Reviewerly/exHarmony huggingface dataset:
Spatiotemporal Characteristics of 360-Degree Basic Attention

Abstract The spatiotemporal characteristics of basic attention are important for understanding attending behaviours in real-life situations, and they are useful for evaluating the accessibility of visual information. However, although people are encircled by their 360-degree surroundings in real life, no study has addressed the general characteristics of attention to 360-degree surroundings. Here, we conducted an experiment using virtual reality technology to examine the spatiotemporal characteristics of attention in a highly controlled basic visual context consisting of a 360-degree surrounding. We measured response times and gaze patterns during the 360-degree search task and examined the spatial distribution of attention and its temporal variations in a 360-degree environment based on the participants’ physical position. Data were collected from both younger adults and older adults to consider age-related differences. The results showed the fundamental spatiotemporal characteristics of 360-degree attention, which can be used as basic criteria to analyse the structure of exogenous effects on attention in complex 360-degree surroundings in real-life situations. For practical purposes, we created spherical criteria maps of 360-degree attention, which are useful for estimating attending behaviours to 360-degree environmental information or for evaluating visual information design in living environments, workspaces, or other real-life contexts.
Modeling and Simulation in an Educational Context: Teaching and Learning Sciences

In science education, there is a need to evaluate the behavior of dynamic systems. Representing and explaining processes through educational models or simulations enables students to perform activities where it is easier to understand these processes and discover the essential properties of a system. Performing modeling or simulation activities that promote interpretation and understanding of systems are learning activities in which students have the ability to create and test their own perceptions of a given phenomenon. Although such activities enhance the development of skills such as reflection, decision making, creativity and generalization, the use of these activities in educational contexts is very sporadic. The difficulties teachers experience in using this type of activity relate to the types of models that are usually used to represent a system, the specificities needed to represent them, the degree of complexity of modeling and simulation tools to do so, and the lack of preparedness to implement practical research activities. In this research, we present the Modeling for Kids methodology to support the development of georeferenced multisensory modeling and simulation activities for the first cycle of Basic Education. This methodology lists a set of norms that aim to advise the teacher and the student in the accomplishment of these activities. The methodology identifies the strategies, contexts of use, and curriculum areas where these activities can be inserted. It also identifies the processes of analysis and representation of dynamic systems. Due to the importance and advantages of georeferenced multisensory information in learning, the methodology also prescribes that teachers and students use this type of information in modeling and simulation activities.
Visual Servoing in Robotics

Visual servoing is a well-known approach to guide robots using visual information[...]
IMMVP: An Efficient Daytime and Nighttime On-Road Object Detector

It is hard to detect on-road objects under various lighting conditions. To improve the quality of the classifier, three techniques are used. We define subclasses to separate daytime and nighttime samples. Then we skip similar samples in the training set to prevent overfitting. With the help of the outside training samples, the detection accuracy is also improved. To detect objects in an edge device, Nvidia Jetson TX2 platform, we exert the lightweight model ResNet-18 FPN as the backbone feature extractor. The FPN (Feature Pyramid Network) generates good features for detecting objects over various scales. With Cascade R-CNN technique, the bounding boxes are iteratively refined for better results.
VOLATILITY SPILLOVERS AND CORRELATION BETWEEN CRYPTOCURRENCIES AND ASIAN EQUITY MARKET

Although, the growth in the cryptocurrency market slowed down after the meteoric rise in late 2017, the market is still enjoying steady capital inflow. This has made the study of market dynamics between the cryptocurrencies and equity market indispensable. In this paper, the study of the volatility spillovers and correlation between the two has been undertaken by considering five Asian stock indices and four cryptocurrencies ranging from November 2014 to December 2018, to cover three phenomena- Leverage effect, Volatility spillovers and Time varying correlation using EGARCH, Diagonal BEKK and DCC tests respectively. Firstly, the EGARCH test reveals the absence of leverage effect in the returns of cryptocurrenices. Secondly, the multivariate GARCH test shows, out of all the cryptocurrencies taken, the past innovations in Bitcoin affect the future volatility of the equity market returns the most. Lastly, the DCC model reveals evidence of time varying correlation between the markets and Bitcoin. Keywords: Cryptocurrencies; Asian equity market; Volatility spillovers; Dynamic conditional correlation JEL Classifications: G12, G14, G17, C15, C32 DOI: https://doi.org/10.32479/ijefi.8624
Fuzzy Reinforcement Learning and Curriculum Transfer Learning for Micromanagement in Multi-Robot Confrontation

Multi-Robot Confrontation on physics-based simulators is a complex and time-consuming task, but simulators are required to evaluate the performance of the advanced algorithms. Recently, a few advanced algorithms have been able to produce considerably complex levels in the context of the robot confrontation system when the agents are facing multiple opponents. Meanwhile, the current confrontation decision-making system suffers from difficulties in optimization and generalization. In this paper, a fuzzy reinforcement learning (RL) and the curriculum transfer learning are applied to the micromanagement for robot confrontation system. Firstly, an improved Qlearning in the semi-Markov decision-making process is designed to train the agent and an efficient RL model is defined to avoid the curse of dimensionality. Secondly, a multi-agent RL algorithm with parameter sharing is proposed to train the agents. We use a neural network with adaptive momentum acceleration as a function approximator to estimate the state-action function. Then, a method of fuzzy logic is used to regulate the learning rate of RL. Thirdly, a curriculum transfer learning method is used to extend the RL model to more difficult scenarios, which ensures the generalization of the decision-making system. The experimental results show that the proposed method is effective.
A Target Tracking Model for Enterprise Production Monitoring System Based on Spatial Information and Appearance Model

Target tracking is the key function of intelligent production monitoring systems in manufacturing enterprises.In many monitoring systems, however, the fields of view (FOVs) of multiple cameras only partially overlap each other, leaving many blind zones that affect the target tracking effect.To solve the problem, this paper explores the spatial correlation probability of appearance features, and discusses about the brightness transfer function (BTF) between different FOVs.Next, the effects of appearance matching were improved, the target position was reconstructed in world coordinate system, and the spatial features of the target was extracted.On this basis, the author established a target tracking and matching appearance model with spatial information.Finally, the effectiveness of the model was fully verified through a simulation on the multi-camera production monitoring system of a factory.
CVIT’s submissions to WAT-2019

This paper describes the Neural Machine Translation systems used by IIIT Hyderabad (CVIT-MT) for the translation tasks part of WAT-2019.We participated in tasks pertaining to Indian languages and submitted results for English-Hindi, Hindi-English, English-Tamil and Tamil-English language pairs.We employ Transformer architecture experimenting with multilingual models and methods for lowresource languages.
Cryptocurrency Survival Analysis

Cryptocurrencies are one of the greatest technological innovations. Cryptocurrencies are decentralized payment systems in which ownership is demonstrated cryptographically. An overview of ownership of payment units is stored in a data structure called blockchain. Of the thousands of cryptocurrencies, the best known are Bitcoin, Ethereum, Ripple, Litecoin, EOS, Cardano, NEO, Dash, and Monero. In the past, new cryptocurrencies were most often created by modifying the parameters of another cryptocurrency and by launching a new blockchain. Nowadays, new cryptocurrencies are most commonly created as applications on another existing cryptocurrency. Such cryptocurrencies are called tokens. Creating a new cryptocurrency is easy, but its value depends on users’ willingness to pay for its units. If a cryptocurrency loses its users, it becomes worthless. In this article, we analyze over 2,500 cryptocurrencies that are or were previously traded on cryptocurrency exchanges. We have explored the probability that a cryptocurrency will not survive and will be delisted from exchanges. For the different categories of cryptocurrencies according to their previous trading time on exchanges, we have determined the conditional probability of delisting within 1 to 5 years. We found out that the new cryptocurrencies are the riskiest. With the increasing age of the cryptocurrency, the probability of its delisting decreases. <b>TOPICS:</b>Currency, statistical methods, risk management, exchanges/markets/clearinghouses <b>Key Findings</b> • Cryptocurrencies constitute an expanding area for potential participation and investment. Since the first cryptocurrency was created in 2009 (Bitcoin) more than 2,500 cryptocurrencies have been listed on exchanges. • Consider waiting at least a year before buying a new cryptocurrency. More than 70% of cryptocurrencies that become delisted do so in the first year. • Consider waiting five years before buying a new cryptocurrency. Cryptocurrencies that have been trading for five years have a 9% chance of being delisted within one year..
Genetic Programming with Pareto Local Search for Many-Objective Job Shop Scheduling

Genetic programming (GP) has been successfully used to automatically design effective dispatching rules for job shop scheduling (JSS) problems. It has been shown that hybridizing global search with local search can significantly improve the performance of many evolutionary algorithms such as GP because local search can directly improve the exploitation ability of these algorithms. Inspired by this, we aim to enhance the quality of evolved dispatching rules for many-objective JSS through hybridizing GP with Pareto Local Search (PLS) techniques. There are two challenges herein. First, the neighborhood structure in GP is not trivially defined. Second, the acceptance criteria during the local search for many-objective JSS has to be carefully designed to guide the search properly. In this paper, we propose a new algorithm that seamlessly integrates GP with Pareto Local Search (GP-PLS). To the best of our knowledge, it is the first time to combine GP with PLS for solving many-objective JSS. To evaluate the effectiveness of our new algorithm, GP-PLS is compared with the GP-NSGA-III algorithm, which is the current state-of-the-art algorithm for many-objective JSS. The experimental results confirm that the newly proposed method can outperform GP-NSGA-III thanks to the proper use of local search techniques. The sensitivity of the PLS-related parameters on the performance of GP-PLS is also experimentally investigated.
IRIS: Smart Phone Aided Intelligent Reimbursement System Using Deep Learning

Financial reimbursement is considered as a cumbersome process of information extraction from actual invoices. Recently, scanner aided intelligent reimbursement methods using scanned images have been developed to reduce the manpower and process time but it lacks flexibility as well portability. Recently, smart phone is becoming more and more general in daily life and hence it is considered a natural way to develop smart phone aided intelligent reimbursement system (IRIS). However, it is difficult to effectively recognize the text information on the distorted and oblique invoices images in natural scene, which are took by smart phone, and the standard formatted output cannot be realized for the offset and misplaced text. In order to solve these problems, we propose an effective smart phone aided intelligent reimbursement method based on deep learning. First, we preprocess distorted images and then propose a hough transform accumulator (HTA) algorithm, which adds an accumulator based on the Hough transform to achieve tilt correction and image recovery operation of the distorted image. Second, in order to remove the redundant information on the invoice image in the natural scene, we apply the you only look once-version 3 (YOLOv3) algorithm to accurately locate, segment and intercept the key information areas on the invoice image. Third, we adopt the connectionist text proposal network (CTPN) to detect the import text information block areas in invoice images, and densely connected convolutional networks (DenseNets) to identify the detected text. The connectionist temporal classification (CTC) algorithm is added to the Densenets network to achieve alignment of the input and output formats of the text, accurate optical character recognition (OCR) is performed on the intercepted block area invoice image. Finally, we proposed a new algorithm Regular Matching and Recursive Segmentation (RMRS) based on recursive segmentation of regular matching, which performs standard formatted output on misaligned or offset information. The average accuracy of the recognition of optical characters in all block areas on the invoice is as high as 0.991 with a minimum of 0.962.
Sentence-level sentiment analysis via BERT and BiGRU

Sentiment analysis is a significant task in nature language processing (NLP). Acquiring high quality word representations is a key point in the task. Specially we find that the same word has different meaning in different sentence, which should be recognized by computer. This idea cannot be done well by traditional way of word embeddings. In this paper, we propose a BERT(Bidirectional Encoder Representation from Transformers) + BiGRU (Bidirectional Gated Recurrent Unit) model which first put words into vector via BERT model, from which we can gain the contextualized embeddings, then perform the sentiment analysis by BiGRU. Experimental results prove that compared with various of different methods, our model has the best performing.
Experimental Autonomous Deep Learning-Based 3D Path Planning for a 7-DOF Robot Manipulator

Abstract In this paper, we examine the autonomous operation of a high-DOF robot manipulator. We investigate a pick-and-place task where the position and orientation of an object, an obstacle, and a target pad are initially unknown and need to be autonomously determined. In order to complete this task, we employ a combination of computer vision, deep learning, and control techniques. First, we locate the center of each item in two captured images utilizing HSV-based scanning. Second, we utilize stereo vision techniques to determine the 3D position of each item. Third, we implement a Convolutional Neural Network in order to determine the orientation of the object. Finally, we use the calculated 3D positions of each item to establish an obstacle avoidance trajectory lifting the object over the obstacle and onto the target pad. Through the results of our research, we demonstrate that our combination of techniques has minimal error, is capable of running in real-time, and is able to reliably perform the task. Thus, we demonstrate that through the combination of specialized autonomous techniques, generalization to a complex autonomous task is possible.
Exponentially Weighted Random Forest

Random forest (RF) is a supervised, non-parametric, ensemble-based machine learning method used for classification and regression task. It is easy in terms of implementation and scalable, hence attracting many researchers. Being an ensemble-based method, it considers equal weights/votes to all atomic units i.e. decision trees. However, this may not be true always for varying test cases. Hence, the correlation between decision tree and data samples are explored in the recent past to take care of such issues. In this paper, a dynamic weighing scheme is proposed between test samples and decision tree in RF. The correlation is defined in terms of similarity between the test case and the decision tree using exponential distribution. Hence, the proposed method named as Exponentially Weighted Random Forest (EWRF). The performance of the proposed method is rigorously tested over benchmark datasets from the UCI repository for both classification and regression tasks.
Association Rule Mining Based Crime Analysis using Apriori Algorithm

Nowadays, criminal law enforcement is a crucial task due to the increasing of crime rates, limitation of manpower and lack of awareness from the local community.Historical data on crimes activities need to be analyzed in order to get the trend and pattern of crimes for future prevention actions.The aim of this article is to explore the relationship between the category of location and area of criminal records through extracting the patterns that frequently occur by applying Apriori algorithm from Association Rule Mining (ARM) method.CRISP-DM methodology is used to conduct this study that consists of business and data understanding, data preparation, modeling, evaluation and deployment phases.As a result, there are strong rules being created from the high support and confidence in modeling process where the generated rules would be considered as potential rules for pattern visualization in crime analysis.This study brings a high significance for effectiveness and efficiency strategy in criminal law enforcement and it can also be explored for other association rule mining methods for future work enhancement.
Human action recognition based on convolutional neural network and spatial pyramid representation

Detecting and recognizing human action in natural scenarios, such as indoor and outdoor, is a significant technique in computer vision and intelligent systems, which is widely applied in video surveillance, pedestrian tracking and human-computer interaction. Conventional approaches have been proposed based on various features and achieved impressive performance. However, these methods failed to cope with partial occlusion and changes of posture. In order to address these limitations, we propose a novel human action recognition method. More specifically, in order to capture image spatial composition, we leverage a three-level spatial pyramid feature extraction scheme, where each pyramid is encoded by local features. Thereafter, regions generated by a proposal algorithm are fed into a dual-aggregation net for deep representation extraction. Afterwards, both local features and deep features are fused to describe each image. To describe human action category, we design a metric CXQDA based on Cosine measure and Cross-view Quadratic Discriminant Analysis (XQDA) to calculate the similarity among different action categories. Experimental results demonstrate that our proposed method can effectively cope with object scale variations, partial occlusion and achieve competitive performance.
Assessment for Ensuring Adequately Qualified Instructors in Maritime Education and Training Institutions

Introducing new technologies, knowledge, understanding and proficiency for seafarers is a challenging task for maritime instructors in the consideration of maritime safety.Effective teaching strategies depend on qualified instructors working both within the International Convention of Standards of Training, Certification and Watchkeeping (STCW Convention) requirements and having adequate teaching arrangements.According to the STCW Convention every party shall ensure that all instructors of Maritime Education and Training institutions (MET institutions) are appropriately qualified for the particular type and levels of training they are responsible for delivering.This article presents a review of factors that are not included in the STCW Convention requirements and are considered important for the development of instructor's competences and appropriate best practice strategies for teaching and learning.Adoption of a wider set of
A Novel Incremental Learning Algorithm Based on Incremental Vector Support Machina and Incremental Neural Network Learn++

Incremental learning refers to the learning of new information iteratively without having to fully retain the classifier.However, a single classifier cannot realize incremental learning if the classification problem is too complex and scalable.To solve the problem, this paper combines the incremental support vector machine (ISVM) and the incremental neural network Learn++ into a novel incremental learning algorithm called the ISVM-Learn++.The two incremental classifiers were merged by parallel combination and weighted sum combination.The proposed algorithm was tested on three datasets, namely, three databases Ionosphere, Haberman's Survival, and Blood Transfusion Service Center.The results show that the ISVM-Learn ++ achieved a learning rate of 98 %, better than that of traditional incremental learning algorithms.The research findings shed new light on incremental supervised machine learning.
Vulnerability Analysis Challenges of the Mouse Data Based on Machine Learning for Image-Based User Authentication

With the change from the pre-internet era to online society, user authentication technology is required, and for that, password-based authentication technology is generally used. However, the technology has vulnerabilities and security threats that cannot ensure security and reliability, due to the exposure of the keyboard data that comprises a password input from the keyboard. In order to settle this problem, image-based authentication technology has emerged; but the password input from the mouse is not secure, due to the exposure of the mouse data. This problem has led to the emergence of mouse data protection technology. This technology protects mouse data by generating a large number of random mouse positions at any time, thereby inducing an attacker to track any mouse position generated by the defender, even if the attacker takes over the mouse data. Therefore, this mouse protection technology almost completely defends against existing mouse data attack techniques. With mouse data protection technology applied, the challenge of this paper is to verity the feasibility of mouse data attack. For the experiment, we collected both random mouse data generated by the defender and real mouse data input from the user, and verified the security of mouse data using mouse data classification based on machine learning. As a result of the experiment, we have verified the stealing of mouse data by using the proposed method with high quality, even if existing techniques of mouse data attack do not steal real mouse data. The best accuracy is 98%. In other words, the proposed method almost completely classifies the mouse data input from the user. Consequently, this paper derives and verifies the vulnerability and security threat of image-based authentication technology. Moreover, the vulnerability and security threat found in this paper not only constitute a new vulnerability and security threat, but can also be used as a criterion in security analysis and evaluation for image-based authentication technology.