Reviewerly/exHarmony huggingface dataset:
A Comparison of Classification Algorithms Based on The Number of Features

As the most widely known data mining algorithm, classification algorithms have attracted wide attention. K-Nearest Neighbor (KNN) algorithm and decision tree algorithm are the two widely known algorithms in classification algorithms. Sometimes, people not sure how to choose the suitable to solve the classification problems. In this paper, we establish KNN algorithm model and decision tree ID3 algorithm model to analyze the accuracy of the two algorithms in the same data set with different number of features. Through the learning curve and cross validation, we find ID3 algorithm is better than KNN algorithm, and when the number of feature increased the accuracy of KNN is increasing while ID3 is decreased.
A case study on anonymised sharing platforms and digital traces left by their usage

Non-local forms of file storage and transfer provide investigatory concerns. Whilst mainstream cloud providers offer a well-established challenge to those involved in criminal enquiries, there are also a host of services offering non-account based 'anonymous' online temporary file storage and transfer. From the context of a digital forensic investigation, the practitioner examining a suspect device must detect when such services have been utilised by a user, as offending files may not be resident on local storage media. In addition, identifying the use of a service may also expose networks of illegal file distribution, supporting wider investigations into criminal activity. This work examines 16 anonymous file transfer services and identifies and interprets the digital traces left behind on a device following their use to support law enforcement investigations.
The Application of the Dilated Convolution based on Small Object Detection

Object detection is an important part of computer vision applications. It is widely used in robot navigation, intelligent video surveillance, industrial testing, aerospace and other fields. Full application of computer vision can reduce the consumption of human capital. At present, most deep learning-based methods focus on large objects, and we know that large objects always account a large part of an image. Therefore, these methods are not ideal for detecting small objects. The reason is that small objects carry very little information and their ability to express features are also weak. To solve this problem, we use our model and try to enlarge the resolution of feature maps by applying the dilated convolution. The results show that it improves the accuracy of detecting small objects.
A Proposed Continuous Auditing Process for Secure Cloud Storage

In recent time, cloud is one of the most useful resources for storing data without any hassle. Integrity, originality and security of data are the main concerns in cloud computing. Through the data auditing, this problem can be solved in some amount. This proposed work follows the data auditing scheme but with different methodology for making the scheme better and secure. This procedure is divided into three phases: (i) user, (ii) data auditor and (iii) cloud server. Cloud server is used for keeping data file, and data auditor is used to check the integrity of the data which is kept by the user. The user has the permission to perform various types of action like create a block for data and generate encryption for data. The third party auditor mainly generates the encryption method and checks the integrity for data security. For encryption, data encryption standard (DES) algorithm is used as the algorithm which generates the longer ciphertext, conveying better security for data. And for data integrity, secure hash algorithm (SHA-512) is used as it checks for 64-bit words. Also, Rivest–Shamir–Adleman (RSA) is used for measuring digital signature. In this method, user can get the deleted item from cloud bin by accessing into the bin with the digital signature, and all the deleted items are stored into the bin as encrypted mode. In this method, attackers are not feasible to take access into the cloud as RSA is used for digital signature.
Fast multidimensional binary image processing with OpenCL

Binary images are often used in image processing pipelines, and are usually stored in the unpacked format, i.e., 8 bits per pixel. The packed format uses 1 bit per pixel, 8 times less memory, and is a good option when dealing with images too big to fit in RAM. This paper presents a parallel implementation of pixelwise and window operators for packed binary images. The implementation, written in OpenCL, can run in GPUs or multiple core CPUs. The proposed Destination Word Accumulation (DWA) implementation of morphological operations is faster than Leptonica in 2D and up to two orders of magnitude faster than Python and MATLAB in 1D to 5D.
An Ontological Analysis of Challenges Involved in Urban Solid Waste Management

Solid waste is a heterogeneous mixture of solid material (biodegradable, non-biodegradable, and inert) that does not have any further use to the society. Research studies suggest an annual per capita rate of increase in solid waste of 1–1.33%. The increasing quantity of such waste is resulting in serious health, aesthetic, environmental, social, and economic problems due to lack of appropriate planning and management. This necessitates the implementation of suitable policies to reduce the risk at all the stages starting from generation and ending with disposal of waste. Generally, the management of solid waste encompasses generation, segregation, storage, collection, transportation, recycling, processing, and disposal. This article presents a logically constructed ontological framework of urban solid waste management. It gives importance to waste management policy, functional elements of waste management, types of waste, sources of waste generation, people involved in waste management, and benefits of solid waste management. It shows the pathways to address the challenge to achieve sustainable solid waste management in the cities. The ontological framework encapsulates a total of 7 * 8 * 3 * 6 * 11 * 5 = 55,440 possible components of the challenge. A critical analysis based on primary and secondary data on urban solid waste management using the framework in Bengaluru, India will help in developing strategies to deal with solid waste. It can be used to systematically map the state-of-the-research on and the state-of-the-practice of urban solid waste management, to discover the gaps, and to bridge the gaps in waste management policy-making.
Synthesis of quantifier-free DNF sentences from inconsistent samples of strings with EF games and SAT

We present a method to extract knowledge in terms of quantifier-free sentences in disjunctive normal form from noisy samples of classified strings. We show that the problem to find such a sentence is NP-complete, and our approach for solving it is based on a reduction to the Boolean satisfiability problem. Moreover, our method bounds the number of disjuncts and the maximum number of literals per clause since sentences with few clauses and few literals per clause are easier to interpret. As the logic we are considering defines exactly the class of locally threshold testable (LTT) languages, our results can be useful in grammatical inference when the goal is to find a model of an LTT language from a sample of strings. We also use results of the Ehrenfeucht–Fraïssé game over strings in order to handle consistent and inconsistent samples of strings.
A Comparative Analysis of Different Classifiers to Propose a Genetically Optimized Neural Network

Breast cancer is the most serious cancer found in female and sometimes in male. The only way of prevention is early detection. By using machine learning techniques we can detect symptoms at an early stage. As observed, different ML techniques for breast cancer has been implemented already to identify the type of tumor. In this paper we have done a comparative study of different existing classifiers like SVC, DTC, KNN, RFC and MLP to find the classifier which performs best for our dataset. Further, we have adopted the linear regression approach to optimize the feature selection. The obtained results will be given as input to the neural network utilized in genetic algorithm. Since machine learning techniques have the problem of solving the redundant and irrelevant features in the dataset, this leads to undesired result. We have tried to overcome this drawback by optimizing feature selection method to reduce this shortcoming. The optimized dataset will be used by Genetic algorithm to produce the fittest attributes being the most correlated ones (according to our problem). This will help to categorize the cancer being benign or malignant. The dataset used for current study in our demonstration is Wisconsin Breast Cancer Dataset (WBCD) from UCI Machine learning repository. The obtained results indicate that MLP classifier provides better accuracy for diagnosing breast cancer and further genetically optimized neural network architecture has been proposed.
La representación de acciones en forma de tokens en la blockchain

espanolLa naturaleza juridica de los criptoactivos sigue siendo discutible. En las cadenas de bloques se alojan en la forma de tokens, que son basicamente una representacion de valor propia de la tecnologia blockchain, y pueden estar vinculados a un bien o derecho existente en el mundo real, fuera de la cadena de bloques, pero tambien pueden tener un valor completamente independiente. Es una cuestion de mucha actualidad e importancia, lo que confirma la reciente consulta publica llevada a cabo por la Comision Europea. En nuestro trabajo analizamos la situacion en la que las acciones se representan en forma de tokens, ya porque se han emitido en el marco de una ICO, o debido al proceso de tokenizacion de los valores tradicionales existentes previamente. Examinamos la consideracion de tokens como valores negociables y sus consecuencias juridicas, la aplicabilidad de la normativa actual y valoramos la necesidad de adaptar el marco regulatorio al fenomeno de los criptoactivos. EnglishThere is no consensus as to the legal status of the cryptoassets. They take a form of tokens, which basically are a representation of value with the use of blockchain technology, and can either be linked to an asset existing in the real world (off-chain), or they may have an independent value existing only in the blockchain. The issue of cryptoassets and their legal status is currently of high importance, as the recent public consultation carried out by the European Commission confirms. In this article, I analyse the representation of shares in the form of tokens, issued through an ICO, as well as created through the process of tokenization of pre-existing securities. I examine the legal consequences of the tokens being treated as securities, the applicability of the law currently in force and I discuss the need to adapt the regulatory framework to the challenges posed by the cryptoassets.
Amplifying the Polarity Categorization on Twitter Data Using Tweet Polarizer Algorithm and Emoticons Score

Mining is utilized to help individuals to separate important data from huge amount of information. Opinion Mining or Sentiment Analysis concentrates on the exploration and grasp of the feelings from the content generated in social media. It recognizes the supposition or attitude that an individual has towards a point or an article and it looks to distinguish the perspective hidden in the large content range. Knowing clients’ sentiments and giving the best arrangement or administration is an outstanding business sector procedure pursued by each business. In this paper, we center around producing polarities of tweets to know the general feeling on a given word or a trump card string. An algorithm Tweet Polarizer is used in this paper to categorize the tweets. A couple of NLP procedures are used to develop a superior methodology for making the most appropriate and possible fringe for a given tweet and to imagine the few trademark features of customers like from which area he has posted the tweet and when.
Music Scoring for Film Using Fruity Loops Studio

Making music for a film can be said to be quite challenging for some people with the necessity that music can evoke the atmosphere in the film. Determination and placement of audio aspects into visual form are things done in the music scoring process. Of course, it will be very inconvenient and inefficient when making music must be through recording instruments manually through the studio. As technology develops in the world of music production, making music for films can now be made using only a computer. This can happen thanks to the Digital Audio Workstation (DAW) software. Nowadays, various types of DAW are emerging, including one that is quite well known, Fruity Loops Studio or commonly called FL Studio. This study aims to find out how the music scoring process for a film using FL Studio, as a reference for making music for films.
Research on collision detection of ship’s hull outer surface curved forming double mechanical arms

Aiming at the self collision problem of double mechanical arms for ship' s hull outer surface curved forming in the actual process of motion planning, firstly, the reachable space of double manipulator arms is solved, and the possibility of collision between double manipulator arms in space is analyzed. Then, according to the actual processing conditions, the double mechanical arms are simplified to simple geometries such as capsules and spheres, and the collision detection between the mechanical arms is converted to the calculation of the distance between the geometries. For the calculation of the distance between capsules, the method of calculating the distance between space lines is adopted, which greatly simplifies the calculation and reduces the complexity of collision detection. Finally, based on the actual double mechanical arms DH parameters, the simulation is performed using the MATLAB toolbox to verify the correctness of the collision detection algorithm.
Empirical scaling analyzer: An automated system for empirical analysis of performance scaling

The time complexity of algorithms, i.e., the scaling of the time required for solving a problem instance as a function of instance size, is of key interest in theoretical computer science and practical applications. In this work, we present a fully automated tool – Empirical Scaling Analyzer (ESA) – for performing sophisticated and detailed empirical scaling analyses. The methodological approach underlying ESA is based on a combination of automatic function fitting and bootstrap sampling; previous versions of the methodology have been used in prior work to characterize the empirical scaling behaviour of several prominent, high-performance SAT and TSP solvers. ESA is applicable to any algorithm or system, as long as running time data can be collected on sets of problem instances of various sizes. We present results from rigorous stress-testing to critically assess ESA on scenarios with challenging characteristics. We also give an overview of empirical scaling results obtained using ESA.
Prediction of Admissions and Jobs in Technical Courses with Respect to Demographic Location Using Multi-linear Regression Model

With more employment opportunities, we have seen tremendous growth in number of institutions which offer professional courses across globe. One side, there are very well-established institutes where infrastructure, faculty profile and teaching facilities have established a niche, while on the other side, new institutes, continuously looking for recognition from government bodies on the basis of infrastructure only. Studies published on various online and offline surveys show that with increased number of institutes offering professional courses like engineering, the employment potential has come down. This paper describes a multiple linear regression-based model which can predict employment potential of different courses according to a specific region. The model includes the job employment potential as a contributing parameter to approve government recognition to such institutes. The proposed work can help in balancing the number of institutes at a location which are more promising in providing jobs to engineering graduates. Our work predicts employment potential rate of each course. The number of admissions to an institute and number of students getting an employment in campus have been considered as two parameters. These values are used to provide the employment potential for each course. If the predicted value of employment potential of the requested course lies within the range of the value of employment potential at that specific location, then course can be considered for approval. We have observed that our proposed work predicts the employment potential with an accuracy of around 92%.
Covariance Functions for Quantum Linear System Driven by Few Photons

In this paper, we consider that a quantum linear system is driven by two input channels, which contain a single-photon state and an unfactorizable two-photon state. Quantum entanglement between the two channels is analyzed by deriving the explicit forms of the input and output covariance functions. Simulations are conducted by tunning the coupling strengths between the two input channels and the quantum system, which has certain guiding effect on the design and application of quantum optical experiments.
Binocular Camera Parallel Calibration Method Based on Sub-pixel

During the operation of binocular vision equipment, the accuracy of initial calibration will gradually decrease. The aging of hardware platform and the change of temperature and humidity will lead to the decline of camera calibration accuracy. Therefore, the binocular camera needs to be quickly calibrated several times. To solve this problem, a parallel calibration method of binocular camera based on sub-pixel corner detection is proposed. The precision of calibration is improved by selecting the most appropriate number of calibration images. The internal parameter matrix and distortion coefficient matrix are obtained by using the parallel calibration method of monocular camera. Furthermore, the rotation matrix, translation matrix and re-projection errors of binocular camera are obtained. In order to improve the calibration accuracy of left and right cameras, a sub-pixel corner detection method is proposed to extract the corners of chessboard. The experiment results show that the parallel thread calibration algorithm is 23.5% faster than the traditional method. This algorithm can effectively improve the speed of binocular camera calibration while ensuring the camera calibration accuracy.
Is There a Prophet Who Can Predict Software Vulnerabilities?

Shortcuts in software development generate technical debt and software vulnerabilities. We propose a framework that will allow stakeholders an effective way to forecast the trend in software vulnerabilities and allow stakeholders to provide the necessary resources to reduce the attack surface and the probability of software failure. We demonstrated that our method can forecast vulnerabilities in several open-source projects, and seasonality in daily, monthly, and yearly total vulnerabilities. Our preliminary results indicate that we can use forecasting methods up to 90 days out with accuracy. In this paper, we present our technique, methodology of preparation of inputs for the proposed artificial intelligence model, and the results of analysis of three open source projects.
Stochastic Gradient Estimation for Artificial Neural Networks

We investigate a new approach to compute the gradients of artificial neural networks (ANNs), based on the so-called push-out likelihood ratio method. Unlike the widely used backpropagation (BP) method that requires continuity of the loss function and the activation function, our approach bypasses this requirement by injecting artificial noises into the signals passed along the neurons. We show how this approach has a similar computational complexity as BP, and moreover is more advantageous in terms of removing the backward recursion and eliciting transparent formulas. We also formalize the connection between BP, a pivotal technique for training ANNs, and infinitesimal perturbation analysis, a classic path-wise derivative estimation approach, so that both our new proposed methods and BP can be better understood in the context of stochastic gradient estimation. Our approach allows efficient training for ANNs with more flexibility on the loss and activation functions, and shows empirical improvements on the robustness of ANNs under adversarial attacks and corruptions of natural noises.
미디어 리터러시 역량 인식의 전문가 집단 간 동질성과 차별성

This study explored the relative importance and priorities of media literacy competence considering media literacy as the key concept of media education. The perceptions of 5 main expert groups in media education (school teachers & lecturers; NGO activists, academic researchers, government officials...
Experimental Study on the Propagation of Low-Velocity Impact Waves in Sand Using Particle Image Velocimetry

Particle image velocimetry (PIV) is a hybrid experimental–digital technique that allows for the noncontact measurement of the displacements and strains in granular media. It has seen widespread applications in the laboratory studies of geostatic problems, such as slope failures and ground deformation due to foundation punch-through. In recent years, the development of optical cameras with high temporal and spatial resolution opens the possibility of extending the PIV technique to dynamic problems such as those caused by impact. Using PIV for impact measurements requires the images to be taken at very high frame rates. In this paper, an attempt is made to capture the phenomenon of low-velocity impact wave propagation in sand using PIV. Details of the experimental setup are presented, and the key factors that affect the accuracy of the measurements are discussed. The PIV results are processed to obtain the displacement, velocity, and acceleration histories for particle patches at varying distances from the impact source. Comparisons are made against independent concurrent measurements recorded by two accelerometers embedded in the same soil sample, as well as the results from a dynamic finite-element analysis that computes the displacements and stresses in the sand due to the same impact loading. This study indicates that, for dynamic measurements in low-velocity impact experiments, the PIV technique offers a promising nonintrusive method of capturing the displacement, velocity, and acceleration histories over a wide range of locations in the sand mass without needing to embed sensors in the test sample.
Efficient Recursive Implementation of Spatial-Temporal Gaussian Process Regression

The current implementation of the spatial-temporal Gaussian process regression has computational complexity O(NM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ), where N and M are the number of temporal and spatial data, respectively, and thus can only be applied to data with large N but relatively small M. In this work, we show that by exploring the Kronecker structure in the state-space model realization of the spatial-temporal Gaussian process, we can extend the current implementation with a coordinate transformation and an output transformation (corresponding to data preprocessing), such that the computational complexity is reduced to O(M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> +NM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> +NM) and therefore the proposed implementation can be applied to data with large N and moderately large M. Moreover, the proposed implementation can be parallelized and the computational complexity can be further lowered if parallel computing is adopted.