Student performance modelling (SPM) is a critical step to assessing and improving students performances in their learning discourse. However, most existing SPM are based on statistical approaches, which on one hand are based on probability, depicting that results are based on estimation; and on the other hand, actual influences of hidden factors that are peculiar to students, lecturers, learning environment and the family, together with their overall effect on student performance have not been exhaustively investigated. In this paper, Student Performance Models (SPM) for improving students performance in programming courses were developed using M5P Decision Tree (MDT) and Linear Regression Classifier (LRC). The data used was gathered using a structured questionnaire from 295 students in 200 and 300 levels of study who offered Web programming, C or JAVA at Federal University, Oye-Ekiti, Nigeria between 2012 and 2016. Hidden factors that are significant to students performance in programming were identified. The relevant data gathered, normalized, coded and prepared as variable and factor datasets, and fed into the MDT algorithm and LRC to develop the predictive models. The evaluation results obtained indicate that the variable-based LRC produced the best model in terms of MAE, RMSE, RAE and the RRSE having yielded the least values in all the evaluations conducted. Further results obtained established the strong significance of attitude of students and lecturers, fearful perception of students, erratic power supply, university facilities, student health and students attendance to the performance of students in programming courses. The variable-based LRC model presented in this paper could provide baseline information about students performance thereby offering better decision making towards improving teaching/learning outcomes in programming courses.

Its constant technological evolution characterizes the contemporary world, and every day the processes, once manual, become computerized. Data are stored in the cyberspace, and as a consequence, one must increase the concern with the security of this environment. Cyber-attacks are represented by a growing worldwide scale and are characterized as one of the significant challenges of the century. This article aims to propose a computational system based on intelligent hybrid models, which through fuzzy rules allows the construction of expert systems in cybernetic data attacks, focusing on the SQL Injection attack. The tests were performed with real bases of SQL Injection attacks on government computers, using fuzzy neural networks. According to the results obtained, the feasibility of constructing a system based on fuzzy rules, with the classification accuracy of cybernetic invasions within the margin of the standard deviation (compared to the state-of-the-art model in solving this type of problem) is real. The model helps countries prepare to protect their data networks and information systems, as well as create opportunities for expert systems to automate the identification of attacks in cyberspace.

Most of you who are learning data science with Python will have definitely heard already about scikit-learn, the open source Python library that implements a wide variety of machine learning, preprocessing, cross-validation and visualization algorithms with the help of a unified interface. If you're still quite new to the field, you should be aware that machine learning, and thus also this Python library, belong to the must-knows for every aspiring data scientist. That's why DataCamp has created a scikit-learn cheat sheet for those of you who have already started learning about the Python package, but that still want a handy reference sheet. Or, if you still have no idea about how scikit-learn works, this machine learning cheat sheet might come in handy to get a quick first idea of the basics that you need to know to get started. Either way, we're sure that you're going to find it useful when you're tackling machine learning problems!

Software development effort estimation is considered a fundamental task for software development life cycle as well as for managing project cost, time and quality. Therefore, accurate estimation is a substantial factor in projects success and reducing the risks. In recent years, software effort estimation has received a considerable amount of attention from researchers and became a challenge for software industry. In the last two decades, many researchers and practitioners proposed statistical and machine learning-based models for software effort estimation. In this work, Firefly Algorithm is proposed as a metaheuristic optimization method for optimizing the parameters of three COCOMO-based models. These models include the basic COCOMO model and other two models proposed in the literature as extensions of the basic COCOMO model. The developed estimation models are evaluated using different evaluation metrics. Experimental results show high accuracy and significant error minimization of Firefly Algorithm over other metaheuristic optimization algorithms including Genetic Algorithms and Particle Swarm Optimization.

The study of register in computational language research has historically been divided into register analysis, seeking to determine the registerial character of a text or corpus, and register synthesis, seeking to generate a text in a desired register. This article surveys the different approaches to these disparate tasks. Register synthesis has tended to use more theoretically articulated notions of register and genre than analysis work, which often seeks to categorize on the basis of intuitive and somewhat incoherent notions of prelabeled 'text types'. I argue that an integration of computational register analysis and synthesis will benefit register studies as a whole, by enabling a new large-scale research program in register studies. It will enable comprehensive global mapping of functional language varieties in multiple languages, including the relationships between them. Furthermore, computational methods together with high coverage systematically collected and analyzed data will thus enable rigorous empirical validation and refinement of different theories of register, which will have also implications for our understanding of linguistic variation in general.

Traditional photo-id involves a laborious manual process of matching each dolphin fin photograph captured in the field to a catalogue of known individuals. Weexamine this problem in the context of open-set recognition andutilise a triplet loss function to learn a compact representation of fin images in a Euclidean embedding, where the Euclidean distance metric represents fin similarity. We show that this compact representation can be successfully learnt from a fairly small (in deep learning context) training set and still generalise well to out-of-sample identities (completely new dolphin individuals), with top-1 and top-5 test set (37 individuals) accuracy of 90.5 2 and 93.6 1 percent. In the presence of 1200 distractors, top-1 accuracy dropped by 12%; however, top-5 accuracy saw only a 2.8% drop. I. INTRODUCTION Dolphin photo-identification (photo-ID) studies involve photographing dolphindorsal fins during field work and then having a human categorise images into unique individual animals andmatching them with an existing catalogue of known individuals. Individuals are identified by natural features that can be observed on the fins--these features vary between species, but typically include the pattern of nicks and notches on the trailing edge of the fin, the scratches/rake marks/scars on the fin and (for some species) the pigmentation patterns [1]. Matchingof new images from the field against a large catalogue is time consuming, because it requires a human to compare each candidate image to every fin in the catalogue, taking O(mn) average time (where m is the number of new images and n is the number of catalogued individuals). Moreover, as n increases over time as more individuals are added to the catalogue, so does the likelihood of making a mistake, putting catalogue integrity at risk.

Over the past decades, numerous loss functions have been been proposed for a variety of supervised learning tasks, including regression, classification, ranking, and more generally structured prediction. Understanding the core principles and theoretical properties underpinning these losses is key to choose the right loss for the right problem, as well as to create new losses which combine their strengths. In this paper, we introduce Fenchel-Young losses, a generic way to construct a convex loss function for a regularized prediction function. We provide an in-depth study of their properties in a very broad setting, covering all the aforementioned supervised learning tasks, and revealing new connections between sparsity, generalized entropies, and separation margins. We show that Fenchel-Young losses unify many well-known loss functions and allow to create useful new ones easily. Finally, we derive efficient predictive and training algorithms, making Fenchel-Young losses appealing both in theory and practice.

Abstract--Recently, a number of works have studied clustering strategies that combine classical clustering algorithms and deep learning methods. These approaches follow either a sequential way, where a deep representation is learned using a deep autoencoder before obtaining clusters with k-means, or a simultaneous way,where deep representation and clusters are learned jointly by optimizing a single objective function. Both strategies improve clustering performance, however the robustness of these approaches is impeded by several deep autoencoder setting issues, among which the weights initialization, the width and number of layers or the number of epochs. To alleviate the impact of such hyperparameters setting on the clustering performance, we propose a new model which combines the spectral clustering and deep autoencoder strengths in an ensemble learning framework. Extensive experiments on various benchmark datasets demonstrate thepotential and robustness of our approach compared to state-of-the art deep clustering methods. I. INTRODUCTION Learning from large amount of data is a very challenging task. Several dimensionality reduction and clustering techniques thatare well studied in the literature aim to learn a suitable and simplified data representation from original dataset; see for instance [1-3]. While many approaches have been proposed to address the dimensionality reduction and clustering tasks, deep learning-based methods recently demonstrate promisingresults.

We consider prediction with expert advice under the log-loss with the goal of deriving efficient and robust algorithms. We argue that existing algorithms such as exponentiated gradient, online gradient descent and online Newton step do not adequately satisfy both requirements. Our main contribution is an analysis of the Prod algorithm that is robust to any data sequence and runs in linear time relative to the number of experts in each round. Despite the unbounded nature of the log-loss, we derive a bound that is independent of the largest loss and of the largest gradient, and depends only on the number of experts and the time horizon. Furthermore we give a Bayesian interpretation of Prod and adapt the algorithm to derive a tracking regret.

We study the problem of data release with privacy, where data is made available with privacy guarantees while keeping the usability of the data as high as possible --- this is important in health-care and other domains with sensitive data. In particular, we propose a method of masking the private data with privacy guarantee while ensuring that a classifier trained on the masked data is similar to the classifier trained on the original data, to maintain usability. We analyze the theoretical risks of the proposed method and the traditional input perturbation method. Results show that the proposed method achieves lower risk compared to the input perturbation, especially when the number of training samples gets large. We illustrate the effectiveness of the proposed method of data masking for privacy-sensitive learning on $12$ benchmark datasets.