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Quantification of all types of uncertainty helps to establish reliability in any analysis. This research focuses on uncertainty in two attribute levels of wetland classification and creates visualization tools to guide analysis of spatial uncertainty patterns over several scales. A novel variant of confusion matrix analysis compares the Cowardin and Hydrogeomorphic wetland classification systems, identifying areas and types of misclassification for binary and multivariate categories. The specific focus on uncertainty in the paper refers to categorical consistency, that is, agreement between the two classification systems, rather than comparing observed data to ground truth. Consistency is quantified using confusion matrix analysis. Aggregation across progressive focal windows transforms the confusion matrix into a multiscale data pyramid for quick determination of where attribute uncertainty is highly variant, and at what spatial resolutions classification inconsistencies emerge. The focal pyramids summarize precision, recall, and F1 scores to visualize classification differences across spatial scales. Findings show that the F1 scores appear most informative on agreement about wetlands misclassification at both coarse and fine attribute scales. The pyramid organizes multi-scale uncertainty in a single unified framework and can be "sliced" to view individual focal levels of attribute consistency. Results demonstrate how the confusion matrix can be used to quantify the percentage of a study area in which inconsistencies occur reflecting wetland presence and type. The research provides confusion metrics and display tools to focus attention on specific areas of large data sets where attribute uncertainty patterns may be complex, thus reducing land managers' workloads by highlighting areas of uncertainty where field checking might be appropriate, and improving analytics by providing visualization tools to quickly see where such areas occur. [ABSTRACT FROM AUTHOR]

Ransomware is a type of malicious software that encrypts a victim's files and demands payment in exchange for the decryption key. It is a rapidly growing and evolving threat that has caused significant damage and disruption to individuals and organizations around the world. In this paper, we propose a comprehensive ransomware classification approach based on the comparison of similarity matrices derived from static, dynamic analysis, and visualization. Our approach involves the use of multiple analysis techniques to extract features from ransomware samples and to generate similarity matrices based on these features. These matrices are then compared using a variety of comparison algorithms to identify similarities and differences between the samples. The resulting similarity scores are then used to classify the samples into different categories, such as families, variants, and versions. We evaluate our approach using a dataset of ransomware samples and demonstrate that it can accurately classify the samples with a high degree of accuracy. One advantage of our approach is the use of visualization, which allows us to classify and cluster large datasets of ransomware in a more intuitive and effective way. In addition, static analysis has the advantage of being fast and accurate, while dynamic analysis allows us to classify and cluster packed ransomware samples. We also compare our approach to other classification approaches based on single analysis techniques and show that our approach outperforms these approaches in terms of classification accuracy. Overall, our study demonstrates the potential of using a comprehensive approach based on the comparison of multiple analysis techniques, including static analysis, dynamic analysis, and visualization, for the accurate and efficient classification of ransomware. It also highlights the importance of considering multiple analysis techniques in the development of effective ransomware classification methods, especially when dealing with large datasets and packed samples. [ABSTRACT FROM AUTHOR]

Twitter produces a massive amount of data due to its popularity that is one of the reasons underlying big data problems. One of those problems is the classification of tweets due to use of sophisticated and complex language, which makes the current tools insufficient. We present our framework HTwitt, built on top of the Hadoop ecosystem, which consists of a MapReduce algorithm and a set of machine learning techniques embedded within a big data analytics platform to efficiently address the following problems: (1) traditional data processing techniques are inadequate to handle big data; (2) data preprocessing needs substantial manual effort; (3) domain knowledge is required before the classification; (4) semantic explanation is ignored. In this work, these challenges are overcome by using different algorithms combined with a Naïve Bayes classifier to ensure reliability and highly precise recommendations in virtualization and cloud environments. These features make HTwitt different from others in terms of having an effective and practical design for text classification in big data analytics. The main contribution of the paper is to propose a framework for building landslide early warning systems by pinpointing useful tweets and visualizing them along with the processed information. We demonstrate the results of the experiments which quantify the levels of overfitting in the training stage of the model using different sizes of real-world datasets in machine learning phases. Our results demonstrate that the proposed system provides high-quality results with a score of nearly 95% and meets the requirement of a Hadoop-based classification system. [ABSTRACT FROM AUTHOR]

Crime prediction is an effort to cut down the crime rate and criminal activities in the society. This paper concentrates on monitoring the crime trends in India using data from 2001 to 2019. The work analyzes the crime condition in India by following a top-to bottom approach and implementing visualization and machine learning algorithms. Heat map visualization of India is done to focus on specific crime types for all states. Furthermore, various demography of human population like age-groups, sex, caste, etc., are considered to have a larger perspective of the issue. The model also suggest proactive measures based on these findings. [ABSTRACT FROM AUTHOR]

Glaucoma is a progressive eye condition that can lead to permanent vision loss. Therefore, on-time detection of glaucoma is critical for making an effective treatment plan. In recent years, enormous attempts have been made to develop automated glaucoma classification systems using CNNs through images. In contrast, limited methods have been proposed for diagnosing different glaucoma stages. It is mainly owing to the lack of large publicly available labeled datasets. Also, fundus images exhibit a high inter-stage resemblance, redundant features and minute size variations of lesions, making the conventional CNNs difficult to classify multiple stages of glaucoma accurately. To address these challenges, this paper proposes a novel adapter and enhanced self-attention based CNN framework named AES-Net for effective classification of glaucoma stages. In particular, we propose a spatial adapter module on top of the backbone network for learning better feature representations and an enhanced self-attention module (ESAM) to capture global feature correlations among the relevant channels and spatial positions. The ESAM assists in capturing stage-specific and detailed-lesion features from the fundus images. Extensive experiments on two multi-stage glaucoma datasets indicate that our AES-Net surpasses CNN-based existing approaches. The Grad-CAM ++ visualization maps further confirm the effectiveness of our AES-Net. • A spatial adapter and enhanced self-attention based CNN, AES-Net, is proposed. • An enhanced self-attention module is proposed to learn class-specific features. • A comparative analysis with state-of-the-art attention mechanisms is performed. • Results on two datasets indicate the effectiveness of AES-Net over current methods. • The visualization results demonstrate the explainability and interpretability of the model. [ABSTRACT FROM AUTHOR]

Constructing adversarial examples in a black-box threat model injures the original images by introducing visual distortion. In this paper, we propose a novel black-box attack approach that can directly minimize the induced distortion by learning the noise distribution of the adversarial example, assuming only loss-oracle access to the black-box network. To quantify visual distortion, the perceptual distance between the adversarial example and the original image, is introduced in our loss. We first approximate the gradient of the corresponding non-differentiable loss function by sampling noise from the learned noise distribution. Then the distribution is updated using the estimated gradient to reduce visual distortion. The learning continues until an adversarial example is found. We validate the effectiveness of our attack on ImageNet. Our attack results in much lower distortion when compared to the state-of-the-art black-box attacks and achieves 100% success rate on InceptionV3, ResNet50 and VGG16bn. Furthermore, we theoretically prove the convergence of our model. The code is publicly available at https://github.com/Alina-1997/visual-distortion-in-attack. [ABSTRACT FROM AUTHOR]

Image editing technology leads to tampering traces hidden in images hard to be identified, which results in great challenges for image manipulation detection. Therefore, it is necessary to enhance the characteristic response of tampering traces. In this paper, a two-stream network is exploited to extract tampering features from both semantic level and noise level through end-to-end training. At the input end of the network, a multi-scale high-frequency constrained convolution kernel module is proposed to extract multi-channel and multi-scale noise fingerprint information, which enhances the tampering traces. Moreover, we leverage multi-task learning to realize the classification of tampering techniques, the detection and segmentation of tampering regions. The tampered segmentation mask attention is introduced as a novel constraint to be fed back to the detection branch, which further enhances the tampering characteristics and improves the detection performance. Experiments on COVER, CASIA and NIST16 datasets prove that the proposed framework outperforms existing methods quantitatively and qualitatively. The proposed method effectively enhances the characteristics of tampered regions. Moreover, the proposed method has high robustness on JPEG recompression and image resize. • We proposed a multi-scale high-frequency constrained convolution kernel module to extract fingerprint noise information end-to-end, which exploits the domain knowledge of camera model identification. The module expands the receptive field and highlights the contrast of tampering traces at different scales and different channels, which emphasizes the characteristic representation of the tampering traces. • The segmentation mask attention is introduced as a novel constraint to be fed back to the detection branch. The network is forced to pay more attention to the tampered area and enhances the tampering characteristics. The visualization results show that segmentation features can focus on the tampered areas. • The tasks of classification, detection and segmentation are combined as multi-task learning, sharing the characteristics of different tasks to form constraints. The detection performance is further improved. • Extensive experiments on three standard datasets demonstrate that our method outperforms other methods significantly. Moreover, the proposed method has high robustness on JPEG recompression and image resize. [ABSTRACT FROM AUTHOR]

The assignment of labels to data instances is a fundamental prerequisite for many machine learning tasks. Moreover, labeling is a frequently applied process in visual interactive analysis approaches and visual analytics. However, the strategies for creating labels usually differ between these two fields. This raises the question whether synergies between the different approaches can be attained. In this paper, we study the process of labeling data instances with the user in the loop, from both the machine learning and visual interactive perspective. Based on a review of differences and commonalities, we propose the “visual interactive labeling” (VIAL) process that unifies both approaches. We describe the six major steps of the process and discuss their specific challenges. Additionally, we present two heterogeneous usage scenarios from the novel VIAL perspective, one on metric distance learning and one on object detection in videos. Finally, we discuss general challenges to VIAL and point out necessary work for the realization of future VIAL approaches. [ABSTRACT FROM AUTHOR]

Feature extraction transforms high-dimensional data into a new subspace of lower dimensionality while keeping the classification accuracy. Traditional algorithms do not consider the multi-objective nature of this task. Data transformations should improve the classification performance on the new subspace, as well as to facilitate data visualization, which has attracted increasing attention in recent years. Moreover, new challenges arising in data mining, such as the need to deal with imbalanced data sets call for new algorithms capable of handling this type of data. This paper presents a Pareto-based multi-objective genetic programming algorithm for feature extraction and data visualization. The algorithm is designed to obtain data transformations that optimize the classification and visualization performance both on balanced and imbalanced data. Six classification and visualization measures are identified as objectives to be optimized by the multi-objective algorithm. The algorithm is evaluated and compared to 11 well-known feature extraction methods, and to the performance on the original high-dimensional data. Experimental results on 22 balanced and 20 imbalanced data sets show that it performs very well on both types of data, which is its significant advantage over existing feature extraction algorithms. [ABSTRACT FROM AUTHOR]

This paper explores the task of real-time identification of dynamic events leading to a layer of situational awareness that can become a reality due to increased penetration of phasor measurement units in transmission systems. Two underlying models for this task—data driven and physics based—are explored with examples. Challenges, advantages, and drawbacks of each model are discussed based on the availability of data, attributes of such data, and processing options. Potential applications of the task to improve security of power system protection and anomaly detection in the case of a cyberattack are conceptualized. Some known issues in data communications are discussed vis-a-vis the requirements imposed by the proposed task. [ABSTRACT FROM AUTHOR]

Research in the field of supervised classification has mostly focused on the standard, so-called 'flat' classification approach, where the problem classes live in a trivial, one-level semantic space. There is however an increasing interest in the hierarchical classification approach, where a performance gain is expected by incorporating prior taxonomic knowledge about the classes into the learning process. Intuitively, the hierarchical approach should be beneficial in general for the classification of visual content, as suggested by the fact that humans seem to organize objects into hierarchies based on visually perceived similarities. In this paper, we provide an analysis that aims to determine the conditions under which the hierarchical approach can consistently give better performances than the flat approach for the classification of visual content. In particular, we (1) show how hierarchical methods can fail to outperform flat methods when applied to real vision-based classification problems, and (2) investigate the underlying reasons for the lack of improvement, by applying the same methods to synthetic datasets in a simulation. Our conclusion is that the use of high-level hierarchical feature representations is crucial for obtaining a performance gain with the hierarchical approach, and that poorly chosen prior taxonomies hinder this gain even though proper high-level features are used. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to visualize the structure of SCImago Journal & Country Rank (SJR) coverage of the extensive citation network of Scopus journals, examining this bibliometric portal through an alternative approach, applying clustering and visualization techniques to a combination of citation-based links.Design/methodology/approach Three SJR journal-journal networks containing direct citation, co-citation and bibliographic coupling links are built. The three networks were then combined into a new one by summing up their values, which were later normalized through geo-normalization measure. Finally, the VOS clustering algorithm was executed and the journal clusters obtained were labeled using original SJR category tags and significant words from journal titles.Findings The resultant scientogram displays the SJR structure through a set of communities equivalent to SJR categories that represent the subject contents of the journals they cover. A higher level of aggregation by areas provides a broad view of the SJR structure, facilitating its analysis and visualization at the same time.Originality/value This is the first study using Persson’s combination of most popular citation-based links (direct citation, co-citation and bibliographic coupling) in order to develop a scientogram based on Scopus journals from SJR. The integration of the three measures along with performance of the VOS community detection algorithm gave a balanced set of clusters. The resulting scientogram is useful for assessing and validating previous classifications as well as for information retrieval and domain analysis. [ABSTRACT FROM AUTHOR]

Isomap aims to recover the intrinsic geometric structure of manifold by preserving geodesic distances between all pairs of data points. However it is an unsupervised dimensionality-reduction method. Usually, using class label information can increase the discriminating capability, hence a new supervised Isomap is proposed in this paper, dubbed Multi-manifold Discriminant Isomap (MMD-Isomap). First, data points are partitioned into different manifolds according to their class label information. Then, MMD-Isomap aims at seeking an optimal nonlinear subspace to preserve the geometrical structure of each manifold according to the Isomap criterion, meanwhile, to enhance the discriminating capability by maximizing the distances between data points of different manifolds. Finally, the corresponding optimization problem is solved by using a majorization algorithm. Furthermore, two new numerical metrics are designed to measure the performance of dimensionality-reduction method. In both visualization and classification experiments, MMD-Isomap achieves improved performance over many state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Social determining factors such as the adverse influence of globalization, supermarket growth, fast unplanned urbanization, sedentary lifestyle, economy, and social position slowly develop behavioral risk factors in humans. Behavioral risk factors such as unhealthy habits, improper diet, and physical inactivity lead to physiological risks, and "obesity/overweight" is one of the consequences. "Obesity and overweight" are one of the major lifestyle diseases that leads to other health conditions, such as cardiovascular diseases (CVDs), chronic obstructive pulmonary disease (COPD), cancer, diabetes type II, hypertension, and depression. It is not restricted within the age and socio-economic background of human beings. The "World Health Organization" (WHO) has anticipated that 30% of global death will be caused by lifestyle diseases by 2030 and it can be prevented with the appropriate identification of associated risk factors and behavioral intervention plans. Health behavior change should be given priority to avoid life-threatening damages. The primary purpose of this study is not to present a risk prediction model but to provide a review of various machine learning (ML) methods and their execution using available sample health data in a public repository related to lifestyle diseases, such as obesity, CVDs, and diabetes type II. In this study, we targeted people, both male and female, in the age group of >20 and <60, excluding pregnancy and genetic factors. This paper qualifies as a tutorial article on how to use different ML methods to identify potential risk factors of obesity/overweight. Although institutions such as "Center for Disease Control and Prevention (CDC)" and "National Institute for Clinical Excellence (NICE)" guidelines work to understand the cause and consequences of overweight/obesity, we aimed to utilize the potential of data science to assess the correlated risk factors of obesity/overweight after analyzing the existing datasets available in "Kaggle" and "University of California, Irvine (UCI) database", and to check how the potential risk factors are changing with the change in body-energy imbalance with data-visualization techniques and regression analysis. Analyzing existing obesity/overweight related data using machine learning algorithms did not produce any brand-new risk factors, but it helped us to understand: (a) how are identified risk factors related to weight change and how do we visualize it? (b) what will be the nature of the data (potential monitorable risk factors) to be collected over time to develop our intended eCoach system for the promotion of a healthy lifestyle targeting "obesity and overweight" as a study case in the future? (c) why have we used the existing "Kaggle" and "UCI" datasets for our preliminary study? (d) which classification and regression models are performing better with a corresponding limited volume of the dataset following performance metrics? [ABSTRACT FROM AUTHOR]