Reviewerly/exHarmony huggingface dataset:
Effect of Feature Selection on Performance of Internet Traffic Classification on NIMS Multi-Class dataset

Abstract The challenges faced by networks nowadays can be solved to a great extent by the application of accurate network traffic classification. Internet network traffic classification is responsible for associating network traffic with the application generating them and helps in the area of network monitoring, Quality of Service management, among other. Traditional methods of traffic classification including port-based, payload-load based, host-based, behavior-based exhibit a number of limitations that range from high computational cost to inability to access encrypted packets for the purpose of classification. Machine learning techniques based on statistical properties are now being employed to overcome the limitations of existing techniques. However, the high number of features of flows that serve as input to the learning machine poses a great challenge that requires the application of a pre-processing stage known as feature selection. Too many irrelevant and redundant features affect predictive accuracy and performance of the learning machine. This work analyses experimentally, the effect of a collection of ranking-basedfilter feature selection methods on a multi-class dataset for traffic classification. In the first stage, the proposed Top-N criterionis applied to the feature sets obtained, while in the second stage we generate for each Top-N set of features a new dataset which is applied as input to a set of four machine learning algorithms (classifiers).Experimental results show the viability of our model as a tool for selecting the optimal subset of features which when applied, lead to improvement of accuracy and performance of the traffic classification process.
Virtual Reality Ear Training System: A study on Spatialised Audio in Interval Recognition

Ear training is a vital element in music education, analogous to taking dictation in written language. It provides musicians with a crucial skill used to identify pitches, melodies, chords and rhythms. Traditionally, the training is conducted by a tutor using a musical instrument, typically a piano. However, with new technologies emerging, several computer applications to facilitate this aspect of music education have been developed. Nevertheless, none of them utilised the VR technology, that proved to be successful in various scenarios, including educational systems, simulations, etc. In this work, we designed and developed a virtual reality ear training system for interval recognition and investigated its usability and user experience and the effect of spatialised audio in a 3D virtual environment on user performance. The results showed that the system has been successfully designed and provides users with a great experience when using it.
Real-Time Vehicle Detection from Captured Images

Objective of this paper is to primarily detect moving vehicles from real-time captured images so that we can later use them for a variety of purposes like detecting the number of vehicles that are present at a road at a certain point, understanding the motion of the vehicles, analyzing vehicle density at any traffic signal at any specified point of time; and based on our previous knowledge can make a good assumption where the vehicle will be heading towards. Signal-to-noise ratio, PSNER and mean-square-error are computed for each captured image using after detection. After establishing vehicle detection scheme, we can also make an attempt to read vehicle plate numbers further a much broader attempt to automate a traffic flow system using image recognition and probability distribution.
Preserving national language and reaching out English-speaking audience of a scholarly journal

This article reflects on the issue of preserving Russian as a national language at scholarly journals and promoting research outcomes of non-Anglophone authors at an international arena by expanding Englishlanguage contents of local journals. Potential approaches to overcoming disadvantages and poor visibility of Russian journals are explored. Publishing English-only journals is advantageous for international recognition and indexing by global databases. However, that language model may have certain disadvantages in terms of information distribution. The optimal proposed solution could be the publication of Russian and English parallel versions of the journal, identical in the contents, metadata, article information, and identifiers, such as the Digital Object Identifier (DOI). Examples of successful bilingual journals are provided.
First-Order Intuitionistic Epistemic Logic

Intuitionistic epistemic logic (IEL), introduced by Artemov and Protopopescu (2016), accepts the co-reflection axiom: “ $$A \supset KA$$ ” in terms of Brouwer-Heyting-Kolmogorov interpretation. There are two variants for IEL, one of which has the axiom “ $$KA \supset \lnot \lnot A$$ ”, while the other does not have it. The aim of this paper is to study the first-order expansions of these two IELs. Hilbert systems and sequent calculi of the first-order expansion of these two intuitionistic epistemic logic are provided to be proved sound and complete for the intended semantics. We also prove the cut-elimination theorems for both systems. Furthermore, the Craig interpolation theorems of both systems are established by Maehara’s method as consequences of cut-elimination theorems.
Short Paper: An Exploration of Code Diversity in the Cryptocurrency Landscape

Interest in cryptocurrencies has skyrocketed since their introduction a decade ago, with hundreds of billions of dollars now invested across a landscape of thousands of different cryptocurrencies. While there is significant diversity, there is also a significant number of scams as people seek to exploit the current popularity. In this paper, we seek to identify the extent of innovation in the cryptocurrency landscape using the open-source repositories associated with each one. Among other findings, we observe that while many cryptocurrencies are largely unchanged copies of Bitcoin, the use of Ethereum as a platform has enabled the deployment of cryptocurrencies with more diverse functionalities.
Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets

A labeled multi-Bernoulli (LMB) filter is presented to jointly detect and track radar targets. A relevant LMB filter is recently proposed by Rathnayake which assumes that the measurements of different targets do not overlap, leading to the favorable separable likelihood assumption. However, new or close tracks often violate the assumption and lead to a bias in the cardinality estimate. To address this problem, a one-to-one association method between measurements and tracks is proposed. In our method, any target only corresponds to its associated measurements and different tracks have little mutual interference. In addition, an approximate method for calculating the point spread function of radar is developed to improve the computational efficiency of likelihood function. The simulation under low signal-to-noise ratio scenario with closely spaced targets have demonstrated the effectiveness and efficiency of the proposed algorithm.
SLA-Based Agile VM Management in Cloud &amp; Datacenter

Cloud Computing is the main root technology to provide various services over the internet in the current scenario, which is basically powered via virtual machines (VMs) over commodity hardware. As soon as a task arrives in the cloud or data center that task is assigned to a VM as per the task scheduler and resource allocation policy. Whenever cloud or data center do not have sufficient spare resources over VM to assign a task, a new VM is created over host machine which have spare resource capacity in the cloud and/or data center. The VM creation should be in such a manner that will follow the Service Level Agreement (SLA) to maintain the Quality of Service (QoS) for the client. We have proposed SLA-based agile VM management algorithm to minimize the VM creation time and VM response time. We use ghost VM to increase the efficiency of cloud, allocation of new VM is from the ghost VM, and using the average creation time is reduced up to 11.98%. We propose two algorithms first for Admission control and second for Rescheduling of VM's. Our aim is to reduce the time taken in the rescheduling of the VM. Currently, we are assuming excess VM which are eligible to Garbage Collection do not put overhead over the cloud and they can be handled via background service, and with do not affect foreground services directly and they are handled via the separate management service.
Improving Robustness of Medical Image Diagnosis with Denoising Convolutional Neural Networks

Convolutional neural networks (CNNs) are vulnerable to adversarial noises, which may result in potentially disastrous consequences in safety or security sensitive systems. This paper proposes a novel mechanism to improve the robustness of medical image classification systems by bringing denoising ability to CNN classifiers with a naturally embedded auto-encoder and high-level feature invariance to general noises. This novel denoising mechanism can be adapted to many model architectures, and therefore can be easily combined with existing models and denoising mechanisms to further improve robustness of CNN classifiers. This proposed method has been confirmed by comprehensive evaluations with two medical image classification tasks.
Comparison of RBAC and ABAC Security Models for Private Cloud

Protecting the resources from unauthorized users is a big challenge in cloud computing. Role based access control (RBAC) model is a standard method for data access control. It is widely implemented access control in many cloud platforms. As the number of roles increase, the complexity is increased. To get rid of limitations of RBAC, the attribute base access control model has been introduced which has more flexibility. Here permissions are directly associated with the roles. By combining both RBAC and ABAC, a hybrid access control scheme is evolved which is more scalable and dynamic. This paper introduces the various data access control models and compare their characteristics. The characteristics of all these security models are compared.
Improving Pathological Structure Segmentation via Transfer Learning Across Diseases

One of the biggest challenges in developing robust machine learning techniques for medical image analysis is the lack of access to large-scale annotated image datasets needed for supervised learning. When the task is to segment pathological structures (e.g. lesions, tumors) from patient images, training on a dataset with few samples is very challenging due to the large class imbalance and inter-subject variability. In this paper, we explore how to best leverage a segmentation model that has been pre-trained on a large dataset of patients images with one disease in order to successfully train a deep learning pathology segmentation model for a different disease, for which only a relatively small patient dataset is available. Specifically, we train a UNet model on a large-scale, proprietary, multi-center, multi-scanner Multiple Sclerosis (MS) clinical trial dataset containing over 3500 multi-modal MRI samples with expert-derived lesion labels. We explore several transfer learning approaches to leverage the learned MS model for the task of multi-class brain tumor segmentation on the BraTS 2018 dataset. Our results indicate that adapting and fine-tuning the encoder and decoder of the network trained on the larger MS dataset leads to improvement in brain tumor segmentation when few instances are available. This type of transfer learning outperforms training and testing the network on the BraTS dataset from scratch as well as several other transfer learning approaches, particularly when only a small subset of the dataset is available.
A NOTE ON THE CUT-ELIMINATION PROOF IN “TRUTH WITHOUT CONTRA(DI)CTION”

Abstract This note shows that the permutation instructions presented by Zardini (2011) for eliminating cuts on universally quantified formulas in the sequent calculus for the noncontractive theory of truth IKT ω are inadequate. To that purpose the note presents a derivation in the sequent calculus for IKT ω ending with an application of cut on a universally quantified formula which the permutation instructions cannot deal with. The counterexample is of the kind that leaves open the question whether cut can be shown to be eliminable in the sequent calculus for IKT ω with an alternative strategy.
An Atomic Technique For Removal Of Gaussian Noise From A Noisy Gray Scale Image Using LowPass-Convoluted Gaussian Filter

During Acquisition of an image using some digital devices we often observe various types of noises. Between them, one of the major types of noise that we often found is the Gaussian noise. When an image contains Gaussian noise, it produces several impurities which are very difficult to detect and eliminate. As a part of the image restoration process, removal of Gaussian noise from a digital image is always a matter of challenge. Before removal of Gaussian noise, we need to convert the digital image into a grayscale image which may contain different percentages of Gaussian noise. Throughout the last few decades, lots of Gaussian noise removal filters or algorithms have been proposed in different international conference papers and acclaimed journals. But a very few of them were successful as far as detecting and eliminating of Gaussian noise from a digital image is concerned. Moreover, these proposed methods also contain several drawbacks and pitfalls which are creating obstacles regarding the generation of the enhanced output image. In this paper, we propose a unique and atomic technique for removal of Gaussian noise from a digital noisy image which is not only capable of detecting and eliminating Gaussian noise, present in the digital image but also capable of generating an enhanced output image. We also try to establish that our proposed method is giving much better result in comparison to other popular filters or algorithms. In order to do that we have invoked a comparative study in experimental results and analysis portion of these paper by calculating PSNR, MSE and RMSE.
Common-path spatial phase-shift speckle shearography using a glass plate

In this paper, a spatial phase shifting digital speckle pattern shearography setup based on a common path interferometer and modest components is proposed for direct measurement of dynamical deformation gradient of objects. The simplicity, stability, and efficiency of the setup are provided by employing a plane parallel glass plate as a shearing device. The desired field of view can easily be achieved by employing two lenses. Moreover, adjustable spatial carrier frequencies within the speckle pattern which is needed to evaluate the phase difference before and after deformation by Fourier transformation can easily be produced. Ultimately, the slope of the deformations on the test surface can be obtained with tunable sensitivity. To demonstrate our technique, the proposed setup is used to evaluate the phase change in a center-loaded aluminum plate. Experimental results for out of plane deformation gradient are presented and discussed.
A Software Service Supporting Software Quality Forecasting

Software repositories such as source control, defect tracking systems and project management tools, are used to help manage the progress of software projects. The exploitation of such data with techniques like forecasting is becoming an increasing need in several domains to support decision-making processes. However, although there exist several statistical tools and languages supporting forecasting, there is a lack of friendly approaches that enable practitioners to exploit the advantages of creating and using such models in their dashboard tools. Therefore, we have developed a modular and flexible forecasting service allowing the interconnection with different kinds of databases/data repositories for creating and exploiting forecasting models based on methods like ARIMA or ETS. The service is open source software, has been developed in Java and R and exposes its functionalities through a REST API. Architecture details are provided, along with functionalities' description and an example of its use for software quality forecasting.
Experimental evaluation of challenges in designing a resistive pressure sensors

Resistive pressure sensors has become popular and used in different applications. The usage of the pressure sensors in designing wearable solutions requests flexible and light materials. However, these materials are exhibiting challenging behavior in respect to precision, sensitivity and repeatability of measurement. In this paper we present a set of experiments demonstrating typical problems, and also we discuss the causes and possible remedies. The experiments were conducted with pressure sensors implemented using the VelostatTM material and Arduino platform for acquisition of the measurements.
Breaking and Fixing Anonymous Credentials for the Cloud

In an attribute-based credential (ABC) system, users obtain a digital certificate on their personal attributes, and can later prove possession of such a certificate in an unlinkable way, thereby selectively disclosing chosen attributes to the service provider. Recently, the concept of encrypted ABCs (EABCs) was introduced by Krenn et al. at CANS 2017, where virtually all computation is outsourced to a semi-trusted cloud-provider called wallet, thereby overcoming existing efficiency limitations on the user's side, and for the first time enabling "privacy-preserving identity management as a service". While their approach is highly relevant for bringing ABCs into the real world, we present a simple attack fully breaking privacy of their construction if the wallet colludes with other users – a scenario which is not excluded in their analysis and needs to be considered in any realistic modeling. We then revise the construction of Krenn et al. in various ways, such that the above attack is no longer possible. Furthermore, we also remove existing non-collusion assumptions between wallet and service provider or issuer from their construction. Our protocols are still highly efficient in the sense that the computational effort on the end user side consists of a single exponentiation only, and otherwise efficiency is comparable to the original work of Krenn et al.
Exploring the Sensed and Unexpected: Not Looking in Gaze Interaction

Gaze interaction paradigms rely on the user needing to look at objects in the interface to select them or trigger actions. ”Not looking” is an atypical and unexpected interaction to perform, but the eye-tracker can sense it. We illustrate the use of ”not looking” as an interaction dynamic with examples of gaze-enabled games. We created a framework containing a spectrum of five discrete categories for this unexpected use of gaze sensing. For each category, we analyse games that use gaze interaction and make the user look away from the game action up to the extent they close their eyes. The framework is described based on whether specific game events mean the player might not; cannot; should not; must not; or does not look. Finally, we discuss the outcomes of using unexpected gaze interactions and the potential of the proposed framework as a new approach to guide the design of sensing-based interfaces.
When the Data Are Out: Measuring Behavioral Changes Following a Data Breach

Despite the dramatic upswing in the frequency and severity of data breaches, little is known about their effects on online customers’ behavior. To this end, we measure individual-level activity changes for a website that suffered an extreme data breach, one for which widespread media attention precluded the existence of an uninformed control group. Our data includes detailed behavior of members of a matchmaking website for those seeking extramarital affairs. We employ Temporal Causal Inference (TCI) for tree-based, trajectory-matching, using it to assess changes in users’ site engagement in the three weeks following the data breach. TCI offers insights regarding individual-level treatment (i.e., breach) effects, as well how demographic and usage-based covariates align with them. These analyses reveal a decrease in the probability of searching or messaging, and a notable increase in the probability of deleting photos. Intriguingly, users who'd enacted more stringent privacy measures pre-announcement were less likely to protect themselves afterward. Despite the common belief that such severe breaches lead to irretrievable customer disengagement, these initially negative reactions did not persist: by the third week post-announcement, treatment effects largely attenuate. These methods and findings offer both managerial and policy considerations to help protect customers’ privacy.
An Effective Big Data and Blockchain (BD-BC) Based Decision Support Model for Sustainable Agriculture System

In spite of the available advanced technologies and the convergence of each and every field of the society with the advanced technologies, perhaps agriculture is the only field lacking behind in effective usage of advanced technology to tackle the farm problems. Due to this agro business is facing a many problems such as demand-supply synchronization, food wastage, lack of food security, unnecessary creation of demand by holding the supply erratically, and intervention of mediators. Due to which, many times farmers may not get the expected income and consumers are not happy with the varied prices. The incorporation, implementation, and usage of Big Data, Cloud, and Blockchain technologies have given the new paradigm shift in sustainable agriculture research and practices with a sustainable commercial model to comprehend potential benefits and sustain it. Hence there is a need for a technology-based efficient system tailored to the needs of farmers in order to remain competitive and derive better price realization. This work presents a technology-based novel idea to address the problems of agriculture effectively. Implementation results have shown that this model improves the quality of the agricultural system by minimizing the gap among demand and supply of food crops, required by the society from the farmer's end, thus avoiding the loss for farmers and catering the needs of consumers. This leads to gainful crop business for farmers and satisfactory fulfillment of the societal needs.
A novel method for image edge extraction based on the Hausdorff derivative

Image edge extraction based on the differential equations is an important branch of image processing. This paper makes the first attempt to employ the Hausdorff derivative gradient method (HDHM) to extract the image edge. In terms of the visual quality of details, contours, edge integrity, and continuity, the original images and noisy images were extracted by using an appropriate fractal dimension. Numerical results indicate that the HDGM outperforms the Sobel operator, Canny algorithm and traditional gradient method. Moreover, it can be confirmed that the proposed method with multiple values is effective for some images on which the gray value in different parts is quite different. This work extends the Hausdorff derivative to a new field and provides an effective methodology for image edge extraction.