Reviewerly/exHarmony huggingface dataset:
Prema: A Tool for Precise Requirements Editing, Modeling and Analysis

We present Prema, a tool for Precise Requirement Editing, Modeling and Analysis. It can be used in various fields for describing precise requirements using formal notations and performing rigorous analysis. By parsing the requirements written in formal modeling language, Prema is able to get a model which aptly depicts the requirements. It also provides different rigorous verification and validation techniques to check whether the requirements meet users' expectation and find potential errors. We show that our tool can provide a unified environment for writing and verifying requirements without using tools that are not well inter-related. For experimental demonstration, we use the requirements of the automatic train protection (ATP) system of CASCO signal co. LTD., the largest railway signal control system manufacturer of China. The code of the tool cannot be released here because the project is commercially confidential. However, a demonstration video of the tool is available at https://youtu.be/BX0yv8pRMWs.
Automatic recognition of handwritten Arabic using maximally stable extremal region features

Arabic script is inherently cursive in nature; therefore, optical character recognition (OCR) techniques developed for other scripts are generally inappropriate for Arabic. We present an OCR system for the recognition of handwritten Arabic words. Several original contributions have been made. First, we have proposed a novel feature vector to represent handwritten Arabic words based on the ellipsoid approximation of maximally stable extremal regions (MSERs). This feature vector is compact, robust, and well suited to handle writer-induced variation in handwritten script. Second, we present a new database, Saudi Arabian city name (SACN) database consisting of handwritten Saudi Arabian city names, which is the first such publicly available database. Finally, we present promising experimental results on two databases. One of the main experiments compares the proposed MSER feature vector with several well-established feature extraction methods using the proposed SACN database. The proposed feature vector performed better than all the standardized methods, with 100% correct identification rate. Another experiment is designed to compare our system to other recent studies on handwritten Arabic text recognition using the extensively employed IFN/ENIT database. Using the standard experimental protocol, our system outperformed all others with 92.64% correct identification rate.
Segmenting MRI Brain Tumor Images Using Modified Cuckoo Search Optimization with Morphological Reconstruction Filters

Cancer is dangerous diseases and it affects the entire body if necessary steps are taken to prevent the starting stage of tumor, to measure the tumor growth and ensure that tumor is malignant or benign is a challenging task for the radiologist. In our work modified cuckoo search algorithm with morphological reconstruction filters is used to segment the MR brain tumor. All images are collected BRATS 2013 and clinical dataset for proving the efficiency of the algorithm. Segmentation performance measures like Entropy, Mean Square Error, Peak Signal to Noise Ratio, Precision, Recall, and accuracy values are calculated to ensure the accuracy of the proposed work. Our work yields 97 % accuracy which is far better than the existing techniques like Particle Swarm optimization, Genetic Algorithm and Ant Colony Optimization.
Asymmetric Generalized Gaussian Distribution Parameters Estimation based on Maximum Likelihood, Moments and Entropy

In this paper, we address the problem of estimating the parameters of Asymmetric Generalized Gaussian Distribution (AGGD) using three estimation mehods, namely, Maximum Likelihood Estimation (MLE), Moment Matching Estimation (MME) and Entropy Matching Estimaion (EME). For this purpose, these methods are applied on an unimodal histogram fitting of an image corrupted with AGGD noise. Experiments show that the effectiveness of each method comparatively to the other one depends on the variation range of the shape factor.
Statistical Complexity Analysis of Turing Machine tapes with Fixed Algorithmic Complexity Using the Best-Order Markov Model

Sources that generate symbolic sequences with algorithmic nature may differ in statistical complexity because they create structures that follow algorithmic schemes, rather than generating symbols from a probabilistic function assuming independence. In the case of Turing machines, this means that machines with the same algorithmic complexity can create tapes with different statistical complexity. In this paper, we use a compression-based approach to measure global and local statistical complexity of specific Turing machine tapes with the same number of states and alphabet. Both measures are estimated using the best-order Markov model. For the global measure, we use the Normalized Compression (NC), while, for the local measures, we define and use normal and dynamic complexity profiles to quantify and localize lower and higher regions of statistical complexity. We assessed the validity of our methodology on synthetic and real genomic data showing that it is tolerant to increasing rates of editions and block permutations. Regarding the analysis of the tapes, we localize patterns of higher statistical complexity in two regions, for a different number of machine states. We show that these patterns are generated by a decrease of the tape's amplitude, given the setting of small rule cycles. Additionally, we performed a comparison with a measure that uses both algorithmic and statistical approaches (BDM) for analysis of the tapes. Naturally, BDM is efficient given the algorithmic nature of the tapes. However, for a higher number of states, BDM is progressively approximated by our methodology. Finally, we provide a simple algorithm to increase the statistical complexity of a Turing machine tape while retaining the same algorithmic complexity. We supply a publicly available implementation of the algorithm in C++ language under the GPLv3 license. All results can be reproduced in full with scripts provided at the repository.
Deep Learning Based Vietnamese Diacritics Restoration

Diacritics are very important in diacritical languages, because the meaning of sentences can be changed in accordance to diacritics. Writing without diacritics makes the sentences ambiguous; however, there are several reasons make people do not write words with diacritics, such as fast typing, convenience, or texting on unsupported diacritics devices. As a result, these texts are very difficult to process on further natural language processing (NLP) tasks like machine translation, sentiment analysis, or question answering system. Therefore, diacritics restoration is critical for further usage or processing in NLP related tasks. In this study, we propose a method which combines convolutional neural network (CNN) and bidirectional gated recurrent unit (Bi-GRU) to restore diacritics. In addition, we use residual block to resolve vanishing gradient problem of recurrent neural networks. We applied the model for restoring diacritics of Vietnamese language that has the highest ratio of diacritics in words. This approach has character accuracy at 98.63% and word accuracy at 94.77%.
EEG Visualization for Cybersickness Detection During Playing 3D Video Games

The development of Virtual Reality (VR) technology has provided a new choice for the industry of video game developers to explore VR game content. The use of 3D video games is increasingly popular recently among players. However, playing 3D video game can cause negative effects like nausea, headache, eyestrain, and vomiting. This side effect is called cybersickness. Cybersickness is an unpleasant condition that appears due to VR exposure. Cybersickness actually can be measured by using a subjective measurement called SSQ questionnaire, while for more objective method, electroencephalograph (EEG) is implemented. In this study, we visualized the EEG of 9 subjects who played a 3D video game called “Mirror Edge” to capture and investigate the pattern of Cybersickness. The questionnaire results were used as references for determining the cybersickness condition. The visualization was done by using interactive graphical information techniques and topographic mapping methods of frontal area channels (F3 and F4) and occipital areas channels (O1 and O2) of gamma (γ) band. The statistical parameter, such as standard deviation was used to visualize the cybersickness. The results showed an increase of standard deviation value in gamma band when the subject playing video game for a few minutes compared to the baseline condition. This indicates that the cybersickness was initiated. From all of 9 subjects, we found that the average time of cybersickness initiation for channels F3 and F4 are at 97th seconds and at 92nd seconds consecutively, while for channels O1 and O2 are at 21st seconds and at 44th seconds. We conclude that by visualizing the standard deviation value of EEG during playing 3D video games, cybersickness initiation can be determined subjectively.
A Conditional Adversarial Network for Scene Flow Estimation

The problem of Scene flow estimation in depth videos has been attracting attention of researchers of machine vision, due to its potential application in various areas of robotics. The conventional scene flow estimation methods are difficult to use in real-time applications due to their long computational overhead. We propose a conditional adversarial network SceneFlowGAN for scene flow estimation. The proposed SceneFlowGAN uses loss function at two ends: both the generator and the discriminator. The proposed network is a first attempt to estimate scene flow using generative adversarial networks, and is able to estimate both the optical flow and disparity from the input stereo images simultaneously. The proposed method is experimented on a huge RGB-D benchmark sceneflow estimation dataset.
Turning a Smartphone Selfie Into a Studio Portrait

We introduce a novel algorithm that turns a flash selfie taken with a smartphone into a studio-like photograph with uniform lighting. Our method uses a convolutional neural network trained on a set of pairs of photographs acquired in a controlled environment. For each pair, we have one photograph of a subject's face taken with the camera flash enabled and another one of the same subject in the same pose illuminated using a photographic studio-lighting setup. We show how our method can amend lighting artifacts introduced by a close-up camera flash, such as specular highlights, shadows, and skin shine.
3D Face Alignment for Japanese Terracotta Figurines (Haniwa)

The Haniwa belong to one kind of terracotta figurines made in ancient Japan, which have been scanned and saved as 3D models composed of point cloud and triangular meshes for archaeology research. In order to align and analyze the face parts of the Haniwa, this paper proposes an ellipsoid-fitting-based segmentation method to extract the nose points, and a hole detection method to locate the contours of eyes and mouth. Our experiments show the proposed methods can be used to achieve the face alignment for the Haniwa with high precision, and make the corresponding face analysis a reality.
Flexible compression of big data

High volumes of valuable data and information can be easily collected in the current era of big data. As rich and constant sources of big data, an incredible amount of people from different social stratum take part in social networks. Hence, social networks are desired for many research topics. In social networks, users (or social entities) are often linked by some 'following' relationships. As the social networks growing, some famous users account (or social entities) might be followed by a large number of same other users. In this situation, we call those famous users as frequently followed groups, which some researchers (or businesses) may be interested in them for investigating. However, the discovery of those frequently followed groups might be difficult and challenging because the following data in social networks are usually very big but sparse (huge number of users lead to big 'following' data, but each user is likely only following a small number of other users). As a result, in this paper, we present a new compression model, which can be used during mining these very big but sparse social networks for discovering the frequently followed groups of users/social entities.
An Efficient Data Compression Algorithm for Smart Distribution Systems using Singular Value Decomposition

With the advance of smart grid conception a massive wide variety of monitoring devices and a large quantity of smart devices are to be installed in the Smart Distribution Systems to allow broad observability and online monitoring of the behavior of power system. The volume of data produced by the smart distribution systems is a tough challenge for storage, transmission and communication through the power system. As a result, the complexity of the smart distribution system increases tremendously. In the current work, to shrink the issues of data communication and size of data in smart distribution systems, an efficient Singular Value Decomposition based data compression algorithm is introduced. Singular Value Decomposition (SVD) performs compression by approximating the original matrix of a signal using a smaller rank. The data compression using SVD for different ranks are evaluated and it is observed that the proposed algorithm is used to drastically reduce the size of data to be transmitted throughout the smart distribution systems and precisely reconstruct the original data. To justify the proposed algorithm, a set of data are simulated under different circumstances in the IEEE Fourteen bus system. The concert of the proposed model is evaluated using CR (Compression Ratio), SNR(Signal to Noise Ratio), Mean Square error (MSE), and reconstructing ability. Simulation results are compared with the conventional discrete wavelet transform approach. It is perceived that the proposed algorithm results in improved SNR & CR with reduced Mean Square Error.
Illumination-Robust Object Coordinate Detection by Adopting Pix2Pix GAN for Training Image Generation

Illumination effects often result in error or failure in visual object localization. Whereas ConvNet-based object localization frameworks have shown tremendous robustness to the illumination effect, under some strong illumination effects, the system may still fail. In this paper, the authors propose a data augmentation method utilizing pix2pix GAN for automatic generation of object images under various illumination effects. Upon training for the object localization ConvNet, the generated images are included to enrich the training set for better performance under strong illumination effects. Experimental evidence shows that the accuracy of object coordinate detection can be improved significantly. The proposed framework maintains our concept of “one-shot” where the user only needs to take a basis photo of the target object; the rest of the process including image processing and data annotation are all automated.
Hierarchical multi-armed bandits for discovering hidden populations

This paper proposes a novel algorithm to discover hidden individuals in a social network. The problem is increasingly important for social scientists as the populations (e.g., individuals with mental illness) that they study converse online. Since these populations do not use the category (e.g., mental illness) to self-describe, directly querying with text is non-trivial. To by-pass the limitations of network and query re-writing frameworks, we focus on identifying hidden populations through attributed search. We propose a hierarchical Multi-Arm Bandit (DT-TMP) sampler that uses a decision tree coupled with reinforcement learning to query the combinatorial attributed search space by exploring and expanding along high yielding decision-tree branches. A comprehensive set of experiments over a suite of twelve sampling tasks on three online web platforms, and three offline entity datasets reveals that DT-TMP outperforms all baseline samplers by upto a margin of 54% on Twitter and 48% on RateMDs. An extensive ablation study confirms DT-TMP's superior performance under different sampling scenarios.
Image Denoising Based on Overlapped and Adaptive Gaussian Smoothing and Convolutional Refinement Networks

We propose to use overlapped and adaptive Gaussian smoothing (OAGS) and convolutional refinement networks (CRN) to recover images corrupted by salt-and-pepper noise. First, the OAGS method identifies noise pixels and recover them. Then, CRN further improve and restore the recovered results with sharper and clearer edges. Experimental results demonstrate the proposed OAGS+CRN method significantly outperforms state-of-the-art denoising methods.
VoteGeo: An IoT-based Voting Approach to Verify the Geographic Location of Cloud Hosts

We propose VoteGeo, an IoT-based voting approach, to verify the geographic location of the cloud hosts in moderately connected networks, where the correlation between the network delay and the distance is weak. We motive our work by showing that: 1) existing solutions, based on end-to-end delay measurement from a group of landmarks with known locations, are only workable when the relationship between the delay and the distance is strong, and 2) such landmark-based schemes are limited by landmark distribution and deployment costs. In our scheme, to enhance the verification accuracy, we develop a vote-based closest-shortest approach to determine the location of the target cloud host, which utilizes the adjacent landmarks with the shortest delays. In order to reduce deployment costs, we adopt random IoT devices (i.e., IP cameras) as landmarks, which are widely distributed and considerable. To reduce the measurement cost, we design a two-level probing method to determine the geographic location of the target cloud hosts step by step, eventually to the city-level. We introduce a delay threshold method, which does not rely on the IP address of the target cloud hosts, to defend against the middlebox-bypass attacks and the delay attacks (i.e., delay-shortening attacks and delay-adding attacks). The evaluation results show that VoteGeo outperforms existing schemes such as GeoGet [1] and CBG [2].
A Study on Encryption Techniques to Protect the Patient Privacy in Health Care Systems

Today's emerging trends in the applications of wearable healthcare devices integrated with clouds and cloudlets technologies which strengthen the delivery of cost effective and high quality healthcare services. The Internet of Things (IoT), wearable devices, medical devices and healthcare systems provides the facilities to health care centers and doctors to monitor patient's health at their homes using patient's health data which is stored on cloud. Public area network is used to send data to cloud from healthcare devices, which is not secure. Opponent could read and change the data in the network during transmission. However, due to the heterogeneity, different characteristics and small size of smart wearable devices, specific method for the data protection with wearable device has not been yet established. Therefore to protect the privacy of patient's data it needed access control mechanism based on encryption and cryptography. In this article, we review the different models, schemes as well as implementation related to data encryption and cryptography algorithms proposed by different researchers to secure smart wearable medical health care devices. To prevent the unauthorized access and securing the wireless communication as well as protect device integrity and data confidentiality Cryptography is the best approach.
Expressive Numbers of Two or More Hidden Layer ReLU Neural Networks

One of the reasons why neural networks are used in machine learning is their high expressive power, that is, the ability to express functions. Expressive power of neural networks depends on its structures and is measured by some indices. In this paper, we focus on one of these measures named "expressive number", which is based on the number of data that can be expressed. Expressive numbers enable us to see whether the size of a neural network is suitable for the given training data before we conduct machine learning. However, existing works on expressive numbers mainly target single hidden layer neural networks, and little is known about those with two or more hidden layers. In this paper, we give a lower bound of the maximum expressive number of two hidden layer neural networks and an upper bound of that of multilayer neural networks with ReLU activation function. This result shows the expressive number of two hidden layer neural networks is proportional to the product of the numbers of each hidden layer's neurons.
A Zero-Gating Processing Element Design for Low-Power Deep Convolutional Neural Networks

Convolution neural networks (CNNs) have shown great success in many areas such as object detection and pattern recognition. However, the high computational complexity of state-of-the-art deep CNNs makes them extreme difficult to be run on resource-constrained mobile and wearable devices. To address this design challenge, in this paper we first analyzed the filters' weights of pre-trained models from four state-of-the-art CNNs. We found that in all the CNNs that we analyzed, from about 20% (AlexNet) to 43% (VGG-19) of the weights are zeros, which lead to redundant large amounts of computation. Then, based on this observation, a zero-gating processing element (PE) design was proposed for low-power deep CNNs, in which the vast number of zeros in both activation maps and filter weights are explored to eliminate redundant computation for power reduction. We implemented our proposal with VGG-16 using ImageNet dataset. Experiments were conducted for evaluations of area and total power consumption. Compared with the baseline PE design without zero-gating, overall the proposed zero-gating PE can achieve 37% power saving while the corresponding area overhead is less than 8%.
Together We Are Stronger: Evidence-Based Reflections on Industry-Academia Collaboration in Software Testing

For a highly relevant and applied research area like software testing industry-academia collaboration is of uttermost importance. In this paper we reflect on how industry-academia collaboration can be improved based on evidence from four empirical studies. We therefore first present four studies providing evidence on the (1) perceived level of challenges in testing activities, (2) focus areas in industrial and academic software testing conferences, (3) synergies between industrial and academic software testing conferences, as well as (4) the need for consideration of grey literature. Then, we reflect on issues, which we think can improve the link and synergies between industry and academia in software testing, i.e., research topics, guidelines and evidence, value and risk, context and scalability, action research and education as well as grey literature and open science.