Reviewerly/exHarmony huggingface dataset:
Automatic Question Generation System for English Reading Comprehension

This paper presents a web-based automatic question generation (AQG) system to generate reading comprehension questions and multiple-choice (MC) questions on grammar from a given English text. Such system saves teachers’ time on setting questions and facilitates students and their parents to prepare self-learning exercises. Our web-based system can automatically generate Wh-questions (i.e., what, who, when, where, why, and how) and MC grammar questions of selected sentences. Wh-questions can also be generated from user-specified answer phrases. The generation of Wh-questions exploits the pre-trained natural language understanding model, Text-To-Text Transfer Transformer (T5), and an adapted version of the SQuAD 2.0 machine reading comprehension dataset. The generation of MC questions involves identifying regular verbs in a text and using the verb’s lexemes as the answer choices. Our system takes an average time of about 1 s to generate a Wh-question and it generates a MC question almost instantly. User evaluation indicated that our system is easy-to-use and satisfactory in usefulness, usability, and quality, revealing the effectiveness of our system for teachers and parents.
A Fast Selection Method of Landmarks for Terrain Matching Navigation

Under fixed imaging conditions, the landmark selection method based feature traversal analysis has high computational complexity. The hierarchical statistical significance detection method uses global statistical information for feature analysis to overcome the computational complexity problem caused by feature traversal analysis. The frequency domain de-correlation method can remove repeat mode in the image by adaptive Gaussian filtering on the amplitude-frequency characteristics. In this paper, combined the hierarchical statistical saliency detection method with the frequency domain de-correlation method, a fast landmark selection algorithm based on saliency analysis is proposed. Based on the proposed algorithm, the automatic landmark selection architecture for terrain matching navigation was constructed. The selection of landmark points was carried out in the Qin-ling Mountains and the Guangdong and Guangxi hills. The results show that compared with the feature or pixel-based landmark selection method, the landmark selection efficiency of the proposed method is improved by 2 to 3 orders of magnitude. The correct matching rate of candidate landmarks selected in the Qinling Mountains and the Guangdong and Guangxi hills are 73.9% and 88.3% respectively.
RETRACTED: Smart community management portable device design based on embedded wearable device technology

A broad definition Embedded Wearable Device Technology (EWDT) communication unites people around a common cause, interest, or smart community. The most basic kind is based on actual intimacy people in area, or older neighbors are essential to a wearable device relevant to the community's location. The natural part of the network with others around you and the Embedded Wearable Device Technology (EWDT) machine suing in the traditional economic system is continuously evolving. The risk is trapped in the state space model. The function of the expansion focus is to avoid the error money-related arrangement, primitive liquidity traps. Many business analysts avoid focusing on positive inflammation, even when the risk is low. The proof that the existence of an inflation trap has a slightly but beneficial effect on the presence of an inflation buffer. The assessment of the unfortunate work of the Bank sector is the descending, inclining capacity of the expansion target. Finally, these analytical features of the expansion target were verified by numerical methods. The embedded system changes because of the monetary trap. The private sector should Liquidity consider for real expansion goals.
An Open Source Project for Tuning and Analyzing MapReduce Performance in Hadoop and Spark

MapReduce parameter tuning is time consuming, and existing tuning systems are difficult to use. We present an open source project, Catla for Hadoop and Spark, to provide comprehensive MapReduce tuning, prediction, and visualization tools based on a series of optimization and machine learning methods.
Generalized Benford’s Law for Fake Fingerprint Detection

Human fingerprints are considered to be the most extensively used modality in state-of-the-art biometric based security systems. A newly developed potential threat occurs in the form of specialized fake fingerprint, known as the double-identity fingerprint which is created by careful alignment of two genuine fingerprints to ensure smooth ridges at the intersection points and with high chance of being matched by the criminal as well as accomplice. In this paper, we propose a countermeasure to resolve this problem with generalized Benford's law in conjunction with support vector machine classification. The generalized Benford's law is found to validate the probability distribution of first nonzero digits from block-DCT coefficients of genuine fingerprint images. The experimental results support that the synthesized fake double-identity fingerprints do not inherently follow the generalized Benford's law and hence the proposed method can be effective to discriminate from the genuine class with high accuracy.
A Model-Driven Stack-Based Fully Convolutional Network for Pancreas Segmentation

The irregular geometry and high inter-slice variability in computerized tomography (CT) scans of the human pancreas make an accurate segmentation of this crucial organ a challenging task for existing data-driven deep learning methods. To address this problem, we present a novel model-driven stack-based fully convolutional network with a sliding window fusion algorithm for pancreas segmentation, termed MDS-Net. The MDS-Net's cost function includes a data approximation term and a prior knowledge regularization term combined with a stack scheme for capturing and fusing the two-dimensional (2D) and local three-dimensional (3D) context information. Specifically, 3D CT scans are divided into multiple stacks to capture the local spatial context feature. To highlight the importance of single slices, the inter-slice relationships in the stack data are also incorporated in the MDS-Net framework. For implementing this proposed model-driven method, we create a stack-based U-Net architecture and successfully derive its back-propagation procedure for end-to-end training. Furthermore, a sliding window fusion algorithm is utilized to improve the consistency of adjacent CT slices and intra-stack. Finally, extensive quantitative assessments on the NIH Pancreas-CT dataset demonstrated higher pancreatic segmentation accuracy and reliability of MDS-Net compared to other state-of-the-art methods.
Sentiment Analysis Of Reviews Using an Augmented Dictionary Approach

Sentiment analysis refers to the use of text analysis and natural language processing technique that classifies the text, based on the sentiment expressed in the text. It is mainly classified into two different categories depending on their approaches as machine learning and a lexicon-based approach. In this work, some of the lexicon-based approaches have been explored and improved algorithms are proposed in order to achieve more accurate classifications of the reviews. We proposed an Augmented Dictionary Approach which includes domain-specific words, extracted from the reviews. This approach achieves an average accuracy of 64.56 % when evaluated on 26988 reviews from publicly available fields of reviews, sampled from three different fields (reviews of movies, mobile phones, and restaurants). The accuracy ranges from 63 % for mobile phone reviews to 67 % for movie reviews. The results are compared with the simple lexicon-based approach, phrasal sentiment weight, and some of the online tools for sentiment analysis.
State-of-the-art and Future Directions for Using Augmented Reality Head Mounted Displays for First Aid Live Training

Mixed reality and serious games are often advocated as promising technologies in supporting training and increasing the skills necessary to deal with new, complex, and often unexpected situations. This paper explores the feasibility of an Augmented Reality-(AR) based cyber-physical solution to complement live training for the treatment of serious wound injuries after accidents. Based on an experimental study set-up and examining the technology and the involved stakeholders' experiences, this paper explores current possibilities and limitations for using a Microsoft HoloLens Head Mounted Display (HMD) for first aid trauma training.
Saccadic Eye Movement Classification Using ExG Sensors Embedded into a Virtual Reality Headset

Measuring saccadic eye movements when wearing a virtual reality (VR) head-mounted display (HMD) has recently gained a lot of attention, as it allows for enriched user experiences. This has led to an increase in devices showcasing camera-based eye tracking capabilities. Such devices, however, can be orders of magnitude more expensive than conventional off-the-shelf HMDs. In this study, we explore the use of low-cost sensors embedded directly into the faceplate of the HMD to measure electroencephalography (EEG) and electrooculography (EOG) signals. In a "do-it-yourself" manner, we rely on the openBCI biosignal amplifier for data acquisition. A 7-channel system was tested on four participants who attended visually to a moving target in their field-of-view that moved every 10 degrees over a circumference. Time series and handcrafted features were extracted from the measured ExG signals and served as input to two different classifiers: support vector machine (SVM) and a multilayer perceptron (MLP). A hierarchical classification approach was proposed and found to achieve the best results with the fusion of both features sets, resulting in an average accuracy of 76.51% with an SVM. The results are encouraging and suggest that accurate, low-cost classification of saccadic eye movements may be possible.
Put the Students to Work: Generating Questions with Constructive Feedback

Feedback has been long understood to be a vital component of learning. This is even more important in an online environment, where access to teachers is limited or unavailable. When online learning material is interspersed with opportunities for students to test their knowledge, there is a golden opportunity to give feedback and fix misconceptions immediately. All too often this opportunity is missed or mishandled in online learning platforms - either providing no feedback, or providing it at a coarse level of granularity. Furthermore, generating constructive feedback at a fine level of granularity is so time consuming as to be prohibitive. In this work, we build upon literature that has established students can be recruited to generate quality multiple choice questions, by exploring if students can also generate quality feedback to compliment the questions they generate. In the first iteration of an introductory programming course, we tasked 35 students to generate six questions each over three weeks, and analysed this dataset to generate principles of creating good multiple choice questions with constructive feedback. In the second iteration of the course, we repeated the same task, but provided students with the principles. The results showed an increased occurrence of good feedback when compared to the previous iteration with minimal need for additional intervention by teachers.
AutoLiv: Automated Liver Tumor Segmentation in CT Images

Gastrointestinal (GI) cancer consists of a group of ten cancers that affect the various accessory organs of the digestive system and liver cancer is one of them. In India, it is ranked twelfth in terms of new cases, eight in terms of deaths and increasing as per the Global cancer Observatory data of last year. Like other cancers, it can be cured if detected early. But the diagnostic performance of Computerized Tomography (CT) images for Liver cancer is interpreter-dependent and prone to human errors. Medical image segmentation and analysis of tumor can help in Computer-aided diagnosis (CAD). Automatic Segmenting of liver and tumor is a complex task as it depends on the shape, location, texture and intensity. Therefore, to develop a general-purpose algorithm that fits all is not possible. Both these tasks can be performed either manually or in a semi-automated manner. In this paper we present AutoLiv, automated liver-tumor detection in CT images. In the first stage, threshold-based slope difference differentiation (SDD) technique is used for segmentation of liver and using this in the second stage we carry out tumor detection by alternative fuzzy c-means (AFCM) clustering algorithm. MATLAB based results and manual segmentation results are compared. A close correlation is observed between both the manual and automated approach with very high degree of spatial overlap seen in the regions-of-interest (ROIs) isolated by both methods.
Semantic Layout Manipulation with High-resolution Sparse Attention

We tackle the problem of semantic image layout manipulation, which aims to manipulate an input image by editing its semantic label map. A core problem of this task is how to transfer visual details from the input images to the new semantic layout while making the resulting image visually realistic. Recent work on learning cross-domain correspondence has shown promising results for global layout transfer with dense attention-based warping. However, this method tends to lose texture details due to the resolution limitation and the lack of smoothness constraint on correspondence. To adapt this paradigm for the layout manipulation task, we propose a high-resolution sparse attention module that effectively transfers visual details to new layouts at a resolution up to 512x512. To further improve visual quality, we introduce a novel generator architecture consisting of a semantic encoder and a two-stage decoder for coarse-to-fine synthesis. Experiments on the ADE20k and Places365 datasets demonstrate that our proposed approach achieves substantial improvements over the existing inpainting and layout manipulation methods.
Named-Entity Recognition Using Automatic Construction of Training Data From Social Media Messaging Apps

In recent years, social media messaging app data has served as a precious resource to extract useful information, such as critical clues and evidence in legal trials and criminal investigations. Although these data can be of various types, they are mostly in the form of natural language text. Therefore, to extract information from them efficiently, it is essential to research practical natural language processing approaches. This study proposes applying a deep-learning-based named-entity recognition (NER) system as a natural language processing approach for information extraction to these messaging data. In addition, a system for automatically constructing NER training data is presented using the distant supervision method for the training data of deep-learning models. Because social media messaging app data generally include a significant amount of noise, such as typographical and word-spacing errors, a NER system with robustness against these types of noisy data is required to extract information from the messaging data effectively. The results demonstrate that the proposed approach outperforms that of a NER system with manually labeled training data.
Real-time Motion Planning Based on MPC With Obstacle Constraint Convexification For Autonomous Ground Vehicles

This paper proposes a real-time local motion planning algorithm for autonomous vehicles, which is based on model predictive control (MPC) subject to static and dynamic obstacles. In the proposed planner, the constraints of the road boundaries and obstacles are approximated by low-dimensional linear convex hulls. The proposed MPC optimizes the planning and control goals within one optimization problem using a dynamical model. The first element of the computed optimal decision sequence at any time instant can be applied directly to the vehicle, and an additional tracking controller is not required. The performance of the proposed approach is evaluated in both static and dynamic obstacle scenarios within the high-fidelity CarSim simulation environment. The results show that the proposed algorithm can compute the control actions in real-time, and the vehicle can drive safely and smoothly amid static and dynamic obstacles. Moreover, the proposed approach outperforms the potential field method in terms of computational complexity and planning cost, and the MPC-based planner with kinematics in the high driving speed cases.
Chaotic Evolution Algorithm with Elite Strategy in Single-objective and Multi-objective Optimization

We propose a chaotic evolution algorithm with elite strategy. The conventional chaotic evolution algorithm uses each individual to search in its local area. The proposed algorithm searches the parameter space always around the elite individual from the last generation. We evaluate the proposed algorithm in both single-objective and multi-objective optimization problems. In the single objective optimization problem, the elite is the individual has the best fitness value, and in the multi-objective optimization problem, the elites are the individuals in the first Pareto front. We design and evaluate these two algorithms with elite strategy using single- and multi-objective benchmark functions. We design a jump strategy to avoid searching within a local optima areas by applying elite strategy several generations one time. The numerical evaluation results demonstrate the proposed algorithm has strong local exploitation capability in the early generations. The optimization performance of chaotic evolution algorithm has a potential possibility to apply in high dimensional and more complex optimization problems.
MCFD: A Hardware-Efficient Noniterative Multicue Fusion Demosaicing Algorithm

Color demosaicing is an essential step in the camera image processing pipeline, especially on hardware platforms for real-time video applications. Though many demosaicing algorithms have been proposed in the past two decades, there remains a substantial gap between industrial needs and academic research. On one hand, industry requires a high perceptual quality, artifact-free, and low-cost demosaicing algorithm that is noniterative and small window based ready to be implemented on a hardware platform with a limited line buffer. On the other hand, academia is targeting high PSNR/SSIM, with the computation cost and line buffer receiving second priority, and often a frame buffer and iterative operation are used. The cost, large line buffer and iteration requirement make most existing demosaicing algorithms inapplicable on hardware platforms. In this paper, we introduce a novel low-cost demosaicing algorithm to narrow the gap. We keep the operation window size and computation cost as the first priority and achieve both high PSNR/SSIM and visual perceptual quality compared to previous state-of-the-art methods on standard test datasets. We fully investigate the a priori knowledge of natural scene raw images and find several key cues that are beneficial to demosaicing. Our demosaicing algorithm is a smart fusion of these useful cues. Furthermore, we solve typical demosaicing issues that occur in many traditional methods, including false color artifacts, T-section closing, and zippering artifacts. The proposed method has no learning stage, no iteration operation, a small line buffer and a limited number of parameters, so it can easily be applied to hardware platforms.
Ensemble Feature Selection Method Based on Bio-inspired Algorithms for Multi-objective Classification Problem

Feature selection is a challenging task, specifically in achieving an optimal solution. This is due to the difficulties in choosing the most suitable feature selection method as they tend to work individually and cause incorrect feature selection, which in turn affect the classification accuracy. The objective of this research was to utilise the potential of ensemble methods (boosting) with bio-inspired techniques in improving the performance of multi-objective algorithms (ENORA and NSGA-II) in term of optimum features set. The vital stage in this research was the optimisation of both algorithms using suitable bio-inspired search algorithms with an ensemble method. The next step was the confirmation of the optimal selected feature set by performing a classification task. The evaluation metrics were determined based on the number of selected features with good classification accuracy. Eight benchmark datasets with various sizes were carefully chosen to experiment. Experimental results revealed that both algorithms that used the selected bio-inspired search algorithms with an ensemble method have successfully achieved a better solution with an optimum set of features, that is, less number of features with higher classification accuracy on the selected datasets. This discovery implies that the combination of bio-inspired algorithms with an ensemble method (boosting) improves the performance of both algorithms in attribute selection and classification task.
On Blockchain Metatransactions

In cryptocurrencies, transaction fees are typically exclusively paid in the native platform currency. This restriction causes a wide range of challenges, such as deteriorated user experience, mandatory rent payments by decentralized applications, and blockchain community rivalries (e.g., coinism). Ideally, in a truly permissionless blockchain, transaction fees should be payable in any other cryptocurrency via so-called metatransactions. In this paper, we formalize metatransactions, review existing ideas, and describe novel metatransaction design approaches. Under the assumption of sufficient market liquidity, we argue that meta-transactions do not lower the security of cryptocurrency platforms. However, without changing the underlying blockchain, metatransaction designs typically increase transaction costs and reduce the blockchain transaction throughput.
Low-rank parity-check codes over Galois rings

Low-rank parity-check (LRPC) are rank-metric codes over finite fields, which have been proposed by Gaborit et al. (2013) for cryptographic applications. Inspired by a recent adaption of Gabidulin codes to certain finite rings by Kamche et al. (2019), we define and study LRPC codes over Galois rings - a wide class of finite commutative rings. We give a decoding algorithm similar to Gaborit et al.'s decoder, based on simple linear-algebraic operations. We derive an upper bound on the failure probability of the decoder, which is significantly more involved than in the case of finite fields. The bound depends only on the rank of an error, i.e., is independent of its free rank. Further, we analyze the complexity of the decoder. We obtain that there is a class of LRPC codes over a Galois ring that can decode roughly the same number of errors as a Gabidulin code with the same code parameters, but faster than the currently best decoder for Gabidulin codes. However, the price that one needs to pay is a small failure probability, which we can bound from above.
Hardware Implementation of Image Dehazing Mechanism using Verilog HDL and Parallel DCP

In image processing technology, one of the prime applications is the dehazing process. And it is still a challenge in implementing the corresponding algorithm in hardware. In this paper we propose an atmospheric light estimation method without any complex logic. This makes easy implementation on hardware level. We have also used the Independent Transmission Rate Estimation (ITRE) for calculation of transmission coefficient parallel to the calculation of the atmospheric light. This procedure enhances the time complexity and the quality of the dehazed image. Compared to other logic studies this logic shows a competitive response.