Reviewerly/exHarmony huggingface dataset:
PCA and Substring Extraction Technique to Generate Signature for Polymorphic Worm: An Automated Approach

Worms that can contaminate hundreds of thousands of web hosts pose a significant risk to the web. Online worms pose a significant risk to the protection of Internet infrastructure. The recent Intrusion Detection system (IDS) monitor edge network DMZs to determine and filter system malicious. While an IDS can help defend the hosts on the nearby advantage networking of its at denial and conciliation of system, it cannot on your own successfully intervene to halt as well as overturn the spreading of novel Internet worms. Age group of the worm signatures needed by an IDS—the byte patterns desired for monitored website traffic to determine worms—today involves non-trivial man labor and hence substantial delay: as networking operators identify anomalous conduct, they conversate with each other and personally learn package traces to develop a worm signature. However, treatment needs to happen soon in an epidemic to halt a worm's spread. In this paper, an automated signature model process is proposed for “polymorphic worms”. We implemented Principal Component Analysis (PCA) to discover by far the most important data that is actually discussed between polymorphic mask situations, and to use them as signatures. The experimental consequences indicate where the PCA effectively recognized “polymorphic worms by zero false positives as well as low false negatives.”
Phishing Detection for Secure Operations of UAVs

Unmanned Aerial Vehicles (UAV) or drones are used in various domains more and more, including military operations, monitoring, rescue of victims, and transport. Often, UAV resources are developed as web services so that they can be accessed anywhere on the Internet through the World Wide Web. However, this makes them vulnerable to phishing activities of criminals who may try to access these resources and other sensitive information. Therefore, the development of a phishing detection tool based on data mining is presented in this paper. It consists of a browser extension monitoring visited webpages and a backend communicating with the browser extension for the purposes of executing some specific tasks. The browser extension is implemented in JavaScript and the ReactJS framework, and it contains an implementation of classifications with a Bayesian network, decision tree, nearest neighbor classifier and neural network. The backend uses PHP, Python scripts and the Apache HTTP Server. In addition, a browser extension is implemented so that data about webpages can be collected and this data is used for the creation of data mining models. Experimental validation with 10-fold cross-validation and through the browsing of real-world websites show promising results in phishing detection.
Anomaly Detection in Natural Scene Images Based on Enhanced Fine-Grained Saliency and Fuzzy Logic

This paper proposes a simple yet effective method for anomaly detection in natural scene images improving natural scene text detection and recognition. In the last decade, there has been significant progress towards text detection and recognition in natural scene images. However, in cases where there are logos, company symbols, or other decorative elements for text, existing methods do not perform well. This work considers such misclassified components, which are part of the text as anomalies, and presents a new idea for detecting such anomalies in the text for improving text detection and recognition in natural scene images. The proposed method considers the result of the existing text detection method as input for segmenting characters or components based on saliency map and rough set theory. For each segmented component, the proposed method extracts feature from the saliency map based on density, pixel distribution, and phase congruency to classify text and non-text components by exploring a fuzzy-based classifier. To verify the effectiveness of the method, we have performed experiments on several benchmark datasets of natural scene text detection, namely, MSRATD-500 and SVT. Experimental results show the efficacy of the proposed method over the existing ones for text detection and recognition in these datasets.
Acoustic and speaker variation in Dutch /n/ and /m/ as a function of phonetic context and syllabic position

In forensic speech science, nasals are often reported to be particularly useful in characterizing speakers because of their low within-speaker and high between-speaker variability. However, empirical acoustic data from nasal consonants indicate that there is a somewhat larger role for the oral cavity in nasal consonant acoustics than is generally predicted by acoustic models. For example, in read speech, nasal consonant acoustics show lingual coarticulation that differs by nasal consonant, and syllabic position also seems to affect realizations of nasal consonants within speakers. In the current exploratory study, the within- and between-speaker variation in the most frequent nasals in Standard Dutch, /n/ and /m/, was investigated. Using 3695 [n] and 3291 [m] tokens sampled from 54 speakers' spontaneous telephone utterances, linear mixed-effects modeling of acoustic-phonetic features showed effects of phonetic context that differed by nasal consonant and by syllabic position. A subsequent speaker-classification test using multinomial logistic regression on the acoustic-phonetic features seems to indicate that nasals displaying larger effects of phonetic context also perform slightly better in speaker classification, although differences were minor. This might be caused by between-speaker variation in the degree and timing of lingual coarticulatory gestures.
Security aspects in blockchain-based scheduling in mobile multi-cloud computing

The intensive development and growth in the popularity of mobile cloud computing services bring a critical need to introduce new solutions that increase the level of cloud and users security. One of the critical issues in highly distributed computational systems is a task scheduling process. This process may be exposed to many external and internal security threats, like task injection, machine failure or generation of incorrect schedule. These problems are especially important in mobile environments. It can be even more complicated if we take into consideration the personalization of the services offered. Recently, blockchain has been gaining rapidly in popularity, combining high efficiency with applications in distributed and highly personalized computational environments. In this paper, we developed and described a novel model for security-aware task scheduling in cloud computing based on blockchain technology. Unlike other blockchain-based solutions, the proposed model uses Proof of Stake, which does not have high requirements for computing power. A series of conducted experiments confirmed the high efficiency of the proposed model.
BioNumQA-BERT

Biomedical question answering (QA) is playing an increasingly significant role in medical knowledge translation. However, current biomedical QA datasets and methods have limited capacity, as they commonly neglect the role of numerical facts in biomedical QA. In this paper, we constructed BioNumQA, a novel biomedical QA dataset that answers research questions using relevant numerical facts for biomedical QA model training and testing. To leverage the new dataset, we designed a new method called BioNumQA-BERT by introducing a novel numerical encoding scheme into the popular biomedical language model BioBERT to represent the numerical values in the input text. Our experiments show that BioNumQA-BERT significantly outperformed other state-of-art models, including DrQA, BERT and BioBERT (39.0% vs 29.5%, 31.3% and 33.2%, respectively, in strict accuracy). To improve the generalization ability of BioNumQA-BERT, we further pretrained it on a large biomedical text corpus and achieved 41.5% strict accuracy. BioNumQA and BioNumQA-BERT establish a new baseline for biomedical QA. The dataset, source codes and pretrained model of BioNumQA-BERT are available at https://github.com/LeaveYeah/BioNumQA-BERT.
An Embedded Based Integrated Flood Forecasting through HAM Communication

The most essential resource for all living beings on the planet is water. There is only three percentage of fresh water among all water resources and two thirds of the freshwater is locked up in icecaps and glaciers. Of the remaining one percent a fifth is in remote areas where abundant seasonal rainfall and the monsoons deluges and hence the water cannot be used. At the same time flooding is also one of the most frequent and detrimental natural disasters in the world. In this embedded world an advanced and intelligent system is in the need to defend the human life from these disasters. This project provides a solution for the floods that are occurring. The main function of this project is to identify and detect the floods that occur abruptly, through the technique called HAM communication.
Improved retrieval of programming solutions with code examples using a multi-featured score

Developers often depend on code search engines to obtain solutions for their programming tasks. However, finding an expected solution containing code examples along with their explanations is challenging due to several issues. There is a vocabulary mismatch between the search keywords (the query) and the appropriate solutions. Semantic gap may increase for similar bag of words due to antonyms and negation. Moreover, documents retrieved by search engines might not contain solutions containing both code examples and their explanations. So, we propose CRAR (Crowd Answer Recommender) to circumvent those issues aiming at improving retrieval of relevant answers from Stack Overflow containing not only the expected code examples for the given task but also their explanations. Given a programming task, we investigate the effectiveness of combining information retrieval techniques along with a set of features to enhance the ranking of important threads (i.e., the units containing questions along with their answers) for the given task and then selects relevant answers contained in those threads, including semantic features, like word embeddings and sentence embeddings, for instance, a Convolutional Neural Network (CNN). CRAR also leverages social aspects of Stack Overflow discussions like popularity to select relevant answers for the tasks. Our experimental evaluation shows that the combination of the different features performs better than each one individually. We also compare the retrieval performance with the state-of-art CROKAGE (Crowd Knowledge Answer Generator), which is also a system aimed at retrieving relevant answers from Stack Overflow. We show that CRAR outperforms CROKAGE in Mean Reciprocal Rank and Mean Recall with small and medium effect sizes, respectively.
Phrase Composing Tool using Natural Language Processing

In this fast-running world, machine communication plays a vital role. To compete with this world, human-machine interaction is a necessary thing. To enhance this, Natural Language Processing technique is used widely. Using this technique, we can reduce the interaction gap between the machine and human. Till now, many such applications are developed which are using this technique.This tool deals with the various methods which are used for development of grammar error correction. These methods include rule-based method, classifier-based method and machine translation-based method. Also, models regarding the Natural Language Processing (NLP) pipeline are trained and implemented in this project accordingly. Additionally, the tool can also perform speech to text operation.
Random Forest Classification with MapReduce in Holonic Multiagent Systems

Multiagent systems are a dominant field of study within artificial intelligence. Holons are a special kind of agent implementation found in multiagent systems which has not received much attention in recent mainstream AI topics, such as machine learning. Their self-similar structure is both stable and coherent, and likewise consists of one or more holons. This paper studies how holonism, introduced through recursive modelling techniques, benefit multiagent systems. It also expands the scope of multiagent learning applications by proposing a new architecture for executing Decision Tree and Random Forest machine learning models that are both novel in terms of parallelism and extensibility in order to solve classification problems. The models were designed using a structured based approach of computer programs, with a special focus on recursive structures. Results obtained show that when the algorithms are applied in a classification problem domain, the algorithms are able to perform consistent with their expected behaviour.
Benchmarking Robustness of Deep Learning Classifiers Using Two-Factor Perturbation

Deep learning (DL) classifiers are often unstable in that they may change significantly when retested on perturbed images or low quality images. This paper adds to the fundamental body of work on the robustness of DL classifiers. We introduce a new two-dimensional benchmarking matrix to evaluate robustness of DL classifiers, and we also innovate a four-quadrant statistical visualization tool, including minimum accuracy, maximum accuracy, mean accuracy, and coefficient of variation, for benchmarking robustness of DL classifiers. To measure robust DL classifiers, we create comprehensive 69 benchmarking image sets, including a clean set, sets with single factor perturbations, and sets with two-factor perturbation conditions. After collecting experimental results, we first report that using two-factor perturbed images improves both robustness and accuracy of DL classifiers. The two-factor perturbation includes (1) two digital perturbations (salt & pepper noise and Gaussian noise) applied in both sequences, and (2) one digital perturbation (salt & pepper noise) and a geometric perturbation (rotation) applied in both sequences. All source codes, related image sets, and results are shared on the GitHub website at https://github.com/caperock/robustai to support future academic research and industry projects.
Water Wave Optimization Based Data Clustering Model

Abstract This paper presents data clustering model by adopting water wave optimization (WWO) algorithm. In recent times, metaheuristics have gained significance to improve the efficiency of clustering algorithms. Cluster accuracy results express the effectiveness of the clustering algorithm. In this work, WWO is adopted to improve the accuracy for data clustering. On the basis of WWO, clustering model has been proposed. The proposed algorithm aims to improve data clustering accuracy. Several standard datasets from UCI repository are considered for assessing the simulation results and results are evaluated using accuracy and f-score. The Friedman test is applied for statistical analysis to validate the proposed model. Experimental results proved that proposed clustering model succeeds to achieve higher accuracy rate.
FreeVM: A Server Release Algorithm in DataCenter Network

With the development of 5G access technology and the corresponding explosive growth of user requests, service providers have to activate more and more physical machines (PM) in cloud datacenters. This simple expansion of PMs results in not only low utilization of servers but also high maintenance cost. Existing researches that try to release unnecessary servers by migrating VMs always face a severe challenge-the large searching space for the optimal VM placement solution. In this paper, we prose a two-stage variable neighborhood searching (STVNS) algorithm, named FreeVM, which can significantly reduce the number of occupied servers. FreeVM works harmoniously with all kinds of VM placement schemes under various scenarios. We conducted an extensive series of experiments using real traces, and the results show that FreeVM can release at least 15% physical machines compared with existing solutions.
Comparing Different Pre-Trained Models Based on Transfer Learning Technique in Classifying Mammogram Masses

Computer-aided diagnosis (CADx) systems have been applied successfully to many tasks such as diagnosing breast cancer. These systems use classification algorithms to differentiate between malignant and benign lesions based on features extracted from the image data. Recently, more advanced models such as deep convolutional neural networks (CNNs) have been developed that learn features directly from full images; this direct learn process yields fine-grained, hidden features that contain more information than do analytically hand-crafted extracted features. However, building a custom deep-learning model requires extensive computational resources and large amounts of labelled training data. To solve this problem, transfer learning capitalizes on existing rich deep learning models, allowing model creation with significantly reduced training data and time. This paper discusses the potential usefulness of transfer learning for CADx breast cancer task. This is done through investigating many pre-trained models and different fine-tuning techniques to find the optimal model proper for each breast tissue category (Fatty and Dense). We believe that the performance gain from each fine-tuned model specific to breast density is much better for distinguishing benign and malignant masses.
Pattern Matching Using Face Recognition System

In last few years, face recognition system is using very large scale on identifying the user. It is an application to perform large number of machine-based visual task and accident avoiding system used by 3D models like appearance from the different angles using edge detections. In this paper, a technique is used to reduce the computational cost and increase the accuracy of the facial recognition system by integrating it with the iris recognition. Iris and face images are manipulated by used Open CV and Python tool. This algorithm will compare all the histograms and produce best label and confidence which gives a better face recognition.
Credit Card Fraud Detection Using K-Means and Fuzzy C-Means

Machine learning (ML) proven to be an emerging technology from small-scale to large-scale industries. One of the important industries is banking, where ML is being adapted all over the world by employing online banking. The online banking is using ML techniques in detecting fraudulent transactions like credit card fraud detection, etc. Hence, in this chapter, a Credit card Fraud Detection (CFD) system is devised using Luhn's algorithm and k-means clustering. Moreover, CFD system is also developed using Fuzzy C-Means (FCM) clustering instead of k-means clustering. Performance of CFD using both clustering techniques is compared using precision, recall and f-measure. The FCM gives better results in comparison to k-means clustering. Further, other evaluation metrics such as fraud catching rate, false alarm rate, balanced classification rate, and Mathews correlation coefficient are also calculated to show how well the CFD system works in the presence of skewed data.
Research on optimization of energy consumption monitoring point layout on user side

In industrial production, the consumption of electric energy is very large. Most enterprises have the problem of repeated monitoring of energy consumption, so the optimization of energy consumption monitoring points has its significance. Firstly, the monitoring points were selected according to the energy efficiency fluctuation coefficient. Then, according to the electrical wiring mode, the proposed monitoring points were optimized for the second time by using the Quantum-behaved Particle Swarm Optimization (QPSO). Finally, the concept of maximum redundancy is put forward to screen and optimize the proposed monitoring points, and the experiment is carried out in an enterprise. The results show that after the first two steps of optimization, the number of monitoring points can be significantly decreased. After the third step of optimization, a more reasonable scheme can be selected under the condition of the same number of monitoring points.
Learning Two-View Correspondences and Geometry via Local Neighborhood Correlation

Seeking quality feature correspondences (also known as matches) is a foundational step in computer vision. In our work, a novel and effective network with a stable local constraint, named the Local Neighborhood Correlation Network (LNCNet), is proposed to capture abundant contextual information of each correspondence in the local region, followed by calculating the essential matrix and camera pose estimation. Firstly, the k-Nearest Neighbor (KNN) algorithm is used to divide the local neighborhood roughly. Then, we calculate the local neighborhood correlation matrix (LNC) between the selected correspondence and other correspondences in the local region, which is used to filter outliers to obtain more accurate local neighborhood information. We cluster the filtered information into feature vectors containing richer neighborhood contextual information so that they can be used to more accurately determine the probability of correspondences as inliers. Extensive experiments have demonstrated that our proposed LNCNet performs better than some state-of-the-art networks to accomplish outlier rejection and camera pose estimation tasks in complex outdoor and indoor scenes.
Natural numerical networks for Natura 2000 habitats classification by satellite images

Natural numerical networks are introduced as a new classification algorithm based on the numerical solution of nonlinear partial differential equations of forward-backward diffusion type on complete graphs. The proposed natural numerical network is applied to open important environmental and nature conservation task, the automated identification of protected habitats by using satellite images. In the natural numerical network, the forward diffusion causes the movement of points in a feature space toward each other. The opposite effect, keeping the points away from each other, is caused by backward diffusion. This yields the desired classification. The natural numerical network contains a few parameters that are optimized in the learning phase of the method. After learning parameters and optimizing the topology of the network graph, classification necessary for habitat identification is performed. A relevancy map for each habitat is introduced as a tool for validating the classification and finding new Natura 2000 habitat appearances.
Educating for compassion and mutual accommodation

Macdonald (2020) argues that there are four pillars of civilization- freedom, science and technology, mutual accommodation and compassion. The emphasis in the West since the Renaissance has been on freedom and science. The latter two pillars are needed if civilization is to thrive. In contrast to many countries where nativism, authoritarianism, and populism is manifest, Canada has been able to engage in mutual accommodation so that Quebec did not separate and is “unique among Western nations in the absence of seriously poisoned immigration politics”. The paper outlines ways to educate for mutual accommodation and compassion and describes two schools in Toronto that demonstrate these practices.
Relation between the roughness, linear entropy and visibility of a quantum state, the Jaynes–Cummings model

In this work, an analysis of the Jaynes–Cummings Model is conducted in the parameter spaces, composed of Roughness, Concurrence/Linear Entropy and Visibility. The analysis was carried out without including the effects of the environment and with the inclusion of a dispersive environment. As Roughness measures the state’s degree of non-classicality, its inclusion in the analysis allows to identify points in the dynamics that are not usually perceived by traditional analysis. It is observed that the parameter space is almost completely occupied when the dispersive term is small, and is concentrated in the region of less roughness and less purity as the dispersive coefficient is increased.