Reviewerly/exHarmony huggingface dataset:
Self-training Pupil Detection Based on Mouse Click Calibration for Eye-Tracking Under Low Resolution

Pupil detection is an indispensable step in eye-tracking, however, there are lots of difficulties in locating the pupil under low-resolution environments. This study proposes a self-training method of pupil detection for eye-tracking under low resolution. The system is applied to a typical application scenario as using a personal computer, a webcam with a resolution of 640 ×480 is used in our study. We first generate an initial pupil pattern by verifying the color intensity of the eye regions and estimate the gaze according to the matched pupil position in the further frames. After that, we utilize the computer users' actions of mouse clicks to calibrate the estimation result and annotate the position of the pupil to train the weight map of the pupil pattern. The experimental result shows that the proposed method could minimize the effect of bright spots, moreover, the precision of pupil detection and eye-tracking is increased. The proposed method will be potentially used in light-weight applications of human-computer interaction.
An l1-Norm Regularized Copula Based Feature Selection

In this paper, we develop a novel feature selection method called RCFS (Regularized Copula based Feature Selection) based on regularized copula. We use l1 regularization, as it penalizes the redundant co-efficient of features and makes them zero, resulting in non-redundant effective features set. Scale-invariant property of copula ensures good performance in noisy data, thereby improving the stability of the method. Three different forms of copula viz., Gaussian copula, Empirical copula, and Archimedean copula are used with l1 regularization. Results prove a significant improvement in the accuracy of the prediction model than any non regularized feature selection method. The number of optimal features to achieve a fixed accuracy value is also less than any other non regularized feature selection techniques.
Semantic Gated Network for Efficient News Representation

Learning an efficient news representation is a fundamental yet important problem for many tasks. Most existing news-relevant methods only take the textual information while abandoning the visual clues from the illustrations. We argue that the textual title and tags together with the visual illustrations form the main force of a piece of news and are more efficient to express the news content. In this paper, we develop a novel framework, namely Semantic Gated Network (SGN), to integrate the news title, tags and visual illustrations to obtain an efficient joint textual-visual feature for the news, by which we can directly measure the relevance between two pieces of news. Particularly, we first harvest the tag embeddings by the proposed self-supervised classification model. Besides, news title is fed into a sentence encoder pretrained by two semantically relevant news to learn efficient contextualized word vectors. Then the feature of the news title is extracted based on the learned vectors and we combine it with features of tags to obtain textual feature. Finally, we design a novel mechanism named semantic gate to adaptively fuse the textual feature and the image feature. Extensive experiments on benchmark dataset demonstrate the effectiveness of our approach.
Secured payment gateway for authorizing E-commerce websites and transactions using Machine Learning Algorithm

Internet has become one of the most important aspects in day-to-day life. Due to enormous amount of information available on the internet maintaining Confidentiality and Privacy of data transmitted across the network is still an issue. Many people are unaware of the right place to share their details. Lack of knowledge leads to hacking and misuse of personal and confidential information by many intruders. To reduce this, a novel approach has been introduced, which analyses on all the sites in the network and approves only valid sites. Machine learning algorithms are used in the system to analyze the sites and help the users know about the integrity of the site. E-commerce being one of the major parts of the internet, providing payment information in the payment gateway also is a challenging task. To provide authorization for the e-commerce transactions , this system is developed which assures the integrity of site along with identification of fraudulence in payment gateway.
Detection and recognition on traffic sign in complex scene

Road traffic sign detection and recognition problem has always been a hot and difficult point in the field of intelligent driving. Complex road conditions always take challenges to the detection and identification of traffic signs. This paper attempts to use machine learning and deep learning algorithms to detect and identify traffic signs. The study is realized in two basic parts. The first part is detection on traffic sign. A color probability model based on YCbCr space and an improved color enhancement method based on MSER are proposed to detect Candidate Areas (CA). By using these two methods, most negative samples in CA can be reduced. Then, in the Region of Interest (ROI) feature extraction stage, PHOG features with strong spatial information expression ability are combined with SVM to classify and process. The second part is recognition on traffic sign. An improved lenet-5 model is used to promote speed and accuracy of the recognition. It has been proven that the proposed model has the characteristics of shorter classification time, higher classification accuracy and better generalization ability by comparing the other algorithm with ours used in GTSDB data set.
Applied Aspects of the Integrated Problem Solving System with Natural Language Interface

This paper describes a concept of automatic problem solving system which implies close integration of linguistic processing, ontological representation of the problem, heuristic-oriented solution and interactive visualization. The concept is based on Polya methodology adapted for computer implementation. The system was implemented as a dummy program and tested in "school geometry" area. Experiments confirmed prospects of the concept and feasibility of its use for creation of a qualitatively higher level training system were carried out.
ConfNet2Seq

Conversational and task-oriented dialogue systems aim to interact with the user using natural responses through multi-modal interfaces, such as text or speech. These desired responses are in the form of full-length natural answers generated over facts retrieved from a knowledge source. While the task of generating natural answers to questions from an answer span has been widely studied, there has been little research on natural sentence generation over spoken content. We propose a novel system to generate full length natural language answers from spoken questions and factoid answers. The spoken sequence is compactly represented as a confusion network extracted from a pre-trained Automatic Speech Recognizer. This is the first attempt towards generating full-length natural answers from a graph input (confusion network) to the best of our knowledge. We release a large-scale dataset of 259,788 samples of spoken questions, their factoid answers and corresponding full-length textual answers. Following our proposed approach, we achieve comparable performance with best ASR hypothesis.
Image Annotation Method Based on Graph Volume Network

The emergence of image annotation technology solves the problem of the rapid search of massive image data. This paper proposes a multi-label image annotation method based on graph convolutional network (GCN). First, it constructs a directed graph on the predictive object label to capture the dependencies between the labels and a set of interdependent object classifiers. The Resnext network is then used to extract image features, and the classifier is applied to the classification of extracted features, making the entire network end-to-end trainable. Finally, experiments on the Corel 5K and IAPR TC12 datasets show that the correct rate of image annotation is increased by 6.7% and the system execution speed is increased by 5.75%.
An Adaptive Low-Rank Modeling-Based Active Learning Method for Medical Image Annotation

Active learning is an effective solution to interactively select a limited number of informative examples and use them to train a learning algorithm that can achieve its optimal performance for specific tasks. It is suitable for medical image applications in which unlabeled data are abundant but manual annotation could be very time-consuming and expensive. However, designing an effective active learning strategy for informative example selection is a challenging task, due to the intrinsic presence of noise in medical images, the large number of images, and the variety of imaging modalities. In this study, a novel low-rank modeling-based multi-label active learning (LRMMAL) method is developed to address these challenges and select informative examples for training a classifier to achieve the optimal performance. The proposed method independently quantifies image noise and integrates it with other measures to guide a pool-based sampling process to determine the most informative examples for training a classifier. In addition, an automatic adaptive cross entropy-based parameter determination scheme is proposed for further optimizing the example sampling strategy. Experimental results on varied medical image datasets and comparisons with other state-of-the-art multi-label active learning methods illustrate the superior performance of the proposed method.
Backpropagation Network Optimization Using One Step Secant (OSS) Algorithm

Abstract Education is one of the main indicators in national development efforts. The government in its efforts to realize national goals to educate the nation’s life has made a policy for compulsory 9-year schools, namely elementary and junior high schools. To find out how many residents use the education facilities provided by the government can be seen through school enrollment rates (SPR). A high School Participation Rate (SPR) means showing greater opportunities to access education in general. This study aims to optimize artificial neural networks with the One Step Secant (OSS) algorithm. Artificial Neural Network (ANN) is part of the artificial intelligence system (Artificial Intelligence, AI) which is one of the artificial representations of the human brain that always tries to simulate the learning process in the human brain. The sample data used for optimization is SPR Indonesia data by province. Using 4 architectural models with 5 input variables, 1 shadow layer and 1 output. The best results obtained between architectures 5-4-1, 5-8-1, 5-16-1 and 5-32-1 are architectures 5-16-1. Obtained prediction accuracy comparisons using the One Step Secant (OSS) algorithm and standard algorithms namely 96.97% and 100%. The standard algorithm is superior in accuracy, the One Step Secant (OSS) algorithm is superior in terms of iterations.
A comparison of joint reversible data hiding methods in encrypted remote sensing satellite images

Abstract The distribution of remote sensing satellite data from the National Remote Sensing Data Bank (BDPJN) of the National Institute of Aeronautics and Space (LAPAN) to users via the internet requires security so that it is not used illegally by unauthorized parties. Encryption and reversible data hiding are two effective and popular privacy protection and confidential communication solutions. With encryption, data is randomized so it can’t be read. Whereas with reversible data hiding, the receiver can extract hidden data and restore the original image without distortion. In this paper, some remote sensing satellite images are used as input in the simulation that is analyzed and compared based on the three methods of joint reversible data hiding in encrypted images, i.e. Zhang’s work, Hong’s work, and Fatema’s work. The experimental results show that Hong’s et al. method reveals the best performance of the three methods. For example, when the block size is 8x8, the bit error rate (BER) of the SPOT-6 test image of Hong’s et al. method was 12.06%, which is slightly lower than the 14.01% Zhang’s method and 11.89% Fatema’s et al. method. Likewise, the quality of remote sensing satellite image (image_spot6) recovery represented by the peak signal to noise ratio (PSNR) of Hong’s et al. method is 49.96 dB, which is quite higher than the 48.26 dB of Zhang’s method and 48.98 dB of Fatema’s et al. method.
Digital Forensics Techniques Overview

This article discusses the important issue of the use of forensic technology in computer forensics. The basic techniques of computer forensics existing today, such as real-time tracking, sandboxing, data and password recovery, and so on, are examined. The advantages and disadvantages of existing methods are given and recommendations are made on the development of computer forensics techniques in the future. Conclusions are drawn up on the problem of using forensic technology in computer forensics.
Knowledge Based Diagnostic Approach for Enterprise Storage Systems

An enterprise storage system stores and gives access to valuable user data and hence is required to operate as a highly available system. One of the key steps to desired reliability is to perform continuous monitoring and diagnosing of a system state. A standard approach to technical diagnostic infers a diagnostic model. However, the complexity of a storage system and the lack of a common diagnostic methodology pose a challenge in creating such a model. This paper presents an ontology-based approach to diagnosing of a storage system, which integrates an expert knowledge of diagnostic parameters, typical storage configurations and common failure modes. An implementation of the approach is presented, as well as considerations of the benefits and limitations of the ontology-based diagnostic model.
Eigan: Enhanced Inpainting Generative Adversarial Network

Generating coherent texture in a repaired region, especially at the boundary, is one of the challenges in image inpainting. To maintain the coherence in the edge transitional region, we propose an efficient framework, enhanced inpainting generative adversarial network (EIGAN). EIGAN is composed of a multi-resolution intersection encoder, a dual decoder, and an adversarial patch discriminator. The encoder and the decoder are used to process multi-resolution semantic information in a parallel and cross-connected fashion. Specially, we utilize a soft-contextual attention module embedded into the decoder to capture contextual information so that the attention module can help the decoder to generate coherent texture and content. Moreover, to improve the details, we introduce an advanced patch discriminator with relativistic adversarial loss named adversarial patch discriminator. Experimental results show that our improvements can enhance the coherence and quality of the completed image, and enable EIGAN to outperform the state-of-the-art image inpainting methods.
Predicting the Outbreak of Conflict in Online Discussions Using Emotion-Based Features

Anti-social online behaviour, such as harassment or vulgarity, leading to conflicts aimed at destroying any merit of the discussions, is a serious problem for the Internet community. Recognising the characteristics of conflict discussions and modelling their trajectory might help to predict and prevent derailing. My PhD thesis focuses on using emotion labels as such characteristics and building an explainable prediction model based on them. As a part of the thesis we have proposed a new dataset of discussions containing knowledge about their emotion-based features. It is a set of dialogues from Wikipedia Talk Pages annotated during a crowdsourcing experiment with labels from Plutchik’s model of emotions and described with EmoWordNet lexicon scores. With this explainable model we hope to introduce a new way of moderating Internet discussions and provide useful educational tool.
Detection of Coronal Holes Using Hough Simulated Parameterized Online Region-Based Active Contour Method

In this paper a novel technique of Hough transformed simulated parameterized online region-based active contour method (PORACM) has been proposed for the real-time detection and extraction of coronal holes (CHs) in solar disk (SD) image without disturbing resolution of the image. For the purpose a circular contour based on Hough transform has been initialized on the SD image. Now based on the formulation of PORACM the initialized circular contour is made to move towards the boundary of the desired region. At the final stage when the contour position does not change any more is considered as the final contour and the region inside contour is extracted as the CHs of the given SD image. The proposed technique has been implemented on benchmark SD images and has been compared with the available work on CHs detection. The generated results shows capability of the proposed technique in detection and extraction of CHs in SD image.
Design and Development of SS Reversible Logic Gate and it’s Application as Adder &amp; Subtractor

In contemporary research, reversible logic design becomes very convenient for its ultra-low power consumption. The reversible logic design plays a great role in different design technologies like CMOS, bioinformatics, optical information processing, nanodevices, cryptography etc. All the outputs of the reversible gate perform some specific set of Boolean functions. So many research works have already been done on this reversible technique. In the present paper, new gate, namely SS reversible logic gate, its input-output table and execution of half addersubtractor and full adder-subtractor are presented.
An End-to-end Obstacle Avoidance Algorithm for Autonomous Vehicles Based on Global Path Constraint

In recent years, local obstacle avoidance algorithm for autonomous vehicles has matured significantly. Existing methods can be divided into two stages: path planning and motion planning, which have the disadvantage of high computational complexity and falling into local optimum easily. In this paper, we propose an end-to-end obstacle avoidance method based on obstacle location information and global path. Firstly, a mathematical model is constructed to transform the obstacle information into local error. Secondly, the states of the vehicle at several subsequent moments are estimated. Finally, the PD controller is utilized to control the vehicle, which considers the local error and the global attitude error. The result of our method is compared with standard DWA algorithm and the action of real human drivers regarding the path smoothness, lateral acceleration and the pose after the obstacle avoidance. The experimental result shows the advantage of our method. There is no need to select the goal point of each period with the constraint of the expected pose. We can achieve the goal with a smooth trajectory and high efficiency.
Tri-Structured-Sparsity Induced Joint Feature Selection and Classification for Hybrid Noise Resilient Multilabel Learning

Multilabel learning handles the problem that instances are associated with multiple labels. In practical applications, multilabel learning often suffers from imperfect training data. Typically, labels may be noisy or features may be corrupted, or both. Most existing multilabel learning models only consider either label noise or feature noise. Theoretically, ignoring any kind of noise in the learning process may lead to an unreasonable model, and thus affect the multilabel learning performance. In this paper, we propose a robust multilabel learning model, Tri-structured-Sparsity induced Joint Feature Selection and Classification (TriS-JFSC), to handle the data with hybrid noise. Specifically, the proposed TriS-JFSC model employs the tri-structured-sparsity regularization bridged with a label enhancement matrix to simultaneously smooth the feature and label noise, and embed a feature selection scheme that can simultaneously learn label-shared features and label-specific ones to boost the multilabel learning performance. Furthermore, by employing Alternating Direction Method of Multipliers (ADMM) method, a simple but efficient optimization algorithm is designed to solve the proposed TriS-JFSC model. Finally, the extensive experiments performed on several benchmark datasets demonstrate that our TriS-JFSC model outperforms other state-of-the-art learning methods.
Determining Optimal Skillsets for Business Managers Based on Local and Global Job Markets: A Text Analytics Approach

ABSTRACT We are experiencing a significant shift in management practices—moving from intuition, experience, and gut‐feeling driven decision‐making to one that is driven by data, evidence, and computational sciences. This shift, which is often called the analytics revolution , is not only changing the business landscape from a practical perspective but also redefining the white‐collar job market. The goal of this study is to employ a data‐ and analytics‐driven approach to analyze a large and feature‐rich dataset (which is composed of the recent job postings and the extant published literature) to characterize the state of the current business job market, especially to magnify the analytics related features and expectations including the geographic (i.e., local vs. global) differentiators. Using several graphical and tabular representations, in this paper we report on our thought‐provoking findings that collectively illustrate the changing face of knowledge, skills, and abilities required by the current local and global business job markets.
Cancerous Cell Detection Using Affine Transformation with Higher Accuracy and Sensitivity

Cancerous cells are detected by affine transformation by working on multi-resolution, multi-lateral, multi-cluster image patterns for prediction analysis. Accuracy, sensitivity and specificity of a few images are calculated for the affected cells, where results provide less confusion value of transformed images than the originals. Authenticity of the cell is investigated by computing true and false positive and negative rates. Difference in maximum peak amplitude for Z-transformed image and Z-transformed affine image is calculated to evaluate the importance of the transform procedure. From the assessment of image, status of the cancerous cell can be easily revealed, whether it is benign or malignant, and the corresponding decision can be taken for shielding of other cells.
Person Sensor-Aided Scene Recognition and Understanding Based on CG Technology

Person sensor-aided scene recognition and detailed understanding based on CG Technology is studied in this paper. Researchers have been working on enabling computers to quickly locate and recognize textual information in images of natural scenes. To achieve this target, we analyze the CG tool, computer graphics is used as the science that uses mathematical algorithms to transform two-dimensional or three-dimensional graphics into various forms of the grids in a computer display. Applications in this field mainly refer to the effective control, generation, storage, and communication of multimedia information by means of the computer control or then grassroots information system. The optimization of the core network structure not only allows the network to fully extract low-level features, but also reduces the interference of complex scenes on object recognition after two capsule layers have undergone two-layer dynamic routing and screening decisions. This paper proposes the novel framework for the optimal model. The experiment proves the performance.