Reviewerly/exHarmony huggingface dataset:
Classification and Performance Evaluation of Phishing Email or URL Using Random Forest

Phishing email turns out to be more risks issue in online bank truncation preparing issue just as long range interpersonal communication destinations like Facebook, twitter, Instagram. Typically phishing is completing by ridiculing of email or text installed in email body, which will incite clients to enter their qualification. Preparing on phishing approach isn’t so much powerful in light of the fact that clients are not for all time recollect their preparation stunts, cautioning messages. It is absolutely rely upon the client activity which will be performed on certain time on notice messages given by programming while at the same time working any URL. The present world is online advanced world. Each client’s web for online correspondence and internet banking measures for their every day life for their own just as expert work. While utilizing on the web email or banking exchange administrations, clients may share their accreditation or disregard security cautioning due to occupied planned or ignorance of web safety efforts. These explanation causes phisher to trap to in phishing.
Trends and Scenarios of Higher Education Development in the Digital Economy

Despite the fact that trends that influence the development of higher education have been sufficiently studied, researchers agree only on the general features of the image of higher education of the future. In this work, the trends are systematized in view of two groups: technological and social trends, which, in their turn, are divided into trends that have influence in the period of pre-school, school, university, and post-graduate education, when person is a full member of labor market. The performed analysis of trends allowed distinguishing the junctions of the two key tendencies: development of labor market with social transformations and development of the system of “human–machine” interaction with development of cognitive technologies. Based on the distinguished key junctions, four scenarios of development of higher education are formulated. The examples of strategic rates of universities are offered for each scenario. The offered scenarios of higher education’s development allow understanding which consequences for a university will be brought by various tendencies, and how the technologies of the digital economy influence the education market. The developed approach is an important supplement to the tools of strategic planning of the organizations of higher education.
SPMM: A Model Taxonomy for Designing and Managing Quality System

Software engineering is a discipline of Computer Science in which the new sub-areas are constantly added, especially in the area of quality, data management, and architectural design. Nowadays software development languages and processes are rapidly changing to deliver high-quality software products, i.e., usable systems, hybrid, and fulfill users&#x2019; needs. This paper aims to identify and classify different process models proposed by the researchers based on characteristics of software quality, data management, and software integration and redesign. From a study of several models through a systematic mapping study, we identify different parameters and presented them in a traceability matrix. The parameters are classified into six areas. This paper provides an in-depth theoretical insight into the models and characteristics. A systematic mapping study was conducted through a literature review. The methodology used in this paper is both qualitative and quantitative. Initially, through a systematic mapping study, we study different models working on different parameters. And then proposed a model that can cover all the aspects of software implementation and management. We select ERP systems for it. Later we perform the GAP analysis and statistical evaluation of the model. It has been observed that all of the models are area specific either focused on quality parameters or management issues or architectural-based. The proposed model covers all aspects. The primary research shows that industrialists also need a better model for quality implementation. Our statistical analysis can serve as a decision-making tool for them to add to their decision-making processes. The other could use it to further enhance the framework for quality management. This model will enhance further in the future for better implementation.
Review on Sentiment Analysis and Polarity Classification of Sarcastic Sentences using Deep Learning in Social Media

With the high proliferation of social media into people’s daily life and existence, more and more users of such social media platforms are using these to express their opinions. This has given rise to the need to carry out sentiment analysis to obtain an understanding of opinions, emotions, and attitudes toward person or aspects or products or services, etc. Sarcasm in sentiment analysis is an expression of emotion containing utterances that are conveying the meaning in an opposite way to what the user really wants to say. While, on the face of it, it seems an easy task of classification but, in reality, sarcasm detection is a particularly difficult task in the domain of natural language processing and continues to be a NP-hard problem. In this survey paper, we have specifically looked into the recent (from 2016 to 2021) works of various authors who have leveraged deep learning and briefed how different methodologies have evolved from use of contexts to moving to multi-modality, cross disciplines like quantum theory, neuro, fuzzy, etc., and its consequences on the results.
Unsupervised Representation Learning via Information Compression

This paper explores a new paradigm for decomposing an image by seeking a compressed representation of the image through an information bottleneck. The compression is achieved iteratively by refining the reconstruction by adding patches that reduce the residual error. This is achieved by a network that is given the current residual errors and proposes bounding boxes that are down-sampled and passed to a variational auto-encoder (VAE). This acts as the bottleneck. The latent code is decoded by the VAE decoder and up-sampled to correct the reconstruction within the bounding box. The objective is to minimise the size of the latent codes of the VAE and the length of code needed to transmit the residual error. The iterations end when the size of the latent code exceeds the reduction in transmitting the residual error. We show that a very simple implementation is capable of finding meaningful bounding boxes and using those bounding boxes for downstream applications. We compare our model with other unsupervised object discovery models.
Improved Hamming Diversity Measure for Set-Based Optimization Algorithms

Proper evaluation of the performance of a population-based algorithm often requires a good understanding of the search behaviour of the population over time. One approach towards understanding search behaviour is to measure the diversity of the population, which is an indicator of how similar the search agents in the population are to one another. Historically, diversity metrics have focused on populations with a continuous-valued, fixed-length solution encoding. Two diversity measures, Jaccard diversity and Hamming diversity, for populations with a set-based solution encoding were recently proposed. It was shown that Jaccard diversity accurately represents the diversity of sets over time while Hamming diversity under represents diversity in the same scenarios. This paper proposes a simple improvement to the Hamming diversity measure, that makes it equivalent to the Jaccard diversity measure, accurately quantifying set-based population diversity.
A Prediction of breast cancer based on Mayfly Optimized CNN

Breast cancer patients are prone to major problems of greater mortality related to their health. Breast cancer diagnosis is time demanding, and a system that can automatically diagnose breast cancer in its early stage has to be developed due to the low availability of systems. The principal explanation may be that radiologists are misinterpreting suspected injuries due to technical difficulties in picture properties and diverse breast densities, hence increasing the false ratio. Timely intervention is important in developing an upto-date forecasting method that can reduce disease complications with increased recovery. For the categorization of benign and malignant tumours, various Machine Learning (ML) and Deep Learning (DL) algorithms were applied. However the detection by typical machine learning methods of breast abnormalities misinterprets an inconsistent feature extraction process that causes problems, the call-back to patients for biopsies to eradicate suspicions. Deep learning approaches for a reliable pronostic and categorization of breast cancer have therefore been developed. This research study focuses on the prediction of breast cancer using Convolutional Neural Network (CNN). The input images are removed by noises and segmentation is carried out by Otsu Thresholding technique. After that, CNNis applied for final prediction, where the learning rate of the CNN is optimized by using Mayfly Algorithm. The experiments are validated to test the efficiency of proposed CNN and proved that it achieved 97&#x0025; of accuracy for the publicly available dataset.
Cross-Domain Activity Recognition Using Shared Representation in Sensor Data

Existing models based on sensor data for human activity recognition are reporting state-of-the-art performances. Most of these models are conducted based on single-domain learning in which for each domain a model is required to be trained. However, the generation of adequate labelled data and a learning model for each domain separately is often time-consuming and computationally expensive. Moreover, the deployment of multiple domain-wise models is not scalable as it obscures domain distinctions, introduces extra computational costs, and limits the usefulness of training data. To mitigate this, we propose a multi-domain learning network to transfer knowledge across different but related domains and alleviate isolated learning paradigms using a shared representation. The proposed network consists of two identical causal convolutional sub-networks that are projected to a shared representation followed by a linear attention mechanism. The proposed network can be trained using the full training dataset of the source domain and a dataset of restricted size of the target training domain to reduce the need of large labelled training datasets. The network processes the source and target domains jointly to learn powerful and mutually complementary features to boost the performance in both domains. The proposed multi-domain learning network on six real-world sensor activity datasets outperforms the existing methods by applying only 50% of the labelled data. This confirms the efficacy of the proposed approach as a generic model to learn human activities from different but related domains in a joint effort, to reduce the number of required models and thus improve system efficiency.
Union-Net: Lightweight Deep Neural Network Model Suitable for Small Data Sets

Deep learning can achieve better performance with large data sets. In real applications, it is expensive to obtain big data. Therefore, research on deep learning for small data sets has been paid more and more attention. Judging from the published research results, the tasks of completing small data sets mostly use pre-trained models based on big data, which are realized through migration learning and fine-tuning technologies. There are two risks in using pre-trained models for commercial applications: One is the copyright issue of the pre-trained model or the data sample used by the pre-trained model. The other is that a suitable pre-training model needs to be selected for learning goals in a specific field. Therefore, this paper proposes a lightweight deep network model Union-net that adapts to small data sets. The structure of the model is as follows: Convolutional network units with the same input and different combinations constitute a union module. Each union module is equivalent to a multiple convolution layer (named union convolution in this paper). The serial input and output of three union modules form a neural network. The results of the parallel output of the three union modules are merged and added, and the result of the addition is used as the input of the last layer of convolutional layer. So as to form a "shallow network" structure. The model can solve the problem of the traditional deep network model that the network is too deep and the conduction path is too long, which leads to the loss of the underlying information transmission. Based on the public data sets CIFAR10 and 17Flower, using multiple classic models and Union-net models, four small data set multi-class classification experiments and one large data set multi-class classification experiment were carried out respectively.
Fidelity-Preserved Reversible Data Hiding in JPEG Images Based on Adaptive Position Selection

Reversible data hiding (RDH) in JPEG images has gained considerable attention from scholars in recent years. However, it is not easy for JPEG images to exploit the pixel redundancy for RDH design like uncompressed images. In this paper, we propose a fidelity-preserved RDH in JPEG images. The proposed scheme embeds the secret data using the histogram shifting method after considering different distortion costs of non-zero AC coefficients at different positions. Accord-ing to the block entropy obtained by summing the squares of all pixels of each $$8\times 8$$ block, the quantified DCT blocks are divided into the smoothed blocks and texture blocks, and the obtained smoothed blocks are sorted in ascending order. The AC coefficients with values 1 and/or −1 in the smoother blocks are used for the embedding, the non-zero AC coefficients are shifted toward the right or the left at most by one, and the others remain invariant. Experiment results have verified the effectiveness of the proposed method.
Rp-Net: A Pointnet++ 3d Face Recognition Algorithm Integrating Rops Local Descriptor

In order to enhance the facial features of 3D face and improve the recognition degree of 3D face in weak light or dark environment, a 3D face recognition algorithm based on point cloud depth learning is proposed in this paper. Firstly, the 3D face is automatically detected from the 3D raw data and preprocessed, including nose tip detection and face clipping, removing spikes and filling holes, and surface normals; Then, the local features are described and classified by integrating the rotating projection statistical local feature descriptor(RoPS) into PointNet++ network; Finally, the nearest neighbor distance ratio is used for feature matching. The algorithm is tested on the benchmark of Bosphorus data set and CASIA-3D data set, and achieved high recognition performance.
A Feature Selection Technique-Based Approach for Author Profiling

Author profiling is a technique of analyzing social media content like texts and images produced by users and classifying authors into classes such as age, gender, language variety, location, and so on according to the characteristics of their content. Authorship profiling techniques are extensively used in many applications like forensic analysis, security, marketing, education, reputation management, fake profiles prediction, and sentiment analysis. The process of prediction is initially started with identification of suitable stylistic features which differentiates the writing style of authors. Most of the authors are successful to extract good number of stylistic features, but they are not achieved good accuracies for author profiles prediction. Several researchers got good accuracies for profiles prediction when the content-based features like most frequent words, most frequent n-grams of words, part of speech tags, and characters are used. In this paper, the experiment started with the terms which are most frequent in the dataset. It was recognized that the accuracy is not increased for profiles prediction because of irrelevant and redundant terms in the vocabulary set. The experiment continued with feature selection algorithms to identify the best informative features as well as avoiding the redundant features. The documents are represented as vectors by using these top informative features. In this work, we proposed a new feature selection algorithm based on the distributional information of the terms in different classes. Various machine learning algorithms are used in this experiment to assess the efficiency of the proposed feature selection algorithm. The proposed work attained best accuracies for prediction of age and gender than most of the popular methods in author profiling.
An Enhanced Algorithm of Bees Colony for Finding Best Threshold in Medical Images

This study investigates a solution to the status of the threshold medical images segmentation using an enhanced algorithm of bees colony (EABC). In medical image processing, effectively segmenting images into recognized objects is critical for classification and object recognition. On a given image, the fractional image threshold segmentation approach is modeled into N-level thresholds and then optimized using the EABC. For the fractional image order error threshold, the proposed scheme results are compared to those of other algorithms in the literature. In comparison to existing methods, the proposed scheme is more efficient in a variety of situations.
WA-SCV Analysis for Scope Creep Management in a Software Project Requirements

Scope creep in the case of a project management environment is a profound challenge. Globally, many of the system development project failures are attributed to the scope creep change failures. Many of the earlier studies have discussed the cause and effect analysis, challenges, and gaps in the case of the project scenarios. However, one of the common issues addressed in the organizational elements for project management and scope creep management is about having the right metrics or assessment models that can support sustainable ways of assessing the scope creep. As the scope creep is more attributed to the personal perspectives of the resources, this manuscript provides the scope of the weighted average model of project scope creep analysis scenario. The model proposed in this manuscript is the WA-SCV (weighted average analysis of scope creep variation), wherein the ratings of impact for various project attributes are collected as datasets using the pre-defined weighted average value. The weighted average model discussed in this study is assessed on a case scenario. The results indicate the potential of the proposed model to be used for scope creep analysis.
Double Dressing by Both the Number of Atoms and the Number of Photons Inside Cavity

We study a system of N two-level atoms interacting with a coherent field a resonant cavity in a self-consistent manner. No direct correlation between the atoms, such as induced by dipole-dipole interaction, is included. Initially all atoms are not exited, and there are n 0 photons in the cavity. We show that the system reveals collective-like behavior, which stems from mutual influence of the atoms and coherent field in the cavity. In distinction from conventional dressing of atomic states by strong electromagnetic field, the system of N two level atoms in a resonant cavity is actually dressed not only by the number of photons but also by the number of atoms N . This double dressing results in $N$-dependent oscillation frequency, [[EQUATION]] . At [[EQUATION]] the spectral density contains, apart from the main peak at the collective atomic frequency [[EQUATION]] , additional features at odd sub-harmonics.
Use of a Home-Based Social Robot to Help Prevent Hypothermia and Dehydration in Older Adults

A 20-year increase in life expectancy among the general population since the 1960s (World Bank) brings with it several health-related challenges, especially if people are to continue to live independently in their own homes. Long term conditions such as stroke, diabetes and dementia and their impact on independent living are well studied, however ways to prevent more intermittent conditions such as dehydration - the loss of body fluid, and hypothermia - the decrease of body temperature, both of which can result in hospitalization, have been studied less. Our research is focused on developing a novel Ambient Assisted Living (AAL) system, HyDeAdCo (Hypothermia and Dehydration Advising Companion), to help detect when an older person might be at risk of becoming dehydrated or developing hypothermia and advise them on how to reduce this risk. HyDeAdCo is an Internet of Things (IoT) system comprised of a social robot interface, commercial-of-the-shelf (COTS) devices and cloud computing to analyse, store, and send responses to the user based on sensor data. This paper describes the premise of the HyDeADCo prototype system and presents the role of the social robot, Temi, that acts as an interface between the user and the system in order that the risk of a person becoming dehydrated or developing hypothermia is reduced.
Medical image super-resolution with deep networks

This chapter explores super-resolution (SR) algorithms with deep networks and their applications in medical imaging. SR algorithms aim to improve image quality by enhancing image resolution so that more details are apparent. In medical imaging modalities such as computed tomography and magnetic resonance imaging, SR algorithms can improve image resolution without requiring changes to the imaging protocols. Being able to discern regions of interest better, physicians can potentially provide more reliable diagnoses. Similarly, many downstream image processing tasks such as registration and segmentation can benefit from increased resolution and thus provide more accurate results. In recent years, the state-of-the-art SR methods have advanced rapidly due to the advent of deep neural networks. In this chapter, we first introduce basic concepts and a brief history of SR methods. We then summarize SR with deep networks in medical imaging by focusing on three aspects: data schemes, network architectures, and loss functions. Finally, we examine real-world applications of SR in different medical imaging modalities and tasks. Through the use of deep networks, SR is likely to become routine in medical imaging applications in the future.
Using Multi-level Segmentation Features for Document Image Classification

Document Image classification is a crucial step in the processing pipeline for many purposes (e.g. indexing, OCR, keyword spotting) and is being applied at early stages. At this point, textual information about the document (OCR) is usually not available and additional features are required in order to achieve higher recognition accuracy. On the other hand, one may have reliable segmentation information (e.g. text block, paragraph, line, word, symbol segmentation results), extracted also at pre-processing stages. In this paper, visual features are fused with segmentation analysis results in a novel integrated workflow and end-to-end training can be easily applied. Significant improvements on popular datasets (Tobacco-3482 and RVL-CDIP) are presented, when compared to state-of-the-art methodologies which consider visual features.