Reviewerly/exHarmony huggingface dataset:
Exploring Hybrid Linguistic Feature Sets to Measure Filipino Text Readability

Proper identification of the difficulty level of materials prescribed as required readings in an educational setting is key towards effective learning in children. Educators and publishers have relied on readability formulas in predicting text readability. While these formulas abound in the English language, limited work has been done on automatic readability assessment for the Filipino language. In this study, we build upon the previous works using traditional (TRAD) and lexical (LEX) linguistic features by incorporating language model (LM) features for possible improvement in identifying readability levels of Filipino storybooks. Results showed that combining LM predictors to TRAD and LEX, forming a hybrid feature set, increased the performances of readability models trained using Logistic Regression and Support Vector Machines by up to ≈ 25% - 32%. From the results of performing feature selection using Spearman correlation and Information Gain on the feature set, we found out that traditional, numeric based features such as word counts and polysyllable word counts are still contributive towards accurate identification of readability levels in Filipino although not by itself. Future directions of the study include extracting more diverse feature sets such as syntactic and morphological predictors.
Probabilistic Estimation of Threat Intrusion in Embedded Systems for Runtime Detection

With billions of networked connected embedded systems, the security historically provided by the isolation of embedded systems is no longer sufficient. Millions of new malware are created every month and zero-day attacks are becoming an increasing concern. Therefore, proactive security measures are no longer enough to provide protection to embedded systems. Instead, reactive approaches that detect attacks that can circumvent the proactive defenses and react upon them are needed. Anomaly-based detection is a common reactive approach employed to detect malware by monitoring anomalous deviations in the system execution. Timing-based anomaly detection detects malware by monitoring the system's internal timing, which offers unique protection against mimicry malware compared to sequence-based anomaly detection. However, previous timing-based anomaly detection methods focus on each operation independently at the granularity of tasks, function calls, system calls, or basic blocks. These approaches neither consider the entire software execution path nor provide a quantitative estimate of the presence of malware. This article presents a novel model for specifying the normal timing for execution paths in software applications using cumulative distribution functions of timing data in sliding execution windows. A probabilistic formulation is used to estimate the presence of malware for individual operations and sequences of operations within the paths. Operation and path-based thresholds are determined during the training process to minimize false positives. Finally, the article presents an optimization method to assist system developers in selecting which operations to monitor based on different optimization goals and constraints. Experimental results with a smart connected pacemaker, an unmanned aerial vehicle, and seven sophisticated mimicry malware implemented at different levels demonstrate the effectiveness of the proposed approach.
Sentiment Analysis of Twitter Data Using Techniques in Deep Learning

Twitter is a trending platform in social media. Twitter has a capacity of storing huge amount of data about current news. So, every citizen in this society is indirectly connected to the news, to know about the current scenario a platform is needed, that platform is our Twitter. In Twitter, there are a lot of trending topics which may be either good or bad. There is ‘n’ number of comments and suggestions regarding each topic. So, to know whether a particular topic is good or bad, every comment mentioned cannot be studied by the user. There comes the sentiment analysis which makes our work easier. The data can be mined utilising the Twitter exploration function, which helps for data cleaning. To analyse the data, a deep learning algorithm is used. To advance the correctness of analysing data, long short-term memory (LSTM) and recurrent neural networks (RNN) are used. Hence, this paper will how Twitter data will get extracted and analysing tweets on a particular topic.
Coarse-to-Fine Gaze Redirection with Numerical and Pictorial Guidance

Gaze redirection aims at manipulating the gaze of a given face image with respect to a desired direction (i.e., a reference angle) and it can be applied to many real life scenarios, such as video-conferencing or taking group photos. However, previous work on this topic mainly suffers of two limitations: (1) Low-quality image generation and (2) Low redirection precision. In this paper, we propose to alleviate these problems by means of a novel gaze redirection framework which exploits both a numerical and a pictorial direction guidance, jointly with a coarse-to-fine learning strategy. Specifically, the coarse branch learns the spatial transformation which warps input image according to desired gaze. On the other hand, the fine-grained branch consists of a generator network with conditional residual image learning and a multi-task discriminator. This second branch reduces the gap between the previously warped image and the ground-truth image and recovers finer texture details. Moreover, we propose a numerical and pictorial guidance module (NPG) which uses a pictorial gazemap description and numerical angles as an extra guide to further improve the precision of gaze redirection. Extensive experiments on a benchmark dataset show that the proposed method outperforms the state-of-the-art approaches in terms of both image quality and redirection precision. The code is available at https://github.com/jingjingchen777/CFGR
A Hamilton-Jacobi Formulation for Time-Optimal Paths of Rectangular Nonholonomic Vehicles

We address the problem of optimal path planning for a Dubins-type nonholonomic vehicle in the presence of obstacles. Most current approaches are either split hierarchically into global path planning and local collision avoidance, or neglect some of the ambient geometry by assuming the car is a point mass. We present a Hamilton-Jacobi formulation of the problem that resolves time-optimal paths and considers the geometry of the vehicle.
Sub-pixel displacement measurement based on the combination of a gray wolf optimizer and gradient algorithm

The digital speckle correlation method (DSCM) aims to measure the displacement of the interesting points by matching the subset around the same point between the undeformed image and the deformed image. It is an effective and powerful optical metrology method for deformation measurement. Considering that the gray wolf optimizer (GWO) is one of the most popular metaheuristic algorithms to calculate the unknown search spaces in the field of optical engineering, a sub-pixel displacement measurement technique based on the GWO and gradient algorithm is proposed. First, the zero-mean normalized cross correlation function is applied to analyze the correlation between the reference image and deformed image subsets. Second, by exploiting the global searching ability of the GWO algorithm, the initial integer pixel value is obtained and further viewed as the initialization displacement. Finally, the final sub-pixel displacement is generated by using a Barron gradient algorithm. Compared with the state-of-the-art methods on synthetic speckle images, the proposed method can effectively measure the displacement and deformation of rigid bodies. Furthermore, the experiments on the real images demonstrate the effectiveness of our presented framework.
Extending Underground Wireless Communications for the Warfighter

Recent advances have seen a rapid acceleration of the development of autonomous platforms to explore indoor facilities, especially for defense and security applications. However, a significant and largely unresolved drawback remains loss of communication signal with an outside operator following any significant penetration by the platform into the environment being explored. This project explores the ability to autonomously create and self-heal a mesh network of wireless communication repeaters to maintain signal between a forward-operating Unmanned Aerial System (UAS) exploring the indoor space and human operator on the exterior. This work proposes a system that uses a quad rotor UAS to deploy small, mobile pucks containing Wi-Fi-enabled microcomputers along its route within an indoor environment. In the event that one of these pucks becomes disabled, the other pucks rearrange to self-heal the network through a novel locomotion method. A prototype of the proposed system was developed and tested, successfully demonstrating subsystem functionality. The distributed communications network was able to extend wireless communication indoors, beyond line of sight, through several hallway corridors, and into multiple rooms.
Is Machine Learning Software Just Software: A Maintainability View

Artificial intelligence (AI) and machine learning (ML) is becoming commonplace in numerous fields. As they are often embedded in the context of larger software systems, issues that are faced with software systems in general are also applicable to AI/ML. In this paper, we address ML systems and their characteristics in the light of software maintenance and its attributes, modularity, testability, reusability, analysability, and modifiability. To achieve this, we pinpoint similarities and differences between ML software and software as we traditionally understand it, and draw parallels as well as provide a programmer's view to ML at a general level, using the established software design principles as the starting point.
Direct multi-element analysis of natural toothbrush by electrothermal vaporization into inductively coupled plasma optical emission spectrometry

Direct analysis of ground natural toothbrush by ETV-ICPOES is accurate, precise and much faster than total digestion and ICPOES analysis.
RNNP: A Robust Few-Shot Learning Approach

Learning from a few examples is an important practical aspect of training classifiers. Various works have examined this aspect quite well. However, all existing approaches assume that the few examples provided are always correctly labeled. This is a strong assumption, especially if one considers the current techniques for labeling using crowd-based labeling services. We address this issue by proposing a novel robust few-shot learning approach. Our method relies on generating robust prototypes from a set of few examples. Specifically, our method refines the class prototypes by producing hybrid features from the support examples of each class. The refined prototypes help to classify the query images better. Our method can replace the evaluation phase of any few-shot learning method that uses a nearest neighbor prototype-based evaluation procedure to make them robust. We evaluate our method on standard mini-ImageNet and tiered-ImageNet datasets. We perform experiments with various label corruption rates in the support examples of the few-shot classes. We obtain significant improvement over widely used few-shot learning methods that suffer significant performance degeneration in the presence of label noise. We finally provide extensive ablation experiments to validate our method.
Patch-CNN: Deep learning for logo detection and brand recognition

Automatic logo detection and recognition is significantly growing due to the increasing requirements of intelligent documents analysis and retrieval. The main problem to logo detection is intra-class variation, which is generated by the variation in image quality and degradation. The problem of misclassification also occurs while having tiny logo in large image with other objects. To address this problem, Patch-CNN is proposed for logo recognition which uses small patches of logos for training to solve the problem of misclassification. The classification is accomplished by dividing the logo images into small patches and threshold is applied to drop no logo area according to ground truth. The architectures of AlexNet and ResNet are also used for logo detection. We propose a segmentation free architecture for the logo detection and recognition. In literature, the concept of region proposal generation is used to solve logo detection, but these techniques suffer in case of tiny logos. Proposed CNN is especially designed for extracting the detailed features from logo patches. So far, the technique has attained accuracy equals to 0.9901 with acceptable training and testing loss on the dataset used in this work.
Performance Evaluation of Markerless 3D Skeleton Pose Estimates with Pop Dance Motion Sequence

The evaluation of markerless pose estimation performed by OpenPose has been getting much attention from researchers of human movement studies. This work aims to evaluate and compare the output joint positions estimated by the OpenPose with a marker-based motion-capture data recorded on a pop dance motion. Although the marker-based motion capture can accurately measure and record the human joint positions, this particular set-up is expensive. The framework to compare the outputs of the markerless method to the ground truth marker-based joint remains unknown, especially for complex body motion. Synchronization, camera calibration, and 3D reconstruction by fusing the outputs of the markerless method (OpenPose) are discussed. In this case study, the comparison results illustrate that the mean absolute errors for each key points are less than 700 mm. Contribution: This work contributes for human movement science by evaluating the OpenPose markerless 3D reconstruction pose with the marker-based motion-capture data recorded on pop dance motion.
i-Walk Intelligent Assessment System: Activity, Mobility, Intention, Communication

We present the i-Walk system, a novel framework for intelligent mobility assistance applications. The proposed system is capable of automatically understanding human activity, assessing mobility and rehabilitation progress, recognizing human intentions and communicating with the patients by giving meaningful feedback. To this end, multiple sensors, i.e. cameras, microphones, lasers, provide multimodal data in order to allow for user monitoring, while state-of-the-art and beyond algorithms have been developed and integrated into the system to enable recognition, interaction and assessment. More specifically, i-Walk performs in real-time and consists of four main sub-modules that interact automatically to provide speech understanding, activity recognition, mobility analysis and multimodal communication for seamless HRI. The i-Walk assessment system is evaluated on a database of healthy subjects and patients, who participated in carefully designed experimental scenarios that cover essential needs of rehabilitation. The presented results highlight the efficacy of the proposed framework to endow personal assistants with intelligence.
SinGAN-GIF: Learning a Generative Video Model from a Single GIF

We propose SinGAN-GIF, an extension of the image-based SinGAN [27] to GIFs or short video snippets. Our method learns the distribution of both the image patches in the GIF as well as their motion patterns. We do so by using a pyramid of 3D and 2D convolutional networks to model temporal information while reducing model parameters and training time, along with an image and a video discriminator. SinGAN-GIF can generate similar looking video samples for natural scenes at different spatial resolutions or temporal frame rates, and can be extended to other video applications like video editing, super resolution, and motion transfer. The project page, with supplementary video results, is: https://rajat95.github.io/singan-gif/.
Deep Learning Techniques for Colorectal Cancer Tissue Classification

The objective assessment of histological images is of paramount importance for the early diagnosis of colorectal cancer (CRC). However, the subjectivity of the evaluation interobserver variation and the traditional visual assessment is time-consuming and costly. On the other hand, the automatic recognition and analysis for digital pathology, the challenging remains due to the variability of the histological images containing more than one tissue type and characteristics of the histological images. In this work, we applied different deep learning techniques based on Convolutional Neural Networks (CNNs) to differentiate colorectal cancer from healthy tissues and benign lesions. Firstly, we trained the deep convolution neural network models and in the parts of network layers are extracting modified parameters to identify different tissue types that are abundant in histological images of CRC. Secondly, we used open dataset of histological images of human colorectal cancer including eight different types of tissue to assess the identification rate. In our results, the colorectal cancer tissue identification accuracy rates are significantly superior to the existing known methods. Therefore, the proposed approach could help doctors to enhance their diagnostic abilities and make better clinical decisions for patients.
Calibration refinement for a fringe projection profilometry system based on plane homography

This paper presents a calibration parameters refinement specifically for a fringe projection profilometry system to assure the final accuracy, even when using an imperfect calibration target. Unlike existing camera-projector calibration methods, we arrange a refinement in subsequent process of target measurement. Following the trend of additionally estimating the target’s geometry, a novel formulation is presented that allows point reconstruction from the infinity homography, thereby introducing the target geometry from the scene. The final objective function is built on the reprojection error and implemented under an equality constraint of the fundamental matrix. In our approach, the fundamental matrix is estimated from the reliable feature correspondences exclusively provided by the structured light system. Experiments are conducted on both synthetic and real datasets to evaluate the performance of our proposed approach.
One-Shot Image Recognition Using Prototypical Encoders with Reduced Hubness

Humans have the innate ability to recognize new objects just by looking at sketches of them (also referred as to proto-type images). Similarly, prototypical images can be used as an effective visual representations of unseen classes to tackle few-shot learning (FSL) tasks. Our main goal is to recognize unseen hand signs (gestures) traffic-signs, and corporate-logos, by having their iconographic images or prototypes. Previous works proposed to utilize variational prototypical-encoders (VPE) to address FSL problems. While VPE learns an image-to-image translation task efficiently, we discovered that its performance is significantly hampered by the so-called hubness problem and it fails to regulate the representations in the latent space. Hence, we propose a new model (VPE++) that inherently reduces hubness and incorporates contrastive and multi-task losses to increase the discriminative ability of FSL models. Results show that the VPE++ approach can generalize better to the unseen classes and can achieve superior accuracies on logos, traffic signs, and hand gestures datasets as compared to the state-of-the-art.
A novel evolutionary approach for learning syntactic features for cross domain opinion target extraction

Aspect-Based Sentiment Analysis (ABSA) is the problem of mining textual user reviews to gauge the orientation of a user towards the various characteristics of a product. Identifying words in the review towards which the user holds a sentiment, known as Aspect Target Extraction (ATE) or Opinion Target Extraction (OTE), is a crucial step in ABSA. Several start-of-the-art techniques, mostly employing Deep Learning methods, have been proposed to tackle this problem. However, when attempting ATE in a new domain, most of the supervised methods are crippled by a lack of labeled data within the domain and do not perform well with models built on other unrelated domains. Other unsupervised methods based on linguistic rules framed by experts have been proposed to mitigate the problem of the lack of data but this requires manual engineering of rules, which may not capture all aspect terms. Different from the previous approaches, we propose evolutionary methods to generate pattern graphs, which encode rules to identify cross-domain aspect terms. The proposed methods aim to mine linguistic patterns, which are quality indicators of the presence of aspect words. The automated pattern learning technique using the evolutionary approach enables automated learning of rules. This approach eliminates the need for human experts to coin the same, while at the same time enabling the production of complicated rules, which can identify cross-domain aspects effectively. Two slightly different approaches are proposed — the first based on identifying opinion targets by matching the learned pattern graphs against existing sentences and the second based on using the learned pattern graphs to construct features for building classification models for aspect identification. We conduct extensive experiments using 10 real-life datasets of varying sizes and different characteristics, and demonstrate the superiority of the proposed methods for cross-domain ATE. We find that the proposed approaches perform better as compared to the existing methods in 69 of the 90 cases, or in other words, 76% of the cases.
Matching Intensity for Image Visibility Graphs: A New Method to Extract Image Features

Recently, the image visibility graphs (IVG) had introduced as simple algorithms by which images map into complex networks.However, current methods based on IVG use global statistical behaviors of the resulting graph to extract image features, which leads to loss of the local structural information of the image.To extract more informative image features by using the concept of IVG, we propose a new concept called matching intensity for image visibility graphs (MIIVG).The key idea of MIIVG is to separate the image into segments and represent the structural behavior of each with reference patterns and corresponding matching intensity.Theoretical analysis shows that the operation of MIIVG can be simplified to convolution operation and provides 256 convolution kernels with clear and apparent physical meaning, through which we can extract image features from multi-viewpoints and obtained more informative image features.Theoretical analysis and experiments demonstrate that MIIVG has a remarkable computing speed and is sufficiently stable against noise.Its high performance in image feature extraction we confirmed by two experiments.In keypoint matching experiments, MIIVG achieves a competitive result compared with SIFT.In texture classification experiments, compared with LBP, MIIVG is superior to LBP in calculation speed and classification effect.Compared with several current deep learning models, they all have the best feature extraction effect and very fast, but the features extracted by MIIVG are more concise.Also, MIIVG hardware requirements are lower, so it is easier to deploy.It is worth mentioning that MIIVG achieved 99.7% classification accuracy on the Multiband datasets, which is a state of the art performance on texture classification task of Multiband datasets and fully demonstrates the effectiveness of MIIVG.
A Novel Semi-supervised Multi-label Twin Support Vector Machine

Multi-label learning is a meaningful supervised learning task in which each sample may belong to multiple labels simultaneously. Due to this characteristic, multi-label learning is more complicated and more difficult than mul... | Find, read and cite all the research you need on Tech Science Press