Reviewerly/exHarmony huggingface dataset:
MAMF: A Multi-Level Attention-Based Multimodal Fusion Model for Medical Visual Question Answering

Medical Visual Question Answering (VQA) targets at accurately answering clinical questions about images. The existing medical VQA models show great potential, but most of them ignore the influence of word-level fine-grained features which benefit filtering out irrelevant regions in medical images more precisely. We present a Multi-level Attention-based Multimodal Fusion model named MAMF, aiming at learning a multi-level multimodal semantic representation for medical VQA. First, we develop a Word-to-Image attention and a Sentence-to-Image attention to obtain the correlations of word embeddings and question feature to image feature. In addition, we propose an attention alignment loss which contributes to adjust the weights of image regions gained from word embeddings and question feature to emphasize relevant regions for improving the quality of predicted answers. Results on VQA-RAD and PathVQA datasets suggest that our MAMF significantly outperforms the related state-of-the-art baselines.
Evolutionary technique for high utility sequential pattern mining

In this paper we propose an evolutionary approach based on bio-inspired Bat algorithm for discovering high utility sequential patterns. Our test results show that the proposed approach is efficient in finding sequences with high utility efficiently. This opens up new opportunities for further research on other evolutionary techniques for high utility sequential pattern mining.
Age and gender prediction using machine learning

Because they are essential to the creation of intelligent systems that can quickly learn and categorise objects, pattern recognition &amp; automated categorization constitute two of the computer science topics that are most actively researched. A crucial role is played by biometrics, that are often employed in systems like this for security reasons. Facial recognition is now being prioritised as the main biometric method. This research aims to develop a system that employs neural networks based on convolution to identify an individual&apos;s gender and age from merely a picture or video feed of a person&apos;s face. The IMDB, WIKI, and ADIENCE datasets were used to build and evaluate several CNN network models with different topologies. The findings demonstrated that CNN had achieved substantial advancements.
Experiencias de aprendizaje virtual desde la netnografía

Learning in virtual environments has served to channel the acquisition of knowledge and new skills from interaction with others in that environment. That is why it is desired from the research carried out, to know the learning experiences in virtual communities from netnography, in order to describe at various points the various elements that make up learning from virtual environments and how it can contribute both in the teacher as well as in the students. The present study addresses in a reflexive way the boom that cybernaut learning has had and the scenarios that have allowed the advancement of the use of technology in education. Speaking of the culture of participation that has to be formed in research netizens and the elements that make up the sustainability of some platforms for educational use. Finally, it can be highlighted that, from an epistemic perspective, the learning that is acquired through virtuality is based on the modification of learning that was face-to-face to a new way of acquiring skills totally online and based on active and collaborative methodologies. mostly.
SentiUrdu-1M: A large-scale tweet dataset for Urdu text sentiment analysis using weakly supervised learning

Low-resource languages are gaining much-needed attention with the advent of deep learning models and pre-trained word embedding. Though spoken by more than 230 million people worldwide, Urdu is one such low-resource language that has recently gained popularity online and is attracting a lot of attention and support from the research community. One challenge faced by such resource-constrained languages is the scarcity of publicly available large-scale datasets for conducting any meaningful study. In this paper, we address this challenge by collecting the first-ever large-scale Urdu Tweet Dataset for sentiment analysis and emotion recognition. The dataset consists of a staggering number of 1, 140, 821 tweets in the Urdu language. Obviously, manual labeling of such a large number of tweets would have been tedious, error-prone, and humanly impossible; therefore, the paper also proposes a weakly supervised approach to label tweets automatically. Emoticons used within the tweets, in addition to SentiWordNet, are utilized to propose a weakly supervised labeling approach to categorize extracted tweets into positive, negative, and neutral categories. Baseline deep learning models are implemented to compute the accuracy of three labeling approaches, i.e., VADER, TextBlob, and our proposed weakly supervised approach. Unlike the weakly supervised labeling approach, the VADER and TextBlob put most tweets as neutral and show a high correlation between the two. This is largely attributed to the fact that these models do not consider emoticons for assigning polarity.
A Stacked Autoencoder Based Meta-Learning Model for Global Optimization

As optimization problems continue to become more complex, previous studies have demonstrated that algorithm performance varies depending on the specific problem being addressed. Thus, this study proposes an adaptive data-driven recommendation model based on the stacked autoencoder. This approach involves the use of a meta-learning autoencoder that is stacked with multiple supervised autoencoders, generating deep meta-features. Then the proper algorithms are identified to address the new problems. To verify the feasibility of this proposed model, experiments are conducted using benchmark functions. Experimental results indicate that both instance-based and model-based meta-learners are well suited to the advanced model, and the performance is promising.
Label Efficient Regularization and Propagation for Graph Node Classification

An enhanced label propagation (LP) method called GraphHop was proposed recently. It outperforms graph convolutional networks (GCNs) in the semi-supervised node classification task on various networks. Although the performance of GraphHop was explained intuitively with joint node attribute and label signal smoothening, its rigorous mathematical treatment is lacking. In this paper, we propose a label efficient regularization and propagation (LERP) framework for graph node classification, and present an alternate optimization procedure for its solution. Furthermore, we show that GraphHop only offers an approximate solution to this framework and has two drawbacks. First, it includes all nodes in the classifier training without taking the reliability of pseudo-labeled nodes into account in the label update step. Second, it provides a rough approximation to the optimum of a subproblem in the label aggregation step. Based on the LERP framework, we propose a new method, named the LERP method, to solve these two shortcomings. LERP determines reliable pseudo-labels adaptively during the alternate optimization and provides a better approximation to the optimum with computational efficiency. Theoretical convergence of LERP is guaranteed. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of LERP. That is, LERP outperforms all benchmarking methods, including GraphHop, consistently on five common test datasets, two large-scale networks, and an object recognition task at extremely low label rates (i.e., 1, 2, 4, 8, 16, and 20 labeled samples per class).
The Conceptual Design Framework for XR Marketing

The concept of XR (Extended Reality) Marketing is growing rapidly and businesses across many different fields are using virtual and augmented reality more than ever before. Despite the ability of XR technology to create immersive and engaging experiences, there is still a lack of understanding about how marketing experiences should be designed using this technology. The aim of this paper is to explore and analyze the conceptual design framework for creating engaging and immersive marketing experiences in XR. The findings of the study show that storytelling, gamification and User-Centered Design (UCD) are the most effective key elements for designing engaging and immersive XR marketing experiences. Storytelling enables the design of a narrative that captures the consumer's attention. Gamification adds a layer of interactivity and competition to the design, making the experience more fun and memorable. User-Centered Design (UCD) ensures that the experience is designed around the needs and preferences of the consumer, making it more effective and personalized. Ultimately, this research aims to provide a guide on how the design framework can be used to create well-functioning XR marketing experiences.
Navigation Planning for Efficient Area Covering for Surface Cleaning

Autonomous surface cleaning robots need to cover surface area completely for efficient cleaning the surface area. This is more complex in a dynamic environment where route must be re-planned to avoid the collision with the dynamic objects and also when it could lack relevance in the dynamic environments. In earlier complete area coverage planning (CACP) approaches, it was difficult to allow the automation break out of stalemate in the smallest amount of more leisure spent when the area's immediate relatives were either challenges or had already visited. In this paper author presents a novel method to cover the complete path to address deadlock issues in a dynamic setting. By indicating the precedence of directions, a scheduling chart that was inspired by limited memory model could lower incidence of stalemate. A global backtracking system simultaneously directs the robot to quickly advance to the next unexplored location using the studied global environmental data. In complex scenarios, the authors' proposed algorithm can offer an optimal collision-free protection, according to experiments done in classroom-like settings. Considering coverage percentage and overlap duration, the suggested strategy performs better than comparable methods.
Scale-Aware Tracking Method with Appearance Feature Filtering and Inter-Frame Continuity

Visual object tracking is a fundamental task in computer vision that requires estimating the position and scale of a target object in a video sequence. However, scale variation is a difficult challenge that affects the performance and robustness of many trackers, especially those based on the discriminative correlation filter (DCF). Existing scale estimation methods based on multi-scale features are computationally expensive and degrade the real-time performance of the DCF-based tracker, especially in scenarios with restricted computing power. In this paper, we propose a practical and efficient solution that can handle scale changes without using multi-scale features and can be combined with any DCF-based tracker as a plug-in module. We use color name (CN) features and a salient feature to reduce the target appearance model’s dimensionality. We then estimate the target scale based on a Gaussian distribution model and introduce global and local scale consistency assumptions to restore the target’s scale. We fuse the tracking results with the DCF-based tracker to obtain the new position and scale of the target. We evaluate our method on the benchmark dataset Temple Color 128 and compare it with some popular trackers. Our method achieves competitive accuracy and robustness while significantly reducing the computational cost.
Development of a Local Area Network with Netsupport in Learning in a Computer Laboratory

Technological developments are increasingly advanced so as to increase the use of computer laboratories in higher education, especially in the learning process. However, this is not matched by the completeness of adequate infrastructure, which causes problems when using the laboratory. This study aims to develop and update a local area network with net support for learning in computer labs 5 and 6. This research uses the network development life cycle (NDLC) method. Data collected by questionnaire technique. The results of this study indicate that the validator test assessment from the innovation aspect of the IT infrastructure component is 89.17% so that the level of validity can be interpreted as valid in use, from the information system aspect it is 93.55%, so it can be interpreted as valid in use, practicality (group A) practicality test of 92.60%, so that it can be interpreted as practical to use, practicality (group B) practicality test is 94%, so the level of practicality can be interpreted as practical to use, practicality (Lecturer) practicality test is 95.23%, so that the practicality level can be interpreted as practical used, the effectiveness (group A) of the effectiveness test was 94.55%, so that the level of effectiveness could be interpreted as effectively used, the effectiveness (group B) of the effectiveness test was 93.70%, so the level of effectiveness could be interpreted as effectively used, effectiveness (labor assistant ) effectiveness test of 93.58%, so that the level of effectiveness can be interpreted as effectively used. So that it can be concluded that the development of a local area network network with netsupport in computer laboratory learning is feasible to use.
Recommender Systems: An Overview, Challenges and Optimization Strategies, Future Scope

Contracts can be considered as one of the fundamental legal instruments that reach deep into our daily life. Numerous numbers of contracts are entered into with a view to creating obligations and rights for the contracting parties. For the creation of a legal relation the contracting parties enter into a valid contract to perform their agreed task and there are also certain chances that the contract agreed to be performed may be breached. In this case, in order to protect the goals of the contracting parties along with providing them with justice, the parties who have been victims of the unlawful act of violation shall be granted such remedies. Apart from these in several cases the contract which was made between the parties is terminated due to various causes and the contracting parties of that contract may be relieved from performing their agreed task. Keywords: Contract, Agreement, Performance, Obligation, Breach, Remedies, Termination
Methodology for Building a Prototype System for Complex Data Analysis of Thematic Sites

One of the modern directions of obtaining information for making informed decisions is the analysis of data from open Internet sources, the analysis of media containing hundreds of thousands of publications. It is critically important not only to obtain reliable information, but also the time needed to obtain and analyze it. The purpose of the research in this work is the development and testing of a complex methodology for quickly building a prototype of a system for complex analysis of thematic sites. A technology of interconnected methods, methodologies, and tools for building a graph database, a knowledge graph, data analysis using methods and mo dels of machine learning with the provision of analytical results to users has been created. The main task of this work is to use these technologies to analyze data from well-known world sites in order to build a prototype of a systems for complex analysis of data from Internet sources.
OpenCitations described

OpenCitations is an infrastructure organization for open scholarship dedicated to the publication of open bibliographic and citation data.
jMetrik Guide

This guide will walk you through getting your data into jMetrik and running an item analysis. jMetrik does many more things than just classical test theory (CTT) item analysis, though. You may want to explore more as you learn. Rasch/IRT analysis, DIF analysis, multiple models, etc., are all available! But for now, let's just look at some items with CTT analyses.
PELATIHAN BAHASA INGGRIS BAGI WARGA DESA MENDEN UNTUK MENGHADAPI ERA GLOBALISASI

The use of English is very important nowadays. As the English teachers, we have a duty to share our knowledge to the society. English training is one activity that can be applied. In Menden Village, Kebonarum District, Klaten Regency, we were held this English training to the people who live there to become the participants. This activity is aimed to foster participants’ interest in mastering English, to provide additional basic knowledge of English, and to provide a way of learning English with specific strategies. It is hoped that this activity can be useful to their daily life to face the globalization era.
Analysis of Sindh School Monitoring System Smartphone App of Government Schools of Sindh

The attendance of employees is crucial in both public and private organizations as it indicates their dedication. In the schools of Government of Sindh the monitoring of attendance of teachers has become increasingly challenging, for that, the Government of Sindh has introduced an android based smart phone app called the Sindh School Monitoring Management System (SSMS app) in April 2016 to collect, analyze and disseminate the real-time data regarding school, teachers and students. The app was implemented in 15 districts in the first phase and has since been expanded to cover the entire province, resulting in improved teachers attendance, education quality and school monitoring at government schools. Previously, attendance was tracked manually, which was not secured and had flaws such as teachers not adhering to policy for signature, timing and signing in advance. While the SSMS app has improved the performance, it still faces implementation and design challenges. This research examines the SSMS app in detail, highlighting its weaknesses and aiming to improve its efficiency through a questionnaire designed with input from experts. An analysis is presented based on the feedback of monitoring assistants (MAs) on the indicators such as teacher’s attendance, punctuality, multi modal representation of SSMS, monitoring, data collection, data loading, data fetching.
Reconstruction Method for Multi-Vehicle Trajectories on Arterials Driven by Multi-Source Data

Vehicle trajectories contain enriched spatial and temporal traffic information. In this study, the vehicle trajectory data were obtained after the data-fusion process of multi-source heterogeneous data on arterials. Both the piecewise cubic Hermite interpolation algorithm and cubic spline interpolation algorithm were used to reconstruct the single vehicle trajectories. A cross-validation method was applied in the comparison for obtaining the optimal model. Based on the reconstructed vehicle trajectories, an interpolation method was used to predict the unrecorded multi-vehicle trajectories by interpolating the time of unknown vehicles. The results show that the piecewise cubic Hermite interpolation can achieve better performance in reconstructing the single-vehicle trajectory and it is effective in predicting the missing trajectories. This study supports the spatial-temporal analysis of vehicle trajectories, traffic-state estimation, and transportation optimization.
Earthquake Preparedness Knowledge and Practice of Secondary Level

This paper attempts to analyze earthquake preparedness knowledge and practice of secondary-level students in Nepal. In this paper, earthquake preparedness knowledge and practice refer to the understanding and implementation of measures to minimize the potential damage and loss of life caused by earthquakes, including proper building design and construction, emergency planning, and regular drills and training for individuals and communities. Earthquake preparedness knowledge and practice are essential for secondary-level school students because the losses of mortalities, morbidities, and economics from earthquakes cannot be predicted in the world. In this paper, a descriptive research design and quantitative approach as well as primary sources of data were used. Primary data were collected from semi-structured questionnaires with the students of Grades 9 and 10 of the government school of Bhaktapur, Bagmati Province. Of the total 96 students were censused using the rule of thumb method. The findings show that about 91.7% reported they heard the earthquake preparedness, 16.9% prepared with a Go bag, and 2.1% practiced duck, cover, and hold methods during the shock of the earthquake. The result reveals that about 50 percent of the total students have adequate knowledge of earthquake preparedness while they have limited practice. It can conclude that adequate preparedness practices were needed for secondary-level students. In addition, they should be aware of and understand the causes, nature, and effects of natural hazards skills to enable proactive prevention and mitigation of disasters.
Automatic retrieval of health case reports for public needs using deep learning techniques

Purpose Online medical repositories provide a platform for users to share information and dynamically access abundant electronic health data. It is important to determine whether case report information can assist the general public in appropriately managing their diseases. Therefore, this paper aims to introduce a novel deep learning-based method that allows non-professionals to make inquiries using ordinary vocabulary, retrieving the most relevant case reports for accurate and effective health information. Design/methodology/approach The dataset of case reports was collected from both the patient-generated research network and the digital medical journal repository. To enhance the accuracy of obtaining relevant case reports, the authors propose a retrieval approach that combines BERT and BiLSTM methods. The authors identified representative health-related case reports and analyzed the retrieval performance, as well as user judgments. Findings This study aims to provide the necessary functionalities to deliver relevant health case reports based on input from ordinary terms. The proposed framework includes features for health management, user feedback acquisition and ranking by weights to obtain the most pertinent case reports. Originality/value This study contributes to health information systems by analyzing patients' experiences and treatments with the case report retrieval model. The results of this study can provide immense benefit to the general public who intend to find treatment decisions and experiences from relevant case reports.
Attribute Lookup

Compared to static compile languages such as Java and C# Python is a dynamic and flexible language. This is also true of how attributes within a class definition are handled. In this chapter we will look in more detail about how Python manages attributes and method lookups, how unknown attribute and method lookup are handled and how you can intercept such lookups yourself.
ADTs, Queues and Stacks

There are a number of common data structures that are used within computer programs that you might expect to see within Python’s list of collection or container classes; these include queues and stacks. However, in the basic collection classes these are missing. However, we can either create our own implementations or use one of the extension libraries that provide such collections.