Reviewerly/exHarmony huggingface dataset:
Malicious URL Classification Model Based on Improved Sparrow Search Algorithm

With the development of the Internet, malicious URL attacks have shown a complex and changeable situation, and have always been one of the main factors that threaten network security. In order to detect malicious URLs more effectively, a random walk strategy is proposed to improve the Sparrow search algorithm, which is compared and analyzed with the Sparrow search algorithm, the gray wolf optimization algorithm and the whale optimization algorithm. After a benchmark test function experiment, it is verified that the improved sparrow search algorithm has the characteristics of fast convergence speed and high optimization precision. In order to further verify the practicability of the improved algorithm, the optimal solution of the random walk strategy optimization sparrow search algorithm is used as the initial neuron connection weight and threshold information of the BP neural network and applied to the classification of malicious URLs. The results show that the random walk strategy optimization sparrow search algorithm has a certain ability to optimize the BP network, which can improve the classification accuracy of malicious URLs.
Confidences for Commonsense Reasoning

Abstract Commonsense reasoning has long been considered one of the holy grails of artificial intelligence. Our goal is to develop a logic-based component for hybrid – machine learning plus logic – commonsense question answering systems. A critical feature for the component is estimating the confidence in the statements derived from knowledge bases containing uncertain contrary and supporting evidence obtained from different sources. Instead of computing exact probabilities or designing a new calculus we focus on extending the methods and algorithms used by the existing automated reasoners for full classical first-order logic. The paper presents the CONFER framework and implementation for confidence estimation of derived answers.
The Fusemate Logic Programming System

Abstract Fusemate is a logic programming system that implements the possible model semantics for disjunctive logic programs. Its input language is centered around a weak notion of stratification with comprehension and aggregation operators on top of it. Fusemate is implemented as a shallow embedding in the Scala programming language. This enables using Scala data types natively as terms, a tight interface with external systems, and it makes model computation available as an ordinary container data structure constructor. The paper describes the above features and implementation aspects. It also demonstrates them with a non-trivial use-case, the embedding of the description logic $$\mathcal ALCIF$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>A</mml:mi> <mml:mi>L</mml:mi> <mml:mi>C</mml:mi> <mml:mi>I</mml:mi> <mml:mi>F</mml:mi> </mml:mrow> </mml:math> into Fusemate’s input language.
Security and Privacy for Edge Artificial Intelligence

Presents the introductory editorial for this issue of the publication.
A 2020 perspective on “DeRec: A data-driven approach to accurate recommendation with deep learning and weighted loss function”

The development of Internet comes up with the prosperity of E-commerce all over the world. In order to promote sales and save consumers’ labor in commodity browsing, recommender systems are proposed by E-commerce platforms to provide online consumers with products and services of their potential interests. The primary challenge in recommendation roots in the intricacy in quantifying users’ preferences on items with the reality of data sparsity and the computation complexity. Hence, more and more researchers are attempting deep learning techniques to deal with the challenge with the hope of using advanced algorithms to alleviate the intricacy. Word embedding is used to learn the association of items in a space of low dimensionality. Multi-layer perception is used to learn users’ preferences on items in a data-driven manner with a customized loss function. The future work of recommender systems includes three folds. The one is to make use of multi-source data to combine implicit and explicit user behavior data to address the problem of data sparsity. The second is dynamic recommendation with the changing users’ preferences on items and make recommender systems light-weight and useable in complex scenarios. The third is to provide effective and verifiable recommendation under the premise of user privacy protection
EXTEND CENTRE’S RESOURCES FOR INCREASING GENERAL DIGITAL LITERACY

The era of powerful technology progress raises the question of digital literacy of population. The lack of knowledge and basic skills within the majority of potential users among common people slows down the development of the IT industry as whole as far as their products are focused on consumers. It is impossible to educate IT specialists, engineers or any other highly qualified professionals without paying appropriate attention to digital literacy of population. Due to "mass start" of daily IT usage among the population of different age group it comes to influence the informational sphere as well as the learning process as it is the significant part of the education. So there appears a special category of users that may be called as a digital-mass-consumer-user. This category of users is mainly oriented on consuming digital services but is not aware of digital competence. Thus, while developing modern consumer-oriented technologies, it is necessary to improve the level of general public's digital literacy. Extend Centres could conduct such type of courses for general public. The aim of the article is to study the digital literacy as a significant component for modern education process and in particular for specialists in engineering. There were used the method of analysis, interview survey and comparison. Special attention is given to digital etiquette and digital security. These components of digital literacy acquire far too much concern while digital literacy as a phenomenon of both modern digital and social environments affects industrial, economical, social and other areas of life.
Design and Implementation of Fractional Order Integral Filter for Denoising of Echocardiographic Images

Denoising techniques demand the areas of analysis which are capable of suppressing noise and also preserving the edges and structure with respect to image quality metrics, visual quality assessment and clinical validation. Denoising is an essential step of preprocessing for improving the diagnostic value of the images. Fractional order calculus is a demanding area of research in various fields of image processing. Fractional Calculus based Denoising filter is proposed in the paper. Denoising is accomplished by the application of filter mask on each pixel of the noisy image. Fractional order integral filters are higher in accuracy in comparison to the integer-order integral filters. The adaptive fractional integral algorithm for image denoising is the thrust area of research in image processing and can prove to be more fruitful. The fractional order denoising algorithm requires structures of nxn filter masks of digital images. The algorithm is tested to both subjective and objective standards of perceiving ability of the human visual system. Simulations using MATLAB are carried out on real echocardiographic as well as standard images. The filter is tested on synthetic Images like Lena Image, Checkerboard image and cameraman image, only the synthetic images are corrupted by adding speckle noise of different noise variances. Further, the filter was tested on real echocardiographic images in different views; since there is no reference noise-free real echo image available as the gold standard for the performance verification of the proposed denoising filter which is only based on the results of standard images. The thrust area includes a faster implementation of the proposed method compared to other methods of denoising. The results obtained using the proposed filter performs better than other recently proposed methods, both in terms of quantitative and qualitative measures. Quantitative metrics being used are Peak signal to noise ratio (PSNR), Signal to noise ratio (SNR) and Mean square error (MSE). Further, the order of the filter is adaptive which is more significant for image processing like segmentation and feature extraction. The proposed denoising technique not only improves the quality of low contrast images but also effectively reduces speckle noise in echo and synthetic images.
AKSALont: Automatic transliteration application for Balinese palm leaf manuscripts with LSTM Model

This study aims to develop an automatic transliteration application for the Balinese palm leaf manuscripts into the Latin/Roman alphabet. The input for this system is the digital image of the original text from the ancient Balinese palm leaf manuscripts, not from the Balinese script, which is printed using a font on a computer. In this study, a segmentation-free transliteration machine using the LSTM model was implemented. In addition, the implementation of the AKSALont application is carried out for the interactions on a web-based platform using cross-platform interoperability. The experimental results show that the machine can transliterate Balinese characters on the Balinese palm-leaf manuscript images properly with a CER of 19.78 % using 10.475 test data. With a web-based online platform, AKSALont has been able to open wider access for the public to the web-based content with an online platform collection.
An algorithmic approach to determine expertise development using object-related gaze pattern sequences

Eye tracking (ET) technology is increasingly utilized to quantify visual behavior in the study of the development of domain-specific expertise. However, the identification and measurement of distinct gaze patterns using traditional ET metrics has been challenging, and the insights gained shown to be inconclusive about the nature of expert gaze behavior. In this article, we introduce an algorithmic approach for the extraction of object-related gaze sequences and determine task-related expertise by investigating the development of gaze sequence patterns during a multi-trial study of a simplified airplane assembly task. We demonstrate the algorithm in a study where novice (n = 28) and expert (n = 2) eye movements were recorded in successive trials (n = 8), allowing us to verify whether similar patterns develop with increasing expertise. In the proposed approach, AOI sequences were transformed to string representation and processed using the k-mer method, a well-known method from the field of computational biology. Our results for expertise development suggest that basic tendencies are visible in traditional ET metrics, such as the fixation duration, but are much more evident for k-mers of k > 2. With increased on-task experience, the appearance of expert k-mer patterns in novice gaze sequences was shown to increase significantly (p < 0.001). The results illustrate that the multi-trial k-mer approach is suitable for revealing specific cognitive processes and can quantify learning progress using gaze patterns that include both spatial and temporal information, which could provide a valuable tool for novice training and expert assessment.
Detecting performance difficulty of learners in colonoscopy: Evidence from eye-tracking

Eye-tracking can help decode the intricate control mechanism in human performance. In healthcare, physicians-in-training requires extensive practice to improve their healthcare skills. When a trainee encounters any difficulty in the practice, they will need feedback from experts to improve their performance. The personal feedback is time-consuming and subjected to bias. In this study, we tracked the eye movements of trainees during their colonoscopic performance in simulation. We applied deep learning algorithms to detect the eye-tracking metrics on the moments of navigation lost (MNL), a signature sign for performance difficulty during colonoscopy. Basic human eye gaze and pupil characteristics were learned and verified by the deep convolutional generative adversarial networks (DCGANs); the generated data were fed to the Long Short-Term Memory (LSTM) networks with three different data feeding strategies to classify MNLs from the entire colonoscopic procedure. Outputs from deep learning were compared to the expert’s judgment on the MNLs based on colonoscopic videos. The best classification outcome was achieved when we fed human eye data with 1000 synthesized eye data, where accuracy (90%), sensitivity (90%), and specificity (88%) were optimized. This study built an important foundation for our work of developing a self-adaptive education system for training healthcare skills using simulation.
Deep Learning for Histopathological Image Analysis

Anatomical Pathology dates back to the nineteenth century when Rudolf Virchow introduced his concept of cellular pathology and when the technical improvements of light microscopy enabled wide-spread use of structural criteria to define diseases. Since then, the quality of optical instruments has been constantly evolving. However the central element of the diagnostic process remains the knowledge and experience of pathologists visually classifying observations according to internationally agreed guidelines (e.g., World Health Organisation (WHO) classification), and much of the pre-analytical steps of specimen preparation (e.g., fixation, embedding, sectioning, staining) is only partially automated and still requires many manual steps. Thanks to the recent advent and cost-effectiveness of digital scanners, tissue histopathology slides can now be fully digitized and stored as Whole Slide Images (WSI). With the availability and analysis of a much larger set of variables combined with sophisticated imaging and analytic techniques, the traditional paradigm of pathology based on visually descriptive microscopy can be complemented and substantially improved by digital pathology, utilizing screen-based visualization of digital tissue sections and novel analysis tools potentially combining the conventional evaluation by pathologists with a computer-based diagnostic aid system. A central element of such evolving medical utilities and decision support systems will be image analysis, a field in which Deep Learning (DL) has recently made immense progress and especially the development of very large Artificial Neural Networks (ANNs) that are revolutionizing the field. Indeed, they have surpassed all existing image processing methods in most fields (segmentation, object detection, classification, etc.). All current methods applied to histopathological image analysis will be presented as well as the future technological issues and challenges of this discipline.
Mining Numbers in Text: A Survey

Both words and numerals are tokens found in almost all documents but they have different properties. However, relatively little attention has been paid in numerals found in texts and many systems treated the numbers found in the document in ad-hoc ways, such as regarded them as mere strings in the same way as words, normalized them to zeros, or simply ignored them. Recent growth of natural language processing (NLP) research areas has change this situations and more and more attentions have been paid to the numeracy in documents. In this survey, we provide a quick overview of the history and recent advances of the research of mining such relations between numerals and words found in text data.
Bengali News Classification Using Long Short-Term Memory

The process of grouping similar news into a predefined category is news classification. Due to the increase on Bengali news contents, published by a good number of news portals, every day. The necessity of classifying news into appropriate groups became necessary for potential readers. Traditional machine learning (ML) classification requires a good number of labeled data and also participation of human users. Nowadays, deep learning is being used in text classification and it is achieving good accuracy in compare to traditional ML. This research was developed in basis of improvement on Bengali news text classification and to show the improved accuracy using long short-term memory (LSTM). We have used a dataset containing 13,445 news and which were collected from various newspapers. The dataset has five types of news they are entertainment, national, sports, city, and state news. This experiment has achieved a good accuracy of 84%. There were a minimum number of Bengali news classification research has already been done. This experiment work has compared with those existing work and also states the future improvement areas.
Towards Navigation by Reasoning over Spatial Configurations

We deal with the navigation problem where the agent follows natural language instructions while observing the environment.Focusing on language understanding, we show the importance of spatial semantics in grounding navigation instructions into visual perceptions.We propose a neural agent that uses the elements of spatial configurations and investigate their influence on the navigation agent's reasoning ability.Moreover, we model the sequential execution order and align visual objects with spatial configurations in the instruction.Our neural agent improves strong baselines on the seen environments and shows competitive performance on the unseen environments.Additionally, the experimental results demonstrate that explicit modeling of spatial semantic elements in the instructions can improve the grounding and spatial reasoning of the model.
Performance Prediction of a Reverse Osmosis Desalination System Using Machine Learning

One of the major challenges that membrane manufacturers, commercial enterprises and the scientific community in the field of membrane-based filtration or reverse osmosis (RO) desalination have to deal with is system performance retardation due to membrane fouling.In this respect, the prediction of fouling or system performance in membrane-based systems is the key to determining the mid and long-term plant operating conditions and costs.Despite major research efforts in the field, effective methods for the estimation of fouling in RO desalination plants are still in infancy, for example, most of the existing methods, neither consider the characteristics of the membranes such as the spacer geometry, nor the efficiency and the day to day chemical cleanings.Furthermore, most studies focus on predicting a single fouling indicator, e.g., flux decline.Faced with the limits of mathematical or numerical approach, in this paper, machine learning methods based on Multivariate Temporal Convolutional Neural networks (MTCN), which take into account the membrane characteristics, feed water quality, RO operation data and management practice such as Cleaning In Place (CIP) will be considered to predict membrane fouling using measurable multiple indicators.The temporal convolution model offers one the capability to explore the temporal dependencies among a remarkably long historical period and has potential use for operational diagnostics, early warning and system optimal control.Data collected from a Desalination RO plant will be used to demonstrate the capabilities of the prediction system.The method achieves remarkable predictive accuracy (root mean square error) of 0.023, 0.012 and 0.007 for the relative differential pressure and permeates Total Dissolved solids (TDS) and the feed pressure, respectively.
Modelling cryptographic distinguishers using machine learning

Abstract Cryptanalysis is the development and study of attacks against cryptographic primitives and protocols. Many cryptographic properties rely on the difficulty of generating an adversary who, given an object sampled from one of two classes, correctly distinguishes the class used to generate that object. In the case of cipher suite distinguishing problem, the classes are two different cryptographic primitives. In this paper, we propose a methodology based on machine learning to automatically generate classifiers that can be used by an adversary to solve any distinguishing problem. We discuss the assumptions, a basic approach for improving the advantage of the adversary as well as a phenomenon that we call the “blind spot paradox” . We apply our methodology to generate distinguishers for the NIST (DRBG) cipher suite problem. Finally, we provide empirical evidence that the distinguishers might statistically have some advantage to distinguish between the DRBG used.
Study on the semi-supervised learning-based patient similarity from heterogeneous electronic medical records

A new learning-based patient similarity measurement was proposed to measure patients' similarity for heterogeneous electronic medical records (EMRs) data.We first calculated feature-level similarities according to the features' attributes. A domain expert provided patient similarity scores of 30 randomly selected patients. These similarity scores and feature-level similarities for 30 patients comprised the labeled sample set, which was used for the semi-supervised learning algorithm to learn the patient-level similarities for all patients. Then we used the k-nearest neighbor (kNN) classifier to predict four liver conditions. The predictive performances were compared in four different situations. We also compared the performances between personalized kNN models and other machine learning models. We assessed the predictive performances by the area under the receiver operating characteristic curve (AUC), F1-score, and cross-entropy (CE) loss.As the size of the random training samples increased, the kNN models using the learned patient similarity to select near neighbors consistently outperformed those using the Euclidean distance to select near neighbors (all P values < 0.001). The kNN models using the learned patient similarity to identify the top k nearest neighbors from the random training samples also had a higher best-performance (AUC: 0.95 vs. 0.89, F1-score: 0.84 vs. 0.67, and CE loss: 1.22 vs. 1.82) than those using the Euclidean distance. As the size of the similar training samples increased, which composed the most similar samples determined by the learned patient similarity, the performance of kNN models using the simple Euclidean distance to select the near neighbors degraded gradually. When exchanging the role of the Euclidean distance, and the learned patient similarity in selecting the near neighbors and similar training samples, the performance of the kNN models gradually increased. These two kinds of kNN models had the same best-performance of AUC 0.95, F1-score 0.84, and CE loss 1.22. Among the four reference models, the highest AUC and F1-score were 0.94 and 0.80, separately, which were both lower than those for the simple and similarity-based kNN models.This learning-based method opened an opportunity for similarity measurement based on heterogeneous EMR data and supported the secondary use of EMR data.
Detection of Phishing Websites Using Machine Learning Approach

Phishing attacks are growing in the similar manner as e-commerce industries are growing. Prediction and prevention of phishing attacks is a very critical step towards safeguarding online transactions. Data mining tools can be applied in this regard as the technique is very easy and can mine millions of information within seconds and deliver accurate results. With the help of machine learning algorithms like, Random Forest, Decision Tree, Neural network and Linear model we can classify data into phishing, suspicious and legitimate. This can be done based on unique features of phishing websites and user does not need to check individual websites. Rather we can identify and predict phishing, suspicious and legitimate websites by extracting some unique features. The aim of this work was to develop model to safeguard users from phishing attack. In this paper Random Forest, Decision Tree, Linear model and Neural Network algorithms have been used on a phishing dataset. The results of these algorithms have then been compared in terms of accuracy, error rate, precision, and recall.