Unsupervised Learning Techniques

A method for maximizing mutual information between segment representations and the generated sequence of phonemes for unsupervised speech recognition using GAN for better control over the inclusion of unrelated textual information in the transcription, allowing experiments with deeper generators.

This thesis provides a theoretical description of on-line unsupervised learning from high-dimensional data. In particular, the learning dynamics of the on-line Hebbian algorithm is studied for the following two popular statistical models—principal component analysis (PCA) and independent component analysis (ICA). The methods of statistical mechanics are used to elucidate the critical transient stages of the learning process.

In the case of on-line PCA, the statistical mechanics approach allows the derivation of a set of deterministic differential equations for a small number of macroscopic self-averaging order parameters which enables an exact calculation of the evolution of the error function in the limit of large data dimension. In the case of ICA learning, it is found that the interesting macroscopic order parameters are not self-averaging in general and the transient dynamics is relatively slow with large fluctuations. The parameter trajectory of the on-line Hebbian ICA algorithm studied here passes through a sequence of metastable states, each of which can be described by a diffusion process in a polynomial potential. The proper treatment of the fluctuations of the order parameters for this case is given by the Fokker-Planck equation.

For both models a natural gradient version of the studied algorithms is constructed using the fact that the parameter space of our models is constrained to a Stiefel manifold of orthogonal rectangular matrices. In the case of on-line ICA, the natural gradient variant exhibits the same stochastic trapping in sub-optimal metastable states during transient learning as in the case of the standard Hebbian ICA algorithm and there is only an advantage in using the natural gradient variant asymptotically.

The problem of the sensitivity of on-line learning to the choice of learning algorithms is considered. Recommendations for proper adjustment of learning parameters in order to improve the performance of studied algorithms are given. Numerical simulations for finite-sized systems are in good agreement with the theoretical results.

The current body of literature lacks studies related to organizational managers' classification of systems thinking (ST) skills based on both their overall systemic tendency and the organizational ownership structure. The purpose of this study is to assess and classify the ST skills of senior managers who currently work in a complex business environment. Initially, we clustered managers' overall systemic thinking (OST) using the Bayesian latent class analysis (BLCA) method into two distinct clusters: managers with upper OST (holistic thinker) and managers with lower OST (reductionist thinker). Further, we classified managers' ST skills into two predefined classes to understand the characteristics of each group better. A total of 51 senior managers from two different organizational structures participated in this study. Results show that the ST skills of managers in public are more towards the upper OST/holistic cluster, whereas managers from the private sector have an inclination towards the lower OST/reductionist cluster.

Stock worth measures have reliably pulled in the thought of various agents and authorities. Standard speculation holds that stock trades are sporadic in nature, and it is senseless to endeavour to envision them. Since various components are incorporated, anticipating the stock worth itself is a troublesome issue. Until further notice, the market continues like a popularity based machine, yet as time goes on, it acts like a measuring machine, so it can foresee exhibit designs over a progressively broadened time period. The utilization of machine learning advancement and various counts in stock worth examination and figure is a promising field. In this paper, we first rapidly plot the logical classification of the stock markets and taxonomy of stock market prediction methods. By then, we will focus on some investigation achieves stock assessment and deciding. We look at the particular, technical, fundamental, and long short term memory network strategies used in stock examination. Finally, we present a couple of challenges and investigation opportunities in this field.

The area of predictive maintenance has taken a lot of prominence in the last couple of years due to various reasons. With new algorithms and methodologies growing across different learning methods, it has remained a challenge for industries to adopt which method is fit, robust and provide most accurate detection. Fault detection is one of the critical components of predictive maintenance; it is very much needed for industries to detect faults early and accurately. In a production environment, to minimize the cost of maintenance, sometimes it is required to build a model with minimal or no historical data. In such cases, unsupervised learning would be a better option model building. In this paper, we have chosen a simple vibration data collected from an exhaust fan, and have fit different unsupervised learning algorithms such as PCA T 2 statistic, Hierarchical clustering, K-Means, Fuzzy C-Means clustering and model-based clustering to test its accuracy, performance, and robustness. In the end, we have proposed a methodology to benchmark different algorithms and choosing the final model

The objective of this paper is to discuss a state-of-the-art of methodology and algorithms for integrating fuzzy sets and neural networks in a unique framework for dealing with pattern recognition· problems, in particular classification procedures. Methods of pattern recognition are studied in two mainstreams, namely supervised and unsupervised data learning.
We propose our own definition of fuzzy neural integrated networks. This criterion is proposed as a unifying framework for comparison of 'algorithms. In the first part pf the this paper, classification methods
based on rule sets or numerical data are reviewed, together with specific methods for handling classification in image processing. In the second
part of this paper, several fuzzy neural clustering models are reviewed and compared. These models are: i) Self-Organizing Map (SOM); ii) Fuzzy Learning Vector Quantization (FLVQ); iii) Carpenter-Grossberg-Rosen
Fuzzy Adaptive Resonance Theory (CGR Fuzzy ART); iv) Growing Neural Gas (GNG); and v) Fully self-Organizing Simplified Adaptive Resonance Theory (FOSART).

Machine learning is one of the most exciting technology& it gives the computer that makes it more similar to humans .It is actively being used today, perhaps in many more places than one would expect. Learning is a natural human behaviour which has been made an essential aspect of the machines as well. There are various techniques used for the same. Traditional machine learning algorithms have been applied in many application areas. This paper presents the most commonly used machine learning algorithms such as supervised, unsupervised, reinforcement. In each of our study we found that the results of various machine-learning algorithm depends on application areas on which they are applied. Our review of study not only suggests that these techniques are competitive with traditional estimators on one data set, but also illustrate that these methods are sensitive to the data on which they are trained.

Thanks to more powerful hardware and a new generation of learning algorithms, artificial intelligence is supporting the automation of a number of tasks and activities that are changing the job landscape as much as they have impacted on our everyday life. The first part of the chapter introduces artificial intelligence from its origins: its definition and its main research and application areas. The nature and the importance of machine learning for artificial intelligence applications are presented in the second part of the chapter. Existing approaches to machine learning are also classified and illustrated. The third part describes artificial intelligence tools and solutions by supported functionalities and automated tasks. Cases of applications in tourism are provided, from the best known and widely adopted, e.g., personal assistants, to the most challenging, i.e., semantic systems. Future trends and risks related to the applications of artificial intelligence are considered in the last part of the chapter.

The ‘special sauce’ that makes machine learning so interesting and what captures our imagination is not the ability of the machine to behave according to what you have already taught it in terms of explicit inputs, it is the implicit ability of the machine to construct relationships and patterns between input/output

"This paper compares various approaches to argument structure. We start out presenting the lexical proposal that we want to defend in this paper. We then introduce phrasal proposals that are common in Construction Grammar. A historical section describes the oscillation between early lexical proposals in Categorial Grammar, phrasal approaches in phrase structure grammar (early Transformational Grammar, GPSG) back to lexical approaches in HPSG and Minimalism. We argue that there were good reasons for returning to lexical models and that the respective issues are not addressed in phrasal approaches. We go on discussing approaches that assume that sematnically compatible verbs are inserted into phrasal constructions and point out that lexical specification of valence plays an important role in various levels of description and that phrasal models can not account for this. A similar criticism applies to so-called Neodavidsonian approaches, which are discussed in a separate section.

We will show that certain relations between constructions cannot be captured with inheritance or unification but require transformations or lexical rules, and hence in non-transformational syntax the lexical approach is the only option.

Three sections are devoted to arguments from language acquisition, psycholinguistics, and statistics. We show that contrary to frequent claims the respective experiments do not provide evidence for phrasal constructions.

We conclude that argument structure properties should be represented together with lexical items. "

Scope of the book:
This book focusses on the technical concepts of deep learning and its associated branch Neural Networks for the various dimensions of image processing applications. The proposed volume intends to bring together researchers to report the latest results or progress in the development of the above-mentioned areas. Since there is a deficit of books on this specific subject matter, the editors aim to provide a common platform for researchers working in this area to exhibit their novel findings.
Topics of Interest:
This book solicits contributions, which include the fundamentals in the field of Deep Artificial Neural Networks and Image Processing supported by case studies and practical examples. Each chapter is expected to be self-contained and to cover an in-depth analysis of real life applications of neural networks to image analysis.

The k-means algorithm is generally the most known and used clustering method. There are various extensions of k-means to be proposed in the literature. Although it is an unsupervised learning to clustering in pattern recognition and machine learning, the k-means algorithm and its extensions are always influenced by initializations with a necessary number of clusters a priori. That is, the k-means algorithm is not exactly an unsupervised clustering method. In this paper, we construct an unsupervised learning schema for the k-means algorithm so that it is free of initializations without parameter selection and can also simultaneously find an optimal number of clusters. That is, we propose a novel unsupervised k-means (U-k- means) clustering algorithm with automatically finding an optimal number of clusters without giving any initialization and parameter selection. The computational complexity of the proposed U-k-means clustering algorithm is also analyzed. Comparisons between the proposed U-k-means and other existing methods are made. Experimental results and comparisons actually demonstrate these good aspects of the proposed U-k- means clustering algorithm.

This paper presents the corpus developed by the LIUM for Automatic Speech Recognition (ASR), based on the TED Talks. This corpus was built during the IWSLT 2011 Evaluation Campaign, and is composed of 118 hours of speech with its accompanying automatically aligned transcripts. We describe the content of the corpus, how the data was collected and processed, how it will be publicly available and how we built an ASR system using this data leading to a WER score of 17.4%. The official results we obtained at the IWSLT 2011 evaluation campaign are also discussed.

Supervised and unsupervised seismic facies classification methods are slowly gaining popularity in hydrocarbon exploration and production workflows. Unsupervised clustering is data driven, unbiased by the interpreter beyond the choice of input data and brings out the natural clusters present in the data. There are several competing unsupervised clustering techniques, each with advantages and disadvantages. In this dissertation, I demonstrate the use of various classification techniques on real 3D seismic data from various depositional environments. Initially, I use the popular unsupervised Kohonen self-organizing maps (SOMs) algorithms on case studies based on offshore deep-water Gulf of Mexico field and onshore heterogeneous Mississippian Chert reservoir from Oklahoma, USA. However, Kohonen SOM suffers from the absence of any proper convergence criterion and rules for parameter selection. These shortcomings are addressed by the more recent development of generative topographic mapping (GTM) algorithm. GTM is based on a probabilistic unsupervised classification technique. I extend the GTM workflow to consider multi-attribute inversion volumes and do seismic facies classification for a Barnett shale survey and another case study on Veracruz Basin survey in Southern Mexico that was acquired over a heterogeneous conglomerate reservoir.Finally, I introduce limited supervision using user defined clusters into both the SOM and GTM algorithms.
The final product of my dissertation is a suite of algorithms, workflows, user interfaces and user documentation allowing others to build upon and extend this research.

For more than a century, the methods for data representation and the exploration of the intrinsic structures of data have developed remarkably and consist of supervised and unsupervised methods. However, recent years have witnessed the flourishing of big data, where typical dataset dimensions are high and the data can come in messy, incomplete, unlabeled, or corrupted forms. Consequently, discovering the hidden structure buried inside such data becomes highly challenging. From this perspective, exploratory data analysis plays a substantial role in learning the hidden structures that encompass the significant features of the data in an ordered manner by extracting patterns and testing hypotheses to identify anomalies. Unsupervised generative learning models are a class of machine learning models characterized by their potential to reduce the dimensionality, discover the exploratory factors, and learn representations without any predefined labels; moreover, such models can generate th...

The k-means clustering algorithm is the oldest and most known method in cluster analysis. It has been widely studied with various extensions and applied in a variety of substantive areas. Since internet, social network, and big data grow rapidly, multi-view data become more important. For analyzing multi-view data, various multi-view k-means clustering algorithms have been studied. However, most of multi-view k-means clustering algorithms in the literature cannot give feature reduction during clustering procedures. In general, there often exist irrelevant feature components in multi-view data sets that may cause bad performance for these clustering algorithms. There also exists high feature dimension in multi-view data sets so it is necessary to consider reducing its dimension for clustering algorithms. In this paper, a learning mechanism for the multi-view k-means algorithm to automatically compute individual feature weight is constructed. It can reduce these irrelevant feature components in each view. A new multi-view k-means objective function is firstly proposed for constructing the learning mechanism for feature weights in multi-view clustering. A schema for eliminating irrelevant feature(s) with small weight(s) is then considered for feature reduction. Therefore, a new type of multi-view k-means, called a feature-reduction multi-view k-means (FRMVK), is proposed. The computational complexity of FRMVK is also analyzed. Numerical and real data sets are used to compare FRMVK with other feature-weighted multi-view k-means algorithms. Experimental results and comparisons actually demonstrate the effectiveness and usefulness of the proposed FRMVK clustering algorithm.

The present article reviews work on morphological analysis, a subfield of computational linguistics. Special focus is given on statistical approach for morpheme segmentation. We delineate morphological analysis as a problem of persuading a narrative of some kind. However, this paper considered the problem of morpheme segmentation. Morpheme segmentation is also referred to as stemming; Stemming is a minimum unit of language that carries a meaning as a root word. In this paper, we concisely discuss the history and motivation of this problem. By referring numbers of papers from reputed journals and conferences, we here present a brief classification of the work done and unfavorably discuss the most significant viewpoints of major unsupervised techniques in the field. While summarizing the achievements so far, we give a clear direction for future work in the related field with justified points of view.

Data analysis is a primal skill of human beings. Cluster analysis, primitive exploration of data based on little or no prior knowledge of the structure underlying it, consists of research developed across various disciplines. Artificial Neural Networks on the other hand are abstractions of learning systems based on the human brain. The brain basically learns from experience, associations and disassociations, and from error improvement, and clusters some new object based on comparing known features from known objects. In this paper, we propose a modified back-propagation algorithm for neural network that performs supervised learning using a set of feature vectors.

Learning good representations is of crucial importance in deep learning. Mutual Information (MI) or similar measures of statistical dependence are promising tools for learning these representations in an unsupervised way. Even though the mutual information between two random variables is hard to measure directly in high dimensional spaces, some recent studies have shown that an implicit optimization of MI can be achieved with an encoder-discriminator architecture similar to that of Genera-tive Adversarial Networks (GANs). In this work, we learn representations that capture speaker identities by maximizing the mutual information between the encoded representations of chunks of speech randomly sampled from the same sentence. The proposed encoder relies on the SincNet architecture and transforms raw speech waveform into a compact feature vector. The discriminator is fed by either positive samples (of the joint distribution of encoded chunks) or negative samples (from the product of the marginals) and is trained to separate them. We report experiments showing that this approach effectively learns useful speaker representations, leading to promising results on speaker identification and verification tasks. Our experiments consider both unsupervised and semi-supervised settings and compare the performance achieved with different objective functions.

i. Two paradoxes
Paradigm Function Morphology (PFM), Construction Morphology (CxM), Amorphous morphology (A-morphM): There are no morphemes BUT there is morphology. (Inflectional) morphemes are listed as markings (exponents) without meaning in the lexicon.
Thus, e.g., PFM defines form and meaning based on the paradigm function (PF): PF(áL,σñ) = áR,σñ (Stewart & Stump 2007). The PF value of a paradigm cell áL,σñ of the lexeme (L) is the pairing of this cell's realization R with the morphosyntactic property set σ.

Distributed Morphology (DM): There is no morphology by itself BUT there are abstract morphemes.
DM relies on syntactic structures and 'morpheme' is an abstract unit that refers to a syntactic terminal node and its content, not to the phonological expression of that terminal. Morphology is distributed between syntax and phonology. Vocabulary items are exponents that relate form and meaning but they insert late (post syntactically).

ii. Our view
There is morphology and there are morphemes.
There are three possible ways to approach the relation of meaning and form in morphology: A. Form and meaning emerge simultaneously B. The association is from meaning to form C. The association is from form to meaning The most important difference between these scenarios consists in the fact that in scenarios B and C meaning may be assigned at the level of word, i.e. one may claim that morphemes do not have meaning of their own or even that there are no morphemes at all (in scenario B).

Language is a positional system.
With respect to meaning, (forms of) morphemes can be evaluated: 1) in isolation (as building blocks of morphology, e.g. English-s,-en,-ed,-er); 2) based on their position in the word form (i.e. templatically, e.g. inflection is outside derivation; prefixes, suffixes, infixes, interfixes are also established positionally); 3) based on combinations with other morphemes, e.g. E. writer -s but not *small-er-s, thus two different-er suffixes, one that derives nouns (agents) and one that expresses comparative degree of adjectives.

Morphemes associate form and meaning, like in scenario A, but this association is not trivial and involves scenarios B and C at the different stages of derivation and in comprehension and production.

Machine learning has become popular today as so many of its algorithms are now commonly used in different data science projects in various industries especially in the health care sector. It is imperative for researchers and medical professionals to be assisted by machine learning methods in early detection of diseases such as heart disease which is one major killer of humans in our world today. Machine learning algorithms are excellent at learning from data, and since healthcare providers generate huge amount of data on a daily basis, these algorithms can thrive in this field. In this research study, a comparative analytical approach was taken in the determination of which algorithm performs better under the given condition. Various experiments were carried out using cross validation of 5 and 10 folds, to ensure that models created can generalize well enough. This study makes use of data from University of California, Irvine (UCI) machine learning database containing 303 instances with 14 attributes. The collected data is scaled using Min-Max normalization technique. Different popular models are built using supervised machine learning classification algorithms on the scaled data such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), and Gradient Boosting ensemble method. These algorithms are also evaluated using standard performance metrics such as precision, recall, and F1-score. From the experiments carried out, it can be concluded that SVM performs better as it out performs the other algorithms.

Abstrak Pemilihan jurusan bagi siswa merupakan langkah positif yang dilakukan untuk memfokuskan siswa sesuai dengan potensi yang dimiliki, hal ini dianggap penting karena dengan adanya jurusan, siswa diharapkan mampu mengembangkan kemampuan akademis sesuai bidang yang dikuasai. Pada penelitian sebelumnya, telah dilakukan pengujian dengan metode Naive Bayes yang bertujuan untuk mengkasifikasikan jurusan siswa bedasarkan kriteria yang menunjang dengan studi kasus pada siswa Madrasah Aliyah Swasta PAB 6 Helvetia, dan didapatkan hasil pengujian dari 100 data siswa dengan tingkat keakuratan 90%. pada penelitian ini, dilakukan optimalisasi metode yang digunakan sebelumnya dengan menerapkan teknik Unsupervised Discretization yang akan mentransformasikan kriteria numerik/kontinyu menjadi kriteria kategorikal dan mengeliminasi satu kriteria yang dianggap tidak mempengaruhi keakuratan hasil pengujian, dengan begitu keakurasian hasil klasifikasi dapat meningkat. Dari 120 data siswa yang diuji, terbukti bahwa hasil klasifikasi penerapan teknik unsupervised discretization pada metode naive bayes naik dari 90% menjadi 92.8%. Abstract Selection of majors for students is a positive step that is done to focus students in accordance with their potential, it is considered important because with the majors, students are expected to develop academic ability according to the controlled field. In previous research, Naive Bayes method has been tested to classify the students department based on the supportive criterias (case study on Madrasah Aliyah PAB 6 Helvetia), and the test result of 100 students data, the classification accuracy is about 90%. in this study, optimizaton is done with a method used earlier by applying Unsupervised Discretization techniques that would transform numerical / continuous criteria into categorical criteria and eliminating one criterion that is considered not affect the accuracy of test results. thus the accuracy of classification results could increase. 120 students data is tested, it is evident that the results of the classification of the application of unsupervised discretization techniques on the Naive Bayes method rose from 90% to 92.8%. 1. PENDAHULUAN Perkembangan ilmu pengetahuan dan teknologi berkembang begitu pesat bahkan telah merambah ke semua aspek kehidupan. Mulai dari bisnis, sosial hingga pendidikan. Peran pendidikan sangat penting dalam mendukung perkembang teknologi yang hampir sudah merambah ke semua bidang. Hal ini tentu juga berpengaruh pada penentuan jurusan bagi siswa SMA/MA sederajat, yang mana penentuan jurusan siswa tersebut merupakan proses untuk memfokuskan siswa dalam bidang konsentrasi tertentu, hal ini dilakukan agar setiap siswa dapat mempelajari lebih dalam mata pelajaran-mata pelajaran yang sesuai dengan konsentrasi yang telah ditentukan untuk siswa tersebut. Yang menjadi masalah ialah sistem yang tengah berjalan pada sekolah Madrasah Aliyah Swasta PAB 2 Helvetia Medan, tidak sepenuhnya efektif dikarenakan para siswa diberikan angket untuk menentukan jurusan apa yang diminati tanpa mempertimbangkan kriteria lain yang mungkin memiliki andil untuk menentukan kelayakan siswa tersebut dalam hal memilih jurusan. Padahal proses penentuan jurusan untuk siswa merupakan langkah penting dalam mempersiapkan siswa untuk berkonsentrasi pada bidang yang siswa tersebut minati saat harus melanjut pada jenjang pendidikan Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK)

The learning dynamics of on-line independent component analysis is analysed in the limit of large data dimension. We study a simple Hebbian learning algorithm that can be used to separate out a small number of non-Gaussian components from a high-dimensional data set. The de-mixing matrix parameters are confined to a Stiefel manifold of tall, orthogonal matrices and we introduce a natural gradient variant of the algorithm which is appropriate to learning on this manifold. For large input dimension the parameter trajectory of both algorithms passes through a sequence of unstable fixed points, each described by a diffusion process in a polynomial potential. Choosing the learning rate too large increases the escape time from each of these fixed points, effectively trapping the learning in a sub-optimal state. In order to avoid these trapping states a very low learning rate must be chosen during the learning transient, resulting in learning time-scales of O(N^2) or O(N^3) iterations where N is the data dimension. Escape from each sub-optimal state results in a sequence of symmetry breaking events as the algorithm learns each source in turn. This is in marked contrast to the learning dynamics displayed by related on-line learning algorithms for multilayer neural networks and principal component analysis. Although the natural gradient variant of the algorithm has nice asymptotic convergence properties, it has an equivalent transient dynamics to the standard Hebbian algorithm.

Automatic detection of gait events has primarily been confined to methods which require a heuristic or biometric determination of threshold values for each event, which are then stipulated as conditions while defining algorithms. This takes away from the complete automation of the process, since subject-wise threshold calculation must be done manually or using force plate data. Machine learning algorithms and neural network approaches have been proposed, but unsupervised machine learning alogrithms (K Means clustering, complete linkage) are yet to be explored and employed. Some algorithms proposed in the last two decades have presented purely automatic identification (Zeni et al., Ghoussayni et al., etc.), but the data used is either gyroscopic or captured using Vicon optical motion cameras. Microsoft Kinect is an inexpensive alternative to Vicon cameras for conducting gait analysis, and provides data for joint kinematics, especially lower limb movement data, which is pertinent to gait event detection. This project has used kinematic data collected on treadmill trials only through 3 Kinect V2s integrated using FusionKit software, and introduces machine learning techniques to perform gait event detection.

Manufacturing industries have been on a steady path considering for new methods to achieve near-zero downtime to have flexibility in the manufacturing process and being economical. In the last decade with the availability of industrial internet of things (IIoT) devices, this has made it possible to monitor the machine continuously using wireless sensors, assess the de gradation and predict the failures ahead of time. Condition-based predictive maintenance has made a significant influence in monitoring the asset and predicting the failure ahead of time. This has minimized the impact on production, quality, and maintenance cost. Numerous approaches have been in proposed over the years and implemented in supervised learning. In this paper, challenges of supervised learning such as need for historical data and incapable of classifying new faults accurately will be overcome with a new methodology using unsupervised learning for rapid implementation of predictive maintenance activity which includes fault prediction and fault class detection for known and unknown faults using density estimation via Gaussian Mixture Model Clustering and K-means algorithm and compare their results with a real case vibration data.

There are many situations where we need to separate data into clusters without any labels being provided. This is an example of Unsupervised learning. In this assignment we apply K-Means algorithm for unsupervised learning on the given dataset and analyse the effect of various parameters including number of clusters and initialization method on the accuracy of clustering.

During the past decade, the size of 3D seismic data volumes and the number of seismic attributes have increased
to the extent that it is difficult, if not impossible, for interpreters to examine every seismic line and time
slice. To address this problem, several seismic facies classification algorithms including k-means, self-organizing
maps, generative topographic mapping, support vector machines, Gaussian mixture models, and artificial neural
networks have been successfully used to extract features of geologic interest from multiple volumes. Although
well documented in the literature, the terminology and complexity of these algorithms may bewilder the average
seismic interpreter, and few papers have applied these competing methods to the same data volume. We have
reviewed six commonly used algorithms and applied them to a single 3D seismic data volume acquired over the
Canterbury Basin, offshore New Zealand, where one of the main objectives was to differentiate the architectural
elements of a turbidite system. Not surprisingly, the most important parameter in this analysis was the choice of
the correct input attributes, which in turn depended on careful pattern recognition by the interpreter. We found
that supervised learning methods provided accurate estimates of the desired seismic facies, whereas unsupervised
learning methods also highlighted features that might otherwise be overlooked.

Automatic detection of antonymy is an important task in Natural Language Processing (NLP) for Information Retrieval (IR), Ontology Learning (OL) and many other semantic applications. However,
current unsupervised approaches to antonymy detection are still not fully effective because they cannot discriminate antonyms from synonyms. In this paper, we introduce APAnt, a new AveragePrecision-based measure for the unsupervised discrimination of antonymy from synonymy using Distributional Semantic Models (DSMs). APAnt makes use of Average Precision to estimate the extent and salience of the intersection among the most descriptive contexts of two target words. Evaluation shows that the proposed method is able to distinguish antonyms and synonyms with high accuracy across different parts of speech,
including nouns, adjectives and verbs. APAnt outperforms the vector cosine and a baseline model implementing the co-occurrence hypothesis.

Motivation: The genotype assignment problem consists of predicting, from the genotype of an individual , which of a known set of populations it originated from. The problem arises in a variety of contexts, including wildlife forensics, invasive species detection and biodiversity monitoring. Existing approaches perform well under ideal conditions but are sensitive to a variety of common violations of the assumptions they rely on. Results: In this article, we introduce Mycorrhiza, a machine learning approach for the genotype assignment problem. Our algorithm makes use of phylogenetic networks to engineer features that encode the evolutionary relationships among samples. Those features are then used as input to a Random Forests classifier. The classification accuracy was assessed on multiple published empirical SNP, microsatellite or consensus sequence datasets with wide ranges of size, geographical distribution and population structure and on simulated datasets. It compared favorably against widely used assessment tests or mixture analysis methods such as STRUCTURE and Admixture, and against another machine-learning based approach using principal component analysis for dimensionality reduction. Mycorrhiza yields particularly significant gains on datasets with a large average fixation index (FST) or deviation from the Hardy-Weinberg equilibrium. Moreover, the phylogenetic network approach estimates mixture proportions with good accuracy. Availability and implementation: Mycorrhiza is released as an easy to use open-source python package at github.com/

The security challenge on IoT (Internet of Things) is one of the hottest and most relevant topics at the moment particularly the few security challenges. The Botnet is one of the security challenges that most impact for several purposes. The network of private devices infected by malicious software and controlled as a group without the owners' awareness and every one of them running one or more bots is called Botnets. Frequently, it is utilized for sending spam, stealing data, and performing DDoS attacks. One of the methods that been used for identifying the Botnet is the Unsupervised Learning technique. This study will examine several Unsupervised Learning methods such as; K-Means , K-medians, Mini Batch K-Means, Hierarchical Clustering, Artificial Neural networks (ANN), DBSCAN (Density-based spatial clustering of applications with noise), Gaussian Mixture Model (GMM), Learning Automata-based Clustering (LAC), and Affinity Propagation (AP) for identifying the Botnet in IoT with the aim of finding which Unsupervised Learning technique can achieve the highest accuracy and fastest detection.

El estudio del comercio internacional es una de las áreas de investigación clásicas de las ciencias económicas. Su caracterización empírica, a la vez que se remonta en el tiempo, constituye hoy en día un espacio de aplicación para nuevas técnicas, basadas en el uso intensivo de datos. El aumento en la disponibilidad de información curada para el conjunto de los países permite explorar nuevas técnicas de análisis. A su vez, las múltiples dimensiones del problema impiden que una única técnica de análisis permita capturar la totalidad del fenómeno. El presente trabajo se propone realizar diversas propuestas metodológicas para el estudio del comercio internacional, de forma tal que en su conjunto permitan obtener una visión novedosa de los datos. Para ello, se utilizan técnicas ampliamente difundidas en otros campos, como el análisis de grafos y los modelos generativos bayesianos, y se considera el comercio agregado entre países, así como la composición de las canastas exportadoras de los mismos.

The amount of data managed in many academic institutions has increased in recent years, particularly in all the research work done by undergraduate students, who simply use empirical techniques for keyword selection, forgetting existing technical methods to assist their students in this process. Information and communication technologies, such as the platform for integrated research and academic work with responsibility (PILAR), which records information about research projects, such as titles, summaries, and keywords in their various modalities, have gained relevance and importance in the management of these. We proved algorithms with these records of research projects that have been analysed in this study, and predictions were made for each of the nine (09) models of unsupervised machine learning algorithms that were implemented for each of the 7430 records from the dataset. The most efficient way of extracting keywords for this dataset was the TF-IDF method, obtaining 72% accuracy and [0.4786, SD 0.0501] in average extraction time for each thesis file processed by this model.

We consider the problem of stable region detection and segmentation of deformable shapes. We pursue this goal by determining a consensus segmentation from a heterogeneous ensemble of putative segmentations, which are generated by a clustering process on an intrinsic embedding of the shape.
The intuition is that the consensus segmentation, which relies on aggregate statistics gathered from the segmentations in the ensemble,
can reveal components in the shape that are more stable to deformations than the single baseline segmentations.
Compared to the existing approaches, our solution exhibits higher robustness and repeatability throughout a wide spectrum of non-rigid transformations. It is computationally efficient, naturally extendible to point clouds, and remains semantically stable even across different object classes. A quantitative evaluation on standard datasets confirms the potentiality of our method as a valid tool for deformable shape analysis.

The volume of SMS messages sent on a daily basis globally has continued to grow significantly over the past years. Hence, mobile phones are becoming increasingly vulnerable to SMS spam messages, thereby exposing users to the risk of fraud and theft of personal data. Filtering of messages to detect and eliminate SMS spam is now a critical functionality for which different types of machine learning approaches are still being explored. In this paper, we propose a system for detecting SMS spam using a semi-supervised novelty detection approach based on one class SVM classifier. The system is built as an anomaly detector that learns only from normal SMS messages thus enabling detection models to be implemented in the absence of labelled SMS spam training examples. We evaluated our proposed system using a benchmark dataset consisting of 747 SMS spam and 4827 nonspam messages. The results show that our proposed method outperformed the traditional supervised machine learning approaches based on binary, frequency or TF-IDF bag-of-words. The overall accuracy was 98% with 100% SMS spam detection rate and only around 3% false positive rate.