Reviewerly/exHarmony huggingface dataset:
Augmented Reality Through the Lens of Eye Tracking

This eye-tracking study compared the usability of Yelp's Augmented Reality application Monocle with its familiar map-based app. Thirty participants (15 in each group) had to find a location in an outdoor mall using one version of the mobile Yelp app while wearing a pair of eye-tracking glasses that recorded the general area of the natural environment or smartphone their eyes focused on. Results showed that AR users could successfully arrive at the destination using the location-based technology that augments a visual display of the physical landscape with digital information. However, Augmented Reality users took longer to find the location than did map users. They also spent more time looking at the mobile smartphone and looked back and forth between the screen and the environment more often than map users. In open-ended questions about preference following the task, 14 of 15 in the AR group said they would likely use the Monocle app in the future to find a location because they could “see exactly” where something was or hated following directions. Since users successfully used this technology and reported enjoying its use, further development of AR for everyday use to complete tasks is encouraged. However, future studies should focus not only on the development of technology but its usability and employ visual communication theories such as Perception Theory (Barry, 2005 Barry, A. M. (2005). Perception theory. In K. Smith, S. Moriarty, G. Barbatsis, & K. Kenney (eds.), Handbook of visual communication: Theory, methods, and media (pp. 45–62). Mahweh, NJ/London, UK: Lawrence Erlbaum. [Google Scholar]) and Information Processing Theory (i.e., Krause, 1982 Krause, W. (1982). Eye fixations and three-term series problems, or: Is there evidence for task independent information units. In R. Groner & P. Fraisse (eds.), Cognition and eye movements (pp. 122–138). Amsterdam, The Netherlands/Berlin, Germany: North Holland/Deutscher Verlag der Wissesnschaften (joint edition). [Google Scholar]) to help understand users' behavior.
word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement

Deep learning natural language processing models often use vector word embeddings, such as word2vec or GloVe, to represent words. A discrete sequence of words can be much more easily integrated with downstream neural layers if it is represented as a sequence of continuous vectors. Also, semantic relationships between words, learned from a text corpus, can be encoded in the relative configurations of the embedding vectors. However, storing and accessing embedding vectors for all words in a dictionary requires large amount of space, and may stain systems with limited GPU memory. Here, we used approaches inspired by quantum computing to propose two related methods, word2ket and word2ketXS, for storing word embedding matrix during training and inference in a highly efficient way. Our approach achieves a hundred-fold or more reduction in the space required to store the embeddings with almost no relative drop in accuracy in practical natural language processing tasks.
Relaxing the Proportionality Assumption in Latent Basis Models for Nonlinear Growth

Change over time is frequently nonlinear, which can present unique statistical challenges. Generally, different approaches for nonlinear growth engage in a tradeoff between interpretable parameters, expedient estimation, or how specific the model must be about the nature of the nonlinearity. Latent basis models are one method that can circumvent tradeoffs that other methods necessitate: it is quick to estimate, simple to interpret, and does not require specification of a particular trajectory. However, latent basis models require a restrictive proportionality assumption that is not required with other methods, which can limit its applicability with empirical data. This paper discusses this proportionality assumption and shows how it can be relaxed by reparameterizing the latent basis model as a multilevel structural equation model. We provide an example to show how relaxing proportionality can improve parameter estimates and person-specific growth curves. We also walkthrough Mplus code to facilitate fitting the model to empirical data.
Video Steganography Using 3D Convolutional Neural Networks

In an steganography we intend to hide information of interest in another data, aiming at secure data transaction, such as hiding an image in another image. The same task could be performed in video steganography. One approach to steganography is through manipulating least significant bits (LSB). Increasing the precision of the approaches in this regard, is still challenging and desired. The attention toward the sequence of video frames is an important issue which affects the accuracy of these methods. In this paper, a 3D convolutional neural network has been employed for the first time, to our knowledge, to perform video steganography. The incentive of exploiting the spatial and temporal features together, pushed us toward using this approach. Implementing the proposed method over the UCF101 dataset, lead to 22.75 bits per pixel improvement and output video frame enhancement level of 1 dB compared to the LSB, on average, respectively.
Algorithm to overcome atmospheric phase errors in SAL data

Synthetic aperture ladar is an emerging sensor technology providing high-resolution imagery of targets from long standoff ranges. Atmospheric turbulence corrupts the collected phase history data with spatially variant phase perturbations, impacting resolution and contrast of reconstructed imagery. We explore the efficacy of model-based reconstruction algorithms with model error corrections to mitigate the deleterious effects of atmospheric turbulence and restore image quality. We present results from model error correction techniques utilizing spatially invariant, spatially variant, and a model-based atmospheric phase error correction. We quantify the performance of all algorithms using an atmospheric ray-trace simulation.
Co-Attentive Equivariant Neural Networks: Focusing Equivariance On Transformations Co-Ocurring in Data

Equivariance is a nice property to have as it produces much more parameter efficient neural architectures and preserves the structure of the input through the feature mapping. Even though some combinations of transformations might never appear (e.g. a face with a horizontal nose) current equivariant architectures consider the set of all possible transformations in the transformation group while generating feature representations. Contrarily, the human visual system is able to attend to the set of relevant transformations occurring in the environment as to assist and improve object recognition. Based on this observation, we modify conventional equivariant feature mappings such that they are able to attend to the set of co-occurring transformations in data. Our experiments show that neural networks utilizing co-attentive equivariant feature mappings consistently outperform those utilizing conventional ones both for fully (rotated MNIST) and partially (CIFAR-10) rotational settings.
Escaping Saddle Points Faster with Stochastic Momentum

Stochastic gradient descent (SGD) with stochastic momentum is popular in nonconvex stochastic optimization and particularly for the training of deep neural networks. In standard SGD, parameters are updated by improving along the path of the gradient at the current iterate on a batch of examples, where the addition of a ``momentum'' term biases the update in the direction of the previous change in parameters. In non-stochastic convex optimization one can show that a momentum adjustment provably reduces convergence time in many settings, yet such results have been elusive in the stochastic and non-convex settings. At the same time, a widely-observed empirical phenomenon is that in training deep networks stochastic momentum appears to significantly improve convergence time, variants of it have flourished in the development of other popular update methods, e.g. ADAM, AMSGrad, etc. Yet theoretical justification for the use of stochastic momentum has remained a significant open question. In this paper we propose an answer: stochastic momentum improves deep network training because it modifies SGD to escape saddle points faster and, consequently, to more quickly find a second order stationary point. Our theoretical results also shed light on the related question of how to choose the ideal momentum parameter--our analysis suggests that $\beta \in [0,1)$ should be large (close to 1), which comports with empirical findings. We also provide experimental findings that further validate these conclusions.
Actor-Critic Provably Finds Nash Equilibria of Linear-Quadratic Mean-Field Games

We study discrete-time mean-field Markov games with infinite numbers of agents where each agent aims to minimize its ergodic cost. We consider the setting where the agents have identical linear state transitions and quadratic cost functions, while the aggregated effect of the agents is captured by the population mean of their states, namely, the mean-field state. For such a game, based on the Nash certainty equivalence principle, we provide sufficient conditions for the existence and uniqueness of its Nash equilibrium. Moreover, to find the Nash equilibrium, we propose a mean-field actor-critic algorithm with linear function approximation, which does not require knowing the model of dynamics. Specifically, at each iteration of our algorithm, we use the single-agent actor-critic algorithm to approximately obtain the optimal policy of the each agent given the current mean-field state, and then update the mean-field state. In particular, we prove that our algorithm converges to the Nash equilibrium at a linear rate. To the best of our knowledge, this is the first success of applying model-free reinforcement learning with function approximation to discrete-time mean-field Markov games with provable non-asymptotic global convergence guarantees.
Additive Powers-of-Two Quantization: A Non-uniform Discretization for Neural Networks

We proposed Additive Powers-of-Two (APoT) quantization, an efﬁcient nonuniform quantization scheme that attends to the bell-shaped and long-tailed distribution of weights in neural networks. By constraining all quantization levels as a sum of several Powers-of-Two terms, APoT quantization enjoys overwhelming efﬁciency of computation and a good match with weights’ distribution. A simple reparameterization on clipping function is applied to generate better-deﬁned gradient for updating of optimal clipping threshold. Moreover, weight normalization is presented to reﬁne the input distribution of weights to be more stable and consistent. Experimental results show that our proposed method outperforms state-of-the-art methods, and is even competitive with the full-precision models demonstrating the effectiveness of our proposed APoT quantization. For example, our 3-bit quantized ResNet-34 on ImageNet only drops 0.3% Top-1 and 0.2% Top-5 accuracy without bells and whistles, while the computation of our model is approximately 2× less than uniformly quantized neural networks.
Protecting intellectual property from insider threats

Purpose The purpose of this paper is to propose a theory of information security intelligence and examine the effects of managers’ information security intelligence (MISI) on employees’ procedural countermeasure awareness and information security policy (ISP) compliance intention. Design/methodology/approach A survey approach and structural equation modeling is utilized. Partial least squares (WarpPLS 6.0) and nonlinear algorithm are employed to analyze and examine the hypotheses. In total, 324 employees from companies in South Korea participated in the survey, which was conducted by a professional survey service company. Findings MISI positively affects employees’ awareness of information security procedural countermeasures; information security knowledge and problem-solving skills have positive effects on procedural countermeasures awareness; MISI increases employees’ compliance intention through procedural countermeasure awareness; and information security procedural countermeasures positively affect employees’ ISP compliance intention. Research limitations/implications This study proposes a theory of information security intelligence and examines its impacts on employees’ compliance intentions. The study highlights the mediating role of information security procedural countermeasures between information security intelligence and employees’ compliance intentions. Practical implications Managers should improve and explicitly demonstrate information security knowledge and problem-solving skills to increase employees’ ISP compliance intention. To protect the organization’s intellectual capital, managers should champion the development and promotion of PCM, rather than leave these functions to the information security group. Originality/value This is the first empirical study to propose and validate MISI.
The Missing Data Encoder: Cross-Channel Image Completion with Hide-and-Seek Adversarial Network

Image completion is the problem of generating whole images from fragments only. It encompasses inpainting (generating a patch given its surrounding), reverse inpainting/extrapolation (generating the periphery given the central patch) as well as colorization (generating one or several channels given other ones). In this paper, we employ a deep network to perform image completion, with adversarial training as well as perceptual and completion losses, and call it the “missing data encoder” (MDE). We consider several configurations based on how the seed fragments are chosen. We show that training MDE for “random extrapolation and colorization” (MDE-REC), i.e. using random channel-independent fragments, allows a better capture of the image semantics and geometry. MDE training makes use of a novel “hide-and-seek” adversarial loss, where the discriminator seeks the original non-masked regions, while the generator tries to hide them. We validate our models qualitatively and quantitatively on several datasets, showing their interest for image completion, representation learning as well as face occlusion handling.
A spatial analysis of soybean land suitability using spatial decision tree algorithm

Soybean is one of the national strategic commodities because of its role as an income and nutrition source for Indonesian people. Until now, the performance of soybean agribusiness is still far from expectations, as indicated by stagnant production and increasing import. One of the problems of the soybean production to achieve self-sufficiency is the unavailability of land allocation that is intended explicitly for planting soybean. This work aims to evaluate the soybean land suitability in Bogor District, West Java Province, Indonesia using the spatial decision tree algorithm. The proposed algorithm has been applied to a spatial dataset consisting of a target layer that represents soybean land suitability and seven explanatory layers that represent land characteristics of Bogor District. The result is a spatial decision tree that generated 26 rules with accuracy of 92.73% and the relief variable as the root node.
Generic Construction of Adaptively Secure Anonymous Key-Policy Attribute-Based Encryption from Public-Key Searchable Encryption

Public-key encryption with keyword search (PEKS) is a cryptographic primitive that allows us to search for particular keywords over ciphertexts without recovering plaintexts. By using PEKS in cloud services, users can outsource their data in encrypted form without sacrificing search functionality. Concerning PEKS that can specify logical disjunctions and logical conjunctions as a search condition, it is known that such PEKS can be (generically) constructed from anonymous attribute-based encryption (ABE). However, it is not clear whether it is possible to construct this types of PEKS without using ABE which may require large computational/communication costs and strong mathematical assumptions. In this paper, we show that ABE is crucial for constructing PEKS with the above functionality. More specifically, we give a generic construction of anonymous key-policy ABE from PEKS whose search condition is specified by logical disjunctions and logical conjunctions. Our result implies such PEKS always requires large computational/communication costs and strong mathematical assumptions corresponding to those of ABE.
High resolution, programmable aperture light field laparoscope for quantitative depth mapping

Recent applications have shown that light field imaging can be useful for developing uniaxial three-dimensional (3D) endoscopes. The immediate challenges in implementation are a tradeoff in lateral resolution and acquiring enough depth information in the physically limited environment of minimally invasive surgery. Here we propose using programmable aperture light field imaging in laparoscopy to capture 3D information without sacrificing the camera sensor’s native, high spatial resolution. This hybrid design utilizes a programmable aperture to preserve the conventional laparoscope’s functionality and, upon demand, to compute a depth map for surgical guidance. A working prototype is demonstrated.
Development of physics mobile learning media in optical instruments for senior high school student using android studio

Abstract Physics learning requires good media in order to convey the relation of physics with everyday life. Lesson material delivered in the form of multimedia is accepted by students better than material in the conventional form. Smartphone-based media is one of the breakthroughs that can be implemented into physics learning. The purpose of this study is to produce an Android-based physics mobile learning media product that are valid and feasible for use in learning. This research is a Research &amp; Development study with procedures adapted from the 4-D model. Based on the results of the development, obtained an Android-based physics mobile learning media product on the Optical Instruments material. The product was developed using the Android Studio 3.2 IDE at the minimum level of API 14 Ice Cream Sandwich. Media products are declared valid and can be used in physics learning with expert assessment results on the Very Good criteria.
Automation of the selection process and graphical display of measuring instruments on single-line diagrams of 6–220 kV substations

This paper describes an algorithm for automated selection and placement of instrumentation in switchgear substations of substations developed in the framework of computer-aided design substations. The algorithm meets the regulatory and technical documentation in this area and allows, based on the design specification, to carry out the process of selecting and placing measuring devices without time-consuming for the engineer of the designer. The developed computer-aided design system is intended as a workplace for an engineer of electrical engineering department of a design institute. The work is carried out with the financial support of a grant from the President of the Russian Federation for young scientists - candidates of sciences MK-939.2019.8.
Fast Adaptation to Super-Resolution Networks via Meta-Learning

Conventional supervised super-resolution (SR) approaches are trained with massive external SR datasets but fail to exploit desirable properties of the given test image. On the other hand, self-supervised SR approaches utilize the internal information within a test image but suffer from computational complexity in run-time. In this work, we observe the opportunity for further improvement of the performance of SISR without changing the architecture of conventional SR networks by practically exploiting additional information given from the input image. In the training stage, we train the network via meta-learning; thus, the network can quickly adapt to any input image at test time. Then, in the test stage, parameters of this meta-learned network are rapidly fine-tuned with only a few iterations by only using the given low-resolution image. The adaptation at the test time takes full advantage of patch-recurrence property observed in natural images. Our method effectively handles unknown SR kernels and can be applied to any existing model. We demonstrate that the proposed model-agnostic approach consistently improves the performance of conventional SR networks on various benchmark SR datasets.
A dockerized framework for hierarchical frequency-based document clustering on cloud computing infrastructures

Abstract Scalable big data analysis frameworks are of paramount importance in the modern web society, which is characterized by a huge number of resources, including electronic text documents. Document clustering is an important field in text mining and is commonly used for document organization, browsing, summarization and classification. Hierarchical clustering methods construct a hierarchy structure that, combined with the produced clusters, can be useful in managing documents, thus making the browsing and navigation process easier and quicker, and providing only relevant information to the users’ queries by leveraging the structure relationships. Nevertheless, the high computational cost and memory usage of baseline hierarchical clustering algorithms render them inappropriate for the vast number of documents that must be handled daily. In this paper, we propose a new scalable hierarchical clustering framework, which uses the frequency of the topics in the documents to overcome these limitations. Our work consists of a binary tree construction algorithm that creates a hierarchy of the documents using three metrics (Identity, Entropy, Bin Similarity), and a branch breaking algorithm which composes the final clusters by applying thresholds to each branch of the tree. The clustering algorithm is followed by a meta-clustering module which makes use of graph theory to obtain insights in the leaf clusters’ connections. The feature vectors representing each document derive from topic modeling. At the implementation level, the clustering method has been dockerized in order to facilitate its deployment on cloud computing infrastructures. Finally, the proposed framework is evaluated on several datasets of varying size and content, achieving significant reduction in both memory consumption and computational time over existing hierarchical clustering algorithms. The experiments also include performance testing on cloud resources using different setups and the results are promising.
Deep convolutional neural networks for detection and classification of tumors in mammograms

Automatic detection and classification of the masses in mammograms are still a big challenge and play a crucial role to assist radiologists for accurate diagnosis. In this paper, we propose a novel Computer-Aided Diagnosis (CAD).System based on one of the regional deep learning techniques, a ROI-based Convolutional Neural Network (CNN) which is called You Only Look Once (YOLO), in this work we used YOLO version three (YOLOv3). YOLO based CAD system can handle detection and classification simultaneously in one framework. It's a little bigger than last time but more accurate. The proposed CAD system contains four steps : preprocessing of mammograms, feature extraction utilizing deep convolutional networks, mass detection with confidence, and finally mass classification using fully connected neural networks (FC-NNs).