Reviewerly/exHarmony huggingface dataset:
A Modified Hough Transform TBD Method for Radar Weak Targets Using Plot’s Quality

In this paper, we propose a modified Hough transform based TBD detection algorithm for radar weak targets using radar plot quality information. Firstly, a general description of the proposed method was presented. And then the quality of radar raw plots was redefined and its calculation algorithms were discussed in detail. Finally, a modification of traditional Hough transform using plot’s quality information was applied and more credible peak detections were conducted and outputted. The effectiveness of the proposed method especially in heavy clutter conditions was verified by simulation experiments.
Context Matters! Identifying Social Context Factors and Assessing Their Relevance for a Socially Assistive Robot

In three online studies we examine which social context factors determine whether a robot should (not) deliver a message/reminder to its user using three scenarios: a smart kitchen (n = 101), a smart living room (n = 96) and a smart office (n = 96). In addition, we varied the nature of the message (urgent/non-urgent; sensitive/non-sensitive). In this late braking report, we present some preliminary insights on the nature of the situations and message types for which participants would prefer the message to be delivered.
Recognition Algorithm of Rice Germ Integrity Base on Improved Inception v3

This paper adopts a deep learning inceptionv3 as a framework, improves a loss function and adds large boundary regularization parameters, thereby increasing an inter-class difference of germ integrity; and moreover, this paper adds new loss function for feedback, identifies an inter-class boundary of the germ integrity and learns different local features, i.e. different germ integrity features of rice grains on different channels. The original dropout is improved and added to an orthogonal classification layer to reduce the unnecessary link weight of different germ integrity classes, thereby identifying the germ integrity of the rice. The experiment result shows that the proposed algorithm has high identification rate for identifying the integrity of the milled rice with germs, and the comprehensive segmentation accuracy is 94.12%. Compared with other classical identification, the accuracy of the algorithm is greatly improved.
Translation into Pali Language from Brahmi Script

The Brahmi script is widely used in ancient time for writing the various types of religious books, information about the rules and regulation of the kingdom of king Ashok and Pali language is used to read the Brahmi script. Pali to English dictionary is available to understand the Pali language. However, any dictionary or translation system related to Brahmi script to Pali script is not available. In order to understand the content in Brahmi script, an automated system for facilitating machine translation of Brahmi text to Pali language is suggested in this study. Detail research on Brahmi script to Pali language has been accomplished, and necessary rules are analyzed. Based on the research and analysis, this study illustrates one-to-one and many-to-one mapping system for Brahmi text to Pali language translation.
Uncertainty in Robot Assisted Second Language Conversation Practice

Moments of uncertainty are common for learners when practicing a second language. The appropriate management of these events could help avoid the development of frustration and benefit the learner's experience. Therefore, its detection is crucial in language practice conversations. In this study, an experimental conversation between an adult second language learner and a social robot is employed to visually characterize the learners' uncertainty. The robot's output is manipulated in prosody and lexical levels to provoke uncertainty during the conversation. These reactions are then processed to obtain Facial Action Units (AUs) and Gaze features. Preliminary results show distinctive behavioral patterns of uncertainty among the participants. Based on these results, a new annotation scheme is proposed, which will expand the data used to train sequential models to detect uncertainty. As future steps, the robotic conversational partner will use this information to adapt its behavior in dialogue generation and language complexity.
On the convergence analysis of asynchronous SGD for solving consistent linear systems

Parallel and distributed stochastic algorithms have drawn significant attention in the realm of big data and machine learning. While the synchronous versions of these algorithms are well understood in terms of their convergence, the convergence analyses of their asynchronous counterparts are not widely studied. In this paper, we propose and analyze a distributed, asynchronous parallel algorithm to solve an arbitrary, consistent linear system by reformulating the system into a stochastic optimization problem as Richtárik and Takác̃ in [1]. We compare the convergence rates of our asynchronous algorithm with the synchronous parallel algorithm proposed by Richtárik and Takáč in [1] under different choices of the hyperparameters—the stepsize, the damping factor, the number of processors, and the delay factor. We show that our asynchronous parallel algorithm enjoys a global linear convergence rate, similar to the synchronous parallel algorithm in [1] under the same setup. We also show that our asynchronous algorithm improves upon the synchronous algorithm in [1] with a better convergence rate when the number of processors is larger than four. Furthermore, our asynchronous parallel algorithm performs asymptotically better than its synchronous counterpart for certain linear systems. Finally, we compute the minimum number of processors required for those systems for which our asynchronous algorithm is better and find that this number can be as low as two for some ill-conditioned problems.
Task-Based Syllabus Design

This chapter examines four different proposals for designing a task-based course. In Prabhu’s Communicational Language Teaching Project, the syllabus has low internal structure, leaving implementation issues to be decided by teachers. In Long’s syllabus, tasks have a training function. Target tasks are identified by a needs analysis and then restructured into pedagogic tasks. Robinson’s is the most ambitious proposal as he proposes a syllabus that takes into account the cognitive complexity of tasks, their propensity for promoting the kinds of interaction that facilitate acquisition, and the cognitive abilities and affective dispositions of individual learners. Finally, Ellis identifies a range of factors that influence task complexity but suggests that sequencing tasks is largely a matter of intuition that can be guided only roughly by these factors. In Ellis’s proposal there is room for a more traditional, structural module to fit alongside a task-based module in a complete course.
Early Detection of Suicidal Predilection on Instagram Social Networking Service

Social Networks has become the biggest medium of expression of one's thoughts and emotions. With more than one-third of the global population expressing their thoughts, opinions and events, these networks become rich with direct indicators about the subject. Multimedia based social networking services are gaining popularity than text-based ones. In this work, we analyze the data from Instagram, which is one of the largest image and video-based social networking service with more than one billion users. This paper analyzes the social media posts on Instagram with the goal of finding when the subject feels low that could potentially lead to a suicidal tendency in the initial stages. In this study, the Instagram posts were analysed using different features that are more inclined to exhibit suicidal behaviour. The best accuracy is achieved by J48 binary tree classifier with a classification accuracy of 87.68%.
Using Features of Encrypted Network Traffic to Detect Malware

Encryption on the Internet is as pervasive as ever. This has protected communications and enhanced the privacy of users. Unfortunately, at the same time malware is also increasingly using encryption to hide its operation. The detection of such encrypted malware is crucial, but the traditional detection solutions assume access to payload data. To overcome this limitation, such solutions employ traffic decryption strategies that have severe drawbacks. This paper studies the usage of encryption for malicious and benign purposes using large datasets and proposes a machine learning based solution to detect malware using connection and TLS metadata without any decryption. The classification is shown to be highly accurate with high precision and recall rates by using a small number of features. Furthermore, we consider the deployment aspects of the solution and discuss different strategies to reduce the false positive rate.
Spectral Method for Analysis of Diffusions and Jump Diffusions

The chapter discusses the use of the spectral form of mathematical description, or the spectral method, for the statistical analysis of stochastic dynamical systems: diffusions and jump diffusions, i.e., for solving Fokker–Planck–Kolmogorov equation and Kolmogorov–Feller equation for the probability density of the state vector for these dynamical systems. The spectral form of mathematical description allows to transform linear partial differential equations or partial integro-differential equations into a system of linear algebraic equations, which determines coefficients according to orthogonal series expansions for the probability density with respect to an arbitrary orthonormal system of functions. As an example for testing, the Dryden wind turbulence model and its modification, allowing to take into account not only continuous random effects but also impulse ones, are considered.
Comparison of SIFT and SURF Methods for Porn Image Detection

Pornography is a global problem. Various efforts to overcome the spread of pornographic videos have been carried out by many researchers in the world with various methods such as the GLCM method for extracting image features based on skin color, the BossaNova method for extracting features in the images, combining two color spaces namely RGB and YCbCr and using segmentation skin color with YCbCr and KNN. Feature extraction used in the previous ones such as GLCM, YCbCr, RGB was vulnerable to changes in image size, translation, and rotation. In contrast to the SIFT and SURF methods that were used in this study. SIFT and SURF method invariant to changes in translation, size, two-dimensional rotation, illumination, can extract many key points and able to show object decryptors better. The number of datasets used was 7997 pornographic and non-pornographic images taken from Yahoo’s dataset (NSW dataset) while 984 images were used as data testing. The feature extraction used the SIFT and SURF methods to get the keypoint and descriptor values. Descriptors of each image were clustered by K-Means. The center of each cluster was used as a visual dictionary’s vocabularies. Then the image representation was performed using BoVW and finally, the classification was done by the KNN algorithm. For the results, the SURF method had better accuracy and time values compared to the SIFT method. The highest accuracy value of the SURF method was in dictionary size 300 with an accuracy of 0. S226 (S2.26%). While the lowest accuracy value was in dictionary size 2600 with an accuracy of 0.6917 (69.17%). For the time variable, the SURF method was slightly faster than the SIFT method with an average value of respectively 2.49 and 2.52
An Efficient Word Embedded Click-Bait Classification of YouTube Titles Using SVM

Most of the online media outlets normally depend on the revenues generated from the clicks made by their viewers and due to presence of their outlets. To increase traffic onto their websites some people come with some misleading headlines as titles for their links. Such misleading headlines are known as Click-baits. These click-baits leave the user disappointed as the content of the address is very different from the headline or the title. The current work focuses on classification of YouTube titles into click-baits and non click-baits using tokenization and word embedding applied to SVM. Upon simulation of the algorithm we are able to increase the accuracy, compared to some of the other classification algorithms.
Proposing Contextually Relevant Advertisements for Online Videos

Online videos are becoming a dominant part of the web. By 2022, it is predicted that online video will make up more than 82% of all consumer internet traffic, 15 times higher than it was in 2017. With the increasing consumption of Online Video Content, it is important for video platforms to understand videos and provide relevant advertisements. To improve viewer satisfaction and maximize advertisement revenue, a key factor needs to be taken into account, Ad applicability to video content. We present a solution that uses a combination of deep learning and natural language processing techniques to provide contextually relevant advertisement for a given input video.
Analysis of Attribute Selection Method Based on IDOE in Software Fault Prediction

This paper presents a new attribute selection method based on Importance Descending Order Exclusion (IDOE) to predict software fault. By using public datasets CM1 and JM1 from PROMISE repository, six machine learning algorithms-Naïve Bayes, LibSVM, Logistic, Multilayer Perceptron, SMO and Random forests integrated in Weka are used for analysis. Firstly, the GeneticSearch and Ranker search method is chosen to select attributes. Secondly, these machine learning algorithms are used in order to predict precision, recall and F-measure of software modules. Then the IDOE method is used to update the attribute datasets. Finally, the different iteration experiment results are compared and analyzed.
Measurement of large, discontinuous displacement from digital images

Abstract One main limitation of conventional digital image correlation (DIC) methods is their incapacity to treat with large, discontinuous displacements. This paper seeks to resolve this issue using a variational method. Specifically, the objective function is written in a form of L 1 ‐norm instead of L 2 ‐norm for discontinuity preservation. This convex, non‐differentiable objective function is resolved by a proximal gradient method. Then, the entire correlation process is embedded into a multi‐level image pyramid, enabling large displacements to be measured accurately. The proposed method is first validated on images with prescribed small displacements; its performance to treat with discontinuous, large displacements is then examined. The results show that the proposed method exhibits an acceptable performance when measuring some extreme displacements up to 16.5 pixels in a 500 × 500 pixels resolution image. Concerning subpixel accuracy, the errors can be lessened to a very low level with optimised parameters even for a dense correlation at each pixel. Good performance is also demonstrated in forms of noise proof and illumination fluctuation tolerance. Finally, an application is conducted on an experiment concerned with cracking in desiccated soils, in which the proposed method succeeds in tracking the nucleation and propagation processes of multiple cracks.
Target detection and tracking based on improved HOG-color feature fusion

Abstract In the process of constructing the target appearance model, the traditional HOG feature does not take into account the relationship between adjacent regions when the image is segmented in the feature extraction process, resulting in the regional aliasing effect, and the single feature is inefficient in target detection and tracking. An improved HOG-color feature fusion method for target detection and tracking is proposed. Firstly, HOG feature and color feature were extracted from the target samples respectively. During the HOG feature extraction process, trilinear interpolation was used to eliminate regional aliasing effect. Secondly, Bhattacharyya distance is used to select the HOG feature after interpolation, and the appropriate feature is selected as the HOG feature. Then combine the HOG feature after selection with the color feature; Finally, the filter is obtained by learning the kernel correlation filter, and the image is correlated detected with the filter, and the response output is obtained. The experimental results show that this method is superior to the traditional HOG-color feature fusion target detection and tracking method in both tracking speed and tracking efficiency.
Load Balancing Enhanced Technique for Static Task Scheduling in Cloud Computing Environments

Cloud computing has become an actual opportunity to supercomputing framework for creating proportional applications that associate enormous computational resources. Though, the difficulty acquired in constructing such parallel cloud mindful applications is higher than the ordinary parallel computing framework. It reports concerns for instance resource finding, heterogeneity, fault tolerance, and Tasks scheduling. Load balanced task scheduling in cloud computing is a vital issue, it is one of the NP-Completeness Problem, So Considered the focus of attention researchers in the cloud computing scope. The conventional Min-min and Max-min are a simple algorithm that creates a schedule that reduces the Makespan, regardless of load balancer, resource utilization, and resource usage effectively. To over-awed such restrictions, in this paper, a new load balance scheduling algorithm (SSLB) is designed and applied. This study focuses on static batch scheduling with the number of accessible resources and Received Tasks. Initially, it uses the Min-min or Max-min allocation map. SSLB utilizes the equal running time of the standard Min-min or Max-min. SSLB algorithm depends on the normal execution time rather than complete time as a determination premise. Experimental outcomes demonstrate the accessibility of load balance in cloud computing framework and overall reduce Makespan better than Min-min and original Max-min, also achieve of resource utilization and resource usage rate.
Root-Cause Analysis Using Ensemble Model for Intelligent Decision-Making

The increase in social media and social media applications made a way for people to opt for online based decision-making. Before this progression, people used to discuss with friends, colleagues and relatives to purchase some products. Currently the social media has taken over the decision-making through online reviews. As a result, large organizations are dealing with such reviews to provide huge benefits. This enormous information available on the web is looked-for by both the organizations and the customers. This has also made decision weariness owing to the devastating number of opinions on a single product. To overcome this issue, root-cause analysis model is proposed for effective decision-making. The root-cause analysis model in turn consists of multiple models namely; Aspect categorization Ontology for aspect extraction, Prediction-based word embedding model, Variegated Ensemble-based Weighted Voting (VEWV) Model for prediction is used to reduce the computational complexity levels and error levels, and Ontology Reinforcement for frequent updates in the ontology system. The experiments were examined with the five-product dataset and compared with recent works. The proposed model shows enhanced performance in terms of precision, recall and F-Measure.
Deep learning and its applications for content-based video retrieval

There is a huge amount of digital information including images, audio, video, and also the textual data that are accessible to everyone and anywhere due to the advances in the Internet and smartphones. Multimedia data are accessible and huge in number because of the high-resolution cameras that are available in smartphones. Multimedia platforms and social media gather these data and facilitate the storage, analytics, and management of the data. In particular, video data are used in areas such as education, broadcasting, entertainment, and digital libraries. The applications that use video data should be efficient to retrieve the videos as quickly as possible and analyze the information that is embedded in it. Most of the applications are based on indexing and searching, that is, the texts associated with the video are used for retrieval. Content-based video retrieval (CBVR) techniques are efficient techniques for video retrieval. In this chapter, techniques for video retrieval using CBVR are outlined with an experiment on a data set for retrieval.
Dihedral Group D4—A New Feature Extraction Algorithm

In this paper, we propose a new feature descriptor for images that is based on the dihedral group D 4 , the symmetry group of the square. The group action of the D 4 elements on a square image region is used to create a vector space that forms the basis for the feature vector. For the evaluation, we employed the Error-Correcting Output Coding (ECOC) algorithm and tested our model with four diverse datasets. The results from the four databases used in this paper indicate that the feature vectors obtained from our proposed D 4 algorithm are comparable in performance to that of Histograms of Oriented Gradients (HOG) model. Furthermore, as the D 4 model encapsulates a complete set of orientations pertaining to the D 4 group, it enables its generalization to a wide range of image classification applications.
A Comparative Study on Disaster Detection from Social Media Images Using Deep Learning

The availability of images of events almost in real time on social media has a prospect in many application developments. A humanitarian technology for disaster type and level assessment can be developed using the images and video available on social media. In this paper, we investigate the potential use of various available deep learning techniques to develop such an application. For our research, based on the use of publicly available image data, we have started collecting disaster images from various sources from South Asia. We created the South Asia Disaster (SAD) image dataset containing 493 images from various online news portals. Using the Keras as our framework to run our models: Visual Geometry Group (VGG-16 and VGG-19), Inception-V3, and Inception-ResNet-V2 (ResNet: Residual Network). However, to boost up the training speed, we dropped the fully connected layer and added a small, fully connected model. To identify the five different disasters: fire disaster, flood disaster, human disaster, infrastructure disaster, and natural disaster; our proposed method with VGG-16 model’s recognition accuracy was 83.37%, which is the highest accuracy on the SAD dataset.
Generalized spatial stick-breaking processes

This paper develops a Bayesian nonparametric model for skewed spatial data with nonstationary dependence structure. A transformed Gaussian model is proposed for the atoms of the kernel stick-breaking process by transforming the margins of a Gaussian process to flexible marginal distributions. This study proves that the correlation structure of the underlying spatial process is nonstationary. Results from both simulated and real datasets demonstrate that the proposed model possesses better spatial prediction performance and offers computational advantages compared to the Bayesian nonparametric model with the Gaussian base measure.