Reviewerly/exHarmony huggingface dataset:
Development of a Hybrid Method for Planning the Movement of a Group of Marine Robotic Complexes in a Priori Unknown Environment with Obstacles

The article deals with the problem of organizing the group movement of marine robotic systems autonomous underwater vehicles (AUV), in a priori unknown environment with obstacles. The presence of numerous studies in this area confirms the relevance of this problem. The proposed method of planning the movement of the group is based on a combined approach, which organizes a multi-level approach to the organization of the movement of marine robotic complexes (MRC). At the upper level, a global planning system based on the randomized tree method is developed and provides for the overall movement of the group, while the lower-level planning system allows objects to plan the movement and interaction of objects in the group at the local level, including allowing for trouble-free movement of objects in space. The optimization problem is calculated on all stages of the band, starting with the process, optimizing group of MRC, ending the ongoing process of minimizing the total distance between the target point and the location of the formation. Completed numerical simulation of the movement of a group of four AUV in an unknown environment with stationary obstacles is carried out. In the end, we consider the results of the proposed method and its further development, in particular its application in the three-dimensional problem statement.
Sentiment Analysis of Amazon Food Review Data

Sentiment classification is a functioning examination area in information mining and information disclosure with different application spaces. Accomplishment of item advancement sites, for example, Amazon, eBay and so forth gets influenced by the nature of the surveys they have for their yields. Every one of these destinations gives a path to the analyst to form the remarks on the basis of the items and assign a remark to it. Considering these remarks, the analysis will be classified as best or worst. By this, a structure can be edified that can identify the sentiment masked in a review, performing sentiment categorization on a gigantic dataset. All particulars can be grouped into primarily two classes, facts and opinions. Facts are assertions about matter and worldly occurrences. And opinions are individual statements that mirror individuals’ assumptions or bits of knowledge about the entities and events. This paper shows the performance of classification algorithms such as Decision Tree, Bernoulli NB, Logistic, and Perceptron using Principal Component Analysis (PCA), applying n-gram (unigram, bigram) on the entire feature set and computing confusion matrix for the dataset.
Agile principles applied in learning contexts

Agile methodologies have been recently proposed to be used in education. In this paper, we propose a rephrasing of the 12 Agile principles for learning context, and we provide concrete application-oriented interpretations for them. Additionally, a practical agile learning methodology is proposed to offer a framework where these principles could be applied. The principles together with the proposed methodology were applied to a concrete use case which is described and the resulting impact is analysed.
Mining Precedence Relations among Lecture Videos in MOOCs via Concept Prerequisite Learning

In recent years, MOOC has gradually become an important way for people to learn knowledge. But the knowledge background of different people is quite different. Moreover, the precedence relations between lecture videos in a MOOC are often not clearly explained. As a result, some people may encounter obstacles due to lack of background knowledge when learning a MOOC. In this paper, we proposed an approach for mining precedence relations between lecture videos in a MOOC automatically. First, we extracted main concepts from video captions automatically. And then, an LSTM-based neural network model was used to measure prerequisite relations among the main concepts. Finally, the precedence relations between lecture videos were identified based on concept prerequisite relations. Experiments showed that our concept prerequisite learning method outperforms the existing methods and helps accurately identify the precedence relations between lecture videos in a MOOC.
Lighter and Faster Cross-Concatenated Multi-Scale Residual Block Based Network for Visual Saliency Prediction

Existing deep architectures for visual saliency prediction face problems like inefficient feature encoding, larger inference times, and a huge number of model parameters. One possible solution is to make the local and global contextual feature extraction computationally less intensive by a novel lighter architecture. In this work, we propose an end-to-end learnable, inter-scale information sharing residual block based architecture for saliency prediction. A series of these blocks are used for efficient multi-scale feature extraction followed by a dilated inception module (DIM) and a novel decoder. We name this network as cross-concatenated multi-scale residual (CMR) block based network, CMRNet. We comprehensively evaluate our architecture on three datasets: SALICON, MIT1003, and MIT300. Experimental results show that our model works at par with other state-of-the-art models. Especially, our model outperforms all the other models with a smaller inference time and a lesser number of model parameters.
An efficient corner detector based on ratio of parallelogram diagonal

Corner is an important local feature of image which has been widely applied on various computer vision and image processing tasks. Here, a contour-based corner detector is developed by using the ratio of parallelogram diagonals (RPD) to estimate the curve curvature. The main advantage of RPD detector is that only once square root operation is required to calculate the curvature value at each point on a contour while maintaining good noise robustness. The contributions of this paper include the following three aspects: First, the motivation of the proposed RPD curvature is illustrated by means of parallelogram theory; second, a complete corner detector is proposed based on RPD curvature; third, comprehensive experiments are carried out and the experimental results show superior performances of the proposed method against another five strong baselines. In these experiments, RPD runs 100% faster than the prior works. Moreover, a mean accuracy of 83.87% is reported on GCM dataset which is an improvement of about 0.9% and a mean accuracy of 74.21% is reported on CPDA dataset which is an improvement of about 0.2%.
Systematic Review and Open Challenges in Hyper-heuristics Usage On Expensive Optimization Problems with Limited Number of Evaluations

Ever since the early introduction of optimization by Kantorovich in 1939 the science and engineering researchers have created vast categories of optimization problems. Throughout the years, these optimization problems moved from classical problems to more challenging complex problems and these transformations were direct results of industrials demands. Consequently, this has given rise to one of the new classes of challenging optimization problems known as expensive optimization. A problem is considered expensive when it involves very high computational costs due to the complex evaluations of high-dimensional and time-consuming objective functions. In this paper, the past researches that were done in this new research domain are presented followed by a discussion of the hyper-heuristics history and how hyper-heuristics is used in solving expensive optimization problems especially in expensive optimization with a limited number of evaluations.
High-Frequency Shape Recovery from Shading by CNN and Domain Adaptation

Importance of structured-light based one-shot scanning technique is increasing because of its simple system configuration and ability of capturing moving objects. One severe limitation of the technique is that it can capture only sparse shape, but not high frequency shapes, because certain area of projection pattern is required to encode spatial information. In this paper, we propose a technique to recover high-frequency shapes by using shading information, which is captured by one-shot RGB-D sensor based on structured light with single camera. Since color image comprises shading information of object surface, high-frequency shapes can be recovered by shape from shading techniques. Although multiple images with different lighting positions are required for shape from shading techniques, we propose a learning based approach to recover shape from a single image. In addition, to overcome the problem of preparing sufficient amount of data for training, we propose a new data augmentation method for high-frequency shapes using synthetic data and domain adaptation. Experimental results are shown to confirm the effectiveness of the proposed method.
WebQuIn-LD: A Method of Integrating Web Query Interfaces Based on Linked Data

The deep web is a huge source of domain-specific information (sale of houses, medical information, e-commerce, science, etc) stored in database servers accessible through HTML forms called web query interfaces (WQIs). Information in the deep web is retrieved by querying one database server at a time, which results inefficient. A more attractive approach is to create an integrated WQI (IWQI) that acts as single entry point to query several database servers at a time for a given domain. Schema matching and string (labels in WQIs) comparison have been the most popular techniques to create IWQIs. In this work, we propose a new method for the integration of web forms based on linked data and the VDIS (View-based Data Integration System) architecture. We present WebQuIn-LD, an alternative and novel approach relying on linked data principles to combine individual WQIs into a single IWQI for a given domain is presented. WebQuIn-LD follows a data integration system architecture, starting from the wrapping of domain-specific WQIs until the creation of the IWQI. A domain-independent ontology is created to describe WQI elements as linked data resources and to exploit semantic integration between the WQI’s elements. WebQuIn-LD was evaluated on performance metrics (precision, recall, and F1) using the state-of-the-art WQIs datasets for different domains (airfares, books, autos, jobs, music, movies, hotels, jobs). The obtained results demonstrate the effectiveness of the linked data approach presented in this work for the WQI integration problem.
Conducting Malicious Cybersecurity Experiments on Crowdsourcing Platforms

Evaluating the effectiveness of defense technologies mandates the inclusion of a human element, specifically if these technologies target human cognition and emotions. One of the biggest challenges that face researchers in the realm of behavioral cybersecurity is participant recruitment. Researchers often rely on college students, the general public, real-world hackers, or a hard-to-reach population (e.g., professional red teamers) to test the effectiveness of cybersecurity defense techniques. However, recruiting participants from these populations has drawbacks, including but not limited to: high cost, time constraints, and manageability and accessibility issues. This research explored the applicability of using two popular crowdsourcing platforms, Amazon Mechanical Turk and Prolific, to conduct web hacking experiments. Our study is the first to use crowdsourcing platforms to run hacking experiments for scientific purposes. While the recruitment is challenging, the paradigm of existing crowdsourcing platforms can be useful for understanding adversarial behavior, as it facilitates access to a diverse set of participants and allows researchers to conduct longitudinal and cross-cultural assessments. In particular, crowdsourcing platforms offer a great opportunity for cybersecurity researchers to investigate the Oppositional Human Factors (OHFs) in a manageable and flexible way.
Using the Overlapping Score to Improve Corruption Benchmarks

Neural Networks are sensitive to various corruptions that usually occur in real-world applications such as blurs, noises, low-lighting conditions, etc. To estimate the robustness of neural networks to these common corruptions, we generally use a group of modeled corruptions gathered into a benchmark. Unfortunately, no objective criterion exists to determine whether a benchmark is representative of a large diversity of independent corruptions. In this paper, we propose a metric called corruption overlapping score, which can be used to reveal flaws in corruption benchmarks. Two corruptions overlap when the robustnesses of neural networks to these corruptions are correlated. We argue that taking into account overlappings between corruptions can help to improve existing benchmarks or build better ones.
Data Augmentation for Breast Cancer Mass Segmentation

In medical imaging, a major limitation of supervised Deep Neural Network is the need of large annotated datasets. Current data augmentation methods, though quite efficient to enhance the performance of deep learning networks, do not include complex transformations. This paper presents a realistic image transformation model mimicking multiple acquisitions obtained from the analysis of a mammography database composed of screening acquisitions with priors. Our transformation model results from the combination of a registration algorithm, an invariant meshing strategy and a reduced model describing motion and local intensity variation in paired images. The extracted data variability was then transferred trough data augmentation to a small database for the training of a deep learning-based segmentation algorithm. Significant improvements are observed compared to usual data augmentation techniques.
Wavelet Frame-Based Image Restoration via $\ell _2$-Relaxed Truncated $\ell _0$ Regularization and Nonlocal Estimation

Wavelet tight frames have been actively investigated for various image restoration problems. In this paper, we introduce an analysis-sparsity model via <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _2$</tex-math></inline-formula> -relaxed truncated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _0$</tex-math></inline-formula> regularization and nonlocal estimation, and the resulted nonconvex minimization problem is tackled by a proximal alternating minimization strategy. Numerical experiments demonstrate that the proposed algorithm is superior to many popular methods in both objective and perceptual quality.
Modified Textural Soil Classification

In this world, different types of soils are present. These soils are consisting of different types of soil particles. The engineering properties of soil are defined on the basis of particles size and consistency limits. Soil was classified into different divisions and subdivisions by different organizations. International classification, highway research board, MIT, AASHTO and many other classifications are available, but these classifications are having limitations in describing or defining the soil and soil mass. Due to this reason, a new classification of soil is proposed with the name modified textural soil classification. The modified textural classification is developed using the concept of two existing soil classifications, namely the international classification system of soil and textural classification system of soil. The original textural classification systems of soil used triangle for soil classification based on particle size distribution considering sand, silt and clay as the type of particles. Modified textural soil classification has introduced two overlapping triangles, coarse and fine triangle based on particle size considering gravel, sand, majla, silt, clay and ultra-clay as the type of particles. In this way, another triangle is formed, namely middle triangle which is more helpful in classifying soil. In modified textural classification, the two triangles are overlapped at the 50%.
Specific Emitter Identification via Variational Mode Decomposition and Histogram of Oriented Gradient

Specific emitter identification (SEI) is a physical-layer-based approach for enhancing wireless communication network security. A well-done SEI method can be widely applied in identifying the individual wireless communication device. In this paper, we propose a novel specific emitter identification method based on variational mode decomposition and histogram of oriented gradient (VMD-HOG). The signal is decomposed into specific temporal modes via VMD and HOG features are obtained from the time-frequency spectrum of temporal modes. The performance of the proposed method is evaluated both in single hop and relaying scenarios and under three channels with the number of emitters varying. Results depict that our proposed method provides great identification performance for both simulated signals and realistic data of Zigbee devices and outperforms the two existing methods in identification accuracy and computational complexity.
RETRACTED: Comparative Chinese and American Mother Tongue Teaching Materials Based on Big Data Technology

Abstract Cultural education has always been regarded as the most important part of the development of a country and society. Education is a topic that people have been exploring. Mother tongue is the first language that people learn in the world. Learning mother tongue has always been important for learning other subjects. It is an important foundation. Since the 21st century, with the development of the Internet, the study of mother tongue has also developed more and more widely with the support of big data. This article mainly introduces the comparative analysis of Chinese and American native language textbooks based on big data technology. This article uses big data to analyze and compare native language textbooks, combined with big data analysis technology, and uses reading feedback to investigate the comparison of Chinese and American native language textbooks. The experimental results of this article show that the big data analysis of Chinese and American native language textbooks has increased the reading volume by 16% and improved the efficiency of learning. Incorporating modern data collection and learning analysis into Chinese and American mother tongue teaching has improved the quality of teaching.
A Comparison of Sequential Prediction Algorithms in IoT Enabled Smart Environments

Sequential prediction is an essential component of Artificial Intelligence (AI) and Machine Learning (ML) in a number of domains. In this paper, we analyze the performance of sequential prediction algorithms that are used to predict the next event in smart environments. We first employ three sequential prediction algorithms, namely LZ78, Active LeZi (ALZ) and the Sequence Prediction via Enhanced Episode Discovery (SPEED) to build sequential trees, and then an efficient prediction algorithm with different window lengths is applied to these trees to predict the next sensor event for a sequence of activities. The sequential algorithms are tested with different window lengths for a dataset consists of 25 sensors. The results show that the best prediction accuracy is achieved by SPEED with a peak accuracy of 92.86%, but with higher storage requirement compared to LZ78 and ALZ.
Detecting of Vertically-Oriented Texts in Images Containing Natural Scenes

Universally, scene text in natural images is an expressive means of communication. Texts available in an image could provide important information which can enhance the interpretation of the image, for instance texts on product packages and road signs. Recently, scene texts detection has been recognized as an important research field in computer vision. However, texts detection in natural scene images has been challenging due to the complicated scene background involving varying fonts, sizes, image resolutions etc. As to date, different methods of detecting texts in natural scene images have been proposed for horizontal texts, arbitrarily-oriented texts and curved texts, but there is a lack of detection of vertically-oriented texts in the natural scene images. Hence, this research proposed a framework for detecting the top-to-bottom, bottom-to-top and horizontally-stacked vertical texts in natural scene images. In this paper, Multi-directional Text Detector (MTD) is modelled and developed to locate the vertically-oriented texts in natural scene images, and the Vertical Scene Texts Dataset-700 (VSTD-700) is developed. The preliminary testing shows that the success rate of detection of MTD is 87% on ICDAR 2013, 73% on ICDAR 2015, 71% on MSRA-TD500 and 87% on VSTD-700 datasets. Hence, the results showed that MTD is able to detect vertically-oriented texts on top of the horizontally-oriented and arbitrarily-oriented texts in natural scene images simultaneously.
Robust Suspicious Action Recognition Approach Using Pose Descriptor

In the current era of technological development, human actions can be recorded in public places like airports, shopping malls, and educational institutes, etc., to monitor suspicious activities like terrorism, fighting, theft, and vandalism. Surveillance videos contain adequate visual and motion information for events that occur within a camera’s view. Our study focuses on the concept that actions are a sequence of moving body parts. In this paper, a new descriptor is proposed that formulates human poses and tracks the relative motion of human body parts along with the video frames, and extracts the position and orientation of body parts. We used Part Affinity Fields (PAFs) to acquire the associated body parts of the people present in the frame. The architecture jointly learns the body parts and their associations with other body parts in a sequential process, such that a pose can be formulated step by step. We can obtain the complete pose with a limited number of points as it moves along the video and we can conclude with a defined action. Later, these feature points are classified with a Support Vector Machine (SVM). The proposed work was evaluated on the benchmark datasets, namely, UT-interaction, UCF11, CASIA, and HCA datasets. Our proposed scheme was evaluated on the aforementioned datasets, which contained criminal/suspicious actions, such as kick, punch, push, gun shooting, and sword-fighting, and achieved an accuracy of 96.4% on UT-interaction, 99% on UCF11, 98% on CASIA and 88.72% on HCA.
Interactive Object Segmentation With Dynamic Click Transform

In the interactive segmentation, users initially click on the target object to segment the main body and then provide corrections on mislabeled regions to iteratively refine the segmentation masks. Most existing methods transform these user-provided clicks into interaction maps and concatenate them with image as the input tensor. Typically, the interaction maps are determined by measuring the distance of each pixel to the clicked points, ignoring the relation between clicks and mislabeled regions. We propose a Dynamic Click Transform Network (DCT-Net), consisting of Spatial-DCT and Feature-DCT, to better represent user interactions. Spatial-DCT transforms each user-provided click with individual diffusion distance according to the target scale, and Feature-DCT normalizes the extracted feature map to a specific distribution predicted from the clicked points. We demonstrate the effectiveness of our proposed method and achieve favorable performance compared to the state-of-the-art on three standard benchmark datasets.