Reviewerly/exHarmony huggingface dataset:
Cryptoanalysis on optical image encryption systems based on the vector decomposition technique in the Fourier domain

In this paper, the security of optical cryptosystems based on the vector decomposition technique in the Fourier domain is analyzed. Compared to the conventional cryptosystem based on the equal modulus decomposition (EMD) technique, an additional EMD structure is introduced in the cascaded EMD-based cryptosystem; hence, the mask including the phase information of the Fourier spectrum is further encoded in the second EMD structure to enhance the security level. However, it is shown that the number of the private keys has not been increased in the cascaded EMD-based cryptosystem, which makes it possible to crack the cascaded EMD-based cryptosystem. Therefore, a chosen-plaintext attack (CPA) and a special attack with an arbitrarily given private key are proposed to retrieve information from encoded images obtained by the cascaded EMD-based cryptosystem. In addition, the security of the cryptosystem based on the random modulus decomposition (RMD) technique is also analyzed. Compared to the EMD-based cryptosystem in which the Fourier spectrum is decomposed into two vectors with equal moduli, the security level of the cryptosystem has been improved by using the RMD technique to decompose the spectrum into vectors with unequal moduli to decrease the number of the amplitude constraints. However, it is found that the arbitrarily given ciphertext provides the attackers enough information to retrieve the precise information of the plaintext without any knowledge of the private keys. A special attack is proposed to crack the RMD-based cryptosystem. This is the first time to report that these two cryptosystems based on the vector decomposition technique are attacked successfully. Numerical simulation is conducted to validate the feasibility and effectiveness of the proposed attacks.
Visual Dialog

We introduce the task of Visual Dialog, which requires an AI agent to hold a meaningful dialog with humans in natural, conversational language about visual content. Specifically, given an image, a dialog history, and a question about the image, the agent has to ground the question in image, infer context from history, and answer the question accurately. Visual Dialog is disentangled enough from a specific downstream task so as to serve as a general test of machine intelligence, while being sufficiently grounded in vision to allow objective evaluation of individual responses and benchmark progress. We develop a novel two-person real-time chat data-collection protocol to curate a large-scale Visual Dialog dataset (VisDial). VisDial v0.9 has been released and consists of $\sim$ ∼ 1.2M dialog question-answer pairs from 10-round, human-human dialogs grounded in $\sim$ ∼ 120k images from the COCO dataset. We introduce a family of neural encoder-decoder models for Visual Dialog with 3 encoders—Late Fusion, Hierarchical Recurrent Encoder and Memory Network (optionally with attention over image features)—and 2 decoders (generative and discriminative), which outperform a number of sophisticated baselines. We propose a retrieval-based evaluation protocol for Visual Dialog where the AI agent is asked to sort a set of candidate answers and evaluated on metrics such as mean-reciprocal-rank and recall $@k$ @ k of human response. We quantify the gap between machine and human performance on the Visual Dialog task via human studies. Putting it all together, we demonstrate the first ‘visual chatbot’! Our dataset, code, pretrained models and visual chatbot are available on https://visualdialog.org .
Visual analysis of attention-based end-to-end speech recognition

An end-to-end speech recognition model consisting of a single integrated neural network model was recently proposed.The end-to-end model does not need several training steps, and its structure is easy to understand.However, it is difficult to understand how the model recognizes speech internally.In this paper, we visualized and analyzed the attention-based end-to-end model to elucidate its internal mechanisms.We compared the acoustic model of the BLSTM-HMM hybrid model with the encoder of the end-to-end model, and visualized them using t-SNE to examine the difference between neural network layers.As a result, we were able to delineate the difference between the acoustic model and the end-to-end model encoder.Additionally, we analyzed the decoder of the end-to-end model from a language model perspective.Finally, we found that improving end-to-end model decoder is necessary to yield higher performance.
Knowledge-Augmented Language Model and its Application to Unsupervised Named-Entity Recognition

Traditional language models are unable to efficiently model entity names observed in text. All but the most popular named entities appear infrequently in text providing insufficient context. Recent efforts have recognized that context can be generalized between entity names that share the same type (e.g., \emph{person} or \emph{location}) and have equipped language models with access to an external knowledge base (KB). Our Knowledge-Augmented Language Model (KALM) continues this line of work by augmenting a traditional model with a KB. Unlike previous methods, however, we train with an end-to-end predictive objective optimizing the perplexity of text. We do not require any additional information such as named entity tags. In addition to improving language modeling performance, KALM learns to recognize named entities in an entirely unsupervised way by using entity type information latent in the model. On a Named Entity Recognition (NER) task, KALM achieves performance comparable with state-of-the-art supervised models. Our work demonstrates that named entities (and possibly other types of world knowledge) can be modeled successfully using predictive learning and training on large corpora of text without any additional information.
Instance Segmentation based Semantic Matting for Compositing Applications

Image compositing is a key step in film making and image editing that aims to segment a foreground object and combine it with a new background. Automatic image compositing can be done easily in a studio using chroma-keying when the background is pure blue or green. However, image compositing in natural scenes with complex backgrounds remains a tedious task, requiring experienced artists to hand-segment. In order to achieve automatic compositing in natural scenes, we propose a fully automated method that integrates instance segmentation and image matting processes to generate high-quality semantic mattes that can be used for image editing task. Our approach can be seen both as a refinement of existing instance segmentation algorithms and as a fully automated semantic image matting method. It extends automatic image compositing techniques such as chroma-keying to scenes with complex natural backgrounds without the need for any kind of user interaction. The output of our approach can be considered as both refined instance segmentations and alpha mattes with semantic meanings. We provide experimental results which show improved performance results as compared to existing approaches.
Uncertain Interval Data EFCM-ID Clustering Algorithm Based on Machine Learning

In clustering problems based on fuzzy c-means (FCM) for uncertain interval data, points within the interval are usually assumed to have uniform distribution, resulting in the difficulty of accurately describing the interval. Furthermore, the clustering results are considerably affected by the initial clustering centers, and the update speed of the membership degree is slow. To address these problems, a new clustering algorithm called uncertain FCM for interval data (EFCM-ID) is presented. On the basis of a quartile, a median quartile-spacing distance measurement for generally distributed interval data based on machine learning is designed to precisely determine these data. Simultaneously, we sample the whole dataset and consider the density centers as the initial clustering centers to increase accuracy. We call this method samplingbased density-center selection (SDCS). To reduce the running time, a new measurement based on competitive-learning theory to update the membership is developed. It accelerates the update speed by different degrees according to value of the membership degree. Experiments conducted on synthetic interval datasets show the feasibility of EFCM-ID.
Low-cost Unsupervised Outlier Detection by Autoencoders with Robust Estimation

Recently, an unsupervised outlier detection method based on the reconstruction errors of an autoencoder (AE), which achieves high detection accuracy, was proposed. This method, however, requires a high calculation cost because of its ensemble scheme. Therefore, in this paper, we propose a novel AE-based unsupervised method that can achieve high detection performance at a low calculation cost. Our method introduces the concept of robust estimation to appropriately restrict reconstruction capability and ensure robustness. Experimental results on several public benchmark datasets show that our method outperforms well-known outlier detection methods and at a low calculation cost.
Readability of Patient Education Texts Presented on the Internet Related to Vaccines

Objective This study aimed to compare the readability and the contents of the patient information texts regarding vaccination on Turkish Internet sites. Material and Methods Websites were searched in the Google search engine using the keywords “what is vaccination?” “benefits of vaccination,” “harms of vaccination,” and “side effects of vaccination” in May 2018. Readability was determined using Ateşman and Bezirci–Yilmaz formulas. According to Ateşman readability formula, the higher the readability of a text, the easier it can be read. Readability of “health-care professional” websites was compared with that of “non–health-care professional” websites. The content of the websites was analyzed by two pediatricians. Results After the exclusion of the website not fulfilling the criteria, 44 websites, of which 20 were created by health-care professionals and 24 by non–health-care professionals, were evaluated. Ateşman readability scores were 53.62 ± 14.27 and 54.30 ± 8.60 for websites created by health-care professionals and non–health-care professionals, respectively. Mean readability level was “moderately difficult” for both groups according to Ateşman formula, and there was no significant difference between the two groups (p = 0.83). Mean readability level was categorized as at “secondary-high school” with Bezirci–Yilmaz readability formula, and there was no significant difference between the two groups (p = 0.68). The percentage of the contents of websites prepared by health-care professionals was found to be significantly higher when compared with non–health-care professionals (p = 0.04). Conclusion It was found that the readability of the patient information texts in the web pages related to vaccination was moderately difficult and that the text content was insufficient. To improve the readability, short sentences should be prepared by health-care professionals with few words and syllables. Existing texts should be revised in the light of current information.
On the Asymptotic Capacity of $X$-Secure $T$-Private Information Retrieval with Graph Based Replicated Storage

The problem of private information retrieval with graph-based replicated storage was recently introduced by Raviv, Tamo and Yaakobi. Its capacity remains open in almost all cases. In this work the asymptotic (large number of messages) capacity of this problem is studied along with its generalizations to include arbitrary $T$-privacy and $X$-security constraints, where the privacy of the user must be protected against any set of up to $T$ colluding servers and the security of the stored data must be protected against any set of up to $X$ colluding servers. A general achievable scheme for arbitrary storage patterns is presented that achieves the rate $(\rho_{\min}-X-T)/N$, where $N$ is the total number of servers, and each message is replicated at least $\rho_{\min}$ times. Notably, the scheme makes use of a special structure inspired by dual Generalized Reed Solomon (GRS) codes. A general converse is also presented. The two bounds are shown to match for many settings, including symmetric storage patterns. Finally, the asymptotic capacity is fully characterized for the case without security constraints $(X=0)$ for arbitrary storage patterns provided that each message is replicated no more than $T+2$ times. As an example of this result, consider PIR with arbitrary graph based storage ($T=1, X=0$) where every message is replicated at exactly $3$ servers. For this $3$-replicated storage setting, the asymptotic capacity is equal to $2/\nu_2(G)$ where $\nu_2(G)$ is the maximum size of a $2$-matching in a storage graph $G[V,E]$. In this undirected graph, the vertices $V$ correspond to the set of servers, and there is an edge $uv\in E$ between vertices $u,v$ only if a subset of messages is replicated at both servers $u$ and $v$.
Infrared bundle adjusting and clustering method for head-mounted display and Leap Motion calibration

Abstract Leap Motion has become widely used due to its ability to recognize intuitive hand gestures or accurate finger positions. Attaching a Leap Motion to a virtual reality head-mounted display (VR HMD) is highly interoperable with virtual objects in virtual reality. However, it is difficult for a virtual reality application to identify the accurate position where the Leap Motion is attached to the HMD. This causes errors in the positions of the actual user’s hands and the virtual hands, which makes the interaction in virtual reality difficult. In this paper, a method that calibrates an output area in VR HMD and a sensing area in Leap Motion is proposed. The difference in the origin coordinate between VR HMD and Leap Motion is derived using the proposed method. The position of the Leap Motion attached to the HMD was determined through an experiment using the proposed calibration technique, and the error was approximately 0.757 cm. Accordingly, it enables more intuitive interactions in virtual reality applications.
Metode SURF dan FLANN untuk Identifikasi Nominal Uang Kertas Rupiah Tahun Emisi 2016 pada Variasi Rotasi

In December 2016, Bank Indonesia (BI) officially launched the 2016 Year Emission Rupiah. With the development of technology, the process of buying and selling are not only possible between humans and humans, but humans with a machine. In addition, the machine must also be able to read and recognize the nominal banknotes in various variations of face and rotation. This is because humans can put money in machines with various variations of face and rotation. This study aims to apply and analyze the level of accuracy of nominal rupiah banknotes identification with the SURF and FLANN methods for rotation variation. Testing for identification of nominal rupiah banknotes is carried out with different rotation variations, namely 0o, 90o, 180o, and 270o. The proposed identification method provides 100% of accuracy.
Cascaded Point Network for 3D Hand Pose Estimation

Recent PointNet-family hand pose methods have the advantages of high pose estimation performance and small model size, and it is a key problem to get effective sample points for PointNet-family methods. In this paper, we propose a two-stage coarse to fine hand pose estimation method, which belongs to PointNet-family methods and explores a new sample point strategy. In the first stage, we use 3D coordinate and surface normal of normalized point cloud as input to regress coarse hand joints. In the second stage, we use the hand joints in the first stage as the initial sample points to refine the hand joints. Experiments on widely used datasets demonstrate that using joints as sample points is more effective and our method achieves top-rank performance.
Progressive Attention Memory Network for Movie Story Question Answering

This paper proposes the progressive attention memory network (PAMN) for movie story question answering (QA). Movie story QA is challenging compared to VQA in two aspects: (1) pinpointing the temporal parts relevant to answer the question is difficult as the movies are typically longer than an hour, (2) it has both video and subtitle where different questions require different modality to infer the answer. To overcome these challenges, PAMN involves three main features: (1) progressive attention mechanism that utilizes cues from both question and answer to progressively prune out irrelevant temporal parts in memory, (2) dynamic modality fusion that adaptively determines the contribution of each modality for answering the current question, and (3) belief correction answering scheme that successively corrects the prediction score on each candidate answer. Experiments on publicly available benchmark datasets, MovieQA and TVQA, demonstrate that each feature contributes to our movie story QA architecture, PAMN, and improves performance to achieve the state-of-the-art result. Qualitative analysis by visualizing the inference mechanism of PAMN is also provided.
Three Dimensional Simulation of Changes in Air Flow on a Jet Engine Desktop Based

Learning about changes in air flow on jet engines requires a clear understanding because there are parts of the jet engine that work according to their functions and are interconnected. People often have difficulty understanding learning about changes in air flow in the jet engine and also about the brayton cycle. This study aims to make a simulation that explains the changes in air flow in a jet engine with a three-dimensional animated form in order to improve understanding of learning about it. The process of making three-dimensional simulations uses animation modeling techniques, texturing, lighting, editing, and rendering. In modeling and texturing on jet engines following the Brayton cycle. The three-dimensional simulation results show that this simulation can be run by computers with Windows 7 and Windows 8 operating systems, and from the results of user test analysis 80.2% agree that this three-dimensional simulation can be understood and can explain changes in airflow in jet engines.
A comment on the "A unified Bayesian inference framework for generalized linear models"

The recent work `A unified Bayesian inference framework for generalized linear models' \cite{meng1} shows that the GLM can be solved via iterating between the standard linear module (SLM) (running with standard Bayesian algorithm) and the minimum mean squared error (MMSE) module. The proposed framework utilizes expectation propagation and corresponds to the sum-product version \cite{Rangan1}. While in \cite{Rangan1}, a max-sum GAMP is also proposed. What is their intrinsic relationship? This comment aims to answer this.
Free-form Video Inpainting with 3D Gated Convolution and Temporal PatchGAN

Free-form video inpainting is a very challenging task that could be widely used for video editing such as text removal. Existing patch-based methods could not handle non-repetitive structures such as faces, while directly applying image-based inpainting models to videos will result in temporal inconsistency (see http://bit.ly/2Fu1n6b ). In this paper, we introduce a deep learn-ing based free-form video inpainting model, with proposed 3D gated convolutions to tackle the uncertainty of free-form masks and a novel Temporal PatchGAN loss to enhance temporal consistency. In addition, we collect videos and design a free-form mask generation algorithm to build the free-form video inpainting (FVI) dataset for training and evaluation of video inpainting models. We demonstrate the benefits of these components and experiments on both the FaceForensics and our FVI dataset suggest that our method is superior to existing ones. Related source code, full-resolution result videos and the FVI dataset could be found on Github https://github.com/amjltc295/Free-Form-Video-Inpainting .
A unified method for completions of posets and closure spaces

In this paper, we introduce the notion of a subset system on the category CL $$_{0}$$ of all $$T_{0}$$ closure spaces. And for each subset system Z, the concept of a Z-convergence space arises as a generalization of sober spaces and monotone convergence spaces. We define a Z-completion of a $$T_{0}$$ closure space X to be a Z-convergence space $$X_{Z}$$ together with a continuous function from X to $$X_{Z}$$ satisfying the universal property. In the case that Z is a hereditary subset system, we prove that: (1) for each $$T_{0}$$ closure space X, the set of all Z-tapered closed subsets of X endowed with the corresponding closure system is a Z-completion of X; (2) the category CS $$_{Z}$$ of all Z-convergence spaces is reflective in the category CL $$_{0}$$ . The Dedekind–MacNeille completion, the Alexandroff completion, the Frink ideal completion, the ideal completion, the Z-completion for posets, the D-completion, the sobrification, the directed completion, etc., are special cases of the Z-completion.
CYBERBULLYING, HAPPINESS, AND STYLE OF HUMOR AMONG PERPETRATORS: IS THERE A RELATIONSHIP?

Purpose: Cyberbullying is becoming a universal concern among parent, teacher, and school. Undesirable consequences have impacted for many teenagers in the world. The current study explores the cyberbullying phenomenon among Indonesian teenagers especially to examine the style of humor and happiness as predictors of cyberbullying perpetrators.&#x0D; Methodology: 156 respondents from a private senior high school at Palangka Raya city was involved in this study. Regression analysis was utilized to test the hypotheses.&#x0D; Results: The result showed that only happiness, self-defeating humor, and aggressive humor were related positively to the reports of cyberbullying conduct.&#x0D; Implications: To lower cyberbullying among adolescents, both the family and the school must collaborate synergistically. Recommendations of the study are explained further in the article.
Analytical Moment Regularizer for Gaussian Robust Networks

Despite the impressive performance of deep neural networks (DNNs) on numerous vision tasks, they still exhibit yet-to-understand uncouth behaviours. One puzzling behaviour is the subtle sensitive reaction of DNNs to various noise attacks. Such a nuisance has strengthened the line of research around developing and training noise-robust networks. In this work, we propose a new training regularizer that aims to minimize the probabilistic expected training loss of a DNN subject to a generic Gaussian input. We provide an efficient and simple approach to approximate such a regularizer for arbitrary deep networks. This is done by leveraging the analytic expression of the output mean of a shallow neural network; avoiding the need for the memory and computationally expensive data augmentation. We conduct extensive experiments on LeNet and AlexNet on various datasets including MNIST, CIFAR10, and CIFAR100 demonstrating the effectiveness of our proposed regularizer. In particular, we show that networks that are trained with the proposed regularizer benefit from a boost in robustness equivalent to performing 3-21 folds of data augmentation.