Reviewerly/exHarmony huggingface dataset:
Image Dust Storm Synthetic Method Based on Optical Model

To address the problem that the dusty image dataset is small and difficult to collect, this paper presents a synthetic method for generating dusty image based on a classical optical model. The proposed method first learns the physical process of generating dusty image according to the classical optical model. Then, the transmission map is estimated and combined with the presupposed dust storm color map as inputs for obtaining a synthetic dusty image. Finally, considering the impact of image scene depth on the synthesis of dusty image, the proposed method selects an appropriate value of input parameter to obtain final synthetic dusty image. Experimental results on an image dataset captured in clear weather show that the synthetic dusty images obtained by the proposed method can be used as a good substitute for real dusty images.
Chinese and English Elementary Discourse Units Recognition Based on Bi-LSTM-CRF Model

Elementary Discourse Unit (EDU) recognition is the basic task of discourse analysis, and the Chinese and English discourse alignment corpus is helpful to the studies of EDU recognition. This paper first builds Chinese-English parallel discourse corpus, in which EDUs are annotated and aligned. Then, we present the framework of Bi-LSTM-CRF EDUs recognition model using word embedding, POS and syntactic features, which can combine the advantage of CRF and Bi-LSTM models. The results show that F1 is about 2% higher than the traditional method. Compared with CRF and Bi-LSTM, the Bi-LSTM-CRF model can combine the advantages of them and obtains satisfactory results for Chinese and English EDUs recognition. The experiment of feature contribution shows that using all features together can get best result, the syntactic feature outperforms than other features.
Adaptive Skewness Kurtosis Neural Network : Enabling Communication Between Neural Nodes Within a Layer

The statistical properties of neural networks are closely associated with their performance. From this perspective, the training process of deep learning models can be divided into two stages corresponding to the procedures of feature extraction and integration. In the feature extraction stage, the mean and variance of the hidden layer changes; during the feature integration stage, the mean and variance remain relatively stable, while the skewness and kurtosis change considerably. Meanwhile, constructing intra-layer connections may improve the performance of neural networks. Consequently, a novel Adaptive Skewness Kurtosis (ASK) structure is proposed, which enables deep learning networks to connect within a layer. On the basis of stabilizing the mean and variance of the layer, the ASK structure adaptively adjusts the skewness and kurtosis of the layer by communicating the connections between neuron nodes in the layer to improve the feature integration ability of the model ultimately. Based on the ASK structure, we propose an ASK neural network (ASKNN) where the ASK structure designed to a standard BP neural network (BPNN) to adjust the high order moments. Compared with the standard BPNN, ASKNN performs better especially when dealing with the data contaminated with noise.
Semantic Enhanced Distantly Supervised Relation Extraction via Graph Attention Network

Distantly Supervised relation extraction methods can automatically extract the relation between entity pairs, which are essential for the construction of a knowledge graph. However, the automatically constructed datasets comprise amounts of low-quality sentences and noisy words, and the current Distantly Supervised methods ignore these noisy data, resulting in unacceptable accuracy. To mitigate this problem, we present a novel Distantly Supervised approach SEGRE (Semantic Enhanced Graph attention networks Relation Extraction) for improved relation extraction. Our model first uses word position and entity type information to provide abundant local features and background knowledge. Then it builds the dependency trees to remove noisy words that are irrelevant to relations and employs Graph Attention Networks (GATs) to encode syntactic information, which also captures the important semantic features of relational words in each instance. Furthermore, to make our model more robust against noisy words, the intra-bag attention module is used to weight the bag representation and mitigate noise in the bag. Through extensive experiments on Riedel New York Times (NYT) and Google IISc Distantly Supervised (GIDS) datasets, we demonstrate SEGRE’s effectiveness.
ARmedViewer, an augmented-reality-based fast 3D reslicer for medical image data on mobile devices: A feasibility study

Background and objective: Medical images obtained by methods such as magnetic resonance imaging (MRI) or computed tomography (CT) are typically displayed as a stack of 2D slices, comprising a 3D volume. Often, the anatomy of interest does not fall neatly into the slice plane but rather extends obliquely through several slices. Reformatting the data to show the anatomy in one slice in conventional medical imaging software can require expertise and time. In this work, we present ARmedViewer, a medical image viewing app designed for mobile devices that uses augmented reality technology to display medical image data. An arbitrary plane for displaying the data can be chosen quickly and intuitively by moving the mobile device. Methods: The app ARmedViewer, compiled for an iOS device, was designed to allow a user to easily select from a list of 3D image datasets consisting of header information and image data. The user decides where to place the data, which can be overlaid on actual human anatomy. After loading the dataset, the user can move and rotate the data as desired. 15 users compared the user experience of the app to a common image viewer by answering two user surveys each, one custom and one standardized. The utility of the app was also tested by having two users find a plane through a 3D dataset that displayed 3 randomly placed lesions. This operation was timed and compared between the app and a standard medical image viewer. Results: ARmedViewer was successfully developed and run on an iPhone XS. User interfaces for selecting, placing, moving, reslicing, and displaying the data were operated with ease, even by naïve users. The custom user survey indicated that freely selecting a slice through the data was significantly more intuitive and easier using the app than using a conventional image viewer on a computer workstation, and changing the viewing angle was also significantly more intuitive. The standardized survey indicated a significantly better user experience for the app in several categories, and never significantly worse. The timed reslicing experiments demonstrated the app being faster than the standard image viewer by an average factor of 9. Conclusions: The newly developed ARmedViewer is a portable software tool for easily displaying 3D medical image data overlaid on human anatomy, allowing for easy choice of the viewing plane by intuitively moving the mobile device.
Intelligent System Based on Wavelets for Automatic Facial Emotion Recognition

Facial emotion recognition is very important for social communication. Whereby through the years it has done many studies and researches about automatic emotion recognition. Generally, the facial emotion recognition systems are composed of the pre-processing phase, the features extraction phase and the classification phase. This article proposes a method for automatic facial emotion recognition in digital images. This method uses histogram equalization to improve special lighting conditions during the pre-processing of images, two-dimensional discrete wavelet transform and PCA algorithms are used to extract and reduce features. Finally it uses a support vector machine linear to predict emotions. The experiments were made using the JAFFE database and CK+. The method achieves more than 93% for average accuracy and it recognizes better the following emotions: Happiness, neutral and surprise. For comparisons, two kinds of classifiers were adopted: Support vector machine and Convolutional Neural Network. Using this last classifier we achieve more than 98% for average accuracy.
Predicting Software Cost Through Entity–Relationship Diagrams: An Empirical View

In order to effectively monitor and handle the entire software development process, software cost estimation plays an important role. The most popular tool for software cost estimation nowadays is Constructive Cost Model II (COCOMO II). This tool uses lines of code (LOC) and function points (FPs) to calculate software size. However, the accurate prediction of software development costs become critical as well as difficult as these LOC & FP are actual implementation details which cannot be gathered in early stages. Thus, ER model—a concept-based model which is constructed during requirement phase (early phase) of the SDLC—is explored for the software cost estimation. In this paper, an attempt has been made to propose a multiple regression model for software cost estimation based on number of entities, attributes, relationship, and path complexity of an ER diagram. The MATLAB tool has been used for analysis of data.
Understanding and Characterizing Changes in Bugs Priority: The Practitioners’ Perceptive

Assigning appropriate priority to bugs is critical for timely addressing important software maintenance issues. An underlying aspect is the effectiveness of assigning priorities: if the priorities of a fair number of bugs are changed, it indicates delays in fixing critical bugs. There has been little prior work on understanding the dynamics of changing bug priorities. In this paper, we performed an empirical study to observe and understand the changes in bugs' priority to build a 3-W model on Why and When bug priorities change, and Who performs the change. We conducted interviews and a survey with practitioners as well as performed a quantitative analysis containing 225,000 bug reports, developers' comments, and source code changes from 24 open-source systems. The interviews with 11 developers from industry aim to establish an initial model to characterize the changes in bugs priority. The survey with an additional 38 developers was to understand their experience in why and when bug priorities change, and who performs the change. Then, we conducted a manual inspection of the collected data on open-source projects to compare our final bugs priority change model with changes identified in practice. Our quantitative results confirmed the outcomes of our interviews and surveys. For instance, we observed frequent changes in bug priorities and their impact on delaying critical bug fixes especially just before shipping a new release. Our findings can enable 1) researchers to build automated tools for checking and validating requests for bug priority changes, 2) practitioners to use a standard format in documenting and approving bug priority changes, and 3) educators to teach the better management of bug priorities.
Improving the Cognitive Levels of Automatic Generated Questions using Neuro-Fuzzy Approach in e-Assessment

Assessment is a fundamental activity to realize the objective of a course and to enhance the teaching learning process. Ensuring quality in the question papers for testing the different cognitive skills is important in the test or examination component of any academic or training domain. Bloom's taxonomy, a popular framework has been used to assess the learning skills of students. This paper describes the methodology to auto-generate questions based on bloom's cognitive levels. Various Natural Language Processing (NLP) techniques are incorporated to construct a textgraph using input statements from the web where native intelligence acquired from ConceptNet interrelates the nodes. The proposed work resolves the complexity of categorising autogenerated questions with similar action verbs. It follows a combinatorial formulation of fuzzy logic to generate cognitively fluent questions. A Fuzzy Restricted Boltzman machine coupled with Gaussian Markov based softmax is used in the proposed architecture. Experiments reveal the significance of the proposed system in generating cognitively fluent questions when the same action verb gets interlinked with different cognitive level of auto-generated questions.
Reinforcement Learning for N-player Games: The Importance of Final Adaptation

This paper covers n-tuple-based reinforcement learning (RL) algorithms for games. We present a new algorithm for temporal difference (TD) learning which works seamlessly on various games with arbitrary number of players. This is achieved by taking a player-centered view where each player propagates his/her rewards back to previous rounds. We add a new element called Final Adaptation RL (FARL) to this algorithm. Our main contribution is that FARL is a vitally important ingredient to achieve success with the player-centered view in various games. We report results on seven board games with 1, 2 and 3 players, including Othello, ConnectFour and Hex. In most cases it is found that FARL is important to learn a near-perfect playing strategy. All algorithms are available in the GBG framework on GitHub.
Estimating the distribution density function using a DOG wavelet

Abstract It is often necessary to obtain information on the distribution form of a random variable within the framework of statistical data analysis. There are several ways of estimating probability density distributions. Wavelet density estimators distribution of a random variable, built with DOG wavelet, would be considered. Three methods are proposed for determining the normalizing parameter, which provides a correction for the non-orthogonality of the DOG wavelet. In addition, studies have been carried out on the impact on quality wavelet estimates of parameters such as sample size, a number of coefficients of expansion, a function by series in the expression for wavelet density estimates. In the course of this process, it was noted that the quality of estimates on the smoothing parameter is substantial. Moreover, there is its best meaning. The conclusion is made that the estimate is based on DOG wavelet, provides quality recovery density functions.
Quantile Regression Hindsight Experience Replay

Efficient learning in the environment with sparse rewards is one of the most important challenges in Deep Reinforcement Learning (DRL). In continuous DRL environments such as robotic manipulation tasks, Multi-goal RL with the accompanying algorithm Hindsight Experience Replay (HER) has been shown an effective solution. However, HER and its variants typically suffer from a major challenge that the agents may perform well in some goals while poorly in the other goals. The main reason for the phenomenon is the popular concept in the recent DRL works called intrinsic stochasticity. In Multi-goal RL, intrinsic stochasticity lies in that the different initial goals of the environment will cause the different value distributions and interfere with each other, where computing the expected return is not suitable in principle and cannot perform well as usual. To tackle this challenge, in this paper, we propose Quantile Regression Hindsight Experience Replay (QR-HER), a novel approach based on Quantile Regression. The key idea is to select the returns that are most closely related to the current goal from the replay buffer without additional data. In this way, the interference between different initial goals will be significantly reduced. We evaluate QR-HER on OpenAI Robotics manipulation tasks with sparse rewards. Experimental results show that, in contrast to HER and its variants, our proposed QR-HER achieves better performance by improving the performances of each goal as we expected.
Very Fast Keyword Spotting System with Real Time Factor Below 0.01

In the paper we present an architecture of a keyword spotting (KWS) system that is based on modern neural networks, yields good performance on various types of speech data and can run very fast. We focus mainly on the last aspect and propose optimizations for all the steps required in a KWS design: signal processing and likelihood computation, Viterbi decoding, spot candidate detection and confidence calculation. We present time and memory efficient modelling by bidirectional feedforward sequential memory networks (an alternative to recurrent nets) either by standard triphones or so called quasi-monophones, and an entirely forward decoding of speech frames (with minimal need for look back). Several variants of the proposed scheme are evaluated on 3 large Czech datasets (broadcast, internet and telephone, 17 hours in total) and their performance is compared by Detection Error Tradeoff (DET) diagrams and real-time (RT) factors. We demonstrate that the complete system can run in a single pass with a RT factor close to 0.001 if all optimizations (including a GPU for likelihood computation) are applied.
Neural Network with Adaptive Learning Rate

Over the last two decades, the neural network has surprisingly arisen as an efficient tool for dealing with numerous real-life applications. Optimization of the hyperparameter of the neural network attracted many researchers in industrial and research areas because of its great effect on the quality of the solution. This paper presents a new adaptation for the learning rate with shock (ALRS) as the learning rate is considered one of the most important hyperparameters. The experimental results proved that the new adaptation leads to improved accuracy with a simpler structure for the neural network regardless of the initial value of the learning rate.
Digital Implementation of Sigmoid Function in Artificial Neural Network Using VHDL

This paper deals with the top-down design methodology of an artificial neural network (ANN) whose architecture is implemented in VHDL. The simulation of this architecture is carried out on Xilinx ISE 8.1. This realization gives flexibility to user to change the description at their own will. The basic elements of artificial neurons constitute the set of input nodes that receive the corresponding input signal or pattern vector, a set of synaptic connections whose strengths are represented by a set of weights activation function Φ that relates the total synaptic input to the output (activation) of the neuron. This paper strives to achieve the linearity of sigmoidal function because it is a nonlinear function whose realization is very difficult and expensive on hardware due of infinite exponential series. To overcome this problem, the proposed paper focuses on the linearity of sigmoid function by taking piecewise linear approximation which is in the form of y = ax + b.
Survey on Privacy-Preserving Machine Learning Protocols

Machine learning, especially deep learning, is a hot research field in academia, and it is revolutionizing industry. However, the privacy-preserving problems are not solved. In this paper, we investigate the privacy-preserving technology in machine learning application. We first introduce the models in privacy-preserving machine learning protocols, and then we give an overview of the main privacy-preserving machine learning technologies. At last, we analyze the existing problems.
Dynamic optical interconnects for quantum secure distributed nodes and quantum processing

Quantum Key Distribution (QKD) has emerged as the technology to offer ultimate security, posing additional challenges in the physical layer due to its single-photon nature. Co-existence with classical data and switching aspects are discussed in dynamic-QKD applications for inter- data-centre communications and switched entanglement-based quantum distribution.
Learner-Independent Targeted Data Omission Attacks

In this paper we introduce the data omission attack—a new type of attack against learning mechanisms. The attack can be seen as a specific type of a poisoning attack. However, while poisoning attacks typically corrupt data in various ways including addition, omission and modification, to optimize the attack, we focus on omission only, which is much simpler to implement and analyze. A major advantage of our attack method is its generality. While poisoning attacks are usually optimized for a specific learner and prove ineffective against others, our attack is effective against a variety of learners. We demonstrate this effectiveness via a series of attack experiments against various learning mechanisms. We show that, with a relatively low attack budget, our omission attack succeeds regardless of the target learner.
Intelligent Text Clustering Based on Semantics Similarity

Clustering text documents have become an increasingly important problem in recent years due to the availability of a huge amount of unstructured data in various forms, such as the web, social networks, and other information networks. It aims to organise and classify large document groups into smaller groups of meaning. This process is crucial because it is challenging to deal with a large amount and an increasing number of digital data. The documents are organised and classified to facilitate faster information retrieval (IR) and try to extract information with semantic knowledge. The process above enables the retrieve information, browsed and understood instead of clustering texts in the traditional way, i.e. compilation of data without descriptive concepts. As textual data has become a diverse set of vocabulary hence, there is an urgent need for text aggregation techniques based on semantic similarity is the primary solution to this problem as it is grouped into groups according to meaning rather than keywords. Several Papers that are using semantic similarity in various scopes. It has been reviewed in this research; some of them which are using similarity based on semantic using document cluster. For developing an effective and efficient clustering methodology to take care of the semantic structure of the text documents, and a compare of between them is presented according to (algorithms, tools, and assessment methods). Finally, extensive study and comparison of work are presented.
Stereo Refinement Based on Gradient Domain Guided Filtering

The weighted median filtering with bilateral weights is currently a popular solution for disparity refinement in stereo vision, but computational complexity is an issue. In this paper, a linear disparity refinement approach based on the gradient domain guided image filtering (GDGIF) is proposed. The advantage of the proposed method is that the weights are adaptive to image structure. As such, the proposed method can remove errors in small regions, similar to salt-and-pepper noise, and large areas while edges are well preserved. Experimental results show that the proposed method can improve accuracy of disparity maps and the edges in disparity maps are better preserved than those by the state-of-the-art algorithms. Especially, the proposed disparity refinement is more efficient.
Recursive axiomatizations for representable posets

We use model theoretic techniques to construct explicit first-order axiomatizations for the classes of posets that can be represented as systems of sets, where the order relation is given by inclusion, and existing meets and joins of specified countable cardinalities correspond to intersections and unions respectively.
Target detection in sonar image based on faster RCNN

Aiming at the limitation of underwater target detection method, a detection method based on Faster RCNN is proposed. Using a variety of convolution neural networks to extract image features, constructing a region recommendation network (RPN) to extract the region that may contain the target, and then using the detection network to determine the target area. The application of transfer learning method to ensure that the limited amount of data can also be trained and achieve good results. The experimental results show that the average accuracy of the proposed method in underwater target detection task can be improved by about 14% compared with the basic RCNN method and about 18% higher than the more new YOLO method.