Anomaly Identification Model for Telecom Users Based on Machine Learning Model Fusion

With the development of economic globalization and modern information and communication technology, the situation of communication fraud is becoming more and more serious. How to identify fraudulent calls accurately and effectively has become an urgent task in current telecommunications operations. Affected by the sample set and the current state of the art, the current machine learning methods used to identify the imbalanced distribution dataset of positive and negative samples have low recognition accuracy. Therefore, in this paper, we propose a new hybrid model solution that uses feature construction, feature selection and imbalanced classes handling. A stacking model fusion algorithm composed of a two-layer stacking framework with several state-of-the-art machine learning classifiers is adopted. The results show that the risk user identification model based on mobile network communication behavior established by our stacking model fusion algorithm can accurately predict the category labels of telecom users and improve the risk of telecom users. The generalization performance of the identification is high, which provides a certain reference for the telecommunications industry to identify risk users based on mobile network communication behaviors

Multimodal Classification of Urban Micro-Events

In this paper we seek methods to effectively detect urban micro-events. Urban micro-events are events which occur in cities, have limited geographical coverage and typically affect only a small group of citizens. Because of their scale these are difficult to identify in most data sources. However, by using citizen sensing to gather data, detecting them becomes feasible. The data gathered by citizen sensing is often multimodal and, as a consequence, the information required to detect urban micro-events is distributed over multiple modalities. This makes it essential to have a classifier capable of combining them. In this paper we explore several methods of creating such a classifier, including early, late, hybrid fusion and representation learning using multimodal graphs. We evaluate performance on a real world dataset obtained from a live citizen reporting system. We show that a multimodal approach yields higher performance than unimodal alternatives. Furthermore, we demonstrate that our hybrid combination of early and late fusion with multimodal embeddings performs best in classification of urban micro-events

Compressive Sequential Learning for Action Similarity Labeling

Human action recognition in videos has been extensively studied in recent years due to its wide range of applications. Instead of classifying video sequences into a number of action categories, in this paper, we focus on a particular problem of action similarity labeling (ASLAN), which aims at verifying whether a pair of videos contain the same type of action or not. To address this challenge, a novel approach called compressive sequential learning (CSL) is proposed by leveraging the compressive sensing theory and sequential learning. We first project data points to a low-dimensional space by effectively exploring an important property in compressive sensing: the restricted isometry property. In particular, a very sparse measurement matrix is adopted to reduce the dimensionality efficiently. We then learn an ensemble classifier for measuring similarities between pairwise videos by iteratively minimizing its empirical risk with the AdaBoost strategy on the training set. Unlike conventional AdaBoost, the weak learner for each iteration is not explicitly defined and its parameters are learned through greedy optimization. Furthermore, an alternative of CSL named compressive sequential encoding is developed as an encoding technique and followed by a linear classifier to address the similarity-labeling problem. Our method has been systematically evaluated on four action data sets: ASLAN, KTH, HMDB51, and Hollywood2, and the results show the effectiveness and superiority of our method for ASLAN

A Literature Review of Fault Diagnosis Based on Ensemble Learning

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance

Machine Learning Architectures for Video Annotation and Retrieval

PhDIn this thesis we are designing machine learning methodologies for solving the problem of video annotation and retrieval using either pre-defined semantic concepts or ad-hoc queries. Concept-based video annotation refers to the annotation of video fragments with one or more semantic concepts (e.g. hand, sky, running), chosen from a predefined concept list. Ad-hoc queries refer to textual descriptions that may contain objects, activities, locations etc., and combinations of the former. Our contributions are: i) A thorough analysis on extending and using different local descriptors towards improved concept-based video annotation and a stacking architecture that uses in the first layer, concept classifiers trained on local descriptors and improves their prediction accuracy by implicitly capturing concept relations, in the last layer of the stack. ii) A cascade architecture that orders and combines many classifiers, trained on different visual descriptors, for the same concept. iii) A deep learning architecture that exploits concept relations at two different levels. At the first level, we build on ideas from multi-task learning, and propose an approach to learn concept-specific representations that are sparse, linear combinations of representations of latent concepts. At a second level, we build on ideas from structured output learning, and propose the introduction, at training time, of a new cost term that explicitly models the correlations between the concepts. By doing so, we explicitly model the structure in the output space (i.e., the concept labels). iv) A fully-automatic ad-hoc video search architecture that combines concept-based video annotation and textual query analysis, and transforms concept-based keyframe and query representations into a common semantic embedding space. Our architectures have been extensively evaluated on the TRECVID SIN 2013, the TRECVID AVS 2016, and other large-scale datasets presenting their effectiveness compared to other similar approaches

AntiPhishStack: LSTM-based Stacked Generalization Model for Optimized Phishing URL Detection

The escalating reliance on revolutionary online web services has introduced heightened security risks, with persistent challenges posed by phishing despite extensive security measures. Traditional phishing systems, reliant on machine learning and manual features, struggle with evolving tactics. Recent advances in deep learning offer promising avenues for tackling novel phishing challenges and malicious URLs. This paper introduces a two-phase stack generalized model named AntiPhishStack, designed to detect phishing sites. The model leverages the learning of URLs and character-level TF-IDF features symmetrically, enhancing its ability to combat emerging phishing threats. In Phase I, features are trained on a base machine learning classifier, employing K-fold cross-validation for robust mean prediction. Phase II employs a two-layered stacked-based LSTM network with five adaptive optimizers for dynamic compilation, ensuring premier prediction on these features. Additionally, the symmetrical predictions from both phases are optimized and integrated to train a meta-XGBoost classifier, contributing to a final robust prediction. The significance of this work lies in advancing phishing detection with AntiPhishStack, operating without prior phishing-specific feature knowledge. Experimental validation on two benchmark datasets, comprising benign and phishing or malicious URLs, demonstrates the model's exceptional performance, achieving a notable 96.04% accuracy compared to existing studies. This research adds value to the ongoing discourse on symmetry and asymmetry in information security and provides a forward-thinking solution for enhancing network security in the face of evolving cyber threats

A Social Network Image Classification Algorithm Based on Multimodal Deep Learning

The complex data structure and massive image data of social networks pose a huge challenge to the mining of associations between social information. For accurate classification of social network images, this paper proposes a social network image classification algorithm based on multimodal deep learning. Firstly, a social network association clustering model (SNACM) was established, and used to calculate trust and similarity, which represent the degree of similarity between users. Based on artificial ant colony algorithm, the SNACM was subject to weighted stacking, and the social network image association network was constructed. After that, the social network images of three modes, i.e. RGB (red-green-blue) image, grayscale image, and depth image, were fused. Finally, a three-dimensional neural network (3D NN) was constructed to extract the features of the multimodal social network image. The proposed algorithm was proved valid and accurate through experiments. The research results provide a reference for applying multimodal deep learning to classify the images in other fields

Disaster Analysis using Satellite Image Data with Knowledge Transfer and Semi-Supervised Learning Techniques

With the increase in frequency of disasters and crisis situations like floods, earthquake and hurricanes, the requirement to handle the situation efficiently through disaster response and humanitarian relief has increased. Disasters are mostly unpredictable in nature with respect to their impact on people and property. Moreover, the dynamic and varied nature of disasters makes it difficult to predict their impact accurately for advanced preparation of responses [104]. It is also notable that the economical loss due to natural disasters has increased in recent years, and it, along with the pure humanitarian need, is one of the reasons to research innovative approaches to the mitigation and management of disaster operations efficiently [1]

Learning from limited labeled data - Zero-Shot and Few-Shot Learning

Human beings have the remarkable ability to recognize novel visual concepts after observing only few or zero examples of them. Deep learning, however, often requires a large amount of labeled data to achieve a good performance. Labeled instances are expensive, difficult and even infeasible to obtain because the distribution of training instances among labels naturally exhibits a long tail. Therefore, it is of great interest to investigate how to learn efficiently from limited labeled data. This thesis concerns an important subfield of learning from limited labeled data, namely, low-shot learning. The setting assumes the availability of many labeled examples from known classes and the goal is to learn novel classes from only a few~(few-shot learning) or zero~(zero-shot learning) training examples of them. To this end, we have developed a series of multi-modal learning approaches to facilitate the knowledge transfer from known classes to novel classes for a wide range of visual recognition tasks including image classification, semantic image segmentation and video action recognition. More specifically, this thesis mainly makes the following contributions. First, as there is no agreed upon zero-shot image classification benchmark, we define a new benchmark by unifying both the evaluation protocols and data splits of publicly available datasets. Second, in order to tackle the labeled data scarcity, we propose feature generation frameworks that synthesize data in the visual feature space for novel classes. Third, we extend zero-shot learning and few-shot learning to the semantic segmentation task and propose a challenging benchmark for it. We show that incorporating semantic information into a semantic segmentation network is effective in segmenting novel classes. Finally, we develop better video representation for the few-shot video classification task and leverage weakly-labeled videos by an efficient retrieval method.Menschen haben die bemerkenswerte Fähigkeit, neuartige visuelle Konzepte zu erkennen, nachdem sie nur wenige oder gar keine Beispiele davon beobachtet haben. Tiefes Lernen erfordert jedoch oft eine große Menge an beschrifteten Daten, um eine gute Leistung zu erzielen. Etikettierte Instanzen sind teuer, schwierig und sogar undurchführbar, weil die Verteilung der Trainingsinstanzen auf die Etiketten naturgemäß einen langen Schwanz aufweist. Daher ist es von großem Interesse zu untersuchen, wie man effizient aus begrenzten gelabelten Daten lernen kann. Diese These betrifft einen wichtigen Teilbereich des Lernens aus begrenzt gelabelten Daten, nämlich das Low-Shot-Lernen. Das Setting setzt die Verfügbarkeit vieler gelabelter Beispiele aus bekannten Klassen voraus, und das Ziel ist es, neuartige Klassen aus nur wenigen (few-shot learning) oder null (zero-shot learning) Trainingsbeispielen davon zu lernen. Zu diesem Zweck haben wir eine Reihe von multimodalen Lernansätzen entwickelt, um den Wissenstransfer von bekannten Klassen zu neuartigen Klassen für ein breites Spektrum von visuellen Erkennungsaufgaben zu erleichtern, darunter Bildklassifizierung, semantische Bildsegmentierung und Videoaktionserkennung. Genauer gesagt, leistet diese Arbeit hauptsächlich die folgenden Beiträge. Da es keinen vereinbarten Benchmark für die Zero-Shot- Bildklassifikation gibt, definieren wir zunächst einen neuen Benchmark, indem wir sowohl die Evaluierungsprotokolle als auch die Datensplits öffentlich zugänglicher Datensätze vereinheitlichen. Zweitens schlagen wir zur Bewältigung der etikettierten Datenknappheit einen Rahmen für die Generierung von Merkmalen vor, der Daten im visuellen Merkmalsraum für neuartige Klassen synthetisiert. Drittens dehnen wir das Zero-Shot-Lernen und das few-Shot-Lernen auf die semantische Segmentierungsaufgabe aus und schlagen dafür einen anspruchsvollen Benchmark vor. Wir zeigen, dass die Einbindung semantischer Informationen in ein semantisches Segmentierungsnetz bei der Segmentierung neuartiger Klassen effektiv ist. Schließlich entwickeln wir eine bessere Videodarstellung für die Klassifizierungsaufgabe ”few-shot video” und nutzen schwach markierte Videos durch eine effiziente Abrufmethode.Max Planck Institute Informatic