Reviewerly/exHarmony huggingface dataset:
Efficient and Lossless Polynomial-Based Secret Image Sharing for Color Images

In today's era of developed network, it is particularly important to ensure the security of information. Secret image sharing is a kind of secret information protection technology with loss tolerance by sharing a secret image to a number of shadow images. Most of the researches on secret image sharing focus on grayscale images, but the images we use in our daily life are mostly in color, so the research on the secret image sharing for color image is very meaningful and has great application value. The traditional color image sharing scheme is to share and recover R, G, and B color planes separately, which cause that the time of sharing and recovering one color image is at least triple that of sharing and recovering one grayscale image with the same size as the color image. In this paper, we propose an efficient and lossless secret image sharing scheme for color images. We concatenate the R, G, and B values of every color pixel into a long integer and share it by the polynomial-based secret image sharing, thus the sharing efficiency of color images is greatly improved by reducing the number of sharing operations to 1/3 of the traditional scheme. And we achieve lossless recovery by taking GF(16777259) as the finite field of polynomial operations. 16777259 is the smallest prime number greater than 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">24</sup> - 1, so we intend to screen the random numbers to drop the invalid share values. Theoretical analyses and experiments are used to prove the effectiveness of the proposed scheme.
ARAML: A Stable Adversarial Training Framework for Text Generation

Most of the existing generative adversarial networks (GAN) for text generation suffer from the instability of reinforcement learning training algorithms such as policy gradient, leading to unstable performance.To tackle this problem, we propose a novel framework called Adversarial Reward Augmented Maximum Likelihood (ARAML).During adversarial training, the discriminator assigns rewards to samples which are acquired from a stationary distribution near the data rather than the generator's distribution.The generator is optimized with maximum likelihood estimation augmented by the discriminator's rewards instead of policy gradient.Experiments show that our model can outperform state-of-the-art text GANs with a more stable training process.
How to account for mispellings: Quantifying the benefit of character representations in neural content scoring models

Character-based representations in neural models have been claimed to be a tool to overcome spelling variation in in word token-based input. We examine this claim in neural models for content scoring. We formulate precise hypotheses about the possible effects of adding character representations to word-based models and test these hypotheses on large-scale real world content scoring datasets. We find that, while character representations may provide small performance gains in general, their effectiveness in accounting for spelling variation may be limited. We show that spelling correction can provide larger gains than character representations, and that spelling correction improves the performance of models with character representations. With these insights, we report a new state of the art on the ASAP-SAS content scoring dataset.
A CNN-Based Image Compression Scheme Compatible with JPEG-2000

We propose a convolutional neural network (CNN) based image compression scheme that is compatible with JPEG-2000. Specifically, our scheme reuses the existing JPEG-2000 encoders to achieve bitstream, and features two components in addition to JPEG-2000: bitstream re-compression and decoder-side post-processing. First, we propose an advanced arithmetic codec that adopts CNN-based probability estimation to exploit the correlation between wavelet coefficients within and across subbands. Second, we propose a CNN-based post-processing method to improve the quality of reconstructed images. Experimental results show that the proposed two CNN-based components both help improve the compression efficiency by a significant margin.
Classifying Hate Speech Using a Two-Layer Model

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An Analysis of Source-Side Grammatical Errors in NMT

The quality of Neural Machine Translation (NMT) has been shown to significantly degrade when confronted with source-side noise.We present the first large-scale study of stateof-the-art English-to-German NMT on real grammatical noise, by evaluating on several Grammar Correction corpora.We present methods for evaluating NMT robustness without true references, and we use them for extensive analysis of the effects that different grammatical errors have on the NMT output.We also introduce a technique for visualizing the divergence distribution caused by a sourceside error, which allows for additional insights.
Efficient Implementation of Second-Order Stochastic Approximation Algorithms in High-Dimensional Problems

Stochastic approximation (SA) algorithms have been widely applied in minimization problems when the loss functions and/or the gradient information are only accessible through noisy evaluations. Stochastic gradient (SG) descent-a first-order algorithm and a workhorse of much machine learning-is perhaps the most famous form of SA. Among all SA algorithms, the second-order simultaneous perturbation stochastic approximation (2SPSA) and the second-order stochastic gradient (2SG) are particularly efficient in handling highdimensional problems, covering both gradient-free and gradientbased scenarios. However, due to the necessary matrix operations, the per-iteration floating-point-operations (FLOPs) cost of the standard 2SPSA/2SG is O(p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ), where p is the dimension of the underlying parameter. Note that the O(p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) FLOPs cost is distinct from the classical SPSA-based per-iteration O(1) cost in terms of the number of noisy function evaluations. In this work, we propose a technique to efficiently implement the 2SPSA/2SG algorithms via the symmetric indefinite matrix factorization and show that the FLOPs cost is reduced from O(p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) to O(p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). The formal almost sure convergence and rate of convergence for the newly proposed approach are directly inherited from the standard 2SPSA/2SG. The improvement in efficiency and numerical stability is demonstrated in two numerical studies.
Content Adaptive Optimization for Neural Image Compression

A data processing system includes a computing platform having a hardware processor and a memory storing a data compression software code. The hardware processor executes the data compression software code to receive a series of compression input data and encode a first compression input data of the series to a latent space representation of the first compression input data. The data compression software code further decodes the latent space representation to produce an. input space representation of the first compression input data corresponding to the latent space representation, and generates f refined latent values for re-encoding the first compression input data based on a comparison of the first compression input data with its input space representation. The data compression software code then re-encodes the first compression input data using the refined latent values to produce a first compressed data corresponding to the first compression input data.
SwICS: Section-Wise In-Text Citation Score

Since past several years, finding relevant documents from plethora of web repositories has become prime attention of the scientific community. To find out relevant research articles, state-of-the-art techniques employ content, metadata, citations, and collaborative filtering based approaches. Among all of them, citation based approaches hold strong potential because most of the time, authors cite relevant papers. Bibliographic coupling is one of the well-known citation based approaches for recommending relevant papers. In this paper, we present an approach SwICS that harnesses number of common references between pair of documents as similarity measure whereas the distribution of in-text citations within the text are not analyzed. The proposed approach explores the in-text citation frequencies within contents of the paper and in-text citation patterns between different logical sections for bibliographically coupled papers. For evaluation, the employed data set contains 1150 research documents are obtained from a well-known autonomous citation index known as: CiteSeer. A comprehensive user study is conducted to build a gold standard for comparing the proposed approach. The approach is compared with the state-of-the-art bibliographic coupling and content similarity based techniques. The comparison results revealed that proposed approach significantly performs better than the contemporary approaches. The comparison result with gold standard yielded an average of 0.73. The average gain achieved by the proposed approach is 60% from state-of-the-art: bibliographic coupling. Whereas, the correlation between gold standard and content based approach remains 20%. The proposed approach can play a significant role for search engines and citation indexers in terms of improving the quality of their results.
Collaborative configuration approaches in software product lines engineering: A systematic mapping study

In the context of software product line engineering, collaborative configuration is a decision-making process where multiple stakeholders contribute in building a single product specification. Several approaches addressing collaboration during configuration have already been proposed, but we still have little hard evidence about their effectiveness and little understanding about how collaborative configuration process should be carried out. This paper presents a classification framework to help understand existing collaborative configuration approaches. To elaborate it, a systematic mapping study was conducted guided by three research questions and 41 primary studies was selected out of 238 identified ones. The proposed framework is composed of four dimensions capturing main aspects related to configuration approaches: purpose, collaboration, process and tool. Each dimension is itself multi-faceted and a set of attributes is associated to each facet. Using this framework, we position and classify existing approaches, structure the representation of each approach characteristics, highlight their strengths and weaknesses, compare them to each other, and identify open issues. This study gives a solid foundation for classifying existing and future approaches for product lines collaborative configuration. Researchers and practitioners can use our framework for identifying existing research/technical gaps to attack, better scoping their own contributions, or understanding existing ones.
LITHOLOGICAL CLASSIFICATION USING MULTI-SENSOR DATA AND CONVOLUTIONAL NEURAL NETWORKS

Abstract. Deep learning has been used successfully in computer vision problems, e.g. image classification, target detection and many more. We use deep learning in conjunction with ArcGIS to implement a model with advanced convolutional neural networks (CNN) for lithological mapping in the Mount Isa region (Australia). The area is ideal for spectral remote sensing as there is only sparse vegetation and besides freely available Sentinel-2 and ASTER data, several geophysical datasets are available from exploration campaigns. By fusing the data and thus covering a wide spectral range as well as capturing geophysical properties of rocks, we aim at improving classification accuracies and support geological mapping. We also evaluate the performance of the sensors on their own compared to a joint use as the Sentinel-2 satellites are relatively new and as of now there exist only few studies for geological applications. We developed an end-to-end deep learning model using Keras and Tensorflow that consists of several convolutional, pooling and deconvolutional layers. Our model was inspired by the family of U-Net architectures, where low-level feature maps (encoders) are concatenated with high-level ones (decoders), which enables precise localization. This type of network architecture was especially designed to effectively solve pixel-wise classification problems, which is appropriate for lithological classification. We spatially resampled and fused the multi-sensor remote sensing data with different bands and geophysical data into image cubes as input for our model. Pre-processing was done in ArcGIS and the final, fine-tuned model was imported into a toolbox to be used on further scenes directly in the GIS environment. The tool classifies each pixel of the multiband imagery into different types of rocks according to a defined probability threshold. Results highlight the power of using Sentinel-2 in conjunction with ASTER data with accuracies of 75% in comparison to only 70% and 73% for ASTER or Sentinel-2 data alone. These results are similar but examining the different classes shows that there are significant improvements for classes such as dolerite or carbonate sediments that are not that widely distributed in the area. Adding geophysical datasets reduced accuracies to 60%, probably due to an order of magnitude difference in spatial resolution. In comparison, Random Forest (RF) and Support Vector Machines (SVMs) that were trained on the same data only achieve accuracies of 46 % and 36 % respectively. Most insecurity is due to labelling errors and labels with mixed lithologies. However, results show that the U-Netmodel is a powerful alternative to other classifiers for medium-resolution multispectral data.
Collaboration TPS Learning Model and m-Learning Based on Android for Understanding of Trigonometry Concepts with Different Cognitive Style

This research aimed to examine the effect of collaboration Think Pair Share (TPS) Leaning Model and m-Learning based on Android on the understanding of trigonometry concepts and to investigate the interaction between collaborative learning and cognitive style on the understanding of trigonometry concepts.This research involved 80 pre-service teachers of Mathematics Department at STKIP Santu Paulus Ruteng, Province of East Nusa Tenggara, Indonesia.The quasiexperimental research with control class without pre-test was done.Before the experiment, the Group Embedded Figures Test (GEFT) was used to identify students cognitive styles either Field-Dependent (FD) or Field-Independent (FI).The research data were obtained from a test consisting of five questions to measure students' understanding of trigonometry concepts.The data were then analyzed using two-way ANOVA.The results of the research show that 1) the collaboration of TPS Leaning Model and m-Learning based on Android leads to the higher achievement of the students on the understanding of trigonometry concepts if compared to students in conventional groups (expository).2) there was no interaction effect between the collaborative learning and cognitive style of students' understanding of trigonometry concepts.
A Cause-Based Classification Approach for Malicious DNS Queries Detected Through Blacklists

Some of the most serious security threats facing computer networks involve malware. To prevent this threat, administrators need to swiftly remove the infected machines from their networks. One common way to detect infected machines in a network is by monitoring communications based on blacklists. However, detection using this method has the following two problems: no blacklist is completely reliable, and blacklists do not provide sufficient evidence to allow administrators to determine the validity and accuracy of the detection results. Therefore, simply matching communications with blacklist entries is insufficient, and administrators should pursue their detection causes by investigating the communications themselves. In this paper, we propose an approach for classifying malicious DNS queries detected through blacklists by their causes. This approach is motivated by the following observation: a malware communication is divided into several transactions, each of which generates queries related to the malware; thus, surrounding queries that occur before and after a malicious query detected through blacklists help in estimating the cause of the malicious query. Our cause-based classification drastically reduces the number of malicious queries to be investigated because the investigation scope is limited to only representative queries in the classification results. In experiments, we have confirmed that our approach could group 388 malicious queries into 3 clusters, each consisting of queries with a common cause. These results indicate that administrators can briefly pursue all the causes by investigating only representative queries of each cluster, and thereby swiftly address the problem of infected machines in the network.
Evaluation of Mobile Augmented Reality Application for Building Evacuation

Building occupants must know how to properly exit a building should the need ever arise. Being aware of appropriate evacuation procedures eliminates (or reduces) the risk of injury and death occurring during an existing catastrophe. Augmented reality (AR) is increasingly being sought after as a teaching and training tool because it offers a visualization and interaction capability that captures the learner’s attention and enhances the learner’s capacity to retain what was learned. Utilizing the visualization and interaction capability that AR offers and the need for emergency evacuation training, this paper explores mobile AR application (MARA) constructed to help users evacuate a building in the event of an emergency such as a building fire, active shooter, earthquake, and similar circumstances. The MARA was built for Android-based devices using Unity and Vuforia. Its features include the use of intelligent signs (i.e. visual cues to guide users to the exits) to help users evacuate a building. Inter alia, this paper discusses the MARA’s implementation and its evaluation through a user study utilizing the Technology Acceptance Model (TAM) and the System Usability Scale (SUS) frameworks. The results demonstrate the participants’ opinions that the MARA is both usable and effective in helping users evacuate a building.
Double Regularization Matrix Factorization Recommendation Algorithm

With the development of social networks, the research of integrated social information recommendation models has received extensive attention. However, most existing social recommendation models are based on the matrix factorization technique which ignore the impact of the relationships between items on users’ interests, resulting in a decline of recommendation accuracy. To solve this problem, this paper proposes a double regularization matrix factorization recommendation algorithm. The algorithm first uses attribute information and manifold learning to calculate similarity. Then, the matrix factorization model is constrained through the regularization of item association relations and user social relations. Experimental results on real datasets show that the proposed method can effectively alleviate problems such as cold start and data sparsity in the recommender system and improve the recommendation accuracy compared with those of existing methods.
Depth-Controllable Very Deep Super-Resolution Network

Deep learning techniques not only have surpassed humans in several computer vision tasks, but also have achieved state-of-the-art performance on the super-resolution (SR) task, which enhances the resolution of reconstructed images from the observed low-resolution (LR) images. Very Deep Super- Resolution (VDSR) is a popular architecture with decent performance on the SR task. In general, the deeper the VDSR network, the better the performance. Say, on a computing device with limited computational resources, the SR task must support multiple resolutions or multiple output rates. For this multi-rate SR task, the state-of-the-art architectures require multiple networks with varying depths. However as the number of supported rates increases, so does the number of networks imposing incremental computational burden on the computing device. We propose a depth-controllable network and training principles for the multi-rate SR task. The proposed network is configured as a single network regardless of the number of supported rates. The inverse auxiliary loss and contiguous/progressive skip connections are presented to train the network end-to-end throughout the varying number of layers without biasing the performances of specific depths. With three data sets and three scaling factors, the proposed network is compared to the baseline network VDSR, along with the Super-Resolution Convolutional Neural Network (SRCNN). Our network not only requires a single network at varying rates, but also performs as well as the baseline networks in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). Our model outperforms the baseline networks when the depth of layers is more than eleven.
Multiparty Quantum Private Comparison of Size Relation Based on Single-Particle States

In order to reduce resource consumption and improve transmission efficiency, in this paper, using single-particle states, a multiparty quantum privacy comparison size relationship (QPCSR) protocol is proposed. Without leaking the compared private message, the blinded secret integer is encoded into the relative phase of single-particle by performing unitary operation. After each of the transformed quantum states has been measured, respectively, the comparison result of n participants' secret integers is obtained. Security analysis shows that this protocol not only resists outside attack and participant attack, but also resists the semi-honest third-party (TP) attack. Compared with the other similar quantum privacy comparison equal relationship (QPCER) protocols, the proposed protocol has better flexibility and universality; compared with the other similar QPCSR protocols, it has lower resource consumption and higher communication efficiency.
Closed-loop solution method of active vision reconstruction via a 3D reference and an external camera

Laser-plane-based vision reconstruction is an important approach for 3D profile measurement and optical inspection. This study develops a 3D reconstruction method to measure the object with a flexible method. The active vision reconstruction includes an external camera and a 3D reference with an internal camera and laser projector. The 3D calibration object, the external camera, and the 3D reference constitute a closed loop. The internal camera, the 3D calibration object, and the 3D reference compose the other closed loop. The intrinsic invariants of the 3D reference with an internal camera and laser projector are determined by the two loops above. The benefit of the method is to cancel the position constraint for the active vision system with the cubic LED reference and the monocular external camera. Furthermore, as the external camera captures only the images of the 3D reference, the reconstruction system of the projector and the camera is flexible to observe the occlusion location on the measured object. The methods, including the calibration model and reconstruction model of the measurement system, are verified by experiments to demonstrate the potential applications.