Reviewerly/exHarmony huggingface dataset:
Who Put That There? Temporal Navigation of Spatial Recordings by Direct Manipulation

Spatial recordings allow viewers to move within them and freely choose their viewpoint. However, such recordings make it easy to miss events and difficult to follow moving objects when skipping through the recording. To alleviate these problems we present the Who Put That There system that allows users to navigate through time by directly manipulating objects in the scene. By selecting an object, the user can navigate to moments where the object changed. Users can also view trajectories of objects that changed location and directly manipulate them to navigate. We evaluated the system with a set of sensemaking questions in a think-aloud study. Participants understood the system and found it useful for finding events of interest, while being present and engaged in the recording.
Again, Together: Socially Reliving Virtual Reality Experiences When Separated

To share a virtual reality (VR) experience remotely together, users usually record videos from an individual's point of view and then co-watch these videos. However, co-watching recorded videos limits users to reliving their memories from the perspective from which the video was captured. In this paper, we describe ReliveInVR, a new time-machine-like VR experience sharing method. ReliveInVR allows multiple users to immerse themselves in the relived experience together and independently view the experience from any perspective. We conducted a 1x3 within-subject study with 26 dyads to compare ReliveInVR with (1) co-watching 360-degree videos on desktop, and (2) co-watching 360-degree videos in VR. Our results suggest that participants reported higher levels of immersion and social presence in ReliveInVR. Participants in ReliveInVR also understood the shared experience better, discovered unnoticed things together and found the sharing experience more fulfilling. We discuss the design implications for sharing VR experiences over time and space.
Quantum Correlation via Skew Information and Bell Function Beyond Entanglement in a Two-Qubit Heisenberg XYZ Model: Effect of the Phase Damping

In this paper, we analyze the dynamics of non-local correlations (NLCs) in an anisotropic two-qubit Heisenberg XYZ model under the effect of the phase damping. An analytical solution is obtained by applying a method based on the eigenstates and the eigenvalues of the Hamiltonian. It is observed that the generated NLCs are controlled by the Dzyaloshinskii–Moriya interaction, the purity indicator, the interaction with the environment, and the anisotropy. Furthermore, it is found that the quantum correlations, as well as the sudden death and sudden birth phenomena, depend on the considered physical parameters. In particular, the system presents a special correlation: the skew-information correlation. The log-negativity and the uncertainty-induced non-locality exhibit the sudden-change behavior. The purity of the initial states plays a crucial role on the generated nonlocal correlations. These correlations are sensitive to the DM interaction, anisotropy, and phase damping.
DB4ML - An In-Memory Database Kernel with Machine Learning Support

In this paper, we revisit the question of how ML algorithms can be best integrated into existing DBMSs to not only avoid expensive data copies to external ML tools but also to comply with regulatory reasons. The key observation is that database transactions already provide an execution model that allows DBMSs to efficiently mimic the execution model of modern parallel ML algorithms. As a main contribution, this paper presents DB4ML, an in-memory database kernel that allows applications to implement user-defined ML algorithms and efficiently run them inside a DBMS. Thereby, the ML algorithms are implemented using a programming model based on the idea of so called iterative transactions. Our experimental evaluation shows that DB4ML can support user-defined ML algorithms inside a DBMS with the efficiency of modern specialized ML engines. In contrast to DB4ML, these engines not only need to transfer data out of the DBMS but also hardcode the ML algorithms and thus are not extensible.
Mobile or hostile? Using smartphones in learning English as a foreign language<sup>1</sup>

The purpose of this paper is to investigate the views of freshmen on using their smartphones in learning English as a foreign language. The study was designed based on a quantitative associational model. The data were collected from the 428 freshmen attending the English preparation programs run at İnönü and Fırat Universities, Turkey, using “Smartphone Use in Learning Foreign Language Scale”. The results showed that participants use their smartphones more frequently to engage in English listening and speaking activities than reading and writing activities. The participants also believe that smartphones scarcely have adverse effects on learning English. However, it was also found that the more time students spend online, the more they suffer from the adverse effects of smartphones in terms of language learning. Results about gender revealed that female students use their smartphones more frequently than their male peers to engage in formal language learning activities. Significant differences related to frequency of smartphone use for both formal and informal language learning purposes were detected, with the Business Administration and Molecular Biology and Genetics department students showing higher frequency. No statistically significant difference was found between students’ views about using smartphones for language learning according to the types of Internet connection (Wi-Fi, mobile data, or both).
Progressive Feature Matching: Incremental Graph Construction and Optimization

We present a novel feature matching algorithm that systematically utilizes the geometric properties of image features such as position, scale, and orientation, in addition to the conventional descriptor vectors. In challenging scenes, in which repetitive structures and large view changes are present, it is difficult to find correct correspondences using conventional approaches that only use descriptors, as the descriptor distances of correct matches may not be the least among the candidates. The feature matching problem is formulated as a Markov random field (MRF) that uses descriptor distances and relative geometric similarities together. Assuming that the layout of the nearby features does not considerably change, we propose the bidirectional transfer measure to gauge the geometric consistency between the pairs of feature correspondences. The unmatched features are explicitly modeled in the MRF to minimize their negative impact. Instead of solving the MRF on the entire features at once, we start with a small set of confident feature matches, and then progressively expand the MRF with the remaining candidate matches. The proposed progressive approach yields better feature matching performance and faster processing time. Experimental results show that the proposed algorithm provides better feature correspondences in many challenging scenes, i.e., more matches with higher inlier ratio and lower computational cost than those of the state-of-the-art algorithms. The source code of our implementation is open to the public.
Simple Rules to Guide Expert Classifications

Summary Judges, doctors and managers are among those decision makers who must often choose a course of action under limited time, with limited knowledge and without the aid of a computer. Because data-driven methods typically outperform unaided judgements, resource-constrained practitioners can benefit from simple, statistically derived rules that can be applied mentally. In this work, we formalize long-standing observations about the efficacy of improper linear models to construct accurate yet easily applied rules. To test the performance of this approach, we conduct a large-scale evaluation in 22 domains and focus in detail on one: judicial decisions to release or detain defendants while they await trial. In these domains, we find that simple rules rival the accuracy of complex prediction models that base decisions on considerably more information. Further, comparing with unaided judicial decisions, we find that simple rules substantially outperform the human experts. To conclude, we present an analytical framework that sheds light on why simple rules perform as well as they do.
Asymmetric multi-stage CNNs for small-scale pedestrian detection

A critical bottleneck in pedestrian detection is the detection of small-scale pedestrians, which have low contrast and blurry shapes in images and videos. Considered that the body shape of a pedestrian is always rectangular (the height is greater than the width), we propose an asymmetric multi-stage network (AMS-Net) for small-scale pedestrian detection. The proposed method has two main advantages. (1) It considers the asymmetry of a pedestrian’s body shape in pedestrian detection. The rectangular anchors are used to generate various rectangular proposals that have a height greater than the width. In addition, asymmetric rectangular convolution kernels are adopted for capturing the compact features of the pedestrian body. (2) The proposed AMS-Net gradually rejects the non-pedestrian boxes according to coarse-to-fine features in a three-stage framework. The proposed AMS-Net significantly improves the performance of pedestrian detection on the Far subset of the Caltech testing set (the miss rate decreases from 60.79% to 51.36%). It also achieves competitive performance on the INRIA, ETH, KITTI and CityPersons benchmarks.
Recurrent GANs Password Cracker For IoT Password Security Enhancement

Text-based passwords are a fundamental and popular means of authentication. Password authentication can be simply implemented because it does not require any equipment, unlike biometric authentication, and it relies only on the users' memory. This reliance on memory is a weakness of passwords, and people therefore usually use easy-to-remember passwords, such as "iloveyou1234". However, these sample passwords are not difficult to crack. The default passwords of IoT also are text-based passwords and are easy to crack. This weakness enables free password cracking tools such as Hashcat and JtR to execute millions of cracking attempts per second. Finally, this weakness creates a security hole in networks by giving hackers access to an IoT device easily. Research has been conducted to better exploit weak passwords to improve password-cracking performance. The Markov model and probabilistic context-free-grammar (PCFG) are representative research results, and PassGAN, which uses generative adversarial networks (GANs), was recently introduced. These advanced password cracking techniques contribute to the development of better password strength checkers. We studied some methods of improving the performance of PassGAN, and developed two approaches for better password cracking: the first was changing the convolutional neural network (CNN)-based improved Wasserstein GAN (IWGAN) cost function to an RNN-based cost function; the second was employing the dual-discriminator GAN structure. In the password cracking performance experiments, our models showed 10%-15% better performance than PassGAN. Through additional performance experiments with PCFG, we identified the cracking performance advantages of PassGAN and our models over PCFG. Finally, we prove that our models enhanced password strength estimation through a comparison with zxcvbn.
Hyperbolic Knowledge Graph Embeddings for Knowledge Base Completion

Learning embeddings of entities and relations existing in knowledge bases allows the discovery of hidden patterns in them. In this work, we examine the contribution of geometrical space to the task of knowledge base completion. We focus on the family of translational models, whose performance has been lagging. We extend these models to the hyperbolic space so as to better reflect the topological properties of knowledge bases. We investigate the type of regularities that our model, dubbed HyperKG, can capture and show that it is a prominent candidate for effectively representing a subset of Datalog rules. We empirically show, using a variety of link prediction datasets, that hyperbolic space allows to narrow down significantly the performance gap between translational and bilinear models and effectively represent certain types of rules.
Multivariate Time Series Forecasting Based on Elastic Net and High-Order Fuzzy Cognitive Maps: A Case Study on Human Action Prediction Through EEG Signals

Fuzzy cognitive maps (FCMs) have been successfully applied to time series forecasting. However, it still remains challenging to handle multivariate long nonstationary time series, such as EEG data, which may change rapidly and have patterns of trend. To overcome this limitation, in this article, we propose a fast prediction model to deal with multivariate long nonstationary time series based on the combination of elastic net and high order fuzzy cognitive map (HFCM), which is termed as ElasticNet <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HFCM</sub> . The designed FCM models each variable by one node and the high-order FCM helps to capture the patterns of trend. A case study on predicting human actions through the Electroencephalogram (EEG) data in the form of multichannel long nonstationary time series is investigated based on the proposed prediction model. Specifically, we first predict EEG signals based on the historical data, then a 1D-convolutionary neural network (1D-CNN) is developed to classify the predicted time series. The experimental results on the Grasp-and-Lift dataset show that the proposal can predict the EEG data with lower prediction error compared with the other regression methods. The area under the curve scores obtained on the Grasp-and-Lift dataset by 1D-CNN are higher than those obtained by state-of-the-art classification methods for EEG data in most cases. These results illustrate that the proposal can predict and classify multivariate long nonstationary time series with high accuracy and efficiency.
Epidote Trace Element Chemistry as an Exploration Tool in the Collahuasi District, Northern Chile

Abstract The Collahuasi district of northern Chile hosts several late Eocene-early Oligocene world-class porphyry Cu-Mo deposits, including Rosario, Ujina, and Quebrada Blanca deposits, and associated high-sulfidation epithermal mineralization at La Grande. Mineralization is hosted by intermediate to felsic intrusive and volcanic rocks of the upper Paleozoic to Lower Triassic Collahuasi Group, which experienced lower greenschist facies regional metamorphism prior to mineralization. Extensive hydrothermal alteration zones surround the porphyry and epithermal deposits, associated with hypogene ore-forming processes. However, outside of the observed sulfide halo the limits of geochemical anomalism associated with mineralization are difficult to define due to mineralogical similarities between weak, distal propylitic alteration and regional metamorphism affecting the host rocks. Recent advancements in laser ablation-inductively coupled plasma-mass spectrometry analysis of epidote from hydrothermal alteration zones around porphyry and skarn deposits have shown that low-level hypogene geochemical anomalies can be detected at distances farther from the center of mineralization than by conventional rock chip sampling. Selective analysis of propylitic epidote from the Collahuasi district indicates that anomalous concentrations of distal pathfinder elements in epidote, including As (&amp;gt;50 ppm), Sb (&amp;gt;25 ppm), Pb (&amp;gt;100 ppm), and Mn (&amp;gt;5,000 ppm), were detectable 1.5 to 4.0 km from deposit centers. Significantly, the concentrations of these trace elements in epidote were obtained from samples that contained whole-rock concentrations of &amp;lt;25 ppm As, &amp;lt;2 ppm Sb, &amp;lt;100 ppm Pb, and &amp;lt;5,000 ppm Mn. Systematic increases in Cu, Mo, and Sn concentrations in epidote near deposit centers, and corresponding decreasing As, Sb and Pb concentrations, also provide effective tools for assessing the fertility and locating the centers of porphyry mineralization. In addition, anomalous concentrations in epidote of Cu (up to 1 wt %) and Zn (up to 6,000 ppm) effectively discriminate epidote associated with high-sulfidation epithermal veins in the Collahuasi district (e.g., La Grande, Poderosa-Rosario) from alteration associated with porphyry mineralization.
Deep Learning-Based Single Image Super-Resolution: An Investigation for Dense Scene Reconstruction with UAS Photogrammetry

The deep convolutional neural network (DCNN) has recently been applied to the highly challenging and ill-posed problem of single image super-resolution (SISR), which aims to predict high-resolution (HR) images from their corresponding low-resolution (LR) images. In many remote sensing (RS) applications, spatial resolution of the aerial or satellite imagery has a great impact on the accuracy and reliability of information extracted from the images. In this study, the potential of a DCNN-based SISR model, called enhanced super-resolution generative adversarial network (ESRGAN), to predict the spatial information degraded or lost in a hyper-spatial resolution unmanned aircraft system (UAS) RGB image set is investigated. ESRGAN model is trained over a limited number of original HR (50 out of 450 total images) and virtually-generated LR UAS images by downsampling the original HR images using a bicubic kernel with a factor × 4 . Quantitative and qualitative assessments of super-resolved images using standard image quality measures (IQMs) confirm that the DCNN-based SISR approach can be successfully applied on LR UAS imagery for spatial resolution enhancement. The performance of DCNN-based SISR approach for the UAS image set closely approximates performances reported on standard SISR image sets with mean peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index values of around 28 dB and 0.85 dB, respectively. Furthermore, by exploiting the rigorous Structure-from-Motion (SfM) photogrammetry procedure, an accurate task-based IQM for evaluating the quality of the super-resolved images is carried out. Results verify that the interior and exterior imaging geometry, which are extremely important for extracting highly accurate spatial information from UAS imagery in photogrammetric applications, can be accurately retrieved from a super-resolved image set. The number of corresponding keypoints and dense points generated from the SfM photogrammetry process are about 6 and 17 times more than those extracted from the corresponding LR image set, respectively.
Unified generative adversarial networks for multimodal segmentation from unpaired 3D medical images

To fully define the target objects of interest in clinical diagnosis, many deep convolution neural networks (CNNs) use multimodal paired registered images as inputs for segmentation tasks. However, these paired images are difficult to obtain in some cases. Furthermore, the CNNs trained on one specific modality may fail on others for images acquired with different imaging protocols and scanners. Therefore, developing a unified model that can segment the target objects from unpaired multiple modalities is significant for many clinical applications. In this work, we propose a 3D unified generative adversarial network, which unifies the any-to-any modality translation and multimodal segmentation in a single network. Since the anatomical structure is preserved during modality translation, the auxiliary translation task is used to extract the modality-invariant features and generate the additional training data implicitly. To fully utilize the segmentation-related features, we add a cross-task skip connection with feature recalibration from the translation decoder to the segmentation decoder. Experiments on abdominal organ segmentation and brain tumor segmentation indicate that our method outperforms the existing unified methods.
Comparison of VGG and ResNet used as Encoders for Image Captioning

Recent models for image captioning are usually based on an encoder-decoder framework. Large pre-trained convolutional neural networks are often used as encoders. However, different authors use different encoder architectures for their image captioning models. This makes it more difficult to determine the effect that the encoder has on the overall model performance. In this paper we compare two popular convolution networks architectures – VGG and ResNet – as encoders for the same image captioning model in order to find out which method is the best at image representation used for caption generation.The results show that the ResNet outperforms VGG allowing image captioning model achieve higher BLEU-4 score. Furthermore, the results show that the ResNet allows model to achieve a score comparable with the VGG-based model with a less amount of training epochs. Based on this data we can state that encoder plays a big role and can significantly improve model without changing a decoder architecture.
DeepKey

Biometric authentication involves various technologies to identify individuals by exploiting their unique, measurable physiological and behavioral characteristics. However, traditional biometric authentication systems (e.g., face recognition, iris, retina, voice, and fingerprint) are at increasing risks of being tricked by biometric tools such as anti-surveillance masks, contact lenses, vocoder, or fingerprint films. In this article, we design a multimodal biometric authentication system named DeepKey, which uses both Electroencephalography (EEG) and gait signals to better protect against such risk. DeepKey consists of two key components: an Invalid ID Filter Model to block unauthorized subjects, and an identification model based on attention-based Recurrent Neural Network (RNN) to identify a subject’s EEG IDs and gait IDs in parallel. The subject can only be granted access while all the components produce consistent affirmations to match the user’s proclaimed identity. We implement DeepKey with a live deployment in our university and conduct extensive empirical experiments to study its technical feasibility in practice. DeepKey achieves the False Acceptance Rate (FAR) and the False Rejection Rate (FRR) of 0 and 1.0%, respectively. The preliminary results demonstrate that DeepKey is feasible, shows consistent superior performance compared to a set of methods, and has the potential to be applied to the authentication deployment in real-world settings.
Prioritization Based Taxonomy of DevOps Security Challenges Using PROMETHEE

DevOps is a combination of collaborative and multidisciplinary efforts of an organization to control continuous delivery and updates of new software while guaranteeing their reliability and correctness.In the software industry, the implementation of DevOps (development and operations units) faces many challenges that are specifically associated with the security.The objective of this study is to identify and develop a prioritization based taxonomy of DevOps security challenges.The total of eighteen DevOps security challenges were extracted using systematic literature review approach and were further evaluated with experts using questionnaire survey study.Finally, the multi criteria decision making PROMETHEE-II approach was used to prioritize and develop the taxonomy of identified factors and their categories.The implications of PROMETHEE-II approach are novel in this research domain as it has been used successfully in various other domains e.g.medical, banking, internet techniques and management etc.The contribution of this study is not limited to develop the taxonomy based structure of DevOps security challenges, but also the proper prioritization of these challenges by introducing PROMETHEE-II approach in the research field of DevOps.The study results will assist the practitioners to remove the uncertainty and vagueness in the opinion of DevOps experts to secure DevOps implementation for better and continuous software development process.