Reviewerly/exHarmony huggingface dataset:
Terrain constrained path planning for long-haul cables

A method for path planning for a long-haul submarine optical fiber cable connecting two locations on the surface of the Earth is presented. Previous work on path planning takes account of the laying cost of the cable including material, labor, and its survivability, with consideration of risk of future cable break arising from laying of the cable in sensitive and risky areas, such as, in particular, earthquake prone areas. Previous work has also taken account of variation in the cost per unit length to optimize shielding (and associated increased costs) in higher risk areas. The key novelty here is to take account of the important requirement to reduce the likelihood of capsize of a remotely operated cable laying vehicle as it buries the cable in an uneven terrain. This instability risk depends on the direction of the path and slope of the terrain and is included here in the laying cost. Minimization of the cable laying cost and the expected number of potential cable repairs are the two objectives used to formulate the multi-objective optimal control problem. Using a Pareto approach, we solve the problem via dynamic programming and a computationally efficient algorithm based on the Ordered Upwind Method. Numerical results are consistent with an intuitive assessment of path quality, e.g., we can observe that the algorithm avoids high slope areas when better solutions are clearly available. Pareto optimal solutions and an approximate Pareto front are obtained to provide insight and guidance for cable path design that considers trade-offs between cost effectiveness (that includes consideration for stability of the remotely operated cable laying vehicle) and seismic resilience.
EM Monitoring and Classification of IEMI and Protocol-Based Attacks on IEEE 802.11n Communication Networks

The development of connected devices and their daily use is presently at the origin of the omnipresence of Wi-Fi wireless networks. However, these Wi-Fi networks are often vulnerable, and can be used by malicious people to disturb services, intercept sensitive data, or to gain access to the system. In railways, trains are now equipped with wireless communication systems for operational purposes or for passenger services. In both cases, defense strategies have to be developed to prevent the misuses of the networks. The first objective of this study is to propose a monitoring solution, which is independent of the communication networks, to detect the occurrence of attacks. The second objective is to develop a method that is able to classify attacks of different types: the intentional electromagnetic interference, i.e., jamming attacks and the protocol-based attacks. This study focuses on the IEEE 802.11n Wi-Fi protocol. To perform these analyses, we propose to monitor and to analyze electromagnetic (EM) signals received by a monitoring antenna and a receiver collecting the EM spectra. After that, we build a classification protocol following two steps: the first consists in the construction of a support vector machine (SVM) classification model using the collected spectra, and the second step uses this SVM model to predict the class of the attack (if any). A time-based correction of this prediction using the nearest neighbors is also included in this second step.
Open-source pipeline for multi-class segmentation of the spinal cord with deep learning

This paper presents an open-source pipeline to train neural networks to segment structures of interest from MRI data. The pipeline is tailored towards homogeneous datasets and requires relatively low amounts of manual segmentations (few dozen, or less depending on the homogeneity of the dataset). Two use-case scenarios for segmenting the spinal cord white and grey matter are presented: one in marmosets with variable numbers of lesions, and the other in the publicly available human grey matter segmentation challenge [1]. The pipeline is freely available at: https://github.com/neuropoly/multiclass-segmentation.
A Comparative Analysis of Symmetric Algorithms in Cloud Computing: A Survey

Introducing Cloud computing to the globe has changed many conceptual and infrastructural bases for today's and tomorrow's computing.It has made the global thinking migrates rapidly towards cloud based architecture.Clouds bring out a variety of benefits including computing resources configurability, cost controllability, sustainability, mobility and service flexibility.However, the new concepts that clouds introduce such as outsourcing, multi-tenancy, and resource sharing create new challenges and raise a broad range of security and privacy issues.Cryptography is the art-of-science of protecting data privacy by converting it to unreadable format using standard mathematical techniques.This paper provides a comprehensive study for eight of the most common symmetric cryptographic algorithms, namely, DES, 3DES, Blowfish, Twofish, RC2, RC5, RC6 and AES.A comparative analysis based on the structure of the algorithm, encryption and decryption times, throughput and memory utilization has been performed to examine the performance of each algorithm.
Endoscopic Vision Augmentation Using Multiscale Bilateral-Weighted Retinex for Robotic Surgery

Endoscopic vision plays a significant role in minimally invasive surgical procedures. The visibility and maintenance of such direct in situ vision is paramount not only for safety by preventing inadvertent injury but also to improve precision and reduce operating time. Unfortunately, the endoscopic vision is unavoidably degraded due to the illumination variations during surgery. This paper aims to restore or augment such degraded visualization and quantitatively evaluate it during robotic surgery. A multiscale bilateral-weighted retinex method is proposed to remove non-uniform and highly directional illumination and enhance surgical vision, while an objective no-reference image visibility assessment method is defined in terms of sharpness, naturalness, and contrast, to quantitatively and objectively evaluate the endoscopic visualization on surgical video sequences. The methods were validated on surgical data, with the experimental results showing that our method outperforms existent retinex approaches. In particular, the combined visibility was improved from 0.81 to 1.06, while three surgeons generally agreed that the results were restored with much better visibility.
INVESTIGATION OF FILTERING AND OBJECTS DETECTION ALGORITHMS FOR A MULTIZONE IMAGE SEQUENCE

Abstract. The problem of detecting objects on a sequence of images with a complex structure is considered. Optimal and quasi-optimal algorithms for processing multidimensional images have been synthesized and investigated. Improved detection efficiency has been obtained by adequately describing real data using doubly stochastic random fields. The possibility of describing Earth remote sensing data using doubly stochastic models is investigated. The possibility of obtaining significant gains when filtering satellite material and detecting extended objects on it due to the adaptive structure of such models and processing time sequence of multizone images as a single multidimensional dataset is shown. The gains for filtering algorithms in the error variance are about 80% comparing single frame processing, and the gains for detecting algorithms in the signal/noise ratio are about 70% comparing single frame processing.
Deep residual learning for denoising Monte Carlo renderings

Learning-based techniques have recently been shown to be effective for denoising Monte Carlo rendering methods. However, there remains a quality gap to state-of-the-art handcrafted denoisers. In this paper, we propose a deep residual learning based method that outperforms both state-of-the-art handcrafted denoisers and learning-based denoisers. Unlike the indirect nature of existing learning-based methods (which e.g., estimate the parameters and kernel weights of an explicit feature based filter), we directly map the noisy input pixels to the smoothed output. Using this direct mapping formulation, we demonstrate that even a simple-and-standard ResNet and three common auxiliary features (depth, normal, and albedo) are sufficient to achieve high-quality denoising. This minimal requirement on auxiliary data simplifies both training and integration of our method into most production rendering pipelines. We have evaluated our method on unseen images created by a different renderer. Consistently superior quality denoising is obtained in all cases.
Enhanced Bayesian Compression via Deep Reinforcement Learning

In this paper, we propose an Enhanced Bayesian Compression method to flexibly compress the deep networks via reinforcement learning. Unlike the existing Bayesian compression method which cannot explicitly enforce quantization weights during training, our method learns flexible codebooks in each layer for an optimal network quantization. To dynamically adjust the state of codebooks, we employ an Actor-Critic network to collaborate with the original deep network. Different from most existing network quantization methods, our EBC does not require re-training procedures after the quantization. Experimental results show that our method obtains low-bit precision with acceptable accuracy drop on MNIST, CIFAR and ImageNet.
Opposition Intensity-Based Cuckoo Search Algorithm for Data Privacy Preservation

Abstract Privacy-preserving data mining (PPDM) is a novel approach that has emerged in the market to take care of privacy issues. The intention of PPDM is to build up data-mining techniques without raising the risk of mishandling of the data exploited to generate those schemes. The conventional works include numerous techniques, most of which employ some form of transformation on the original data to guarantee privacy preservation. However, these schemes are quite multifaceted and memory intensive, thus leading to restricted exploitation of these methods. Hence, this paper intends to develop a novel PPDM technique, which involves two phases, namely, data sanitization and data restoration. Initially, the association rules are extracted from the database before proceeding with the two phases. In both the sanitization and restoration processes, key extraction plays a major role, which is selected optimally using Opposition Intensity-based Cuckoo Search Algorithm, which is the modified format of Cuckoo Search Algorithm. Here, four research issues, such as hiding failure rate, information preservation rate, and false rule generation, and degree of modification are minimized using the adopted sanitization and restoration processes.
Automated Classification of Malignant and Benign Breast Cancer Lesions Using Neural Networks on Digitized Mammograms

We propose a novel neural network approach for the classification of abnormal mammographic images into benign or malignant based on their texture representations. The proposed framework has the capability of mapping high dimensional feature space into a lower-dimension, in a supervised way. The main contribution of the proposed classifier is to introduce a new neuron structure for map representation and adopt a supervised learning technique for feature classification. This is achieved by making the weight updating procedure dependent on the class reliability of the neuron. We showed high accuracy (95.2%) for our proposed approach in the classification of abnormal real mammographic images when compared to other related methods.
Dropout as a Structured Shrinkage Prior

Dropout regularization of deep neural networks has been a mysterious yet effective tool to prevent overfitting. Explanations for its success range from the prevention of co-adapted weights to it being a form of cheap Bayesian inference. We propose a novel framework for understanding multiplicative noise in neural networks, considering continuous distributions as well as Bernoulli noise (i.e. dropout). We show that multiplicative noise induces structured shrinkage priors on a network's weights. We derive the equivalence through reparametrization properties of scale mixtures and without invoking any approximations. Given the equivalence, we then show that dropout's Monte Carlo training objective approximates marginal MAP estimation. We leverage these insights to propose a novel shrinkage framework for resnets, terming the prior 'automatic depth determination' as it is the natural analog of automatic relevance determination for network depth. Lastly, we investigate two inference strategies that improve upon the aforementioned MAP approximation in regression benchmarks.
Analyzing Student Process Data in Game-Based Assessments with Bayesian Knowledge Tracing and Dynamic Bayesian Networks.

Digital game-based assessments generate student process data that is much more difficult to analyze than traditional assessments. The formative nature of game-based assessments permits students, through applying and practicing the targeted knowledge and skills during gameplay, to gain experiences, receive immediate feedback, and as a result, improve their skill mastery. Both Bayesian Knowledge Tracing and Dynamic Bayesian Networks are capable of updating students’ mastery levels based on their observed responses during the assessment. This paper investigates the use of these two models for analyzing student response process data from an interactive game-based assessment, Raging Skies. The game measures a set of knowledge and skill-based learner outcomes listed in a Canadian Provincial Grade 5 science program-of-study under the Weather Watch unit. To evaluate and compare the performance of Bayesian Knowledge Tracing and Dynamic Bayesian Networks, the classification consistency and accuracy are examined.
Blind stereo image quality assessment inspired by brain sensory-motor fusion

The use of 3D and stereo imaging is rapidly increasing. Compression, transmission, and processing could degrade the quality of stereo images. Quality assessment of such images is different than their 2D counterparts. Metrics, which represent 3D perception by human visual system (HVS), are expected to assess stereoscopic quality more accurately. In this paper, inspired by brain sensory/motor fusion process, by combining the right and left images, we form two synthesized images. Effects of different structural distortions on statistical distributions of the synthesized images are analyzed. Based on the observed statistical changes, features are extracted from these synthesized images that can reveal type and severity of distortions. Then, we propose a stacked neural network model to learn the extracted features and accurately predict the quality of stereo images. This model is tested on 3D images of popular databases. Experimental results show the superiority of this method over state-of-the-art stereo image quality assessment approaches.
Designing a Symbolic Intermediate Representation for Neural Surface Realization

Generated output from neural NLG systems often contain errors such as hallucination, repetition or contradiction. This work focuses on designing a symbolic intermediate representation to be used in multi-stage neural generation with the intention of reducing the frequency of failed outputs. We show that surface realization from this intermediate representation is of high quality and when the full system is applied to the E2E dataset it outperforms the winner of the E2E challenge. Furthermore, by breaking out the surface realization step from typically end-to-end neural systems, we also provide a framework for non-neural based content selection and planning systems to potentially take advantage of semi-supervised pretraining of neural surface realization models.
A class of multi-resolution approximations for large spatial datasets

Gaussian processes are popular and flexible models for spatial, temporal, and functional data, but they are computationally infeasible for large datasets. We discuss Gaussian-process approximations that use basis functions at multiple resolutions to achieve fast inference and that can (approximately) represent any spatial covariance structure. We consider two special cases of this multi-resolution-approximation framework, a taper version and a domain-partitioning (block) version. We describe theoretical properties and inference procedures, and study the computational complexity of the methods. Numerical comparisons and an application to satellite data are also provided.
An Innovative English Teaching System Based on Computer Aided Technology and Corpus Management

With the development of modern science and technology, more and more com-puter technologies have been successfully applied in English teaching. Based on computer aided technology and big data corpus management, this paper improves the traditional teaching method into an innovative teaching mode with a big data corpus as English learning resource. On this basis, a computer multimedia teach-ing system was set up to realize automatic matching of subtitles and vivid restora-tion of contexts. The teaching system achieved excellent results in application ver-ification. The research results can promote computer technology in English teach-ing.
A New Visual Interface for Searching and Navigating Slide-Based Lecture Videos

The rapid development of distance education technologies, e.g. MOOCs, provide learners unprecedented access to high-quality online lecture videos at scale, anytime and anywhere. Unfortunately, these valuable resources are often underutilized by online learners. One prevailing reason is the lack of support for and the resulting difficulty of exploring and locating content of interest among lengthy recordings of course lectures. To address this deficiency, we introduce a novel visual interface that supports efficient search and navigation of video content at fine granularities and with rich semantic clues. The interface is particularly designed for slide-based lecture videos (SBLV), which represent a significant portion of online lecture videos. The interface comprehensively derives versatile semantic clues for video content indexing and visual aid generation according to visual elements, text, and mathematical expressions included on lecture slides, speeches recorded, as well as mouse and cursor pointing actions captured during a lecture. Empowered by such semantically revealing indices and visual assistance, the interface is able to noticeably enhance online learners' capabilities in searching and browsing of content in need from SBLVs. The advantages of the new interface are demonstrated through benchmarked experimental results in comparison with peer methods.
Goal Reasoning in the CLIPS Executive for Integrated Planning and Execution

The close integration of planning and execution is a challenging problem. Key questions are how to organize and explicitly represent the program flow to enable reasoning about it, how to dynamically create goals from run-time information and decide on-line which to pursue, and how to unify representations used during planning and execution.In this work, we present an integrated system that uses a goal reasoning model which represents this flow and supports dynamic goal generation. With an explicit world model representation, it enables reasoning about the current state of the world, the progress of the execution flow, and what goals should be pursued – or postponed or abandoned. Our executive implements a specific goal lifecycle with compound goal types that combine sub-goals by conjunctions, disjunctions, concurrency, or that impose temporal constraints.Goals also provide a frame of reference for execution monitoring. The current system can utilize PDDL as the underlying modeling language with extensions to aid execution, and it contains well-defined extension points for domain-specific code. It has been used successfully in several scenarios.
SMS Spam Detection Based on Long Short-Term Memory and Gated Recurrent Unit

An SMS spam is the message that hackers develop and send to people via mobile devices targeting to get their important information.For people who are ignorant, if they follow the instruction in the message and fill their important information, such as internet banking account in a faked website or application, the hacker may get the information.This may lead to loss their wealth.The efficient spam detection is an important tool in order to help people to classify whether it is a spam SMS or not.In this research, we propose a novel SMS spam detection based on the case study of the SMS spams in English language using Natural Language Process and Deep Learning techniques.To prepare the data for our model development process, we use word tokenization, padding data, truncating data and word embedding to make more dimension in data.Then, this data is used to develop the model based on Long Short-Term Memory and Gated Recurrent Unit algorithms.The performance of the proposed models is compared to the models based on machine learning algorithms including Support Vector Machine and Naïve Bayes.The experimental results show that the model built from the Long Short-Term Memory technique provides the best overall accuracy as high as 98.18%.On accurately screening spam messages, this model shows the ability that it can detect spam messages with the 90.96% accuracy rate, while the error percentage that it misclassifies a normal message as a spam message is only 0.74%.Index Terms-SMS spam, natural language process, deep learning, long short-term memory
Multimodal sentiment analysis using reliefF feature selection and random forest classifier

Multimodal sentiment analysis becomes an emerging research topic due to the rapid growth of modality and diversity of social data. The primary objective of this research paper is to develop an effective feature selection algorithm to determine the optimal features for further improving the performance of multimodal sentiment analysis. Initially, the data were collected from YouTube dataset. Then, feature extraction was carried out on the collected data utilizing Mel-Frequency Cepstral Coefficients (MFCCs), linear predictive coefficient, spectral centroid, spectral flux, Local Binary Pattern (LBP), Histogram of Oriented Gradient (HOG), Latent Semantic Analysis (LSA), and Term Frequency-Inverse Document Frequency (TF-IDF) to extract the feature values from the collected data. After feature extraction, reliefF feature selection algorithm was used for choosing the optimal features. At last, random forest classifier was used for classifying the sentiments of speakers such as neutral, positive, and negative class. The quantitative analysis showed that the proposed system enhanced the classification accuracy up to 5.41% compared to the existing systems.
Interactive Language Learning by Question Answering

Humans observe and interact with the world to acquire knowledge.However, most existing machine reading comprehension (MRC) tasks miss the interactive, information-seeking component of comprehension.Such tasks present models with static documents that contain all necessary information, usually concentrated in a single short substring.Thus, models can achieve strong performance through simple word-and phrase-based pattern matching.We address this problem by formulating a novel text-based question answering task: Question Answering with Interactive Text (QAit) 1 .In QAit, an agent must interact with a partially observable text-based environment to gather information required to answer questions.QAit poses questions about the existence, location, and attributes of objects found in the environment.The data is built using a text-based game generator that defines the underlying dynamics of interaction with the environment.We propose and evaluate a set of baseline models for the QAit task that includes deep reinforcement learning agents.Experiments show that the task presents a major challenge for machine reading systems, while humans solve it with relative ease.