Reviewerly/exHarmony huggingface dataset:
Sequential Feature Explanations for Anomaly Detection

In many applications, an anomaly detection system presents the most anomalous data instance to a human analyst, who then must determine whether the instance is truly of interest (e.g., a threat in a security setting). Unfortunately, most anomaly detectors provide no explanation about why an instance was considered anomalous, leaving the analyst with no guidance about where to begin the investigation. To address this issue, we study the problems of computing and evaluating sequential feature explanations (SFEs) for anomaly detectors. An SFE of an anomaly is a sequence of features, which are presented to the analyst one at a time (in order) until the information contained in the highlighted features is enough for the analyst to make a confident judgement about the anomaly. Since analyst effort is related to the amount of information that they consider in an investigation, an explanation’s quality is related to the number of features that must be revealed to attain confidence. In this article, we first formulate the problem of optimizing SFEs for a particular density-based anomaly detector. We then present both greedy algorithms and an optimal algorithm, based on branch-and-bound search, for optimizing SFEs. Finally, we provide a large scale quantitative evaluation of these algorithms using a novel framework for evaluating explanations. The results show that our algorithms are quite effective and that our best greedy algorithm is competitive with optimal solutions.
Novel adaptive genetic algorithm sample consensus

Random Sample Consensus (RANSAC) is a successful algorithm in model fitting applications when there are numerous outliers within the dataset. Achieving a proper model is guaranteed through the pure exploration strategy of RANSAC. However, finding the optimum result requires exploitation. Genetic Algorithm Sample Consensus (GASAC) is an evolutionary paradigm which adds the exploitation capability to RANSAC. Although GASAC improves the results of RANSAC, it has a fixed strategy for balancing between exploration and exploitation. In this paper, a new paradigm is proposed based on genetic algorithms using an adaptive strategy. We propose an adaptive genetic operator to select the proper number of high fitness individuals as parents and mutate the rest. This operator can adjust the ratio of exploration vs. exploitation phases according to the amount of outliers. Also, a learning method is proposed for the mutation operator to gradually learn which gene is the best replacement for the mutated gene. This operator guides the exploration phase towards good solution areas and therefore produces better individuals for further exploitation. The proposed method is extensively evaluated in two sets of experiments. In all tests, our method outperformed the other methods in terms of both the number of inliers found and the speed of the algorithm.
Spectral-based convolutional neural network without multiple spatial-frequency domain switchings

Recent researches have shown that spectral representation provides a significant speed-up in the massive computation workload of convolution operations in the inference (feed-forward) algorithm of Convolutional Neural Networks (CNNs). This approach results in reducing the computational complexity of the classification task, which makes spectral-based CNN suitable for implementation on embedded platform that typically has constrained resources. However, a major challenge in this approach is that the mathematical formulation of a nonlinear activation function in spectral (frequency) domain is currently not available; hence, computation of the activation functions in each layer has to be performed in the spatial domain. This results in several spatial-frequency domain switchings that are computationally very costly, and as such, it would be advantageous to strictly stay in the frequency domain. Hence, in this work, a novel Spectral Rectified Linear Unit (SReLU) for the activation function is proposed, that makes it possible for the computations to remain in the frequency domain, and therefore avoids the multiple compute-intensive domain transformations. To further optimize the classification speed of the network, an efficient spectral-based CNN model is presented that uses only the lower frequency components by way of fusing the convolutional and sub-sampling layers. Additionally, we provide and utilize a frequency domain equivalent of the conventional batch normalization layer that results in improving the accuracy of the network. Experimental results indicate that the proposed spectral-based CNN model achieves up to 17.02 × and 3.45 × faster classification speed (without considerable accuracy loss) on AT&T face recognition and MNIST digit/fashion classification datasets, respectively, as compared to the equivalent models in the spatial domain, hence outperforming conventional approaches significantly.
Optimal Time-Complexity Speed Planning for Robot Manipulators

In this paper, we consider the speed planning problem for a robotic manipulator. In particular, we present an algorithm for finding the time-optimal speed law along an assigned path that satisfies velocity and acceleration constraints and respects the maximum forces and torques allowed by the actuators. The addressed optimization problem is a finite-dimensional reformulation of the continuous-time speed optimization problem, obtained by discretizing the speed profile with n points. The proposed algorithm has linear complexity with respect to n and to the number of degrees of freedom. Such complexity is the best possible for this problem. Numerical tests show that the proposed algorithm is significantly faster than algorithms already existing in literature.
Solution to the Time-Dependent Coupled Harmonic Oscillators Hamiltonian with Arbitrary Interactions

We show that by using the quantum orthogonal functions invariant, we found a solution to coupled time-dependent harmonic oscillators where all the time-dependent frequencies are arbitrary. This system may be found in many applications such as nonlinear and quantum physics, biophysics, molecular chemistry, and cosmology. We solve the time-dependent coupled harmonic oscillators by transforming the Hamiltonian of the interaction using a set of unitary operators. In passing, we show that N time-dependent and coupled oscillators have a generalized orthogonal functions invariant from which we can write a Ermakov–Lewis invariant.
Maximal Ferrers Diagram Codes: Constructions and Genericity Considerations

This paper investigates the construction of rankmetric codes with specified Ferrers diagram shapes. These codes play a role in the multilevel construction for subspace codes. A conjecture from 2009 provides an upper bound for the dimension of a rank-metric code with given specified Ferrers diagram shape and rank distance. While the conjecture in its generality is wide open, several cases have been established in the literature. This paper contributes further cases of Ferrers diagrams and ranks for which the conjecture holds true. In addition, the proportion of maximal Ferrers diagram codes within the space of all rank-metric codes with the same shape and dimension is investigated. Special attention is being paid to MRD codes. It is shown that for growing field size the limiting proportion depends highly on the Ferrers diagram. For instance, for [m × 2]-MRD codes with rank 2 this limiting proportion is close to 1/e.
Representation Mixing for TTS Synthesis

Recent character and phoneme-based parametric TTS systems using deep learning have shown strong performance in natural speech generation. However, the choice between character or phoneme input can create serious limitations for practical deployment, as direct control of pronunciation is crucial in certain cases. We demonstrate a simple method for combining multiple types of linguistic information in a single encoder, named representation mixing, enabling flexible choice between character, phoneme, or mixed representations during inference. Experiments and user studies on a public audiobook corpus show the efficacy of our approach.
Augmenting Java method comments generation with context information based on neural networks

Code comments are crucial to program comprehension. In this paper, we propose a novel approach ContextCC to automatically generate concise comments for Java methods based on neural networks, leveraging techniques of program analysis and natural language processing. Firstly, ContextCC employs program analysis techniques, especially abstract syntax tree parsing, to extract context information including methods and their dependency. Secondly, it filters code and comments out of the context information to build up a high-quality data set based on a set of pre-defined templates and rules. Finally, ContextCC trains a code comment generation model based on recurrent neural networks. Experiments are conducted on Java projects crawled from GitHub. We show empirically that the performance of ContextCC is superior to state-of-the-art baseline methods.
Propagating sentiment signals for estimating reputation polarity

The emergence of social media and the huge amount of opinions that are posted everyday have influenced online reputation management. Reputation experts need to filter and control what is posted online and, more importantly, determine if an online post is going to have positive or negative implications towards the entity of interest. This task is challenging, considering that there are posts that have implications on an entity's reputation but do not express any sentiment. In this paper, we propose two approaches for propagating sentiment signals to estimate reputation polarity of tweets. The first approach is based on sentiment lexicons augmentation, whereas the second is based on direct propagation of sentiment signals to tweets that discuss the same topic. In addition, we present a polar fact filter that is able to differentiate between reputation-bearing and reputation-neutral tweets. Our experiments indicate that weakly supervised annotation of reputation polarity is feasible and that sentiment signals can be propagated to effectively estimate the reputation polarity of tweets. Finally, we show that learning PMI values from the training data is the most effective approach for reputation polarity analysis.
Quantitative Analysis of Benign and Malignant Tumors in Histopathology: Predicting Prostate Cancer Grading Using SVM

An adenocarcinoma is a type of malignant cancerous tissue that forms from a glandular structure in epithelial tissue. Analyzed stained microscopic biopsy images were used to perform image manipulation and extract significant features for support vector machine (SVM) classification, to predict the Gleason grading of prostate cancer (PCa) based on the morphological features of the cell nucleus and lumen. Histopathology biopsy tissue images were used and categorized into four Gleason grade groups, namely Grade 3, Grade 4, Grade 5, and benign. The first three grades are considered malignant. K-means and watershed algorithms were used for color-based segmentation and separation of overlapping cell nuclei, respectively. In total, 400 images, divided equally among the four groups, were collected for SVM classification. To classify the proposed morphological features, SVM classification based on binary learning was performed using linear and Gaussian classifiers. The prediction model yielded an accuracy of 88.7% for malignant vs. benign, 85.0% for Grade 3 vs. Grade 4, 5, and 92.5% for Grade 4 vs. Grade 5. The SVM, based on biopsy-derived image features, consistently and accurately classified the Gleason grading of prostate cancer. All results are comparatively better than those reported in the literature.
An Energy and Performance Aware Consolidation Technique for Containerized Datacenters

Cloud datacenters have become a backbone for today’s business and economy, which are the fastest-growing electricity consumers, globally. Numerous studies suggest that <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 30% of the US datacenters are comatose and the others are grossly less-utilized, which make it possible to save energy through resource consolidation techniques. However, consolidation comprises migrations that are expensive in terms of energy consumption and performance degradation, which is mostly not accounted for in many existing models, and, possibly, it could be more energy and performance efficient not to consolidate. In this paper, we investigate how migration decisions should be taken so that the migration cost is recovered, as only when migration cost has been recovered and performance is guaranteed, will energy start to be saved. We demonstrate through several experiments, using the Google workload data for 12,583 hosts and approximately one million tasks that belong to three different kinds of workload, how different allocation policies, combined with various migration approaches, will impact on datacenter’s energy and performance efficiencies. Using several plausible assumptions for containerised datacenter set-up, we suggest, that a combination of the proposed energy-performance-aware allocation ( <small>Epc-Fu</small> ) and migration ( <small>Cper</small> ) techniques, and migrating relatively long-running containers only, offers for ideal energy and performance efficiencies.
Digital behavioral-fingerprint for user attribution in digital forensics: Are we there yet?

the need for a reliable and complementary identifier mechanism in a digital forensic analysis is the focus of this study. Mouse dynamics have been applied in information security studies, particularly, continuous authentication and authorization. However, the method applied in security is void of specific behavioral signature of a user, which inhibits its applicability in digital forensic science. This study investigated the likelihood of the observation of a unique signature from mouse dynamics of a computer user. An initial mouse path model was developed using non-finite automata. Thereafter, a set-theory based adaptive two-stage hash function and a multi-stage rule-based semantic algorithm were developed to observe the feasibility of a unique signature for forensic usage. An experimental process which comprises three existing mouse dynamics datasets were used to evaluate the applicability of the developed mechanism. The result showed a low likelihood of extracting unique behavioral signature which can be used in a user attribution process. Whilst digital forensic readiness mechanism could be a potential approach that can be used to achieve a reliable behavioral biometrics modality, the lack of unique signature presents a limitation. In addition, the result supports the logic that the current state of behavioral biometric modality, particularly mouse dynamics, is not suitable for forensic usage. Hence, the study concluded that whilst mouse dynamics-based behavioral biometrics may be a complementary modality in security studies, more will be required to adopt it as a forensic modality in litigation. Furthermore, the result from this study finds relevance in other human attributional studies such as user identification in recommender systems, e-commerce, and online profiling systems, where the degree of accuracy is not relatively high.
Classification of Mammograms Using Texture and CNN Based Extracted Features

In this paper, a modified adaptive K-means (MAKM) method is proposed to extract the region of interest (ROI) from the local and public datasets. The local image datasets are collected from Bethezata General Hospital (BGH) and the public datasets are from Mammographic Image Analysis Society (MIAS). The same image number is used for both datasets, 112 are abnormal and 208 are normal. Two texture features (GLCM and Gabor) from ROIs and one CNN based extracted features are considered in the experiment. CNN features are extracted using Inception-V3 pre-trained model after simple preprocessing and cropping. The quality of the features are evaluated individually and by fusing features to one another and five classifiers (SVM, KNN, MLP, RF, and NB) are used to measure the descriptive power of the features using cross-validation. The proposed approach was first evaluated on the local dataset and then applied to the public dataset. The results of the classifiers are measured using accuracy, sensitivity, specificity, kappa, computation time and AUC. The experimental analysis made using GLCM features from the two datasets indicates that GLCM features from BGH dataset outperformed that of MIAS dataset in all five classifiers. However, Gabor features from the two datasets scored the best result with two classifiers (SVM and MLP). For BGH and MIAS, SVM scored an accuracy of 99%, 97.46%, the sensitivity of 99.48%, 96.26% and specificity of 98.16%, 100% respectively. And MLP achieved an accuracy of 97%, 87.64%, the sensitivity of 97.40%, 96.65% and specificity of 96.26%, 75.73% respectively. Relatively maximum performance is achieved for feature fusion between Gabor and CNN based extracted features using MLP classifier. However, KNN, MLP, RF, and NB classifiers achieved almost 100% performance for GLCM texture features and SVM scored an accuracy of 96.88%, the sensitivity of 97.14% and specificity of 96.36%. As compared to other classifiers, NB has scored the least computation time in all experiments.
A general approach for cure models in survival analysis

In survival analysis it often happens that some subjects under study do not experience the event of interest; they are considered to be "cured."The population is thus a mixture of two subpopulations, one of cured subjects and one of "susceptible" subjects.We propose a novel approach to estimate a mixture cure model when covariates are present and the lifetime is subject to random right censoring.We work with a parametric model for the cure proportion, while the conditional survival function of the uncured subjects is unspecified.The approach is based on an inversion which allows us to write the survival function as a function of the distribution of the observable variables.This leads to a very general class of models which allows a flexible and rich modeling of the conditional survival function.We show the identifiability of the proposed model as well as the consistency and the asymptotic normality of the model parameters.We also consider in more detail the case where kernel estimators are used for the nonparametric part of the model.The new estimators are compared with the estimators from a Cox mixture cure model via simulations.Finally, we apply the new model on a medical data set.
Connected Reading: A Framework for Understanding How Adolescents Encounter, Evaluate, and Engage With Texts in the Digital Age

Abstract Since the emergence of the World Wide Web and e‐reading devices in the late 1990s and early 2000s, reading research has focused on issues of website credibility, search and navigation strategies, and the ability to comprehend text on‐screen as compared with in print. What has been missing, however, are data about the specific texts that adolescents are reading in these digital spaces, what devices they prefer, and the strategies that they employ. Drawing from survey data ( N = 804) and interviews ( n = 23) with participants in grades 7–12 from 12 suburban, urban, and rural schools across the United States, the authors sought to explore what, where, and how adolescents read digitally. The authors propose a new framework of connected reading, a model of print and digital reading comprehension that conceptualizes readers’ interactions with digital texts through encountering (the ways in which readers seek or receive digital texts), evaluating (the ways in which readers make judgments about the usefulness of digital texts), and engaging (the ways in which readers interact with and share digital texts). In light of the findings, the authors argue that it is imperative to reframe discussions about how adolescents are taught to comprehend and interact with a variety of digital texts (e.g., webpages, e‐books, multimedia, social media).
Data agency at stake: MyData activism and alternative frames of equal participation

Data activism has emerged as a response to asymmetries in how data and the means of knowledge production are distributed. This article examines MyData, a data activism initiative developing principles for a new technical and commercial ecosystem in which individuals control the use of personal data. Analyzing material collected at a formative event shaping MyData activism, we examine how more just data arrangements are framed to enhance equal participation. Our analysis shows agreement on what is ultimately at stake: individual data agency and fair competition in the data economy. However, two alternatives are offered for what participation involves. Collaboration with commercial actors favors framing participation as agency in data markets, thereby potentially limiting the scope of what is at stake. The alternative framing presents a rights-based understanding of economic and civic agency, potentially leading to a broader understanding of participation in a datafied society.
User Interactions for Augmented Reality Smart Glasses: A Comparative Evaluation of Visual Contexts and Interaction Gestures

Smart glasses for wearable augmented reality (AR) are widely used in various applications, such as training and task assistance. However, as the field of view (FOV) in the current AR smart glasses is narrow, it is difficult to visualize all the information on the AR display. Besides, only simple interactions are supported. This paper presents a comparative and substantial evaluation of user interactions for wearable AR concerning visual contexts and gesture interactions using AR smart glasses. Based on the evaluation, it suggests new guidelines for visual augmentation focused on task assistance. Three different types of visual contexts for wearable AR were implemented and evaluated: stereo rendering and direct augmentation, and non-stereo rendering and indirect augmentation with/without video background. Also, gesture interactions, such as multi-touch interaction and hand gesture-based interaction, were implemented and evaluated. We performed quantitative and qualitative analyses, including performance measurement and questionnaire evaluation. The experimental assessment proves that both FOV and visual registration between virtual and physical artifacts are important, and they can complement each other. Hand gesture-based interaction can be more intuitive and useful. Therefore, by analyzing the advantages and disadvantages of the visual context and gesture interaction in wearable AR, this study suggests more effective and user-centric guidance for task assistance.