Reviewerly/exHarmony huggingface dataset:
Feature Descriptor for Sri Lankan Batik Patterns Using Hu Moment Invariants and GLCM

Batik is a traditional craft of designing patterned fabrics which hold high artistic value in Sri Lankan culture, where hand-painted wax patterns are coloured using specialist dyeing methods to create the finished product. This paper presents a study of vision-based feature extraction of Batik images considering colour, texture and shape features to develop a comprehensive feature descriptor of Batik motifs. Wax drawn patterns are identified from the digital images of Batik motifs to retrieve an outline of patterns demarcating the different coloured layers generated by multiple stages of dyeing. Motifs with repetitive patterns are identified using the Local Binary Pattern (LBP) as a texture feature vector. Both RGB and L*a*b* colour schemes are studied in the representation of Batik motifs. The colour description is presented using Mini Batch K-Means which out-performed the widely used K-Means clustering method. Hu Moment Invariants are used for shape feature extraction, and Gray Level Co-occurrence Matrix (GLCM) for texture feature extraction. A comprehensive feature descriptor is developed to represent Batik designs, which could be used to recommend similar designs based on the shape and texture features of query images presented by the user.
Research on the Application Status of Educational Apps in Primary School Students' Learning

In order to find out the specific problems about applying the educational apps to the learning of primary school students and find corresponding solutions, education apps can be more scientifically and efficiently integrated with the learning of primary school students, and promote the healthy and happy growth of primary school students, so the corresponding research is made. This research mainly concerns parents' education awareness about the use of new media, parents and students' educational apps interface settings, user experience, and the use of content richness. It is related to the current situation of new media education and primary and middle school students. Please refer to the relevant literature for the usage of the apps, and design the application status of educational apps in students' learning research, questionnaire survey research, etc.
High Accuracy Video Foreground Segmentation Based on Feature Normalization

We propose an efficient foreground segmentation method for video images. The methods using Convolutional Neural Networks (CNNs) have shown high accuracy in the field of video processing. Batch normalization (BN) is a compensation method for CNNs, which controls the weights so as to converge in scale when the learning rate is increased. Therefore it improves the learning speed and accuracy. However, there is a problem that the accuracy decreases when the batch size is small, and it is not optimal for video images where the batch size cannot be increased. In order to solve this problem, a normalization method called Group Normalization (GN), which divides channels into groups and normalizes them, has been attracting attention. In this paper, we improve the accuracy of foreground segmentation of video images by introducing GN. We also propose Layer Instance Normalization (LIN), which is a combination of the two normalization methods. The effectiveness of the proposed methods is demonstrated through simulation.
Abnormal Quantum State Search Based on Parallel Phase Comparison

We discuss the problem of filtering out abnormal states from a larger number of quantum states. For this type of problem with [Formula: see text] items to be searched, both the traditional search by enumeration and classical Grover search algorithm have the complexity about [Formula: see text]. In this letter a novel quantum search scheme with exponential speed up is proposed for abnormal states. First, a new comprehensive quantum operator is well-designed to extract the superposition state containing all abnormal states with unknown number [Formula: see text] with complexity [Formula: see text] in probability 1 via well-designed parallel phase comparison. Then, every abnormal state is achieved respectively from [Formula: see text] abnormal states via [Formula: see text] times’ measurement. Finally, a numerical example is given to show the efficiency of the proposed scheme.
Text Classification on Twitter Data Using Machine Learning Algorithm

Classification of text is the major challenge faced by the industries so that they can extract the required information from that data. With the advent of machine learning, it becomes a lot easier for the engineers to classify the data as per requirement. Writing an effective code and algorithm is required to classify the data accurately. In this paper, we have tried to solve a problem where we have taken a raw dataset which contains tweets by users where the name of the disease in every tweet begins with a hashtag (‘#’) symbol. These disease names are extracted and stored it in a new column preceding to every tweet with the help of python.
Automated Attendance System, Mask Detection and Social Distancing Violation Tracker for Post Covid Scenarios

The lockdown imposed in many countries due to the deadly effects of COVID-19 caused a huge backlog in offices. A lot of employees have a shift in their working hours and changes in their schedule which makes the manual attendance system less efficient. Employees are preoccupied with the workload so much that it becomes difficult to maintain social distancing in the office space. During these times, where a safe and healthy work environment should be promoted, a mistake from a single person can do irreparable damage to his/her peers. In order to tackle the above problems, we have implemented an automated attendance system to record attendance via QR Scanner, a violation tracker implemented using Internet of Things and Machine Learning which tracks the total number of social distancing and mask violations, a website and an app to display the results. Our software provides a clean and easy to use user-interface which gives the ability to the user to login, view his work calendar, take a note of important announcements made at his/her workplace, keep a track of user’s attendance, and generate a QR code which is unique just to the user.
Learning to Localise and Count with Incomplete Dot-annotations

Annotating training data is a time consuming and labor intensive process in deep learning, especially for images with many objects present. In this paper, we propose a method to allow deep networks to be trained on data with reduced numbers of annotations per image in heatmap regression tasks (e.g. object localisation and counting), by applying an asymmetric loss function. This reduction of annotations can be imposed by the researchers by asking annotators to intentionally label only 50% of what they see in each image - a form of ’few-click’ annotation. Our method also has a secondary benefit of counteracting unintentionally missing labels from the annotators. We conduct experiments on wheat spikelet localisation and crowd counting to assess the effectiveness and robustness of our method. Results show that an asymmetric loss function is effective across different models and datasets, even in very extreme cases with limited annotations provided (e.g. 90% of the original annotations reduced). Whilst tuning of the key parameters is required, we find that setting conservative parameter values can help more realistic situations, where only small amounts of data have been missed by annotators.
Chapter 7. English and Spanish discourse markers in translation

The study and annotation of discourse markers (DMs) in the context of translation is a much needed and challenging task not only for descriptive translation studies, but also for Natural Language Processing (NLP) applications. Their various meanings are difficult to identify and annotate, even for trained human experts. In this chapter, a methodology for the analysis and annotation of DMs is proposed, using three highly frequent DMs in English - in fact, actually and really - and their translations into Spanish as a case study. The methodology consists of an initial corpus analysis phase followed by a corpus annotation phase. The corpus analysis provides qualitative and quantitative information on the meanings of these DMs by looking at their translations in large parallel corpora. The corpus annotation phase specifies the annotation procedure, which can be generalized to other DMs and to other language pairs, and form the basis for large-scale cross-linguistic annotation of DMs.
Approach Based on Text Processing and Ontology Applied to the Health Domain of Palliative Care in Panama

Natural language processing (NLP) is a discipline of artificial intelligence (AI) and computational linguistics. NLP facilitates the user’s work and communicating with the computer, as well as to analyze unstructured data such as textual documents. In Panama, doctors in palliative care units write their diagnoses, observations and medications in textual documents, which represents a problem to properly handle the information. The development of an expert system to support clinicians in decision-making for palliative care patients is presented. It uses NLP techniques to extract information from plain textual documents. The information extracted from the textual documents was organized in an ontological domain that can be accessed to feed an efficient information system that manages the information of palliative care patients. The evaluation of the platform was carried out through a case study in a hospital in Panama. The results are favorable, since the data extracted from the created ontology produces responses similar to those of the experts. Therefore, this platform can help clinicians make better decisions regarding the treatment of patients in palliative care.
Path Planning of Mobile Robots in Unknown Environments using Fuzzy Logic Based Hybrid Controller

Obstacle avoidance has always been a serious issue while handling the navigational problems of mobile robots. This paper demonstrates the implementation of a new fuzzy logic hybrid controller to navigate a mobile robot through a dynamically cluttered environment. The proposed hybrid controller is capable to overcome major drawbacks of path planning by taking the shortest path to goal in a minimal possible time and simultaneously avoiding the obstacles on its way and it also aids in the creation of rules and the decision making process to achieve the desired outcome in a variety of complex circumstances. The simulation results reveal that using the proposed hybrid fuzzy technique the robot escapes through barriers of obstacles and arrives at its destination efficiently.
Service Level Agreement Based Secured Data Analytics Framework for Healthcare Systems

Many physical objects are connected to the internet in this modern day to make things easier to work based on the convenience of the user, which reduces human involvement with the help of Internet of Things (IoT) technology.This aids in the capture of large amounts of data, the interchange of information via the internet, and the remote operation of machines. IoT health data is typically in the form of big data and is frequently coupled with the cloud for secure storage. Cloud technology provides a wide range of technological services via the internet, and it is a highly interoperable and on-demand network for a wide range of computing resources. The Service Level Agreement (SLA) is made between the cloud and the patient, and it outlines the services supplied as well as the level of security provided to the user. For fulfilling service, the deployed external cloud has challenges with load balancing and work scheduling. Furthermore, the gathered health data must be effectively processed by medical practitioners. To solve this issue, a Secure Cluster Naive Bayes (CNB) framework is proposed, both with and without Dimensionality Reduction. To preserve its anonymity, the obtained data is hashed and stored in the cloud using the blockchain technology. SLA sessions are organized to prioritize patient data for decryption and prediction. At the doctor’s end, the decrypted data is first filtered and dimensionally reduced before being clustered using a dual K-means clustering technique and classified using the Naive Bayes algorithm. The web-based graphical user interface server is responsible for connecting the IoT device, the cloud, and the doctor. The security performance of the DRCNB and CNB frameworks is evaluated using block chain characteristics, the frequency of SLA violations, and processing and execution time. The DRCNB framework is 91.1% accurate, while the CNB model is 80.73% accurate, making it more accurate than previous models. The new models exceed the prior ones in terms of both security and prediction performance.
The Proposed Data-Driven glassoFCM Method

Fuzzy cognitive maps (FCMs) have gained popularity within the scientific community due to their capabilities in modeling and decision making for complex problems. However, learning FCM models automatically from data without any expert knowledge and/or historical data remains a considerable challenge. Therefore, we propose a novel algorithm, the glassoFCM (graphical least absolute shrinkage and selection operator and Fuzzy Cognitive Map) to bridge that gap in the literature. Specifically, the glassoFCM is a combination of glasso technique with EBIC (Extended Bayesian Information Criterion) regularization and the FCM methodology. To recap, the glasso is a technique originated from machine learning to model data structures or in simple word to learn the undirected structure of a Gaussian Graphical Model and the FCM method is used to simulate different decision-making scenarios.
On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning

We consider Model-Agnostic Meta-Learning (MAML) methods for Reinforcement Learning (RL) problems, where the goal is to find a policy using data from several tasks represented by Markov Decision Processes (MDPs) that can be updated by one step of stochastic policy gradient for the realized MDP. In particular, using stochastic gradients in MAML update steps is crucial for RL problems since computation of exact gradients requires access to a large number of possible trajectories. For this formulation, we propose a variant of the MAML method, named Stochastic Gradient Meta-Reinforcement Learning (SG-MRL), and study its convergence properties. We derive the iteration and sample complexity of SG-MRL to find an $\epsilon$-first-order stationary point, which, to the best of our knowledge, provides the first convergence guarantee for model-agnostic meta-reinforcement learning algorithms. We further show how our results extend to the case where more than one step of stochastic policy gradient method is used at test time. Finally, we empirically compare SG-MRL and MAML in several deep RL environments.
A New Operational Effectiveness Evaluation Method of Command Information System Based on Fuzzy Neural Network

In order to evaluate the operational effectiveness of command information system quickly and accurately, a fuzzy neural network evaluation method based on ANFIS toolbox in MATLAB is proposed. Based on the analysis of the factors influencing the operational effectiveness evaluation of the command information system, an evaluation system is established. The corresponding index value is taken as the input, the effectiveness evaluation value is taken as the output of the neural network, relevant parameters are set, then the neural network is trained, and the membership function of various indexes is obtained. The training and verification results show that this method can effectively evaluate the operational effectiveness of the command information system, and it has certain reference significance for the development of relevant fields in the future.
On Adoptability and Use Case Exploration of Threat Modeling for Mobile Communication Systems

As the attack surface and the number of security incidents in mobile communication networks increase, a common language for threat intelligence gathering and sharing among different parties becomes essential. We addressed this by developing the Bhadra framework [4], a domain-specific conceptual framework that captures adversarial behaviors in end-to-end communication over the mobile networks in our previous work. Nevertheless, the acceptance or adoptability of the framework by the mobile communications industry is still unclear. In this work, we built a threat modeling tool as a companion for Bhadra and conduct a user study with industry experts to evaluate the framework's usefulness and explore its potential use cases besides threat modeling and sharing. Our preliminary results indicate that the mobile communication industry would benefit from a threat modeling framework with a companion tool and its use cases, making it a potential candidate to integrate within work processes.
Out–of-sequence measurement processing method based on multi-sensor fusion filtering

With the swift development of intelligent driving, the state of the art of synchronous perception is widely used in many industrial domains even while there has out-of-sequence measurement problems due to the existence of unsynchronized measurements, different frequencies, or background noises which would be potential barrier of further development of this technique. This paper presents a novel algorithm including the process of effectiveness validation and dual-thread particle filter (DTPF). Compared with the current state of the art, the process of effectiveness validation can efficiently reduce the computational load of dealing with OOSM, and the DTPF could bring about high efficiency, real time, and nonlinear. In the simulation portion of this paper, a two-dimensional target tracking example is given. The numerical results demonstrate that the tracking performance of our algorithm is quite precise while the computation cost is significantly reduced.
Real Time Multi Sensor Multi Target Data Association Over Sensor Network

Data association is an integral process in tracking multiple targets with multiple sensors in a cluttered environment. With the help of data association, we obtain the relationship between sensor measurements and existing tracks. Tracking moving targets from a stationary position is likely a potential problem for losing tracks or track mingling. To overcome this problem, we employ a clustering algorithm solution developed using the nearest neighbour technique with the help of an Extended Kalman Filter. This paper focuses on finding out the real-time clustering solution for each target, having at most one measurement from any heterogeneous sensors at a particular timestamp. Here we employ a novel multi-target tracking algorithm using three basic association techniques, i.e. track-to-track association, measurement-to-measurement association and track-to-measurement association. Our real-time data simulation experiments show that the data association algorithm implemented works effectively and gives a good efficiency in real-time multi-target multi-sensor environ-ments.
Chaotic Harris Hawk Optimization Algorithm for Training Feed-Forward Neural Network

The training of the feed forward neural network (FFNN) can be formulated as an optimization problem. In this paper, we present a new Harris hawk optimization algorithm (HHO) to minimize the mean square error (MSE). To balance between the global and local search of the traditional HHO algorithm, we invoke the chaotic map into it. The proposed algorithm is named the Chaotic Harris Hawks Optimization (CHHO) algorithm. We apply the CHHO for training the feed-forward neural network (FFNN). To verify the efficiency of the CHHO algorithm, we test it on five classification datasets and compare it against eight meta-heuristics algorithms in the literature. The experimental results show that the proposed CHHO algorithm has the best overall performance and has more outstanding performance than other meta-heuristic algorithms in terms of performance metrics.
Technology of Internet of Things Responding to Natural Disasters

Numerous countries have been affected by natural disasters, the seriousness of which consequences is self-evident, such as power failure, building collapse and communication interruption.With the rapid development of science and technology, the Internet of Things (IoT) has become one of the most important part of current information technology.More and more countries and regions have realized the significance of IoT in dealing with natural disasters and are making efforts to put IoT into use.This paper summarizes the latest progress of IoT with fog computing, swarm robots and other advanced technologies in coping with natural disasters on four aspects.It focuses on how the IoT and other information technologies can solve problems encountered in natural disasters to a certain extent.
Methods of teaching physics in the conditions of online interaction

Abstract The article discusses the problems of education caused by the pandemic and the possibilities of electronic educational tools. The challenges of the period that 2020 brought sharply accelerated the development of electronic educational tools, at the same time, revealed the limitations of student control tools. The author of the article worked in the educational system of a school, university and pre-university training in the spring of 2020. This article discusses three options for implementing online classes. All three of these systems have switched to the electronic form of online classes, although this situation was not initially foreseen. The problems that one had to face in the work and the successful moments of the studying in the information educational environment are described. Prospects for the development of online learning are outlined.
Use of Image File Format WebP on Websites in Croatian top Domains

The paper deals with the use of WebP image format on web pages within the Croatian domain (.hr). Overview of usual image formats, JPEG, PNG, GIF and SVG, is given, but the emphasis is on the WebP format, which is the topic of the research. Its main features are described and how images are converted to WebP format from other formats, as well as the ways of using WebP format on web pages are listed. For the purposes of the research, a tool for collecting WebP images from web pages was developed. The mentioned tool found, identified and downloaded WebP images from the home pages of network sites within the. hr domain. Image data was stored in a database so that it could be analyzed further. The research showed that 1.04% (872 web pages) with the Croatian domain (.hr) use the WebP image file format. The largest number of web pages, 371 of them, use between two and five WebP images, while the smallest number of pages (ten of them) use over 50 WebP images. A manual review of web pages that have a larger number of WebP images leads to the conclusion that web shops mostly use this format. Furthermore, a review of WebP images showed that WebP is mostly used in cases where compression with data loss is suitable, for example, for photos.
Blockchain Adoption and Optimal Reinsurance Design

We study blockchain adoption in insurance-reinsurance markets. Unlike standard operational costs related to claim processing that scale linearly with the volume of claims, blockchain operating costs per firm are independent of claim volume and decrease with the adoption rate since verification and storage costs are distributed among the adopters. In a consortium of insurance firms, we quantify how the equilibrium adoption decisions depend on the reinsurance contract characteristics, the risk aversions of insurance companies, the distributions of their potential losses and the blockchain cost structure. When a reinsurance firm acts as a central planner in the blockchain consortium, the optimal adoption rate from planner’s viewpoint is higher than the Nash equilibria adoption rate. We observe that this gap widens when the blockchain cost becomes more sensitive to the adoption level.