Reviewerly/exHarmony huggingface dataset:
Improving CNN linear layers with power mean non-linearity

Nowadays, Convolutional Neural Network (CNN) has achieved great success in various computer vision tasks. However, in classic CNN models, convolution and fully connected (FC) layers just perform linear transformations to their inputs. Non-linearity is often added by activation and pooling layers. It is natural to explore and extend convolution and FC layers non-linearly with affordable costs. In this paper, we first investigate the power mean function, which is proved effective and efficient in SVM kernel learning. Then, we investigate the power mean kernel, which is a non-linear kernel having linear computational complexity with the asymmetric kernel approximation function. Motivated by this scalable kernel, we propose Power Mean Transformation, which nonlinearizes both convolution and FC layers. It only needs a small modification on current CNNs, and improves the performance with a negligible increase of model size and running time. Experiments on various tasks show that Power Mean Transformation can improve classification accuracy, bring generalization ability and add different non-linearity to CNN models. Large performance gain on tiny models shows that Power Mean Transformation is especially effective in resource restricted deep learning scenarios like mobile applications. Finally, we add visualization experiments to illustrate why Power Mean Transformation works.
&lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$L_{21}$ &lt;/tex-math&gt; &lt;/inline-formula&gt;-Norm Based Loss Function and Regularization Extreme Learning Machine

Extreme learning machine (ELM) has gained increasing interests from various research fields recently.Researchers have proposed various extensions to improve its stability, sparsity, and generalization performance.In this paper, we propose a robust and sparse ELM to exploit L 21 -norm minimization of both loss function and regularization (LR21-ELM).Our L 21 -norm-based loss function can diminish the undue influence of noises and outliers of data points compared with the L 2 -norm based loss function and make the learned ELM model more robust and stable.The powerful structural sparse-inducing L 21 -norm regularization is integrated into the ELM objective function to eliminate the potential redundant neurons of ELM adaptively and reduce the complexity of the learning model.We introduce an effective iterative optimization algorithm to solve the L 21 -norm minimization problem.Empirical tests on a number of benchmark datasets indicate that our proposed algorithm can generate a more compact, robust, and discriminative model compared with the original ELM algorithm.
“This is the way ‘I’ create my passwords” ... does the endowment effect deter people from changing the way they create their passwords?

The endowment effect is the term used to describe a phenomenon that manifests as a reluctance to relinquish owned artifacts, even when a viable or better substitute is offered.It has been confirmed by multiple studies when it comes to ownership of physical artifacts. If computer users also "own", and are attached to, their personal security routines, such feelings could conceivably activate the same endowment effect. This would, in turn, lead to their over-estimating the "value" of their existing routines, in terms of the protection they afford, and the risks they mitigate. They might well, as a consequence, not countenance any efforts to persuade them to adopt a more secure routine, because their comparison of pre-existing and proposed new routine is skewed by the activation of the endowment effect. In this paper, we report on an investigation into the possibility that the endowment effect activates when people adopt personal password creation routines. We did indeed find evidence that the endowment effect is likely to be triggered in this context. This constitutes one explanation for the failure of many security awareness drives to improve password strength. We conclude by suggesting directions for future research to confirm our findings, and to investigate the activation of the effect for other security routines.
Personalization Recommendation Algorithm Based on Trust Correlation Degree and Matrix Factorization

The rapid development of the Internet of Things (IoT) and e-commerce has brought a lot of convenience to people's lives. IoT applications generate a large number of services and user data. It is necessary to design a personalized recommendation technology suitable for the users of IoT services and improve the user experience. In this paper, a recommendation algorithm with trusted relevance combined with matrix factorization is proposed. By establishing an effective trust metric model, the user's social information is integrated into the recommendation algorithm. First, the social network concentric hierarchical model is used to consider the direct or indirect trust relationship, and more trust information is integrated for the matrix factorization recommendation algorithm. Then, we design the trust relevance, comprehensively considering the trust factors and interest similar factors. Our experiments were performed on the Dianping datasets. The recommendation algorithm using matrix factorization and trusted relevance degree has higher prediction accuracy than the basic matrix decomposition and social matrix factorization in terms of accuracy and stability.
Content-Based Image Retrieval Using Color Volume Histograms

Human visual perception has a close relationship with the HSV color space, which can be represented as a cylinder. The question of how visual features are extracted using such an attribute is important. In this paper, a new feature descriptor; namely, a color volume histogram, is proposed for image representation and content-based image retrieval. It converts a color image from RGB color space to HSV color space and then uniformly quantizes it into 72 bins of color cues and 32 bins of edge cues. Finally, color volumes are used to represent the image content. The proposed algorithm is extensively tested on two Corel datasets containing 15[Formula: see text]000 natural images. These image retrieval experiments show that the color volume histogram has the power to describe color, texture, shape and spatial features and performs significantly better than the local binary pattern histogram and multi-texton histogram approaches.
Timeline2GUI: A Log2Timeline CSV parser and training scenarios

Crimes involving digital evidence are getting more complex due to the increasing storage capacities and utilization of devices. Event reconstruction (i.e., understanding the timeline) is an essential step for investigators to understand a case where a prominent tool is Log2Timeline (a tool that creates super timelines which is a combination of several log files and events throughout a system). While these timelines provide great evidence and help to understand a case, they are complex and require tools as well as training scenarios. In this paper we present Timeline2GUI an easy-to-use python implementation to analyze CSV log files create by Log2Timeline. Additionally, we present three training scenarios – beginner, intermediate and advanced – to practice timeline analysis skills as well as familiarity with visualization tools. Lastly, we provide a comprehensive overview of tools.
An image segmentation method of a modified SPCNN based on human visual system in medical images

An image segmentation method of a modified simplified pulse-coupled neural network (MSPCNN) based on human visual system (HVS) is proposed for medical images. The method successfully determines the stimulus input of the MSPCNN according to the characteristics of PCNN and HVS. In order to accomplish the goal, we attempt to deduce the sub-intensity range of central neurons firing by introducing neighboring firing matrix Q and calculating intensity distribution range based on a new MSPCNN(NMSPCNN), and then reveal the way how sub-intensity range parameter Sint generates the stimulus input Sioij closer to HVS. Besides, we try to substitute the above stimulus input into the MSPCNN to extract more suitable lesions for medical images. In contrast to prevalent PCNN models, the MSPCNN has higher segmentation accuracy rates and lower computational complexity because of the parameter setting method. Finally, a good proposed method comparing with the state-of-the-art methods has a better performance, presenting the overall metric OEM with MIAS of 0.8784, DDSM of 0.8606 and gallstones of 0.8585.
Local Feature Descriptor and Derivative Filters for Blind Image Quality Assessment

In this letter, a novel blind image quality assessment (BIQA) technique is introduced to provide an automatic and reproducible evaluation of distorted images. In the approach, the information carried by image derivatives of different orders is captured by local features and used for the image quality prediction. Since a typical local feature descriptor is designed to ensure a robust image patch representation, in this letter, a novel descriptor that additionally highlights local differences enhanced by the filtering is proposed. Furthermore, a set of derivative kernels is introduced. Finally, the support vector regression technique is used to map statistics of described local features into subjective scores, providing an objective quality score for an image. Extensive experimental validation on popular IQA image datasets reveals that the proposed method outperforms the state-of-the-art handcrafted and deep learning BIQA measures.
Developing a Data Dashboard Framework for Population Health Surveillance: Widening Access to Clinical Trial Findings

Population surveillance sites generate many datasets relevant to disease surveillance. However, there is a risk that these data are underutilized because of the volumes of data gathered and the lack of means to quickly disseminate analysis. Data visualization offers a means to quickly disseminate, understand, and interpret datasets, facilitating evidence-driven decision making through increased access to information.This paper describes the development and evaluation of a framework for data dashboard design, to visualize datasets produced at a demographic health surveillance site. The aim of this research was to produce a comprehensive, reusable, and scalable dashboard design framework to fit the unique requirements of the context.The framework was developed and implemented at a demographic surveillance platform at the Africa Health Research Institute, in KwaZulu-Natal, South Africa. This context represents an exemplar implementation for the use of data dashboards within a population health-monitoring setting. Before the full launch, an evaluation study was undertaken to assess the effectiveness of the dashboard framework as a data communication and decision-making tool. The evaluation included a quantitative task evaluation to assess usability and a qualitative questionnaire exploring the attitudes to the use of dashboards.The evaluation participants were drawn from a diverse group of users working at the site (n=20), comprising of community members, nurses, scientific and operational staff. Evaluation demonstrated high usability for the dashboard across user groups, with scientific and operational staff having minimal issues in completing tasks. There were notable differences in the efficiency of task completion among user groups, indicating varying familiarity with data visualization. The majority of users felt that the dashboards provided a clear understanding of the datasets presented and had a positive attitude to their increased use.Overall, this exploratory study indicates the viability of the data dashboard framework in communicating data trends within population surveillance setting. The usability differences among the user groups discovered during the evaluation demonstrate the need for the user-led design of dashboards in this context, addressing heterogeneous computer and visualization literacy present among the diverse potential users present in such settings. The questionnaire highlighted the enthusiasm for increased access to datasets from all stakeholders highlighting the potential of dashboards in this context.
A simple and reliable empirical model with two predictors for estimating 1-minute diffuse fraction

Recent progress in radiometry allows the development of diffuse fraction models based on data stored at high-frequency sampling. At the present only a handful of such models exist in the literature. In this paper, we present a new and simple model for estimating diffuse fraction developed on 1-minute resolution data. The new model (PB) is extensively validated on data from the Baseline Surface Radiation Network, for both diffuse fraction (kd) and direct normal irradiance (Gn). The results show that PB performs well compared to other similar models, despite its structural simplicity. kd is estimated with the best accuracy in locations with temperate climate (TM), the average (median) nRMSE of the tested TM stations being 18.38% (17.79%). The Gn estimates are found to be most accurate at arid locations (AR), the average (median) nRMSE over all tested AR stations being 17.29% (16.96%). We find that the performance of the models varies strongly as a function of the climate zone or when we switch between estimating diffuse and direct normal irradiance components, possibly changing the accuracy-based rankings among models, a fact which should be taken into consideration by users when selecting appropriate models.
Nonparticipation of bangladeshi women in #MeToo movement

In October 2017, an unprecedented social media movement started where women from all around the world publicly shared their untold stories of being sexually harassed using the hashtag #MeToo (or some variants of it). While this movement raised voices against sexual harassment across the globe, many women in South Asia did not participate in this movement. In this paper, we present our study on non-participation of many Bangladeshi women in this movement through an anonymous online survey (n = 180), and an in-depth interview study (n = 30). Our study shows that many Bangladeshi women, despite being supportive of the movement, did not participate in this movement because of several social, cultural, and infrastructural reasons. We use transnational feminism as a theoretical framework to explain their non-participation. We further discuss how the lessons learned from this study help us better understand the participation, functioning, and impact of social media movements.
An Active Learning Approach for Improving the Accuracy of Automated Domain Model Extraction

Domain models are a useful vehicle for making the interpretation and elaboration of natural-language requirements more precise. Advances in natural-language processing (NLP) have made it possible to automatically extract from requirements most of the information that is relevant to domain model construction. However, alongside the relevant information, NLP extracts from requirements a significant amount of information that is superfluous (not relevant to the domain model). Our objective in this article is to develop automated assistance for filtering the superfluous information extracted by NLP during domain model extraction. To this end, we devise an active-learning-based approach that iteratively learns from analysts’ feedback over the relevance and superfluousness of the extracted domain model elements and uses this feedback to provide recommendations for filtering superfluous elements. We empirically evaluate our approach over three industrial case studies. Our results indicate that, once trained, our approach automatically detects an average of ≈ 45% of the superfluous elements with a precision of ≈ 96%. Since precision is very high, the automatic recommendations made by our approach are trustworthy. Consequently, analysts can dispose of a considerable fraction – nearly half – of the superfluous elements with minimal manual work. The results are particularly promising, as they should be considered in light of the non-negligible subjectivity that is inherently tied to the notion of relevance.
A universal taxonomy and survey of forensic memory acquisition techniques

Abstract Main memory analysis plays an increasingly important role in today's digital forensic analysis. It can be used to retrieve encryption keys or to analyze malware that solely resides in RAM. Typically, the memory is acquired prior to analysis. As of today, there exist a large number of different techniques and tools to accomplish this task that all have their own advantages and disadvantages and appear to be incompareable. In this paper, we define a taxonomy of acquisition methods based on a well-defined partial order that generalizes the concept of ring-based privilege separation. Using this taxonomy, we provide a comprehensive survey of state-of-the-art memory acquisition techniques that is agnostic towards the used operating system and the hardware architecture.
Illumination-Aware Multi-Task GANs for Foreground Segmentation

Foreground-background segmentation has been an active research area over the years. However, conventional models fail to produce accurate results when challenged with the videos of challenging illumination conditions. In this paper, we present a robust model that allows accurately extracting the foreground even in exceptionally dark or bright scenes and in continuously varying illumination in a video sequence. This is accomplished by a triple multi-task generative adversarial network (TMT-GAN) that effectively models the semantic relationship between the dark and bright images and performs binary segmentation end-to-end. Our contribution is twofold: first, we show that by jointly optimizing the GAN loss and the segmentation loss, our network simultaneously learns both tasks that mutually benefit each other. Second, fusing features of images with varying illumination into the segmentation branch vastly improve the performance of the network. Comparative evaluations on highly challenging real and synthetic benchmark datasets (ESI and SABS) demonstrate the robustness of TMT-GAN and its superiority over state-of-the-art approaches.
Action recognition and localization with spatial and temporal contexts

Locating human action in spatio-temporal domain among untrimmed videos is an important but challenging task. Recent works have shown that incorporating contextual information leads to a significant improvement in action recognition, but there is still no existing work taking full advantage of context for action localization. While the popular target-centered methods have achieved promising results, they fail to exploit contexts and capture temporal dynamics in actions. In this paper, we propose a principled dynamic model, called spatio-temporal context model (STCM), to simultaneously locate and recognize actions. The STCM integrates various kinds of contexts, including the temporal context that consists of the sequences before and after action as well as the spatial context in the surrounding of target. Meanwhile, a novel dynamic programming approach is introduced to accumulate evidences collected at a small set of candidates in order to detect the spatio-temporal location of action effectively and efficiently. We report encouraging results on the UCF-Sports and UCF-101. It demonstrates that the contextual information is not only helpful for action recognition, but also contributes to action localization.
Visualization and Visual Analytic Techniques for Patterns

This chapter surveys visualization techniques for frequent itemsets, association rules, and sequential patterns. The human is crucial in the process of identifying interesting patterns and thus, mining such patterns and visualizing them is important for the decision making. The complementary feedback loop that a user may use to refine parameters through inspecting the current mining results is broadly described as visual analytics. This survey identifies visual designs for patterns of each category and analyzes and compares their strengths and weaknesses systematically. The comparison and overview help decision-makers selecting the appropriate technique for their tasks and systems while knowing about their limitations.
Enhanced self-adaptive global-best harmony search without any extra statistic and external archive

Harmony search is a music-inspired optimization algorithm. Our findings reveal that the common uniform randomization selection in the harmony search algorithm is less efficient than the Gaussian mutation to explore the global optimal solution, regardless of solving unimodal or multimodal problems under the same parameters settings. To evaluate the effectiveness of a given search strategy, a general measurement that can apply to other algorithms is also proposed. To enhance the search efficiency and effectiveness, a self-adaptive global-best harmony search algorithm is developed. The proposed algorithm takes full advantage of the valuable information hidden in harmony memory to devise a high-performance search strategy and integrates a self-adaptive mechanism to develop a parameter-setting-free technique. Moreover, it is as simple and straightforward to implement as the canonical harmony search algorithm. It does not require any extra statistic and external archive. It well maintains the interesting and distinctive framework of the original version in the evolutionary computation domain. The experimental results show that the proposed algorithm significantly outperforms the recent adaptive variant of the harmony search algorithm and achieves the strongly competitive performances compared with other state-of-the-art adaptive evolutionary algorithms. The proposed algorithm is also successfully applied into the real-world space trajectory optimization problem.
Feature-enriched matrix factorization for relation extraction

Relation extraction aims at finding meaningful relationships between two named entities from within unstructured textual content. In this paper, we define the problem of information extraction as a matrix completion problem where we employ the notion of universal schemas formed as a collection of patterns derived from open information extraction systems as well as additional features derived from grammatical clause patterns and statistical topic models. One of the challenges with earlier work that employ matrix completion methods is that such approaches require a sufficient number of observed relation instances to be able to make predictions. However, in practice there is often insufficient number of explicit evidence supporting each relation type that could be used within the matrix model. Hence, existing work suffer from a low recall. In our work, we extend the work in the state of the art by proposing novel ways of integrating two sets of features, i.e., topic models and grammatical clause structures, for alleviating the low recall problem. More specifically, we propose that it is possible to (1) employ grammatical clause information from textual sentences to serve as an implicit indication of relation type and argument similarity. The basis for this is that it is likely that similar relation types and arguments are observed within similar grammatical structures, and (2) benefit from statistical topic models to determine similarity between relation types and arguments. We employ statistical topic models to determine relation type and argument similarity based on their co-occurrence within the same topics. We have performed extensive experiments based on both gold standard and silver standard datasets. The experiments show that our approach has been able to address the low recall problem in existing methods, by showing an improvement of 21% on recall and 8% on f-measure over the state of the art baseline.