Reviewerly/exHarmony huggingface dataset:
Particle Filter With Unknown Statistics to Estimate Liquid Level in the Silicon Single-Crystal Growth

In the process of silicon single-crystal growth, the detection of melt level is an important step to ensure that the grown silicon single crystal is of high quality. Since there exist many mechanical motions, chemical interactions, thermal convection, and so on, it is extremely difficult to obtain the noise statistics of the liquid-level data. To attain a high-accuracy detection of liquid level with unknown noise statistics, we present an extended set-membership-based particle filter (PF). The key points are as follows. We construct a nonlinear state-space model for liquid-level detection system based on the kinematic principle and laser triangulation measurement principle and cast the detection problem of the liquid level into a state estimation problem. Based on this model, we use the extended set-membership estimation theory to generate an ellipsoid set that contains the true value of the liquid-level state. To overcome the difficulty of drawing particles under the case of unknown statistics noise, we draw particles from a Gaussian distribution in the obtained ellipsoidal set. In order to resample particles when the measurement noise statistic is unknown, we introduce a cost function to obtain the weights of the particles, and the ultimate estimation of liquid level is calculated by the weighted particles after the resampling procedure. Comparing with the extended Kalman PF (EPF) and the cost-reference PF (CRPF), the proposed algorithm has higher estimation accuracy. Some simulation and experiment results are presented to illustrate the effectiveness of the proposed method.
ATLASLang NMT: Arabic text language into Arabic sign language neural machine translation

ATLASLang is a machine translation system from Arabic text language into Arabic sign language (ArSL). The first version of the system (Brour and Benabbou, 2019) is based on two approaches: rule-based Interlingua and example-based approaches. It is a classical machine translation system, its limitations are the linguistic knowledge necessary to develop the rules, also many rules and exceptions required. In the last few years, a neural machine translation has achieved notable results, and several well-known companies including Google (Wu et al., 2016) and Systran (Crego et al., 2016) have starting to exploit it. In this paper, we present a new version of ATLASLang that is implemented using a feed-forward back-propagation Artificial Neural Network. In this version, we have started to translate simple sentences composed from a limited number of words since generally communication with deaf person uses short sentences. We have used the same ATLASLang MTS database of signs and we have utilized morphological characteristics to derive the maximum information from each word in the input of the neural translation system. The two versions of the system are compared using n-gram BLEU score (Papineni et al., 2002), and the results demonstrate that neuron network approaches outperform the other classical approaches.
Image Captioning by Asking Questions

Image captioning and visual question answering are typical tasks that connect computer vision and natural language processing. Both of them need to effectively represent the visual content using computer vision methods and smoothly process the text sentence using natural language processing skills. The key problem of these two tasks is to infer the target result based on the interactive understanding of the word sequence and the image. Though they practically use similar algorithms, they are studied independently in the past few years. In this article, we attempt to exploit the mutual correlation between these two tasks. We propose the first VQA-improved image-captioning method that transfers the knowledge learned from the VQA corpora to the image-captioning task. A VQA model is first pretrained on image--question--answer instances. Then, the pretrained VQA model is used to extract VQA-grounded semantic representations according to selected free-form open-ended visual question--answer pairs. The VQA-grounded features are complementary to the visual features, because they interpret images from a different perspective. We incorporate the VQA model into the image-captioning model by adaptively fusing the VQA-grounded feature and the attended visual feature. We show that such simple VQA-improved image-captioning (VQA-IIC) models perform better than conventional image-captioning methods on large-scale public datasets.
Random Temporal Skipping for Multirate Video Analysis

Current state-of-the-art approaches to video understanding adopt temporal jittering to simulate analyzing the video at varying frame rates. However, this does not work well for multirate videos, in which actions or subactions occur at different speeds. The frame sampling rate should vary in accordance with the different motion speeds. In this work, we propose a simple yet effective strategy, termed random temporal skipping, to address this situation. This strategy effectively handles multirate videos by randomizing the sampling rate during training. It is an exhaustive approach, which can potentially cover all motion speed variations. Furthermore, due to the large temporal skipping, our network can see video clips that originally cover over 100 frames. Such a time range is enough to analyze most actions/events. We also introduce an occlusion-aware optical flow learning method that generates improved motion maps for human action recognition. Our framework is end-to-end trainable, runs in real-time, and achieves state-of-the-art performance on six widely adopted video benchmarks.
A Novel Fiber Intrusion Signal Recognition Method for OFPS Based on SCN With Dropout

Accurate and fast recognition of fiber intrusion signals has always been a fundamental task in the Optical Fiber Pre-warning System (OFPS). However, currently existing recognition models tend to focus on one aspect and lack a comprehensive approach. In this paper, a dropout-based Stochastic Configuration Network (SCN) optical fiber intrusion signal recognition model is first proposed, which is named DropoutSCN. By combining dropout with randomized algorithm models, it not only enhances the fast learning ability, but also improves the generalization performance of the recognition model. In the experiment, compared with traditional Artificial Neural Network (ANN), Random Vector Functional Link (RVFL), and original SCN models, the DropoutSCN model proposed in this paper has the lowest root-mean-square error (RMSE). In terms of time efficiency, it reduces the time delay by about 2.5 times compared with the traditional ANN. In addition, this paper applies dropout to SCN, which provides a feasible thinking and reference for the study of randomized algorithm models.
Anonymous cross-domain authentication scheme for medical PKI system

Nowadays, most information systems of hospitals are established based on Public Key Infrastructure(PKI). Each of them has a certificate authority(CA) act as a trust anchor for all the members it authorized. However, each hospital builds system according to its own understanding and form a lot of isolated domains. In medical application scenario, patients often passively need to change hospitals because of different medical levels and different diseases. For hospital cannot electronically share medical data, which cause lots of waste of resource such as repeated treatment and time consuming. The solution to medical information sharing between hospitals becomes urgent. In this paper, we propose blockchain-based cross-domain authentication scheme for doctor users and designed corresponding protocols to support supervision department and user privacy protection. The security analysis and engineering tests demonstrate that the scheme can better meet the requirements of cross-domain authentication and provide better privacy protection services when sharing medical data.
Analyzing periodicity and saliency for adult video detection

Content-based adult video detection plays an important role in preventing pornography. However, existing methods usually rely on single modality and seldom focus on multi-modality semantics representation. Addressing at this problem, we put forward an approach of analyzing periodicity and saliency for adult video detection. At first, periodic patterns and salient regions are respectively analyzed in audio-frames and visual-frames. Next, the multi-modal co-occurrence semantics is described by combining audio periodicity with visual saliency. Moreover, the performance of our approach is evaluated step by step. Experimental results show that our approach obviously outperforms some state-of-the-art methods.
Neural morphosyntactic tagging for Rusyn

Abstract The paper presents experiments on part-of-speech and full morphological tagging of the Slavic minority language Rusyn. The proposed approach relies on transfer learning and uses only annotated resources from related Slavic languages, namely Russian, Ukrainian, Slovak, Polish, and Czech. It does not require any annotated Rusyn training data, nor parallel data or bilingual dictionaries involving Rusyn. Compared to earlier work, we improve tagging performance by using a neural network tagger and larger training data from the neighboring Slavic languages. We experiment with various data preprocessing and sampling strategies and evaluate the impact of multitask learning strategies and of pretrained word embeddings. Overall, while genre discrepancies between training and test data have a negative impact, we improve full morphological tagging by 9% absolute micro-averaged F1 as compared to previous research.
Multi-Person tracking algorithm based on data association

Aiming at the diversity of human attitudes, appearance similarity and occlusion of real-time road traffic scenarios, this paper proposes a multi-person tracking algorithm based on tracking-by-detection framework, which uses pedestrian depth appearance features and motion features to correlate tracking objects. We use the YOLOv3 algorithm to detect the pedestrian target in the input video sequence, and the detected target is tracked and predicted using the Kalman filter algorithm. The depth appearance and motion information of the target are combined with the Hungarian algorithm to match the prediction and detection results, so as to achieve tracking of multi-Pedestrian targets.
A Neurally-Guided, Parallel Theorem Prover

We present a prototype of a neurally-guided automatic theorem prover for first-order logic with equality. The prototype uses a neural network trained on previous proof search attempts to evaluate subgoals based directly on their structure, and hence bias proof search toward success. An existing first-order theorem prover is employed to dispatch easy subgoals and prune branches which cannot be solved. Exploration of the search space is asynchronous with respect to both the evaluation network and the existing prover, allowing for efficient batched neural network execution and for natural parallelism within the prover. Evaluation on the MPTP dataset shows that the prover can improve with learning.
A Robust Digital Watermarking Algorithm for Text Document Copyright Protection based on Feature Coding

Information hiding has attracted the attention of researchers in recent years because digital contents are generated and share online through different communication channels. The verification of original authorship of digital contents is a crucial task. Digital watermarks are used to provide copyright protection and ownership verification solutions. However, digital text watermarking is a challenging job due to limited research and existing schemes like inter-word and paragraph spacing, line and word shift are used to hide information, which is not robust. If spaces are removed between words, lines, and paragraphs, the hidden information is destroyed. The special properties of Word document like variable, bookmarks, and range are appropriate for information hiding. The proposed scheme uses these special properties for watermarking without affecting the content. The watermark information is embedded in the special properties after encryption. Our proposed algorithm shows the results. After applying different attacks, the proposed scheme is robust 99.9%, excellent on imperceptibility 99.9%, and improve embedding capacity from Bytes to KiloBytes.
Optimal-Margin Evolutionary Classifier

We introduce a novel approach for discriminative classification using evolutionary algorithms. We first propose an algorithm to optimize the total loss value using a modified 0-1 loss function in a 1-D space for classification. We then extend this algorithm for multidimensional classification using an evolutionary algorithm. The proposed evolutionary algorithm aims to find a hyperplane that best classifies instances while minimizing the classification risk. We test particle swarm optimization, evolutionary strategy (ES), and covariance matrix adaptation ES for optimization purposes. After parameter selection, we compare our results with well-established and state-of-the-art classification algorithms, for both binary and multiclass classification, on 23 benchmark classification problems, with and without noise and outliers. We also compare these methods on a seizure detection task for 12 epileptic patients. Results show that the performance of the proposed algorithm is significantly (Wilcoxon test) better than all other methods in almost all problems tested. We also show that the proposed algorithm is significantly more robust against noise and outliers comparing to other methods. The running time of the algorithm is within a reasonable range for the solution of real-world classification problems.
Applying Case-Based Learning for a Postgraduate Software Architecture Course

Software architecture remains a difficult subject for learners to grasp and for educators to teach given its level of abstraction. On the other hand, case-based learning (CBL) is a popular teaching approach used across disciplines especially in business, medicine and law where students work in groups apply their knowledge to solve real-world case studies, or scenarios using their reasoning skills and existing theoretical knowledge. In this paper, we provide how we apply case-based learning to address the challenge in teaching a postgraduate software architecture course. Our learners are postgraduate students taking a master's program in software engineering. We first describe our design of case-based learning for our software architecture course. We then analyse the survey ratings and learners' profile to evaluate the effectiveness of the proposed case-based design. These data are gathered from 9 class runs over a period of 8 years. Our analysis results show the effectiveness of our case-based design and significant relationships between this effectiveness to the learners' years of working experiences and the number of learners. Key contributions in this paper are our proposed case-based design for software architecture and the analysis findings.
The Differential Effects of Interpersonal Justice and Injustice on Computer Abuse

Insider employees have become one of the top security threats to organizations. In order to mitigate their detrimental security behaviors, it is important to understand the thought processes of these insider offenders. Recent security research has examined the role of perceptions of injustice in explaining employee security behaviors. However, there is a paucity of research investigating the differential effects of justice and injustice. Based on regulatory focus theory, this article examines the emotional and behavioral reactions to perceptions of interpersonal justice and injustice. The results show that perceptions of interpersonal injustice are more relevant to employee experiences of hostility than perceptions of interpersonal justice. In addition, the results show that promotion focus and prevention focus have asymmetric effects on the role of emotions in computer abuse. The results have important theoretical contributions to justice and security behavior research and provide critical guidance to organizational security management.
Attention-Guided Coarse-to-Fine Network for 2D Face Alignment in the Wild

Methods based on stacked hourglass networks (SHNs) have achieved great progress in face alignment tasks. However, most of these algorithms have met with limited success in modeling correlations among features. Visual attention mechanisms have shown promise in terms of effectively understanding scenes in various computer vision tasks. In this paper, an attention-guided coarse-to-fine network (AGCFN) based on an attention mechanism is proposed for robust face alignment. Thus, the network is guided to emphasize key information while suppressing less important information. Meanwhile, the fusion of features from different levels is adopted to improve the information flow through the proposed network. Additionally, conditional random fields (CRFs) are introduced to model the spatial interactions between landmarks in the prediction maps. Experimental results obtained on the 300-W dataset, the 300-W private test set, and the WFLW dataset demonstrate the superiority of the proposed method in terms of accuracy and robustness.
The Deeper, the Better: Analysis of Person Attributes Recognition

Research into person attributes recognition has focused on approaches to describe a person in terms of their appearance. Typically, this includes a wide range of traits including age, gender, clothing, and footwear. Although this could be used in a wide variety of scenarios, it generally is applied to video surveillance, where attribute recognition is impacted by low resolution, and other issues such as variable pose, occlusion and shadow. Recent approaches have used deep convolutional neural networks (CNNs) to improve the accuracy in person attribute recognition. However, many of these networks are relatively shallow and it is unclear to what extent they use contextual cues to improve classification accuracy. This paper builds upon prior research by proposing to use a modified ResNet architecture with calibrations that permit us to train networks that are deeper than previously published approaches. Interpretation suggests that this deeper architectures allows the network to take more contextual information into consideration, which helps to improve classification accuracy and generalizability. We present experimental analysis and results for whole body attributes using the PA-100K and PETA datasets and facial attributes using the CelebA dataset.
Learning from african classroom pedagogy to increase student engagement in education technologies

Tablet-based educational technologies provide a supplement to traditional classroom-based early literacy education, especially in regions with limited schooling resources. Prior work has probed how children generally interact with and learn from these technologies, however, there is limited research on student engagement with applications that utilize valuable input techniques such as automatic handwriting and speech recognition. In our study, we designed and field-tested early literacy speech and handwriting recognition applications with the primary aim of maximizing student engagement. We designed the applications based on prior research insights and classroom observations from our target population and field-tested the applications with 283 children living in rural Tanzania. We found that observing a small set of classrooms can produce design insights that increase engagement on tablet-based learning systems on a much larger scale. We also demonstrate the importance of domain familiarity in students' choice to persist through activities while learning with technology.
Calibration of camera intrinsic parameters based on the properties of the polar of circular points

Three-dimensional stratified reconstruction from images requires acquisition of circular points. This study proposes a new algorithm for calibrating camera intrinsic parameters using the properties of the polar of circular points (PCPs). Because the PCPs are asymptotic, which are self-conjugate diameters of the circle, the obtained PCPs can determine the image of the circular points and a group of orthogonal vanishing points in the image plane. On this basis, the camera parameters can be calibrated. The results of the simulation experiment and the data of the real experiment prove that the proposed method is effective and feasible.
Mixtures of Hidden Truncation Hyperbolic Factor Analyzers

The mixture of factor analyzers model was first introduced over 20 years ago and, in the meantime, has been extended to several non-Gaussian analogs. In general, these analogs account for situations with heavy tailed and/or skewed clusters. An approach is introduced that unifies many of these approaches into one very general model: the mixture of hidden truncation hyperbolic factor analyzers (MHTHFA) model. In the process of doing this, a hidden truncation hyperbolic factor analysis model is also introduced. The MHTHFA model is illustrated for clustering as well as semi-supervised classification using two real datasets.
Deep compression of probabilistic graphical networks

Probabilistic Graphical Models (PGMs) are important and active research areas in machine learning and artificial intelligence. The well-known representatives of PGMs include Restricted Boltzmann Machines (RBMs), Deep Belief Networks (DBNs), Deep Boltzmann Machines (DBMs), and their variants. These PGMs open a new dimension of machine learning and directly lead to the revival of deep learning since 2006. Compared with deep deterministic models, such as deep convolutional neural networks (CNNs), PGMs are more flexible, robust and powerful for both unsupervised and supervised learning problems. As the generative models on the one hand, PGMs have the good capability of generating samples after extracting informative features of input data. On the other hand, they also can provide a good initialization of hierarchical structures for supervised learning. The PGMs with deep structures are called deep Probabilistic Graphical Networks (PGNs) due to their hierarchical structures and close relationships with deep neural networks. There are a large number of CNN-based deep deterministic networks proposed for computer vision tasks. However, these deep deterministic models are shown to possess extremely large number of model parameters, and have serious redundancy problems. Therefore, deep compression of these deterministic models have been proposed in the last few years to reduce the number of connections and nodes, while preserving the classification accuracy. This paper is the first attempt to combine deep PGNs and deep compression techniques together to derive sparse versions of the deep probabilistic models. We firstly examine that whether deep PGNs have serious problem of parameter redundancy, and compress them if necessary. From the experimental results on MNIST, Fashion-MNIST and CIFAR-10 dataset, deep PGNs can be compressed by at least 50% of their parameters through a deep pruning and retraining approach, while keeping their capabilities in both generative and discriminative tasks.