Reviewerly/exHarmony huggingface dataset:
The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets

Abstract The analysis of ocean and atmospheric datasets offers a unique set of challenges to scientists working in different application areas. These challenges include dealing with extremely large volumes of multidimensional data, supporting interactive visual analysis, ensembles exploration and visualization, exploring model sensitivities to inputs, mesoscale ocean features analysis, predictive analytics, heterogeneity and complexity of observational data, representing uncertainty, and many more. Researchers across disciplines collaborate to address such challenges, which led to significant research and development advances in ocean and atmospheric sciences, and also in several relevant areas such as visualization and visual analytics, big data analytics, machine learning and statistics. In this report, we perform an extensive survey of research advances in the visual analysis of ocean and atmospheric datasets. First, we survey the task requirements by conducting interviews with researchers, domain experts, and end users working with these datasets on a spectrum of analytics problems in the domain of ocean and atmospheric sciences. We then discuss existing models and frameworks related to data analysis, sense‐making, and knowledge discovery for visual analytics applications. We categorize the techniques, systems, and tools presented in the literature based on the taxonomies of task requirements, interaction methods, visualization techniques, machine learning and statistical methods, evaluation methods, data types, data dimensions and size, spatial scale and application areas. We then evaluate the task requirements identified based on our interviews with domain experts in the context of categorized research based on our taxonomies, and existing models and frameworks of visual analytics to determine the extent to which they fulfill these task requirements, and identify the gaps in current research. In the last part of this report, we summarize the trends, challenges, and opportunities for future research in this area. (see http://www.acm.org/about/class/class/2012 )
Collision Detection for UAVs Based on GeoSOT-3D Grids

The increasing number of unmanned aerial vehicles (UAVs) has led to challenges related to solving the collision problem to ensure air traffic safety. The traditional approaches employed for collision detection suffer from two main drawbacks: first, the computational burden of a pairwise calculation increases exponentially with an increasing number of spatial entities; second, existing grid-based approaches are unsuitable for complicated scenarios with a large number of objects moving at high speeds. In the proposed model, we first identified UAVs and other spatial objects with GeoSOT-3D grids. Second, the nonrelational spatial database was initialized with a multitable strategy, and spatiotemporal data were inserted with the GeoSOT-3D grid codes as the primary key. Third, the collision detection procedure was transformed from a pairwise calculation to a multilevel query. Four simulation experiments were conducted to verify the feasibility and efficiency of the proposed collision detection model for UAVs in different environments. The results also indicated that 64 m GeoSOT-3D grids are the most suitable basic grid size, and the reduction in the time consumption compared with traditional methods reached approximately 50–80% in different scenarios.
On the Efficacy of Ensemble of Constraint Handling Techniques in Self-Adaptive Differential Evolution

Self-adaptive variants of evolutionary algorithms (EAs) tune their parameters on the go by learning from the search history. Adaptive differential evolution with optional external archive (JADE) and self-adaptive differential evolution (SaDE) are two well-known self-adaptive versions of differential evolution (DE). They are both unconstrained search and optimization algorithms. However, if some constraint handling techniques (CHTs) are incorporated in their frameworks, then they can be used to solve constrained optimization problems (COPs). In an early work, an ensemble of constraint handling techniques (ECHT) is probabilistically hybridized with the basic version of DE. The ECHT consists of four different CHTs: superiority of feasible solutions, self-adaptive penalty, ε -constraint handling technique and stochastic ranking. This paper employs ECHT in the selection schemes, where offspring competes with their parents for survival to the next generation, of JADE and SaDE. As a result, JADE-ECHT and SaDE-ECHT are developed, which are the constrained variants of JADE and SaDE. Both algorithms are tested on 24 COPs and the experimental results are collected and compared according to algorithms’ evaluation criteria of CEC’06. Their comparison, in terms of feasibility rate (FR) and success rate (SR), shows that SaDE-ECHT surpasses JADE-ECHT in terms of FR, while JADE-ECHT outperforms SaDE-ECHT in terms of SR.
Exploring Features and Attributes in Deep Face Recognition Using Visualization Techniques

Deep convolutional neural networks (CNNs) currently have achieved state-of-the-art results on face recognition; yet, the understanding behind the success of the deep face model is still lacking. In particular, it is still unclear the inner workings of deep face model. What effective features does a deep face model learn? What do these features represent and what is the sematic meaning of them? This work explores this problem by analyzing the classic network VGGFace using deep visualization techniques. We first explore features computed by neurons, investigating characters of features like diversity, invariance, discrimination. It's worth noting that the middle layer is the least robust to transform, which contradicts the conventional view that robustness to transform increases as the network going deeper. The most significant phenomenon we find is that high level features are correspond with complex face attributes which human could not describe using a few words. We present a quantitative analysis on these face attributes perceived by deep CNNs, understanding them and the complex relationships between them. Additionally, we also focus on the significant point, the pose invariance in face recognition. Our research is the first work to understand the inner works of deep face models, elucidating some particular phenomena in deep face recognition.
Virtual reality as a tool for teaching library design

Virtual reality (VR) offers potential for teaching and learning, though there seems to be no prior use of VR for teaching library and information science. This paper reports on a project that used VR for teaching library design, specifically for designing a library for customers with physical impai rments. A survey and interviews were used to collect data. The results suggest that students prefer using VR to more traditional forms of learning because it makes physical problems seem more real than they would if presented in a book or video.
Driver Gaze Zone Dataset With Depth Data

Drivers' inattention and distraction detection are crucial features for driver monitoring and driver assistance systems (DAS) and a key point to avoid car crashes. Currently available datasets mainly track head pose and only a few were collected inside a car. Therefore, we present a novel public dataset designed to train algorithms that estimate driver gaze zone in real driving conditions. We provide labeled frame-by-frame images from 19 car points. Depth 3D images were also captured and they are aligned with infrared images. This new dataset is the largest one available for this purpose and the only one that provides 2D and 3D data aligned at pixel using a Intel <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> RealSense™ camera. Finally, in order to establish a baseline, we present a gaze zone estimator built using a transfer learning method.
Generalized PVO‐based dynamic block reversible data hiding for secure transmission using firefly algorithm

Abstract In this paper, we proposed a novel generalized pixel value ordering–based reversible data hiding using firefly algorithm (GPVOFA). The sequence of minimum and maximum number pixels value has been used to embed the secret data while prediction and modification are held on minimum, and the maximum number of pixel blocks is used to embed the secret data into multiple bits. The host image is divided into the size of noncoinciding dynamic blocks on the basis of firefly quadtree partition, whereas rough blocks are divided into a larger size; moreover, providing more embedding capacity used small flat blocks size and optimal location in the block to write the information. Our proposed method becomes able to embed large data into a host image with low distortion. The rich experimental results are better, as compared with related preceding arts.
FCSR-GAN: End-to-end Learning for Joint Face Completion and Super-resolution

Combined variations such as low-resolution and occlusion often present in face images in the wild, e.g., under the scenario of video surveillance. While most of the existing face enhancement approaches only handle one type of variation per model, in this paper, we propose a deep generative adversarial network (FCSR-GAN) for joint face completion and face super-resolution via one model. The generator of FCSR-GAN aims to recover a high-resolution face image without occlusion given an input low-resolution face image with partial occlusions. The discriminator of FCSR-GAN consists of two adversarial losses, a perceptual loss, and a face parsing loss, which assure the high quality of the recovered face images. Experimental results on several public-domain databases (CelebA and Helen) show that the proposed approach outperforms the state-of-the-art methods in jointly doing face super-resolution (up to 4×) and face completion from low-resolution face images with occlusions.
Exemplar-Based Image Inpainting With Multi-Resolution Information and the Graph Cut Technique

Filling holes in an image is achieved in a manner similar to peeling the onion.The order of filling affects the image inpainting results, especially concerning the content of complex images.When highresolution images are used to extract edge information, they are susceptible to high-frequency information, such as complex textures and noise.Furthermore, edge information is extracted in different resolutions, while the main contour information of the image can be obtained more easily.In this paper, multi-resolution information is used to prioritize which target patches in an image to fill, which helps to elucidate the optimal sequence for image repair.Multi-resolution images provide more information than single-resolution images, and similar patches are computed on multi-resolution images to obtain multiple candidate patches.Similar patch calculations use a variety of information on colors, gradients, and boundaries to more accurately search for similar patches.We chose the most reasonable candidate patch by means of the structural similarity index measure (SSIM).When pasting the patch to fill the target region, we used graph cut technology to eliminate blockiness.Compared with the state-of-the-art repair algorithm, the experimental results prove that the proposed repair algorithm can repair the image very well.
Penalized estimation of flexible hidden Markov models for time series of counts

We propose an effectively nonparametric approach to fitting hidden Markov models to time series of counts, where the state-dependent distributions are estimated in a completely data-driven way without the need to specify a parametric family of distributions. To avoid overfitting, a roughness penalty based on higher-order differences between adjacent count probabilities is added to the likelihood, which is demonstrated to produce smooth state-dependent probability mass functions. The feasibility of the suggested approach is assessed in simulation experiments, and further illustrated in two real-data applications, where we model the distributions of (i) major earthquake counts and (ii) acceleration counts of an oceanic whitetip shark (Carcharhinus longimanus) over time. The proposed methodology is implemented in the accompanying R package countHMM, which is available on CRAN.
Improved Search in Hamming Space Using Deep Multi-Index Hashing

Similarity-preserving hashing is a widely used method for nearest neighbor search in large-scale image retrieval tasks. Considerable research has been conducted on deep-network-based hashing approaches to improve the performance. However, the binary codes generated from deep networks may be not uniformly distributed over the Hamming space, which will greatly increase the retrieval time. To this end, we propose a deep-network-based multi-index hashing (MIH) for retrieval efficiency. We first introduce the MIH mechanism into the proposed deep architecture, which divides the binary codes into multiple substrings. Each substring corresponds to one hash table. Then, we add the two balanced constraints to obtain more uniformly distributed binary codes: 1) balanced substrings, where the Hamming distances of each substring are equal for any two binary codes and 2) balanced hash buckets, where the sizes of each bucket are equal. Extensive evaluations on several benchmark image retrieval data sets show that the learned balanced binary codes bring dramatic speedups and achieve comparable performance over the existing baselines.
Measuring diachronic language distance using perplexity: Application to English, Portuguese, and Spanish

Abstract The objective of this work is to set a corpus-driven methodology to quantify automatically diachronic language distance between chronological periods of several languages. We apply a perplexity-based measure to written text representing different historical periods of three languages: European English, European Portuguese, and European Spanish. For this purpose, we have built historical corpora for each period, which have been compiled from different open corpus sources containing texts as close as possible to its original spelling. The results of our experiments show that a diachronic language distance based on perplexity detects the linguistic evolution that had already been explained by the historians of the three languages. It is remarkable to underline that it is an unsupervised multilingual method which only needs a raw corpora organized by periods.
Thermanator

Being warm-blooded mammals, we humans routinely leave thermal residues on various objects with which we come in contact. This includes common input devices, such as keyboards, that are used for entering (among other things) secret information, such as passwords and PINs. Although thermal residue dissipates over time, there is always a certain time window during which thermal energy readings can be harvested from input devices to recover recently entered, and potentially sensitive, information. To-date, there has been no systematic investigation of thermal profiles of keyboards, and thus no efforts have been made to secure them. This serves as our main motivation for constructing a means for password harvesting from keyboard thermal emanations. Specifically, we introduce Thermanator, a new post factum insider attack based on heat transfer caused by a user typing a password on a typical external keyboard. We conduct and describe a user study that collected thermal residues from 30 users entering 10 unique passwords (both weak and strong) on 4 popular commodity keyboards. Results show that entire sets of key-presses can be recovered by non-expert users as late as 30 seconds after initial password entry, while partial sets can be recovered as late as 1 minute after entry. Furthermore, we find that Hunt-and-Peck typists are particularly vulnerable. The take-away of our work is three-fold: (1) using keyboards to enter passwords is even less secure than previously recognized, (2) post factum (either planned or impromptu) thermal imaging attacks are realistic, and (3) we should either stop using keyboards for password entry, or abandon passwords altogether.
Semantic Adversarial Deep Learning

Adversarial examples have emerged as a key threat for machine-learning-based systems, especially the ones that employ deep neural networks. Unlike a large body of research in this area, this Keynote article accounts for the semantic, context, and specifications of the complete system with machine learning components in resource-constrained environments. -Muhammad Shafique, Technische Universität Wien.
Two-parameter Hong-Ou-Mandel dip

A modification of the standard Hong-Ou-Mandel interferometer is proposed which allows one to replicate the celebrated coincidence dip in the case of two-independent delay parameters. In the ideal case where such delays are sufficiently stable with respect to the mean wavelength of the pump source, properly symmetrized input bi-photon states allow one to pinpoint their values through the identification of a zero in the coincidence counts, a feature that cannot be simulated by semiclassical inputs having the same spectral properties. Besides, in the presence of fluctuating parameters the zero in the coincidences is washed away: still the bi-photon state permits to recover the values of parameters with a visibility which is higher than the one allowed by semiclassical sources. The detrimental role of loss and dispersion is also analyzed and an application in the context of quantum positioning is presented.
Deep Potential Geo-Social Relationship Mining for Point-of-Interest Recommendation

Point-of-Interest (POI) recommendation has been an important research topic in data mining and the popularity of location-based social networks (LBSNs) has significantly contributed to POI recommendation.The existing POI recommendation models mostly adopt various explicit social relationships under geographical space.The implicit relationships among users under a certain geographical region are rarely taken into account, though they have major influence on user behaviors.Due to the above limitations, we were motivated to introduce an innovative Deep Potential Geo-Social Relationship mining model for POI Recommendation (DPGSR-PR).The proposed DPGSR-PR performs the personalization of geographical features, towards the determination of user check-in behaviors and choices in specific domains, which is achieved by estimating and considering kernel density.Moreover, user preferences and personalized geo-social influence are incorporated into a geo-social recommendation framework under a holistic view.Specifically, the deep potential geo-social relationships include the explicit-implicit user geosocial relationships between users (EIU-GSR) and the implicit common check-in POI-based geo-social relationships (ICP-GSR).The estimation of Kernel density and the two-hop random walk approach are employed in an effort to mine the EIU-GSR.The ICP-GSR is discovered by specifically determining and considering the Jaccard similarity coefficient and kernel density estimation.Due to the fact that the role of both EIU-GSR and ICP-GSR as regularization terms is quite significant, we used their combined impact to obtain a unique recommendation model that employs matrix factorization.The proposed DPGSR-PR was tested on two datasets, which has proven that DPGSR-PR outperforms other well-established models. INDEX TERMS Geo-social relationship, point-of-interest recommendation
Robustness, evolvability and phenotypic complexity: insights from evolving digital circuits

We analyze the relation between robustness to mutations, phenotypic complexity, and evolvability in the context of artificial circuits evolved for the ability to solve a parity problem. We demonstrate that whether robustness to mutations enhances or diminishes phenotypic variability and evolvability depends on whether robustness is achieved through the development of parsimonious (phenotypically simple) solutions, that minimize the number of genes playing functional roles, or through phenotypically more complex solutions, capable of buffering the effect of mutations. We show that the characteristics of the selection process strongly influence the robustness and the performance of the evolving candidate solutions. Finally, we propose a new evolutionary method that outperforms evolutionary algorithms commonly used in this domain.
Cracking the problem with 33

Inspired by the Numberphile video "The uncracked problem with 33" by Browning and Brady Haran ( https://youtu.be/wymmCdLdPvM ), we investigate solutions to $$x^3+y^3+z^3=k$$ for a few small values of k. We find the first known solutions for $$k=33$$ and $$k=795$$ .
Social Media Data in Digital Forensics Investigations

Evidence collected from social media presents valuable information that should not be overlooked. Evidence can be captured from social media using multiple methods including searching publicly viewable content, reviewing content metadata, soliciting and investigating interactions with other users, and utilizing legal holds. After the evidence is gathered, it can be utilized in various ways. Social media evidence can be used to create a timeline of events, show intent or conspiracy, and establish connections between persons. Digital forensics investigations can be used to collect such evidence. The evidence accumulated from social media is far-reaching and widely advantageous. However, there are many legal issues that can affect the collection and ultimate legal admissibility of this evidence. Evidence must be collected using careful, correct procedures and in a manner that ensures its integrity. The ethical implications of the collection of social media evidence also plays a role in these digital forensics investigations. These issues can present adverse circumstances for cases. Nonetheless, social media offers various avenues for the collection and use of its data as evidence within a digital forensics investigation. The advantageous and disadvantageous aspects of this use are discussed in this chapter.
An assessment of SMS fraud in Pakistan

SMS fraud has become a growing concern for those working toward financial inclusion, however, it is often unclear how widespread such threats are in practice. This multi-method study investigates SMS fraud in Pakistan through identification and categorization of fraudulent messages as well as the impact on those who receive such messages. We collect fraudulent SMS messages by various means, including byway of a custom-built Android smartphone application. To complement this, we interview people exposed to SMS fraud and representatives of mobile network operators. Based on our analysis, lottery type fraud schemes dominate SMS fraud in Pakistan, and these schemes have the greatest impact on vulnerable low-income, rural populations. We offer a simple heuristic for fraud detection that has a high accuracy rate and is adaptable to evolving fraud schemes, and conclude with a recommendation for a fraud mitigation strategy to target fraudster call back numbers.
Effective Visual Communication for the Quantitative Scientist

Effective visual communication is a core competency for pharmacometricians, statisticians, and, more generally, any quantitative scientist. It is essential in every step of a quantitative workflow, from scoping to execution and communicating results and conclusions. With this competency, we can better understand data and influence decisions toward appropriate actions. Without it, we can fool ourselves and others and pave the way to wrong conclusions and actions. The goal of this tutorial is to convey this competency. We posit three laws of effective visual communication for the quantitative scientist: have a clear purpose, show the data clearly, and make the message obvious. A concise "Cheat Sheet," available on https://graphicsprinciples.github.io, distills more granular recommendations for everyday practical use. Finally, these laws and recommendations are illustrated in four case studies.