Miracle-dz/UniFAR-FTUs huggingface dataset:
LEARN: Enhanced Network Embedding via Similarity Learning

Learning low-dimensional representations for large-scale networks reinforce numerous network inference tasks such as link prediction, clustering, vertex classiﬁcation, and visualization. While achieving competitive performance on a variety of network analytic tasks, most existing network embedding methods are ineﬃcient when applied to large-scale networks and generally suﬀer from performance issues. In this paper, we present a novel unsupervised network embedding approach to learn low-dimensional representations for large-scale networks which simultaneously preserve network topology and community structure of underlying network. We propose a deep learning model based on Siamese neural network architecture to eﬃciently model the relationships between vertex pairs. In addition, we propose a sampling approach based on vertex domination concept to sample vertices and represent the relationship between them at accurate k-hop distance away from each other which enhances the quality of vertex pairs sampling and reduces the time complexity. The experimental results on several real-world networks show that our method is capable of learning higher quality representations and outperforms several state-of-the-art methods, as evaluated on challenging link prediction, clustering, and visualization tasks.
A Feedback Attention Network for Classification and Visualization of Thyroid Nodules on Ultrasound Images

With the maturity of AI technology, computer-aided diagnosis technology is gradually developing, even surpassing the performance of doctors. However, due to the lack of interpretability of deep learning, we can’t understand what the model has learned, and even determine whether the diagnostic model is credible. In this paper, Feedback Attention Network(FAN) is proposed, which visualizes the lesion area that plays a decisive role in precise prediction while classifying benign and malignant thyroid ultrasound images. The degree of ﬁneness is adjustable, which is more reﬁned than existing visualization methods. In addition, this paper uses the attention to make the model more sensitive to the lesion area and inhibit the inﬂuence of the non-lesion area. The experimental results show that the accuracy of benign and malignant classiﬁcation reaches 90 . 76% ± 2 . 8%. Compared with the state-of-the-art classiﬁcation networks, FAN has higher accuracy and fewer parameters. Above all, the visualization results show that FAN does learn the true nodule features, especially those at the edge of nodules, which proves FAN a credible aided diagnosis model.
Teaching game design through cross-disciplinary content and individualized student deliverables

Video game development is used to teach collaborative software engineering principles. However, when the collaboration is exclusively between computer scientists, a balkanized perspective is unintentionally promoted. A multidisciplinary faculty addressed how to exploit video game development as a vehicle for a collaborative cross-disciplinary experience in technology development for upper-level students from our contributing majors. This paper addresses the issues of curriculum structure, student assessment and cross-disciplinary team teaching. We present a dual model of cross-disciplinary content and individualized deliverables that allows each student to determine how narrowly or broadly to focus his/her personal learning.
OpenStack

LEPECO, Marco Antonio. Study of the Construction of an Environment for Cloud Computing with Openstack . 2017. Monograph of the Specialization Course and 2017. This work presents a solution for implementing a cloud computing model. It will be made use of a specific tool for this computational model, where will be presented your functionalities and main resources, then implemented through a simulation environment where tests will to be done to exploit your resources in order to obtain the expected results , and finally the presentation of the results.
The new Spanish political scenario: Twitter graph and opinion analysis with an interactive visualisation

Twitter, as a social network, has been used extensively as a way to express users opinion about a wide range of topics. Particularly, politics have been one of the main issues in the last years, and consequently, its study in Twitter can lead to valuable conclusions. The methodology presented in this paper is based on the extraction of the followers network of the main politicians in Spain prior to the general election of November of 2019, providing a suitable graph analysis and visualisation. Hence, closeness between different political parties can be spotted with respect to their common followers. This is motivated by the new political scenario, given that in the last five years, it derived from a bipartisan system to a plural one, with six relevant national parties. Additionally, information about each political party is presented interactively with respect to the political profile of those who write about them. Reciprocally, this tool also provides information about the target of this party identified profiles.
Neural Style Transfer for Characters Synthesis via Stack Network

In this paper, we focus on the challenge of characters synthesis problem: synthesizing highly-stylized characters based on just observing very few examples. We treat this problem as image translation problem and propose a cascaded stack network which consists of two stages: first synthesize kinds of rough target characters based on observed characters; then input the rough characters as a stack to synthesize the final decorated target character. (e.g., synthesize rough “A” from other characters and then input these rough “A” as a stack to synthesize the final stylized “A”). Additionally, we establish a large-scale dataset that contains various styles of characters. We evaluate the proposed method as well as other style transfer methods on our collected dataset and a public dataset. The extensive results and quantitative evaluation demonstrate the effective of our method.
Optimized Functional Link Artificial Neural Network for Multi-label Classification

Multi-label classiﬁcation problem is a generalization of the traditional single-label classiﬁcation, which makes the data more complex in nature. To handle the inherent complexity of multi-label data, a compact and eﬃcient network known as functional link artiﬁcial neural network (FLANN) has been explored. FLANNs are known to functionally transform the input space to introduce non-linearity into the data, thus making the task of separating the classes in the output space comparatively simpler. In this paper, six multi-label FLANN models (ﬁve novel and one existing) have been devised for multi-label classiﬁcation to procure the optimal conﬁguration. These six variations of the network have been built using three basis functions - trigonometric, Chebychev, power polynomial and two learning techniques - backpropagation and particle swarm optimization. These fundamentals of FLANN have been thoroughly explored in the single-label domain, but are yet to be experimented for multi-label data. These multi-label FLANN models were tested on four datasets for ten performance metrics. Analysis of the results have generated some interesting conclusions and optimal models for multi-label classiﬁcation.
Web-based Scalable Visual Exploration of Large Multidimensional Data Using Human-in-the-Loop Edge Bundling in Parallel Coordinates

Visual clutter and overplotting are the main challenges for visualizing large multidimensional data in parallel coordinates, which greatly hampers the recognition of patterns in the data. Although many automatic clustering and edge-bundling methods have been used in parallel coordinates to reduce visual clutter and overplotting, a scalable, transparent, and interactive approach that allows analysts to interact with large data and generate interpretable results of visualization in real time is lacking. To solve this problem, we propose an approach, human-in-the-loop edge bundling, to visually explore and interpret large multidimensional data in parallel coordinates. This approach combines data binning-based clustering and density-based con uent drawing, which reduces much data processing time and rendering time. It provides novel interactions, such as splitting, adjusting, and merging clusters, to integrate human judgment into the edge-bundling process. These interactions make the underlying clustering transparent to users, which allow users to generate interpretable visualization without complex data clustering. The scalability of our approach was evaluated through experiments on several large datasets. The results show that our approach is scalable for large multidimensional data, which supports real-time interactions on millions of data items in web browsers without hardware-accelerated rendering and big data infrastructure-based data processing. We used a case study to highlight the e ectiveness of our approach. The results show that our approach provides an interpretable way of visually exploring large multidimensional data in parallel coordinates.
Predictors of Prehypertension among the Island Residents, Maldives

Background: Prehypertension was defined as a systolic blood pressure 120-139 mmHg and/or a diastolic blood pressure of 80-89 mmHg. It has been shown as a precursor of hypertension and a significant risk factor for cardiovascular disease. This study aimed to estimate the prevalence of prehypertension and examine the risk factors. Methods: A cross-sectional study was conducted in adults aged 18 to 65 years at Baa Atoll, the Republic of the Maldives. A total of 407 participants from five islands were randomly recruited. Standard anthropometric measurements and face-to-face interviews were employed to collect the data from December 2013 to January 2014. Results: The prevalence of prehypertension was about 38.1%. Bivariate analysis revealed that there was a statistically significant association between prehypertension and age, education, Body Mass Index (BMI), waist circumference, sources of obtaining health information, and levels of knowledge. Multiple logistic regression showed that the predictors of prehypertension were lower educational levels (AOR: 2.27; 95% CI: 1.62 to 4.58), and high waist circumference (AOR: 2.44; 95% CI: 1.43 to 4.14). Conclusion: Screening for prehypertension should be focused on adults with low education, especially who have high waist circumference, and health system should promote a healthy lifestyle.
A Novel Deep Intrusion Detection Model Based On a Convolutional Neural Network

An intrusion detection system (IDS) plays an important role in information security. However, large amounts of high-dimensional data and unknown attacks pose severe challenges to traditional IDSs. Applying commonly used techniques such as traditional machine learning applied to an IDS relies on complex feature extraction engineering and speciﬁc classiﬁcation algorithms. In this paper, we propose a novel intrusion detection method based on a temporal convolutional network (TCN-IDS) combined with character-level data processing. This method can be applied to both a network intrusion detection system (NIDS) and a host-based intrusion detection system (HIDS), and it achieves a high detection rate without complicated feature engineering. The model we propose has a small number of parameters and consumes little storage (as little as 543 KB depending on the model size) and few resources. Our experimental results on the benchmark dataset NSL-KDD show that the proposed model outperforms previous methods regarding accuracy. TCN-IDS improves the accuracy of intrusion detection and provides a new and eﬃcient deep learning method for intrusion detection.
Correlation and Probability Based Similarity Measure for Detecting Outliers in Categorical Data

Determining the similarity or distance among data objects is an important part in many research fields such as statistics, data mining, machine learning etc. There are many measures available in the literature to define the distance between two numerical data objects. It is difficult to define such a metric to measure the similarity between two categorical data objects since categorical data objects are not ordered. Only a few distance measures are available in the literature to find the similarities among categorical data objects. This paper presents a comparative evaluation of various similarity measures for categorical data and also introduces a novel similarity measure for categorical data based on occurrence frequency and correlation. We evaluated the performance of these similarity measures in the context of outlier detection task in data mining using real world data sets. Experimental results show that the proposed similarity measure outperform the existing similarity measures to detect outliers in categorical datasets. The performances are evaluated in the context of outlier detection task in data mining
Classification of Human?s Perception of Manipulated and Unmanipulated Digital Images Using Neural Network with Network Reduction Techniques

The goal of this study was to develop a feedforward neural network to predict humans’ ability to correctly identifying manipulated or unmanipulated digital images with accuracy above 67%. This prediction accuracy is higher than previous work which utilised expert designed features. We experimented with the distinctiveness network reduction techniques proposed by Gedeon and Harris (1999) on a feedforward network trained by backpropagation. Our research showed that the network reduction technique is useful in reducing the network size to minimize computational resources usage. This was achieved without sacrificing much of the prediction performance for the task of predicting humans’ ability on identifying manipulated images. In addition to this, we compared the effectiveness of the distinctives network reduction technique when the weights were trained with backpropagation versus genetic algorithm. Our results showed that the effectiveness of the network reduction technique is highly dependent on the weight assigned to each hidden neuron . Thus, the network reduction technique performs significantly better when it is applied to the network trained by a genetic algorithm.
URL-based Phishing Attack Detection by Convolutional Neural Networks

Personal sensitive data are frequently obtained with the help of malicious web sites. In this paper, we present a way to detect such malicious URL addresses with high accuracy using convolutional neural networks by analysing only the URL text. The text is encoded with one-hot character-level encoding. The dataset is composed from publicly available datasets. The network is compared with similar methods and is more accurate. Moreover, it converges faster than than the LSTM network.
Three-Stream Convolutional Neural Network for Micro-Expression Recognition

Augmented Reality (AR) is investigated as a unique technology to combine virtual and real world together. The key feature of AR is to demonstrate the extra information in the ﬁeld of view for those who interact with the authentic environment. Two new schemes of AR are proposed, including technical terms as well as psychological approaches. The ﬁndings demonstrate an increase in the number of AR studies in various educational applications. The most reported advantages of AR is to help learners to be engaged in genuine exploration in the real world such as education settings. Some denoted challenges imposed by AR are usability issues and technical problems. The inﬂuence of both technical and psychological issues are discussed, which can shape the mental structure of learners regarding to cognitive theory.
Deep Transfer Learning for Emotion Recognition from Text

Deep learning is almost everywhere and has been shown to achieve human or superhuman level performance on many supervised learning problems. It has been made possible by leveraging the large collection of labelled datasets required for tuning the millions of parameters of neural networks. The downside of deep learning is that the large amount of labelled data is not always available which requires a great deal of human eﬀort and resources to collect, thus limiting the adoption of deep learning to many real life problems. Somehow utilizing unlabelled data or labelled data from a diﬀerent but related task is the only viable solution when labelled data is scarce and one wishes to use deep learning framework. In recent years, emotion recognition in text has become more popular due to its vast potential applications extending into domains such as user interaction, management and marketing, ﬁnance and politics, etc. In this paper we explore methods of transfer learning so that the performance on the low data task of emotion recognition can be improved, we ﬁrst experimented with already existing methods of transfer learning namely parameter ﬁne-tuning and multitask learning. We then propose a novel method of common-space representation learning, which better utilizes the sentiment classiﬁcation data by forcing the network to simultaneously minimize the emotion label prediction loss and reconstruction loss. This method of ours achieves state-of-the-art results on prominently used datasets.
Effiecient Graph Matching by Adjacency Matrix Based Model and Gaussian Smoothing Based Optimization

Graph matching is a fundamental problem in information science and artiﬁcial intelligence, and can be applied to various data with structural characteristics. Approximate methods are necessary for most graph matching tasks due to their NP-complete nature. The continuous relaxation scheme oﬀers an eﬃcient approximate solution, whose performance can be improved by well tackling the nonconvexity of the objective function. Targeting at this problem, this paper proposes an eﬃcient graph matching algorithm using adjacency matrix based graph matching model and generalizing Gaussian smoothing to approximately optimize it. Consequently, the proposed method can better reshape the objective function in the optimization process than existing algorithms, and meanwhile enjoy comparable eﬃcient computation and storage. Experiments on both synthetic dataset and real-world image dataset validate the eﬀectiveness of the proposed method.
ConvDroid: Lightweight Neural Network based Andoird Malware Detection

The explosive amount of Android malware have threatened the security of legitimate users. In Recent years, with the development of the neural network, more and more research is focusing on detecting malware based on the neural network. Where, most of these techniques are depending on the complex feature engineering process and have a resource and time expensive classiﬁcation neural network. In this paper, we propose a novel lightweight convolution neural network model, ConvDroid, with the system call sequences as features. Diﬀerent from the existing n-gram models utilizing system call sequences, we construct a two-layer convolution neural network to learn the global information of sequences instead of limited windows. Furthermore, we assess ConvDroid for accuracy, eﬃciency using a dataset consisting of 3567 malicious applications and 3536 benign ones. Our experiments show that ConvDroid achieves an accuracy of more than 98% with a low time cost. We further demonstrate ConvDroid’s superiority against state-of-the-art approaches.
FacetX: Dynamic Facet Generation for Advanced Information Filtering of Search Results

Searching for information is an important part of our daily life. People are searching for information about jobs, recipes, enter-tainment, places and much more. Many information systems try to support the users in finding the most relevant information to their information need by providing pre-built categories as filter mechanism. However, in most of the systems this support is designed in a very static way and does not consider the dynamic content of the documents in their collections. In this paper we introduce FacetX, an application for the dynamic generation of filter facets for advanced information filtering of search results.
Sw-el'05: Applications of Semantic Web Technologies for E-learning in Conjunction with 3 Rd International Conference on Knowledge Capture (k-cap'05)

Preface The Semantic Web offers new technologies and paradigms to the developers of e-learning applications aiming at providing more intelligent access to and management of Web information and semantically richer modelling of the applications and their users. An important target for (educational) Web application researchers and developers nowadays is to provide means to unite, as much as possible, the efforts in creating information and knowledge components that are easily accessible and usable by third parties. Within the educational space this motivates efforts to achieve semantically rich, well-structured, standardised and verified educational content The Semantic Web with its emerging technologies and standards allows achieving of such reusability, shareability and interoperability among educational Web applications. Conceptualizations (formal taxonomies), ontologies, and the available Web standards, such as XML, RDF, XTM, OWL, DAML-S, and RuleML, allow specification of components in a standard way. The notion of Web services offers a way to make such components mobile and accessible within the wide sea of Web information and applications. The research on e-learning systems involves research interests and efforts from various fields. Starting with the traditional ITS and going towards Web-based ITS, the research on intelligent educational applications strengthens its positions within this context. They all come together in their attempt to serve better the needs of the education community by employing Semantic Web technologies in order to achieve improved e-learning efficiency, adaptation and flexibility for single and group users (e.g. instructors, courseware authors and learners). Knowledge capture is becoming increasingly important in the educational Semantic Web context by offering technologies for acquiring, representing, reasoning, and manipulating information. The ambitious target is to offer manageable, extendable and standardised infrastructure for complementing and collaborating knowledge-based learning applications. In this way, the Semantic Web offers a different perspective on the Intelligent Educational Systems by providing advanced technologies to support more adequate and accurate representations of learners, their learning goals, learning material and contexts of its use, as well as better access and navigation through the learning resources. SW-EL'05 @ K-CAP'05 focuses on the application of Semantic Web technologies for the needs of Web-based educational systems with an emphasis on issues related to knowledge acquisition and management, semantic interoperability, standardization and Semantic Web-based personalization. The following topics are addressed in the attempt to achieve the Educational Semantic Web: • Using ontologies and/or Semantic Web technologies within e-learning applications: