Reviewerly/exHarmony huggingface dataset:
The review of the most characteristic codas in Beethoven's works

An extensive research of the coda in Beethoven's compositions has resulted in writing this paper which competently presents numerous examples of the coda typical of Beethoven's opus. The codas depicted range from those early and short ones, which can be described as outer extensions, to those that represent the codas of the complete cycle, not only of the form that contain them. The selection has not been based on the formal pattern in which they occurred.
The U. S. Geological Survey’s Approach to Analysis Ready Data

Analysis Ready Data (ARD) is a recent concept in Earth observing remote sensing which encompasses many different initiatives by individual imagery providers and collaborative international organizations working towards easing/minimizing data preprocessing required by users. This allows users to spend more time on analysis and less time on downloading, formatting, and ingesting. The U. S. Geological Survey (USGS), the primary provider of Landsat image data, has been making internal strides to provide ARD: moving towards Level-2 surface reflectance and surface temperature as standard products. External cooperation, working toward a common ARD definition, has also been a focus of the USGS by working directly with other governmental or commercial providers, both national and international, or through organizations such as the Committee on Earth Observation Satellites (CEOS) and the Joint Agency Commercial Imagery Evaluation (JACIE) workshop. The USGS is determined to provide users with the most accurate and easy to use data.
Fast proximity graph generation with spark

Since the early 1980s, proximity graphs have served as one of the classical approaches to characterize neighborhood relationships; the most popular proximity graphs include Delaunay Triangulation (DT) and Gabriel graphs (GG). These graphs find their applications in geographical analysis, spatial analysis, pattern recognition, evolutionary biology, computer vision, cluster analysis, and visualization. The emergence of big data has created a need for scalable algorithms to generate proximity graphs for massive datasets. In this paper, we propose a novel approach for creating DT and GG by leveraging the cluster computing capabilities of Apache Spark. To compute DT, we rely on the divide and conquer paradigm. We first partition the spatial dataset into a grid; next, we compute the DT for each grid partition and separate the resulting triangles into the core and boundary triangles; next, we merge the adjacent partitions recursively and recompute boundary triangles; finally, we return the union of obtained triangles as the final result. GG generation is implemented on top of a DT, by removing edges from the DT not intersected with their corresponding Voronoi edge. We evaluate our proximity graph generation algorithms built on top of Spark to create DT and GG for a benchmark of datasets having between one and thirty million data records and compare our framework with existing Python and R libraries to create DT and GG. Our benchmark tests showcase significant performance improvements(2 times on DT and up to 24 times on GG) and the capability for creating DTs and GGs for datasets consisting of thirty million records.
The Mensural Scoring-up Tool

Vocal polyphonic music from 1280 to 1600 is written in mensural notation and it is typically presented in a layout with separate parts. In this paper, we introduce the Mensural Scoring-up Tool, a set of scripts designed to automatically transform the separate-parts representation of the music into a score by dealing with the context-dependent nature of the notation through the implementation of the principles of imperfection and alteration, outlined by Franco of Cologne (ca. 1280). This tool exhibits 97% accuracy in a corpus of fourteenth- and fifteenth-century pieces, including both black and white mensural notation. The new encoding generated by the Scoring-up Tool could be useful for digital libraries that have digitized their collections of mensural music documents since the symbolic score could accompany the digital images providing a representation that makes the music accessible to a larger audience—since these symbolic scores can easily allow for a conversion into modern values and for playback—and that facilitates music analysis for the experts.
Towards Stochastic Simulations of Relevance Profiles

Recently proposed methods allow the generation of simulated scores representing the values of an effectiveness metric, but they do not investigate the generation of the actual lists of retrieved documents. In this paper we address this limitation: we present an approach that exploits an evolutionary algorithm and, given a metric score, creates a simulated relevance profile (i.e., a ranked list of relevance values) that produces that score. We show how the simulated relevance profiles are realistic under various analyses.
On Application of "Artificial Intelligence Plus Large Data" in Public Maths Course Teaching of Engineering Colleges

In recent years, artificial intelligence and large data are attracting increasing public attention, what's more, their practical application is greatly expected by the public. Currently, "artificial intelligence plus large data" is being put into practice gradually in the class-teaching of some certain colleges, with some desirable achievements made. Supported by artificial intelligence and large data, mathematical thinking abilities of related college students have developed profoundly, and accordingly, their comprehensive accomplishment has been strengthened. Based on this, this paper makes the analysis of existing problems in public Maths course teaching of engineering colleges of China, then introduces the development of artificial intelligence plus large data and their relations with public Maths course teaching of engineering colleges, and lastly makes scientific and reasonable exploration into the application of "artificial intelligence plus large data" in public Maths course teaching of engineering colleges, for the purpose of offering some proper help in public Maths course education of engineering colleges of China.
To Scrap the LinkedIn Data to Create the Organization’s Team Chart

From past decades, LinkedIn appears as the professional connection site for both the freelancers and the recruiters. LinkedIn users are ought to post jobs, connect different industries and updates the people with current events. The goal of the paper is to create a report on the structure of the Organization to provide a smooth and efficient reporting hierarchy which involves data Analytics on the LinkedIn Data. So it is required to create an organizational hierarchy. Web Scraping is performed on the LinkedIn site for this intended purpose.
Explore and Rescue Using Humanoid Water Rescuer Robot with AI Technologies

In the present era, there is no requirement for humans to work in risky jobs. Technology has developed to an extreme to replace for humans. The most perfect technology to replace humans in risky jobs is Robotics with Artificial Intelligence. In this paper, we are going to design "Humanoid Water Rescuer". This is worth and new to the job as it supports both rescuing human life and existing aquatic organisms from environment situation. One of the mobile robots is rescuer robots which is portable and small. As it traverses the source to destination and sense the organism and to save the life. With sending information to robots it will diagnosis and treat or given the first aid and it is used as carrier robots to carry the humans to the source. Thus it used in all water rescuing situations and treat life according to the command of the master.
Comparative study of the mechanisms for personalization of information

The relevance of the information provided, its intelligibility and its adaptation to customer usage and preferences are key factors in the rejection or success of an information system. Thus, in a context of information superabundance and heterogeneity, the implementation of mechanisms for personalizing information has become essential. Personalizing information means adapting it to In most information personalization mechanisms, there is agreement on the usefulness of having a user profile. The latter can be seen as a factoring of the invariant part of the user's preferences. There are two types of profiles: the statistical profile and the inferred profile. For the construction and updating of this profile, several approaches and techniques can be used.
Semantic image inpainting with dense and dilated deep convolutional autoencoder adversarial network

The developments of generative adversarial networks (GANs) make it possible to fill missing regions in broken images with convincing details. However, many existing approaches fail to keep the inpainted content and structures consistent with their surroundings. In this paper, we propose a GAN-based inpainting model which can restore the semantic damaged images visually reasonable and coherent. In our model, the generative network has an autoencoder frame and the discriminator network is a CNN classifier. Different from the classic autoencoder, we design a novel bottleneck layer in the middle of the autoencoder which is comprised of four dense-net blocks and each block contains vanilla convolution layers and dilated convolution layers. The kernels of dilated convolution are spread out and result in an effective enlargement of the receptive field. Thus the model can capture more widely semantic information to ensure the consistency of inpainted images. Furthermore, the multiplex of different level’s features in each dense-net block can help the model understand the whole image better to produce a convincing image. We evaluate our model over the public datasets CelebA and Stanford Cars with random position masks of different ratios. The effectiveness of our model is verified by qualitative and quantitative experiments.
Design of Hanman Entropy Network from Radial Basis Function Network

Different learning algorithms have been developed in the literature for training the radial basis function network (RBFN).In this paper, a new neural network named as Hanman Entropy Network (HEN) is developed from RBFN based on the Information set theory that deals with the representation of possibilistic uncertainty in the attribute/property values termed as information source values.The parameters of both HEN and RBFN are learned using a new learning algorithm called JAYA that solves the constrained and unconstrained optimization problems and is bereft of algorithm-specific parameters.The performance of HEN is shown to be superior to that of RBFN on four datasets.The advantage of HEN is that it can use both information source values and their membership values in several ways whereas RBFN uses only the membership function values.
There Are 10 Types of Vectors (and Polynomials)

We present a new 4-move special honest-verifier zero-knowledge proof of knowledge system for proving that a vector of Pedersen commitments opens to a so-called "one-hot'' vector (i.e., to a vector from the standard orthonormal basis) from Zpn. The need for such proofs arises in the contexts of symmetric private information retrieval (SPIR), end-to-end verifiable voting (E2E), and privacy-preserving data aggregation and analytics, among others. The key insight underlying the new protocol is a simple observation regarding the paucity of roots of polynomials of bounded degree over a finite field. The new protocol is fast and yields succinct proofs: For vectors of length n, the prover evaluates Θ(Θlgn) group operations plus Θ(n) field operations and sends just Θ(Θlgn) group and field elements, while the verifier evaluates one n-base multiexponentiation plus Θ(łlgn) additional group operations and sends just 2(λ+lgn) bits to obtain a soundness error less than 2-λ. (A 5-move variant of the protocol reduces prover upload to just λlgn bits for the same soundness error.) We have implemented both our new protocol and its closest competitors from the literature; in accordance with our analytic results, experiments confirm that the new protocols handily outperform existing protocols for all but the shortest of vectors (roughly, for vectors with more than 16-32 elements).
Research on key technology analysis and system design of enterprise patent management system

Patent documents, the paper constructs a patent model tree and establishes a similar patent determination method based on vector space model and patent document features. The paper then proposes to use the patent application number as the association identifier, and associate the ontology and the o ntology instance with the existing patent information database, which improves the speed of establishing the ontology and reduces the maintenance and update of the ontology. At the same time, based on the integration of existing workflow management and knowledge flow management, this paper proposes an integration framework with patent knowledge as the core knowledge and integration link, and describes the nature of knowledge retrieval and the method of importance differentiation. Finally, the paper designs a patent retrieval system, establishes the structural model and functional model of the system and the technical framework of the implementation system. On this basis, the design of the update module of the PMAS system is discussed. Finally, the performance optimization of the PMAS system is designed in detail.
Virtual Reality Empowers New Learning Exploration

VR learning in education is favoured by many academics, and they believe that this approach can accelerate, amplify, and expand the impact of effective teaching and learning experiences. The adoption of virtual reality in higher education is to make the learning process exciting and more effective. It is aligned with the target TLLM (teach less, learn more) initiative in Taylor's University to provide students with the characteristics and skills that will help them survive in an ever-changing technological world. As a 21st century educator, it is essential to be able to implement technology, think forward, embrace change, and have the ability to allow students to learn best when they are taught with their own unique styles and abilities. The principles behind this pedagogy comprise a mix of technology, space, and learning in innovative ways that support a variety of “e-learning” modes to facilitate knowledge transfer—immersive lesson, interaction, and collaboration—and independent self-paced learning that enables learning anytime and anywhere.
Research on Semantic Composition of Smart Government Services Based on Abstract Services

Smart government widely integrates heterogeneous business systems to provide intelligent applications, and improving interdepartmental government coordination capability is a key technical issue that needs to be solved urgently. Service composition technology based on SOA architecture is a good solution. However, due to the inherent heterogeneity and huge scale of smart government, the state space of service composition is very large, which is a great challenge to the efficiency and accuracy of problem solving. Based on analysis of the existing service composition research methods, combined with the domain characteristics of smart government, this paper studies and constructs an abstract government Web service template to reduce the scale of state space for service composition problem. Furthermore, a semantic-based service composability measurement is studied to select service solutions to improve the accuracy.
Closed-form solutions for multistatic target localization with time-difference-of-arrival measurements

This chapter focuses on closed-form least-squares estimation techniques for multistatic target localization using time-difference-of-arrival (TDOA) measurements. To overcome the computational burden and instability of the maximum likelihood estimator (MLE), the nonlinear TDOA measurement equations are algebraically rearranged into a set of linear equations by introducing a nuisance parameter, thus enabling linear least-squares to be performed in closed-form. Such a transformation of the TDOA measurement equations creates two technical challenges. First, the nuisance parameter is dependent on the true target position and this dependence must be taken into account in order to obtain efficient estimates. Second, the measurement matrix in the linearized equations is correlated with the pseudolinear noise vector, leading to bias problems in the linear least-squares solution. In this chapter, we cover a range of algorithms that can effectively tackle these two challenges. Simulation examples are presented to corroborate the performance benefits of the algorithms.
Analysis of Consumer Reviews by Machine Learning Techniques

The dynamic change in the usage of digital economy had drifted the customers towards using online shopping websites for their day to day purchase. Under such circumstances, customer reviews and ratings had been used by these online stores to generate trustworthy and esteemed brand in the digital market. Most of these reviews were unstructured text which had been widely used for sentiment analysis. This paper discusses about various machine learning algorithms proposed by different researchers to analyze the consumer reviews. Based on the existing work, we categorized three broad aspects in consumer reviews namely opinion mining, spam or fake review detection, collaborative filtering. In future work, we plan to propose customer review-oriented decision support system (CRDSS) which can help in consumer decision making process and thus improve the customer review helpfulness and rating prediction.
Role-Based Ecosystem for the Design, Development, and Deployment of Secure Multi-Party Data Analytics Applications

Software applications that employ secure multi-party computation (MPC) can empower individuals and organizations to benefit from privacy-preserving data analyses when data sharing is encumbered by confidentiality concerns, legal constraints, or corporate policies. MPC is already being incorporated into software solutions in some domains; however, individual use cases do not fully convey the variety, extent, and complexity of the opportunities of MPC. This position paper articulates a role-based perspective that can provide some insight into how future research directions, infrastructure development and evaluation approaches, and deployment practices for MPC may evolve. Drawing on our own lessons from existing real-world deployments and the fundamental characteristics of MPC that make it a compelling technology, we propose a role-based conceptual framework for describing MPC deployment scenarios. Our framework acknowledges and leverages a novel assortment of roles that emerge from the fundamental ways in which MPC protocols support federation of functionalities and responsibilities. Defining these roles using the new opportunities for federation that MPC enables in turn can help identify and organize the capabilities, concerns, incentives, and trade-offs that affect the entities (software engineers, government regulators, corporate executives, end-users, and others) that participate in an MPC deployment scenario. This framework can not only guide the development of an ecosystem of modular and composable MPC tools, but can make explicit some of the opportunities that researchers and software engineers (and any organizations they form) have to differentiate and specialize the artifacts and services they choose to design, develop, and deploy. We demonstrate how this framework can be used to describe existing MPC deployment scenarios, how new opportunities in a scenario can be observed by disentangling roles inhabited by the involved parties, and how this can motivate the development of MPC libraries and software tools that specialize not by application domain but by role.
Dynamic Gesture Recognition Based on the Multimodality Fusion Temporal Segment Networks

Gesture recognition is applied in various intelligent scenes. In this paper, we propose the multi-modality fusion temporal segment networks (MMFTSN) model to solve dynamic gestures recognition. Three gesture modalities: RGB, Depth and Optical flow (OF) video data are equally segmented and randomly sampled. Then, the sampling frames are classified using convolutional neural network. Finally, fusing three kinds of modality classification results. MMFTSN is used to obtain the recognition accuracy of 60.2% on the gesture database Chalearn LAP IsoGD, which is better than the result of related algorithms. The results show that the improved performance of our MMFTSN model.
Specifying Ontologies in a Social Networking ImagesÃ¢Â€Â™ Sample Obtained with a Multiagent System

In the present research, there are described some promising initial results of induced ontologies from vocabularies of labels in Metroflog, which were selected using Multi agent Systems utilizing a model based on suppositions. We describe the utility of the ontology of aspects as a supplement to a system that marks with labelling and we present our model and results. We propose a probabilistic reviewed model, using seed ontologies to induce ontology of aspects, and we describe how the model can integrate inside of the community’s logistics of labelling. An innovation of our research is having been able to improve the characterization of a group of labels associated with different images and how our multi-agent system uses a Belief–Desire-Intention (BDI) architecture is much more suitable to expand our context-based vocabulary and better describe the details fine of each image and what they really represent for a social group.
Confused Memory Read Attracts Synthetic Diffusion on the Fly – A Lightweight Image Encryption for IoT Platform

The analogous growth of threat and data communication among the connected devices invites specialised security algorithms for Internet of Things (IoT). The minimal computational capabilities and resource constraints of the processing devices used in IoT architecture do not afford the overhead incurred by the conventional encryption schemes. This paper proposes a lightweight image encryption algorithm that can be realised as an embedded software to run on microcontroller architectures suitable for IoT applications. The proposed algorithm uses the pseudo-random numbers produced by the Linear Feedback Shift Register (LFSR) to perform inherent confusion on the fly via random memory read. A synthetic image generated by extracting the random bits produced by the digitised Lorenz attractor has been used to diffuse the confused pixels on the fly. The proposed algorithm has been realised as embedded software to run on microcontrollers suitable for Internet of Things (IoT) applications. The proposed algorithm achieves better results than similar reliable encryption schemes in terms of security parameters such as entropy, correlation, histogram, PSNR, NPCR and UACI. Further, eliminating the storage of confusion and diffusion key beside the storage of encrypted image employing on the fly encryption process proposed in our algorithm reduces the demand on RAM for about 48 KB as compared to the conventional storage-based encryption schemes.