, a segmentation method called Homogeneity Based Segmentation (HBS) and a clustering algorithm called Top Two Silhouettes (Top2S). The system is evaluated on a set of over 200 manually transcribed multiparty meetings. The evaluation reveals that the system can be further simplified by omitting the use of HBS. Furthermore, we illustrate that SphereDiar achieves state-of-the-art results with two different meeting data sets.