Reviewerly/exHarmony huggingface dataset:
Machine Learning and Deep Learning Based Phishing Websites Detection: The Current Gaps and Next Directions

There are many phishing websites detection techniques in literature, namely white-listing, black-listing, visual-similarity, heuristic-based, and others. However, detecting zero-hour or newly designed phishing website attacks is an inherent property of machine learning and deep learning techniques. By considering a promising solution of machine learning and deep learning techniques, researchers have made a great deal of effort to tackle the this problem, which persists due to attackers constantly devising novel strategies to exploit vulnerability or gaps in existing anti-phishing measures. In this study, an extensive effort has been made to rigorously review recent studies focusing on Machine Learning and Deep Learning Based Phishing Websites Detection to excavate the root cause of the aforementioned problems and offer suitable solutions. The study followed the significant criterion to search, download, and screen relevant studies, then to evaluate criterion-based selected studies. The findings show that significant research gaps are available in the rigorously reviewed studies. These gaps are mainly related to imbalanced dataset usage, improper selection of dataset source(s), the unjustified reason for using specific train-test dataset split ratio, scientific disputes on website features inclusion and exclusion, lack of universal consensus on phishing website lifespans and on what is defining a small dataset size, and run-time analysis issues. The study clearly presented a summary of the comparative analysis performed on each reviewed research work so that future researchers could use it as a structured guideline to develop a novel solution for anti-phishing website attacks.
Enhanced TEMPO Algorithm for Quantum Path Integrals with Off-Diagonal System-Bath Coupling: Applications to Photonic Quantum Networks

Multitime system correlation functions are relevant in various areas of physics and science, dealing with system-bath interaction including spectroscopy and quantum optics, where many of these schemes include an off-diagonal system bath interaction. Here we extend the enhanced time-evolving matrix product operator (eTEMPO) algorithm for quantum path integrals using tensor networks [Phys. Rev. Lett. 123, 240602 (2019)PRLTAO0031-900710.1103/PhysRevLett.123.240602 to open quantum systems with off-diagonal coupling beyond a single two level system. We exemplify the approach on a coupled cavity waveguide system with spatially separated quantum two-state emitters, though many other applications in material science are possible, including entangled photon propagation, photosynthesis spectroscopy, and on-chip quantum optics with realistic dissipation.
Swin Transformer Assisted Prior Attention Network for Medical Image Segmentation

Transformer complements convolutional neural network (CNN) has achieved better performance than improved CNN-based methods. Specially, Transformer is utilized to be combined with U-shaped structure, skip-connections, encoder, and even them all together. However, the intermediate supervision network based on the coarse-to-fine strategy has not been combined with Transformer to improve the generalization of CNN-based methods. In this paper, we propose Swin-PANet, which is applying a window-based self-attention mechanism by Swin Transformer in the intermediate supervision network, called prior attention network. A new enhanced attention block based on CCA is also proposed to aggregate the features from skip-connections and prior attention network, and further refine details of boundaries. Swin-PANet can address the dilemma that traditional Transformer network has poor interpretability in the process of attention calculation and Swin-PANet can insert its attention predictions into prior attention network for intermediate supervision learning which is humanly interpretable and controllable. Hence, the intermediate supervision network assisted by Swin Transformer provides better attention learning and interpretability in network for accurate and automatic medical image segmentation. The experimental results evaluate the effectiveness of Swin-PANet which outperforms state-of-the-art methods in some famous medical segmentation tasks including cell and skin lesion segmentation.
Necessity and Sufficiency for Explaining Text Classifiers: A Case Study in Hate Speech Detection

We present a novel feature attribution method for explaining text classifiers, and analyze it in the context of hate speech detection.Although feature attribution models usually provide a single importance score for each token, we instead provide two complementary and theoreticallygrounded scores -necessity and sufficiencyresulting in more informative explanations.We propose a transparent method that calculates these values by generating explicit perturbations of the input text, allowing the importance scores themselves to be explainable.We employ our method to explain the predictions of different hate speech detection models on the same set of curated examples from a test suite, and show that different values of necessity and sufficiency for identity terms correspond to different kinds of false positive errors, exposing sources of classifier bias against marginalized groups.
A Hybrid System for Handwritten Character Recognition with High Robustness

In the past few decades, the offline recognition of handwritten Indic scripts has received much attention of researchers. Although an intensive research has been reported for various Indic languages, limited research work is carried out for handwritten Odia, Bangla character recognition owing to their complex shapes and the unavailability of the standard datasets. This paper proposes an automated model for recognizing both handwritten of Odia characters and numerals, along with Bangla numerals with maximum optimization efficiency. The proposed model primarily deals with feature optimization parameters which mainly comprises of three parts. Firstly, the fast discrete curvelet transform (FDCT) is used to derive multidirectional features from the character images. Secondly, PCA along with LDA is used to reduce the dimension of the feature vector. The features are finally subjected to both least-squares support vector machine (LS-SVM), and random forest (RF) for classification. The effectiveness of proposed model is evaluated over three benchmark datasets such as Odia handwritten character, Bangla numeral and Odia handwritten numeral. The efficacy of proposed model achieves superiority as compared to state-of-art techniques. The discriminatory prospective of FDCT along with PCA and LDA features is establish more suitable than its counterparts.
Visual Data Analysis with Task-Based Recommendations

General visualization recommendation systems typically make design decisions for the dataset automatically. However, most of them can only prune meaningless visualizations but fail to recommend targeted results. This paper contributes TaskVis, a task-oriented visualization recommendation system that allows users to select their tasks precisely on the interface. We first summarize a task base with 18 classical analytic tasks by a survey both in academia and industry. On this basis, we maintain a rule base, which extends empirical wisdom with our targeted modeling of the analytic tasks. Then, our rule-based approach enumerates all the candidate visualizations through answer set programming. After that, the generated charts can be ranked by four ranking schemes. Furthermore, we introduce a task-based combination recommendation strategy, leveraging a set of visualizations to give a brief view of the dataset collaboratively. Finally, we evaluate TaskVis through a series of use cases and a user study.
“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding

When reading a literary piece, readers often make inferences about various characters' roles, personalities, relationships, intents, actions, etc.While humans can readily draw upon their past experiences to build such a character-centric view of the narrative, understanding characters in narratives can be a challenging task for machines.To encourage research in this field of character-centric narrative understanding, we present LiSCU -a new dataset of literary pieces and their summaries paired with descriptions of characters that appear in them.We also introduce two new tasks on LiSCU: Character Identification and Character Description Generation.Our experiments with several pre-trained language models adapted for these tasks demonstrate that there is a need for better models of narrative comprehension.
The Impact of Co-evolution of Code Production and Test Suites through Software Releases in Open Source Software Systems

The software system evolves and changes with the time, so the test suite must be maintained according to code changes. Maintaining test cases manually is an expensive and time-consuming activity, especially for large test suites, which has motivated the recent development of automated test-repair techniques. Several researchers indicate that software evolution shows a direct impact on test suites evolution, as they have strong relationships and they should be evolved concurrently. This article aims to provide statistical evidence of having this significant relationship between the code production and its associated test suites. Seven systems along with releases are collected and eight metrics were calculated to be used in this study. The result shows how the systems under study are evolving and have a high impact on their test suites, although two metrics provide a negative significant relationship.
Map Reduce Clustering in Incremental Big Data Processing

An advanced Incremental processing technique is planned for data examination in knowledge to have the clustering results inform. Data is continuously arriving by different data generating factors like social network, online shopping, sensors, e-commerce etc. [1]. On account of this Big Data the consequences of data mining applications getting stale and neglected after some time. Cloud knowledge applications regularly perform iterative calculations (e.g., PageRank) on continuously converting datasets. Though going before trainings grow Map-Reduce aimed at productive iterative calculations, it's miles also pricey to carry out a whole new big-ruler Map-Reduce iterative task near well-timed quarter new adjustments to fundamental records sets. Our usage of MapReduce keeps running [4] scheduled a big cluster of product technologies and is incredibly walkable: an ordinary Map-Reduce computation procedure several terabytes of records arranged heaps of technologies. Processor operator locates the machine clean to apply: masses of MapReduce applications, we look at that during many instances, The differences result separate a totally little part of the data set, and the recently iteratively merged nation is very near the recently met state. I2MapReduce clustering adventures this commentary to keep re-calculated by way of beginning after the before affected national [2], and by using acting incremental up-dates on the converging information. The approach facilitates in enhancing the process successively period and decreases the jogging period of stimulating the consequences of big data.
Perspectives on Documenting Methods to Create Ocean Best Practices

This perspective outlines how authors of ocean methods, guides, and standards can harmonize their work across the scientific community. We reflect on how documentation practices can be linked to modern information technologies to improve discoverability, interlinkages, and thus the evolution of distributed methods into common best practices within the ocean community. To show how our perspectives can be turned into action, we link them to guidance on using the IOC-UNESCO Ocean Best Practice System to support increased collaboration and reproducibility during and beyond the UN Decade of Ocean Sciences for Sustainable Development.
Handwritten Digit Recognition using Different Dimensionality Reduction Techniques

The objective of this paper is to introduce to Technologies of linear dimension reduction popularly known as Principal Component Analysis and Linear Discriminant Analysis. PCA reduces the size of data and conserve maximum variance in the form of new variable called principal components where LDA works with minimum class distance and maximizing difference between the classes. Axis of maximum variance is found by PCA while axis of class separability is found by LDA. This method is experimented over and MNIST handwritten digit data set. Our conclusion explains PCA can outperform LDA when training data set a small and recalls values with lesser computational complexity. The present in linear techniques in this paper presents clear understanding and methods in comparative manner.
Innovation spillover effects of information and communications technology in higher education

Е. В. Огурцова, Р. Р. Тугушева
E-Learning Development Management With The Schoology Improving Geographic Learning Literation

Lack of development of instructional media reduces student interest in learning so that it affects student literacy in the learning process. The purpose of this research is to develop E-learning with schoology to improve literacy in geography learning in flora and fauna material. This type of research is development research. The data collection technique was carried out through three data collection methods, namely observation, questionnaires and tests. The data analysis technique used in this research is quantitative data analysis techniques. The results of this study are experts in the validation of E-learning product designs with schoology which consists of three components of the validator's most dominant assessment, namely the element of product quality with a value of one validator of 52%, and two components of the validator's assessment. 78%, and the results of product trials with a value of 0.42. %. So it can be concluded that E-learning with schoology is declared suitable for use in the learning process and is very easy to use based on literacy in geography learning.
Computational thinking assessment at primary school in the context of learning programming

The study was carried out from 2018 to 2020 with the challenge - how to assess the level of computational thinking. The research design is mixed since the disclosure of mutual influence of the components of the chain ‘learning programming – computational thinking – evaluating computational thinking’ requires the use of both qualitative and quantitative research methods. The conceptualisation of the ‘computational thinking’ idea is based on the premise of the impact of abstract thinking and computers on human thinking evolution. The structural interpretation of ‘computational thinking’, consisting of nine components, reflects the presence of a semantic link between teaching programming and the development of abstract computational thinking. Four levels (phenomenological, analytic–synthetic, set–prognostic and axiomatic) of computational thinking have been identified for each of these nine components. The study involved 102 elementary school students who are learning programming in Scratch. The guiding questions and problems we have developed for elementary school students are designed following the characteristics of the four levels of computational thinking. The results of the study showed that the ratio of ‘structural components’ to different levels of computational thinking, with the corresponding characteristics, allows one to determine the degree of its development or its individual components.&#x0D; &#x0D; Keywords: Computational thinking, educational taxonomies, evaluation methodologies, levels of development;
Human Activity Recognition Making Use of Long Short-Term Memory Techniques

The optimisation and validation of a classifiers performance when applied to real world problems is not always effectively shown.In much of the literature describing the application of artificial neural network architectures to Human Activity Recognition (HAR) problems, postural transitions are grouped together and treated as a singular class.This paper proposes, investigates and validates the development of an optimised artificial neural network based on Long-Short Term Memory techniques (LSTM), with repeated cross validation used to validate the performance of the classifier.The results of the optimised LSTM classifier are comparable or better to that of previous research making use of the same dataset, achieving 95% accuracy under repeated 10-fold cross validation using grouped postural transitions.The work in this paper also achieves 94% accuracy under repeated 10-fold cross validation whilst treating each common postural transition as a separate class (and thus providing more context to each activity).
An Audio-based Sequential Punctuation Model for ASR and its Effect on Human Readability

Inserting punctuation marks into the word chain hypothesis produced by automatic speech recognition (ASR) has long been a neglected task.In several application domains of ASR, real-time punctuation is, however, vital to improve human readability.The paper proposes and evaluates a prosody inspired approach and a phrase sequence model implemented as a recurrent neural network to predict the punctuation marks from the audio.In a very basic and lightweight modeling framework, we show that punctuation is possible by state-of-the-art performance, solely based on the audio signal for speech close to read quality.We test the approach on more spontaneous speaking styles and on ASR transcripts which may contain word errors.A subjective evaluation is also carried out to quantify the benefits of the punctuation on human readability, and we also show that when a critical punctuation accuracy is reached, humans are not able to distinguish automatic and human produced punctuation, even if the former may contain punctuation errors.
Feature selection optimisation of software product line using metaheuristic techniques

The role of software product line (SPL) is very important in representing the same system with multiple variants. Feature models are used to define SPL. In this paper, genetic algorithm (GA), hyper-heuristic algorithm and particle swarm optimisation (PSO) have been applied for feature selection optimisation in SPL. Also, an improved fitness function is applied for optimisation of features in SPL. The objective function is designed by taking reusability and consistency of features (components) into consideration. Furthermore, we have used a case study and discussed about software product line in detail. A non-parametric test, i.e., Kruskal-Wallis test has been performed to analyse performance and computation time of 20 to 1,000 features sets and identify core features. Through extensive experimental analysis, it is observed that PSO outperforms GA and hyper-heuristic algorithm.
Higher Education in VUCA-World: New Metaphor of University

In the 21st century universities cannot survive if they simply support an established state of affairs because the modern world is described by the following relation: the rate of change tends to infinity; the transition interval tends to zero. This leads to the fact that universities cannot rest on their laurels and not change. The university that cannot construct new organizational ties loses its magnitude forever. The article describes the specific features of the new reality which are of great importance for building modern organizational systems in universities. Reference points have been being identified and that allows presenting the direction of development that meets the new requirements of the modern world to people, processes, technologies, structures, and systems accordingly to the university. Analysis of the selected reference points leads to the conclusion that in the conditions of dynamic changes and uncertainty of the world the concrete way of the vision of the university’s situation is to see it as if in the light of the modern world. A metaphor is presented, which is based on a comparison of the university internal world with the current reality. It is expressed by the acronym VUCA.
Odia Characters and Numerals Recognition using Hopfield Neural Network Based on Zoning Feature

Odia character and digits recognition area are vital issues of these days in computer vision. In this paper a Hopfield neural network designe to solve the printed Odia character recognition has been discussed. Optical Character Recognition (OCR) is the principle of applying conversion of the pictures from handwritten, printed or typewritten to machine encoded text version. Artificial Neural Networks (ANNs) trained as a classifier and it had been trained, supported the rule of Hopfield Network by exploitation code designed within the MATLAB. Preprocessing of data (image acquisition, binarization, skeletonization, skew detection and correction, image cropping, resizing, implementation and digitalization) all these activities have been carried out using MATLAB. The OCR, designed a number of the thought accuses non-standard speech for different types of languages. Segmentation, feature extraction, classification tasks is the well-known techniques for reviewing of Odia characters and outlined with their weaknesses, relative strengths. It is expected that who are interested to figure within the field of recognition of Odia characters are described in this paper. Recognition of Odia printed characters, numerals, machine characters of research areas finds costly applications within the banks, industries, offices. In this proposed work we devolve an efficient and robust mechanism in which Odia characters are recognized by the Hopfield Neural Networks (HNN).
Computer Vision based System to Detect Abandoned Objects

Security has become the most essential aspect in every walk of life. With ever growing technology, there has been an apparent inclination towards developing smart surveillance systems that do not stop with just monitoring and recording of events, but also possesses the ability to observe the events and alert in case of any discrepancies if observed. One of the impending threats to security in crowded places is the ignorance of un-attended objects. These un-attended objects or abandoned objects may have been left behind intentionally and may contain hazardous things which can cause huge disasters. This paper is focused towards developing a computer vision based approach that analyses the blob areas to detect any abandoned objects and instantaneously send appropriate alert without any human intervention. The uniqueness of this approach is that it handles the occlusion scenario and also quick execution time to detect abandoned objects. The approach has been tested with various benchmark video datasets and real-time video sequences as well. The performance has been measured in terms of accuracy of classifying abandoned objects in a given video sequence and also the execution time taken for computing the outcome. Results indicate good accuracy in terms of abandoned object detection under varying conditions and scenarios and also faster execution time when compared to other contemporary approaches.