Reviewerly/exHarmony huggingface dataset:
Detection of Email Spam using an Ensemble based Boosting Technique

Email is amongst the advanced socializing communication source that is mostly adapted by business and commercial users. Although there are various assets of email as it is quick, cheap, and efficient communication resource, the service is misused by various users for the personal or professional purposes by spreading the useless and extra emails which are termed as email spam. There is the existing research of authors who have used machine learning methods for the detection of email spam and achieved effective precision values, but the individual methods mostly noted with some shortcomings which leads to lack in the performance. In this research work, ensemble based boosting technique on machine learning classifiers of Multinomial Naïve Bayes and J48 classifiers is proposed for the detection of email spam. The system detects email spam by adding the strong features of one classifier into another with the help of Adaboost algorithm. The results of the proposed ensemble technique are evaluated with accuracy and sensitivity parameters. The evaluated results indicate the effectiveness of proposed concept to detect the email spam in comparison with individual methods and other considered existing concepts.
Long-range Forecasting as a Past Value Problem: Using Scaling to Untangle Correlations and Causality

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Geophysical Research Letters. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]Long-range Forecasting as a Past Value Problem: Using Scaling to Untangle Correlations and CausalityAuthorsLeninDel Rio AmadoriDShaun M.LovejoyiDSee all authors Lenin Del Rio AmadoriDCorresponding Author• Submitting AuthorMcGill UniversityiDhttps://orcid.org/0000-0003-4043-472Xview email addressThe email was not providedcopy email addressShaun M. LovejoyiDMcGill UniversityiDhttps://orcid.org/0000-0002-9367-3137view email addressThe email was not providedcopy email address
How Children Gauge Information Trustworthiness in Online Search: Credible or Convenience Searcher?

This study investigates children information search behaviour to inform about their search characteristics. The study uses a qualitative approach by observing and noting the children’s searching process. A total of 30 children aged 7-9 years participated in this study. The unobtrusively observation sessions were video-recorded on children's searching process via the Web. Probing interviews were used to gather understanding on how the children perceive information trustworthiness. The data was analysed using thematic coding approach. The finding highlighted two main characteristics of children search: Credible Searcher and Convenience Searcher. The children in general could gauge information trustworthiness and have confidence with regard to the relevance, reliability and the credibility of the resources. The children do not consider source reputation and currency as an important criterion when seeking information. This study contributes to a better understanding of information trustworthiness as reflected in young children’s information seeking processing.
New Construction Methods on Multiple Output Resilient Boolean Functions with High Nonlinearity

Construction of multiple output functions is one of the most important problems in the design and analysis of stream ciphers. Generally, such a function has to be satisfied with several criteria, such as high nonlinearity, resiliency and high algebraic degree. But there are mutual restraints among the cryptographic parameters. Finding a way to achieve the optimization is always regarded as a hard task. In this paper, by using the disjoint linear codes and disjoint spectral functions, two classes of resilient multiple output functions are obtained. It has been proved that the obtained functions have high nonlinearity and high algebraic degree.
Dilemmas and Challenges of Citizen Information Campaigns: Lessons from a Failed Experiment in India

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VASD2OM: Virtual Auditing and Secure Deduplication with Dynamic Ownership Management in Cloud

In cloud repository amenities, deduplication technology is often utilized to minimize the volume and bandwidth by removing repetitious information and caching only a solitary duplicate of them. Deduplication is extremely productive when many customers deploy the identical information to the cloud repository, but it aggrevates problems pertaining to safety and proprietorship. Proof-of-ownership mechanism authorize any possessor of an identical information to approve to the cloud repository server that he possess the information in a dynamic way. In repository utilities with enormous information, the repository servers may want to minimize the capacity of cached information, and the customers desires to examine the integrity of their information with a reasonable cost. Aiming at realizing integrity auditing and deduplication with effective proprietorship administration of information in the cloud, we propose Virtual Auditing and Secure Deduplication with Dynamic Ownership Management in Cloud (VASD2OM) mechanism. It empowers the distributed server to limit access to the deployed information though the proprietorship transforms actively. Further, the mechanism supports safe and effective virtual auditing of the documents during the download process. In addition, the proposed mechanism lowers the burden of dataowner to audit documents by himself and there is no need to authorize auditing to a Third Party Auditor (TPA). Experimental results demonstrate that the virtual auditing has low auditing time cost relative to the existing public auditing schemes.
Pervasive hybrid two-stage fusion model of intelligent wireless network security threat perception

The rapid development of software, hardware, and communications technologies has helped spread sensors, actuators, and heterogeneous devices connected through the internet, collecting and distributing a large amount of information, opening a unique class of advanced services available at any time. This paper introduced a pervasive hybrid two-stage fusion model (PHTSFM) for the cybersecurity situation evaluation and focused on multi-heterogeneous sensors to determine the impact of security threats on a networked system and precisely evaluate system safety. The multisource information's characteristics in network security analysis and data fusion security algorithms have been introduced. Multiple correlations are utilised to differentiate the normal event or outlier and abnormal event within the least delay. The simulated network assessment reveals that the proposed solution is appropriate for the network environment, and the findings of the test are precise and effective.
Pruned Cascade Neural Network Image Classification

In this paper we propose a new model of deep neural network to build in deeper network. The convoluational neural network is one of the leading Image classification problem. The vanishing gradient problem requires us to use small learning rate with gradient descent which needs many small steps to converge and its take long time to proceed . By using GPU we can process more than one dataset (CIFAR-100) in a particular session. To overcome vanishing gradient problem by using the prune cascade correlation neural network learning algorithm compared to the deep cascade learning in CNN architecture. We improve the filter size, to reduce to the problem by training algorithm that trains in the network from bottom to top approach and its performing attain the task for better image classification in Google Net. We reduce the time complexity (training time ), storage capacity can be used pre training algorithm. Index Terms: Deep Learning, Vanishing gradient, GoogleNet, CNN, Image classification.
English education during the second and third National Curriculum period in South Korea: Focusing on the newspaper article analysis.

This paper explores the historical development of English education in Korea regarding the second and third National Curriculum period. By using the historical research method which analyzes newspaper articles in four major newspapers in Korea, we compared the similarities and differences between these two periods. The research questions are: 1) what are the characteristics of the second National Curriculum? 2) what are the characteristics of the third National Curriculum?, and 3) what is the implication of these previous curricula on the current English education in Korea? It was found that the second National Curriculum Period showed steady development in English education in terms of its emphasis on oral skills instead of the traditional emphasis on reading and grammar skills. However, the lack of coherence among various English textbooks, the lack of English teachers fluent in English, and the exam-oriented social atmosphere were the significant challenges in fully implementing the second National Curriculum. The third National Curriculum Period overlaps with the Yushin Regime propagated by the late President Park Junghee. During this period, a systematic effort to introduce communicative language teaching continued, whereas the exam-orientation persisted which made the English class employ teacher-centered grammar-translation methods. This paper concludes with educational suggestions and future research directions.
<i>Pixel of Matter</i>: New Ways of Seeing with an Active Volumetric Filmmaking System

Using volumetric filmmaking as a medium for artists and designers requires the development of new methodologies and tools. We introduce an installation art project using the active volumetric filmmaking technology to investigate its possibilities in art practice. To do that, we developed a system to film volumetric video in real time, thereby allowing its users to capture large environments and objects without fixed placement or preinstallation of cameras. Active volumetric filmmaking helps us realize the digital reconstruction of physical space in real time and can be expected to ultimately facilitate the coexistence of real and virtual spaces.
TBAC: tree-based access control approach for secure access of PHR in cloud

Personal Health Record (PHR) system is a currently emerging patient-oriented model for sharing the health information through a cloud environment. Previously, single attribute authority-based security scheme was used for sharing the PHRs in the cloud. But, this security scheme is not practically applicable due to the security and privacy issues. The existing access control approaches require more time to encrypt and decrypt the PHR file. This paper proposes a Tree-Based Access Control (TBAC) approach for fine-grained and secure access of the PHR in the cloud environment. In our approach, Tree-based Group Diffie-Hellman (TBGDH) algorithm is used to generate the key instance for the encryption process. The Attribute-based Encryption (ABE) approach is used with different hierarchical levels of the users to protect the personal health data. The access policies are based on the user attribute.
Dark Channel Prior Based Image Dehazing using Different Transmission Map Refinement Methods

Due to existence of haze, the image quality is degraded in the environment. Removal of haze is called dehazing. To dehaze an image Dark Channel Prior is recommended. Dark Channel Prior is an observation, that an image has few pixels whose intensity value is very small or near to zero in most non-sky patches. Such pixels are referred to as dark pixels. Dehazing through Dark Channel Prior is accomplished using four major steps. The steps include estimating atmospheric light, estimating transmission map, refinement of transmission map and image reconstruction. Incorrect estimation of transmission map may lead to some problems. These problems include false textures and blocking artifacts. Many methods are developed to further sharpen transmission map. Here transmission map is refined using soft matting, guided filter and bilateral filter. The comparison of dehazing methods has become difficult due to scarce availability of ground truth images .So we used I-HAZE, a new data set containing 35 picture pairs of hazy pictures and their respective ground truth pictures. A significant benefit of I-HAZE data set is that it allows us to compare different refinement methods used for dehazing with SSIM, PSNR and RMSE which are used for the measurement of finally obtained reconstructed image quality after the removal of haze.
IBM: 2020 Cyber Resilient Organisation Report

Being the target of a cyber attack is a statistical certainty for enterprises of all sizes. Most people understand this now and organisations have, for the most part, become more effective and diligent about planning for attacks and detecting them when they happen. But according to this survey-based report, conducted by the Ponemon Institute, the past five years have actually seen a reduction in organisations' ability to contain attacks while they're happening.
Intelligent models

In recent decades, a vast number of intelligent approaches are applied for different engineering problems. These intelligent approaches are based on different (Artificial Intelligence) AI modeling techniques. In this chapter, various intelligent modeling techniques are discussed in detail. These models include Artificial neural networks (ANN), Fuzzy logic systems (FLS), Adaptive neuro-fuzzy inference system (ANFIS), Support vector machine (SVM), Decision tree (DT), Group method of data handling (GMDH), Genetic programming (GP), Gene expression programming (GEP), Case-based reasoning (CBR), and Committee machine intelligent system (CMIS).
Text Generation

In this book, so far you have used vanilla neural networks, the networks that do not have the ability to remember their past. They accept a fixed-sized vector as input and produce a fixed-sized output. Consider the case of image classification where the input is an image and the output is one of the classes for which the model has been trained. Now, consider a situation where the prediction requires the knowledge of the previous predictions. To give you an example, consider you are watching a movie. Your mind keeps guessing what will be the next scene. The guess depends on what has happened not just in the near past but something that also happened 15 minutes ago or even an hour for a long movie. Our vanilla neural networks, in the way they work, do not have memory to remember what happened in the past and to apply that knowledge to the current guess.
Quantum Computationally Predicate-Binding Commitments with Application in Quantum Zero-Knowledge Arguments for NP

A quantum bit commitment scheme is to realize bit (rather than qubit) commitment by exploiting quantum communication and quantum computation. In this work, we study the binding property of the quantum string commitment scheme obtained by composing a generic quantum perfectly(resp. statistically)-hiding computationally-binding bit commitment scheme (which can be realized based on quantum-secure one-way permutations(resp. functions)) in parallel. We show that the resulting scheme satisfies a stronger quantum computational binding property, which we will call predicate-binding, than the trivial honest-binding. Intuitively and very roughly, the predicate-binding property guarantees that given any inconsistent predicate pair over a set of strings (i.e. no strings in this set can satisfy both predicates), if a (claimed) quantum commitment can be opened so that the revealed string satisfies one predicate with certainty, then the same commitment cannot be opened so that the revealed string satisfies the other predicate (except for a negligible probability).As an application, we plug a generic quantum perfectly(resp. statistically)-hiding computationally-binding bit commitment scheme in Blum’s zero-knowledge protocol for the NP-complete language Hamiltonian Cycle. This will give rise to the first quantum perfect(resp. statistical) zero-knowledge argument system (with soundness error 1/2) for all NP languages based solely on quantum-secure one-way permutations(resp. functions). The quantum computational soundness of this system will follow immediately from the quantum computational predicate-binding property of commitments.
Machine Vision

Machine vision means to endow the machine with visual perception system which provides similar biological visual ability to facilitate situational awareness. The machine vision system converts the ingested object into an image signal by an image capturing device, transmits it to a dedicated image processing system and converts it into a digital signal according to the pixel distribution, width, color, etc. The image system performs various computations on the signals to extract the target characteristics. By discerning the results, the site operations are being controlled. The main research goal of machine vision is to enable computers to have the ability to recognize three-dimensional environmental information through two-dimensional images, and to sense and process geometric information such as shape, position, posture, and motion of the objects in a three-dimensional environment.
Towards a performance analysis of composite web services using Petri nets

The proliferation of web services-based applications, collaboration, interoperability between companies, and more generally demands of web services over the internet are major challenges in the design of performance evaluation in web services discovery and composition. It is required for the web services providers to analyse the performance related to the satisfaction of clients requests. This paper presents an analytical model based on Petri nets, for evaluating the performance of a web services system where requests and web services follow an exponential server. The arrival process of the users requests and web services follows a Poisson distribution. When the web services finish the service, they join the queue. This model follows a Poisson distribution, it is based on Petri nets. We use an analytical method for solving the model. Furthermore, we compute the system's response time, and the average number of clients in the system in terms of the arrival rate of clients requests. We find the limit number of clients in the system from which it begins to be saturated.
Improving the Performance of Lung Cancer Detection at Earlier Stage and Prediction of Reoccurrence using the Neural Networks and Ant Lion Optimizer

Lung cancer is considered to be the one among the most dreaded disease which will be the main reason for the death of individuals and having greater deterioration of death if it is not identified at primitive stage. Because of the fact that Lung cancer could be identified only after spreading to the parts of lungs to a greater extent and it is very tough to predict the presence of lung cancer at the earlier stage. Moreover, it involves greater error in the diagnosing the presence of Lung cancer by Radiologists and Expert Doctors. Therefor it is compulsory to design an intelligent and automated system for accurately predicting the cancer and stage at which the stage of cancer or enhancing the accuracy of prediction for detecting the cancer at earlier which will be much helpful in deciding the treatment type and depth of the treatment based on the extent of disease. Currently application of ANN strategies are the influential ways in supporting expert doctor for examining, complicated medical increase across a wider category of medical application. Back Propagation Network are ideal in recognizing lung cancer and there is no requirement involvement by expert doctors. Maximum number of applications of BPN in medical diagnosis will be utilized in the applications related to decision making of the presence or absence of disease; by which the performance will be reliant over the considered features and allocating the patient with minimum number of classes. Here this research paper establishes the idea of using BPN in the classification of the lung cancer and its stages and the predicting the possibility of recurrence. Along with the BPN, a nature inspired Meta Heuristics that is termed as Ant Lion Optimization Algorithm is used in optimizing the parameters and weights of Back Propagation Network. By using the Ant Lion Optimization Algorithm, the convergence mechanism is improved along with improving the accuracy of the proposed technique and it avoids the chance of getting caught within the clutches of local minima. By using this proposed method BPN network optimized with the help of antlion optimizer more accurate prediction of lung cancer is possible even at primitive stage and the predicting of chance of reoccurrence even after undergoing the appropriate treatment.
A technique for semantic annotation and retrieval of e-learning objects

The primary objective of my research is to design and develop semantic annotation and retrieval model for e-learning document. In training phase, the documents from different domains are taken and the informative words from each document are obtained based on balanced mutual information and frequency of contents in each document. We then use the informative words to identify the superordinates and the objects. The superordinates, the informative words and the objects from each document will give the relation and properties of each document. The relation and properties of each document are then used to cluster the documents. In the testing phase, we give a query or a document as input to the system to retrieve the relevant documents. If a document is given as input, the relation and properties of that document are first identified and it is used to retrieve the relevant documents.
VMware Carbon Black: Global Security Insights Report 2021

There has been a common refrain among cyber security practitioners – seen, not least, in the pages of this publication – that the pandemic has forced radical changes on organisations. And not all of them have been up to the challenge.
Integration of Statistical based Texture and Color Feature for Medical Image Retrieval

Today, the common problem in the domain of computer vision and pattern recognition is content based image retrieval (CBIR). In this paper, a novel image retrieval method using the geometric details based on the correlation among edgels and correlation between pixels has been introduced. The autocorrelation based choridiogram descriptor has been extracted from the image to obtain geometric, texture and spatial information. Color autocorrelogram has been computed to obtain color, texture and spatial information. The proposed method is tested on benchmark heterogeneous medical image database and LIDC-IDRI-CT and VIA/I-ELCAP-CT databases and results are compared with typical CBIR system for medical image retrieval.
Digital forensic standards and digital evidence in Polish criminal proceedings. An updated definition of digital evidence in forensic science

Digital evidence is increasingly popular in criminal proceedings - not only to those commonly referred to as 'cybercrimes'. The credibility criteria of such evidence are in theory governed by a set of basic principles developed within forensic science. According to current theoretical definitions, 'digital evidence' includes any information of probative value stored or transmitted in the form of digital data. It is expected that all digital evidence should be acquired and examined in a forensically sound manner. However, an empirical study based on polish criminal cases' files analysis shows that certain digital forensic guidelines on digital evidence are often ignored in practice, and information of digital origin are often presented only as printouts. A re-evaluation of current theoretical definition of 'digital evidence' is proposed based on a distinction between digital evidence in 'general' (sensu largo) and 'technical' (sensu stricto) sense.