Reviewerly/exHarmony huggingface dataset:
The Application of Security Technology on Computer Software Development

Today's society is the Internet age, the rapid development of computer technology, computer software technology has also been applied in depth, and penetrated into various areas of society, directly affecting people's lives and work. The development of computer software has also become an important symbol of national social progress, and it is highly reflected in the comprehensive strength of the country. This paper starts with computer development and software development, analyzes the security problems and security risks in computer software development, and proposes corresponding security measures to avoid these problems and improve the security of software development technology.
Blockchain based Privacy-Preserving Software Updates with Proof-of-Delivery for Internet of Things

A large number of IoT devices are connected via the Internet. However, most of these IoT devices are generally not perfect-by-design even have security weaknesses or vulnerabilities. Thus, it is essential to update these IoT devices securely, patching their vulnerabilities and protecting the safety of the involved users. Existing studies deliver secure and reliable updates based on blockchain network which serves as the transmission network. However, these approaches could compromise users privacy when updating the IoT devices. In this paper, we propose a new blockchain based privacy-preserving software updates protocol, which delivers secure and reliable updates with an incentive mechanism, as well protects the privacy of involved users. The vendor delivers the updates and it makes a commitment by using a smart contract to provide financial incentive to the transmission nodes who deliver the updates to the IoT devices. A transmission node gets financial incentive by providing a proof-of-delivery. The transmission node uses double authentication preventing signature (DAPS) to carry out the fair exchange to obtain the proof-of-delivery. Specifically, the transmission node exchanges an attribute-based signature from a IoT device by using DAPS. Then, it uses the attribute-based signature as a proof-of-delivery to receive financial incentives. Generally, the IoT device has to execute complex computation for an attribute-based signature (ABS). It is intolerable for resource limited devices. We propose a concrete outsourced attribute-based signature (OABS) scheme to resist the weakness. Then, we prove the security of the proposed OABS and the protocol as well. Finally, we implement smart contract in Solidity to demonstrate the validity of the proposed protocol.
Improved Clustering Technique Using Metadata for Text Mining

Metadata delivers a lot of data for the bunching reason, creating groups without shifting the side or extra data it can result to give an awful nature of groups. In content mining application, the content alongside the side data or metadata is introduced in every single report. The way toward applying bunching method to the records containing content is called as the content grouping. The consistent development in unstructured data makes the content mining applications critical to accomplish the quality data as an objective. Content mining basically changes over unstructured information to organized information. Content mining is the way toward getting brilliant data from unstructured information. Content mining is otherwise called content information mining or content investigation. Unstructured information contains an abundance of data that is exceptionally helpful for cutting-edge recognition of dangers why in light of the fact that prepared security investigator feel hard to examine tremendous volume of information. Multifaceted nature is expanding to dissect those information. Our investigation investigates the using the methods for content mining, content grouping, common dialect handling, machine figuring out how to distinguish security dangers by mining the applicable data from unstructured log messages. Enhanced the bunching procedures comes about a solid potential for expanding the execution by expanding the span of datasets and separating the more highlights from the unstructured log messages. In this paper, it primarily center around the bunching strategies and grouping techniques that are utilized as a part of text mining.
Geological position and structural control of ore mineralization of the Toupugol-Khanmeyshorsky district (the Polar Urals) as a result of remote sensing

Large ring, arc and radial structures were identified based on complex processing of the multispectral satellite imagery of the Landsat apparatus. These structures control the position of gold mineralization of the Toupugol-Khanmeyshorsky ore district (Novogodnenskoe ore cluster, the Polar Urals). The comparative characteristic of this territory with the Turinsk-Auerbakh ore district (Auerbach ore cluster, the Northern Urals) was held. Similar geological features, regularity of the structures and location of gold mineralization in the system of morphostructures were revealed. For the first time, the depths of the formation of magmatic chambers were determined for the Novogodnensky and Auerbach paleovolcanic structures. These chambers were located at depths of ~4 km and ~20 km, respectively in the upper and middle parts of the earth's crust. These are sitting. The Novogodnensky structure is a monogenic structure. It formed under the influence of one leading geological process. The Auerbach structure is larger and more complex structure; it is characterized by a long and multi-stage development. The paleovolcanic reconstruction showed that the root (focal) part of the Auerbach structure plunges from the southeast to the northwest during of the magmatic process evolution. Gold mineralization within the studied areas of the Northern Urals is confined to the intersections of radial faults near the centers of large concentric morphostructures.
Are good managers also good investors? Common factors of managerial and investment performance of majority owners of SICAVs

This research explores and tests the existence of common factors of success in both managerial and investment activities performed by the same individual, defined as ‘manager-investor.’ After reviewing the existing theoretical literature, we identified theoretical developments and evidence that emphasize the role of individual-related characteristics as determinants of the performance of managerial actions as well as investment decisions.  We test the resulting hypothesis using a database comprising information from managerial and investment decisions of 147 entrepreneurs-investors. Panel models and simultaneous equation models (estimated by 3SLS) are proposed as empirical methods in order to assure a proper treatment of the potential unobserved heterogenity and endogeneity issues. Keywords: managerial resources, skills, investment capabilities, SICAV, panel data estimation, three-stage least square method
Identification of Flying Drones in Mobile Networks using Machine Learning

Drone usage is increasing, both in recreational use and in the industry. With it comes a number of problems to tackle. Primarily, there are certain areas in which flying drones pose a security thre ...
Kontribusi Minat Belajar, Motivasi Belajar, dan Fasilitas Belajar terhadap Hasil Belajar Matematika Siswa Kelas X SMK Muhammadiyah Delanggu

The purpose of this study was to examine, (1) the contribution of learning interest, learning motivation, and learning facilities to mathematics learning outcomes, (2) contribution to learning interest in mathematics learning outcomes, (3) contribution to learning motivation towards mathematics learning outcomes, (4) facility contribution learn about mathematics learning outcomes. This type of research is based on a quantitative approach. The study population was 265 students of class X SMK Muhammadiyah Delanggu. The research sample of 159 students was determined by the Slovin formula. The sampling technique uses proportional random sampling. Data collection techniques with questionnaires and documentation. The data analysis technique uses multiple linear regression. The results of the study are: (1) there is a simultaneous contribution of interest in learning, learning motivation, and learning facilities to mathematics learning outcomes with α = 5%, (2) there is a contribution to learning interest in mathematics learning outcomes with α = 5%, (3) there is no contribution to learning motivation towards mathematics learning outcomes with a standard error of 0,171, (4) there is no contribution of learning facilities to the learning outcomes of mathematics with a standard error of 0,137.
Storing Information About Virtual Machines

There are two categories of information that need to be obtained and stored to be able to manipulate and use guest domains.
Texture, Tactility, and Affect in Three Electroacoustic and Multimedia works

Author(s): Miller, Caroline Louise | Advisor(s): Rosenberger, Katharina | Abstract: How to Survive a 100-Hour Workweek, Vivarium, and Subsong, with accompanying essays, investigate multiple, polyvalent, cumulative ways to engrave multimedia works with labor, touch, and affect. I frame a discussion of my three works through alliances with Eve Kosofsky Sedgwick, Katrine Dirkinck-Holmfeld, and Brian Massumi. Sedgwick configures tactile materials in terms of texture and texxture, where two x's signify a material that is dense with freely offered information about how, substantively and historically, it came into being. Katrine Dirckinck-Holmfeld applies Sedgwick's tex[x]tures to the question of digital data, a highly manipulatable medium that doesn't necessarily embed specific histories, to show its expressive, affective potentials. Brian Massumi, in “Autonomy of Affect,” discusses how contexts become affective in resonance with bodies. Working with these allies, my musical explorations of texture, tactility and affect range from corporate affective discipline via wellness industry media in late capitalism, to an exploration of the affect of abjection through a touch-guided dance with pomegranates, to the musical potentials of touch and labor performed in the digital production studio.
A Regularization Approach for Instance-Based Superset Label Learning

Different from the traditional supervised learning in which each training example has only one explicit label, superset label learning (SLL) refers to the problem that a training example can be associated with a set of candidate labels, and only one of them is correct. Existing SLL methods are either regularization-based or instance-based, and the latter of which has achieved state-of-the-art performance. This is because the latest instance-based methods contain an explicit disambiguation operation that accurately picks up the groundtruth label of each training example from its ambiguous candidate labels. However, such disambiguation operation does not fully consider the mutually exclusive relationship among different candidate labels, so the disambiguated labels are usually generated in a nondiscriminative way, which is unfavorable for the instance-based methods to obtain satisfactory performance. To address this defect, we develop a novel regularization approach for instance-based superset label (RegISL) learning so that our instance-based method also inherits the good discriminative ability possessed by the regularization scheme. Specifically, we employ a graph to represent the training set, and require the examples that are adjacent on the graph to obtain similar labels. More importantly, a discrimination term is proposed to enlarge the gap of values between possible labels and unlikely labels for every training example. As a result, the intrinsic constraints among different candidate labels are deployed, and the disambiguated labels generated by RegISL are more discriminative and accurate than those output by existing instance-based algorithms. The experimental results on various tasks convincingly demonstrate the superiority of our RegISL to other typical SLL methods in terms of both training accuracy and test accuracy.
Wavelet regression and additive models for irregularly spaced data

We present a novel approach for nonparametric regression using wavelet basis functions. Our proposal, $\texttt{waveMesh}$, can be applied to non-equispaced data with sample size not necessarily a power of 2. We develop an efficient proximal gradient descent algorithm for computing the estimator and establish adaptive minimax convergence rates. The main appeal of our approach is that it naturally extends to additive and sparse additive models for a potentially large number of covariates. We prove minimax optimal convergence rates under a weak compatibility condition for sparse additive models. The compatibility condition holds when we have a small number of covariates. Additionally, we establish convergence rates for when the condition is not met. We complement our theoretical results with empirical studies comparing $\texttt{waveMesh}$ to existing methods.
Resampling detection of recompressed images via dual-stream convolutional neural network

Resampling detection plays an important role in identifying image tampering, such as image splicing. Currently, the resampling detection is still difficult in recompressed images, which are yielded by applying resampling followed by post-JPEG compression to primary JPEG images. Except for the scenario of low quality primary compression, it remains rather challenging due to the widespread use of middle/high quality compression in imaging devices. In this paper, we propose a new convolution neural network (CNN) method to learn the resampling trace features directly from the recompressed images. To this end, a noise extraction layer based on low-order high pass filters is deployed to yield the image residual domain, which is more beneficial to extract manipulation trace features. A dual-stream CNN is presented to capture the resampling trails along different directions, where the horizontal and vertical streams are interleaved and concatenated. Lastly, the learned features are fed into Sigmoid/Softmax layer, which acts as a binary/multiple classifier for achieving the blind detection and parameter estimation of resampling, respectively. Extensive experimental results demonstrate that our proposed method could detect resampling effectively in recompressed images and outperform the state-of-the-art detectors.
Saliency Detection With Fully Convolutional Neural Network

Saliency detection is an important task in image processing as it can solve many problems and it usually is the first step in for other processes. Convolutional neural networks have been proved to be very effective on several image processing tasks such as classification, segmentation, semantic colorization and object manipulation. Besides, using the weights of a pretrained networks is a common practice for enhancing the accuracy of a network. In this paper a fully convolutional neural network which uses a part of VGG-16 is proposed for saliency detection in images.
A new evaluation framework for topic modeling algorithms based on synthetic corpora

Topic models are in widespread use in natural language processing and beyond. Here, we propose a new framework for the evaluation of probabilistic topic modeling algorithms based on synthetic corpora containing an unambiguously defined ground truth topic structure. The major innovation of our approach is the ability to quantify the agreement between the planted and inferred topic structures by comparing the assigned topic labels at the level of the tokens. In experiments, our approach yields novel insights about the relative strengths of topic models as corpus characteristics vary, and the first evidence of an "undetectable phase" for topic models when the planted structure is weak. We also establish the practical relevance of the insights gained for synthetic corpora by predicting the performance of topic modeling algorithms in classification tasks in real-world corpora.
An Efficient Linkable Group Signature for Payer Tracing in Anonymous Cryptocurrencies

Cryptocurrencies, led by bitcoin launched in 2009, have obtained wide attention due to the emerging Blockchain in recent years. Anonymous cryptocurrencies are highly essential since users want to preserve their privacy when conducting transactions. However, some users might misbehave with the cover of anonymity such as rampant trafficking and extortion. Thus, it is important to balance anonymity and accountability of anonymous cryptocurrencies. In this paper, we solve this issue by proposing a linkable group signature (LGS) for signing cryptocurrency transactions, which can be used to trace a payer's identity in consortium blockchain based anonymous cryptocurrencies, in case the payer tries illegal activities. A payer keeps anonymous if he/she behaves honestly. We prove that the proposed scheme achieves full-anonymity, full-traceability and linkability in the random oracle. Implementation of the proposed LGS scheme demonstrates its high efficiency thus, can be adopted in anonymous cryptocurrencies in reality.
EFFICIENT ATTRIBUTE BASED MULTIPLE GROUP KEY MANAGEMENT FOR SECURE DOCUMENT ACCESS IN CLOUD SECURITY HEALTHCARE SERVICES

The healthcare services are being moved to the cloud processing condition in the ongoing occasions to make them most effective, interoperate and to team up in the exploration and business areas. In spite of the fact that this will upgrade the services offered by the healthcare providers, the vulnerabilities in the security and execution of the open cloud makes the move less operable. And this paper proposed to multiple group key management scheme to diminish the complexities of the archive clients, get to control grid has been presented through which the capacity intricacy is decreased from 0(n) to 0(3) and calculation complexity can be credited to just a single expansion and two subtraction activities. Correspondingly, if there should be an occurrence of a data proprietor, the quantity of subtraction activities has been diminished from 0(n)3 to 0(n) which is the explanation behind decrease in the computational intricacy. Additionally, for the data proprietor, the capacity multifaceted nature acquired is 0(n) as it were. In this way, this work diminishes the overheads caused by both the data proprietors and the cloud clients.
An overview of Artificial neural network

Neural networks represent a brain metaphor for information processing. These models are biologically inspired rather than an exact replica of how the brain actually functions. Neural networks have been shown to be very promising systems in many forecasting applications and business classification applications due to their ability to learn from the data. This article aims to provide a brief overview of artificial neural network.The artificial neural network learns by updating the network architecture and connection weights so that the network can efficiently perform a task. It can learn either from available training patterns or automatically learn from examples or input-output relations.
New dynamic and verifiable multi-secret sharing schemes based on LFSR public key cryptosystem

A verifiable multi-secret sharing (VMSS) scheme enables the dealer to share multiple secrets, and the deception of both participants and the dealer can be detected. After analyzing the security of VMSS schemes proposed by Mashhadi and Dehkordi in 2015, we illustrate that they cannot detect some deception of the dealer. By using nonhomogeneous linear recursion and LFSR public key cryptosystem, we introduce two new VMSS schemes. Our schemes can not only overcome the drawback mentioned above, but also have shorter private/public key length at the same safety level. Besides, our schemes have dynamism.
El blog académico: herramienta didáctica en la formación integral de los estudiantes de Cultura Física

This article shows the result of a field study on the benefits that the academic blog can provide, which is defined as a web page that can accommodate diversity of content, free access and interaction with visitors, as a teaching tool in the processes of teaching-learning in the integral formation of the university student. Arises from the requirements not satisfied by the subject of computer science in the formation of the students of the Career of Physical Culture of the Academic Unit of Social Sciences of the Technical University of Machala. The applied methodology was of exploratory quantitative type, bibliographic review, for the obtaining of data a questionnaire was applied with closed questions of multiple option with unique answer, for the processing of the information an Excel 2013 electronic spreadsheet was used. Most of the students consider the use of the academic blog as a didactic tool in the teaching-learning processes and the level of significance that this contributes to the harmonious development of the future professional, taking advantage of the advancement of technology and the benefits it offers to the development of the Sciences of Education. The objective of this article is to show if the use of the academic blog as a didactic tool influences the teaching-learning processes of the students of the Physical Culture Career of the Academic Social Science Unit of the Technical University of Machala during the academic year 2016-2017 and the contribution to the improvement of his profile as future professionals.
Accuracy analysis of three deterministic sampling nonlinear filtering algorithms

To study the estimation accuracy of nonlinear Kalman filter in transfer alignment system, the Taylor expansion of multivariable function is utilized to analyze the estimation accuracy of the three typical deterministic sampling nonlinear Kalman filters, such as high-degree cubature Kalman filter (HCKF), cubature Kalman filter (CKF) and unscented Kalman filter (UKF). The Taylor expansion analyses demonstrate that CKF and UKF produce truncation error since the fourth degree term, while HCKF can capture fifth degree term of Taylor series expansion. The three nonlinear filtering algorithms are applied to carrier-aircraft transfer alignment system under large misalignment angle. The simulation results show that CKF and UKF have the same accuracy, and HCKF has higher estimation accuracy than the other two, which is consistent with the theoretical analysis results.
Efficiently escaping saddle points on manifolds

Smooth, non-convex optimization problems on Riemannian manifolds occur in machine learning as a result of orthonormality, rank or positivity constraints. First- and second-order necessary optimality conditions state that the Riemannian gradient must be zero, and the Riemannian Hessian must be positive semidefinite. Generalizing Jin et al.'s recent work on perturbed gradient descent (PGD) for optimization on linear spaces [How to Escape Saddle Points Efficiently (2017), Stochastic Gradient Descent Escapes Saddle Points Efficiently (2019)], we propose a version of perturbed Riemannian gradient descent (PRGD) to show that necessary optimality conditions can be met approximately with high probability, without evaluating the Hessian. Specifically, for an arbitrary Riemannian manifold $\mathcal{M}$ of dimension $d$, a sufficiently smooth (possibly non-convex) objective function $f$, and under weak conditions on the retraction chosen to move on the manifold, with high probability, our version of PRGD produces a point with gradient smaller than $ε$ and Hessian within $\sqrtε$ of being positive semidefinite in $O((\log{d})^4 / ε^{2})$ gradient queries. This matches the complexity of PGD in the Euclidean case. Crucially, the dependence on dimension is low. This matters for large-scale applications including PCA and low-rank matrix completion, which both admit natural formulations on manifolds. The key technical idea is to generalize PRGD with a distinction between two types of gradient steps: "steps on the manifold" and "perturbed steps in a tangent space of the manifold." Ultimately, this distinction makes it possible to extend Jin et al.'s analysis seamlessly.