Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of Game Development Tools

This article discusses several engines used for game development. Developers use game engines as a tool to manage complex aspects of the gameplay. The article describes several engines: Unity, Unreal Engine and Cry Engine to choose the best one for the initial stages of development. Research has shown that Unity is a suitable engine for small development teams, Unreal Engine is designed to develop high-quality games with intense graphics, while the feature of Cry Engine is to create realistic natural environments.
Simulation of the Landslide Process on the Territory of Zhalpak-Tash (Kyzyl-Kungoi Site) for Analysis by GIS Technologies

In this work, the landslide process of the area was studied, a digital model of the study area was developed using geographic information systems. Based on the constructed digital model, an analysis of the landslide was made, a three-dimensional model of the area was built in the Agisoft Metashape program. The steepness of the slopes was studied using the 3D Analyst Tools of the ArcGIS program.
Developing English Learning Material for the Hospitality Students

The purpose of the research is to understand the process of LPK ISI (Indonesia Institute of Technology) in developing English materials. This research focuses on hotel and cruise planning and research products. The method used in this research is research and development (Research and Development) by following of research models is ADDIE (Analysis, Design, Development, Implementation, and Evaluation). The procedure in this research the researcher analyses to know the students need what they need and want (teaching goals). After finishing the English course, Design the researcher designs as syllabus, lesson plans, English materials and assessments after that continues to the Development stage, the process is implemented in the classroom, which curriculum; after the learning ends, the researcher evaluates to understand to know the efficacious of the curriculum then, continue to revise the curriculum, the curriculum, which revises the newer for the next batch. Result of this research showed the process of developing English learning material on the students at the three batch until the researcher has done designed English material for the next batch and this research show the product of this research is a textbook, the title is English for Hospitality and Cruise Ship Program.
Color Barcodes from Debut to Present: A Broad Survey on the State of the Art

Efficiently transferring information, which is of major importance for almost all engineering systems, requires a data representative appropriate to maintain the density and consistency of the information dispatched. As such a machine-readable representation of data, barcodes are one of the most recognized and powerful instruments of the purpose in every domain. Likewise, from the perspective of engineering, colors are excellent sources of information exchange, since the transmission of color connotes the conveyance of its entire scalar attributes in the same spatial channel component. Exerting colors on barcodes as an effective way of bursting the data conveyance capacity has been an active area of research for over 50 years. Significant progress has been achieved through efforts in this regard. It is also envisagable that the evident evolution in related technologies exoterically empowers the enhancement of color barcode capabilities as capacity and reliability, thereby further encouraging prospected research in this direction. &#x0D; Herein, a comprehensive survey of the studies on this main area of interest is presented. To help better acquainted with the field, also a taxonomy of the peculiar interference sources and distortion effects is provided, besides, the 3D barcoding process itself and the research areas are described. Most of the relevant works from debut to the present are broadly examined. Rather than presenting a timeline, studies that pertain to similar issues are addressed together. Amongst those related, premising or pivotal ones are preferably cited as far as feasible. Moreover, all perused works are analyzed by the research areas and the results are presented. Also, the issues relatively more prominent as affecting the performance of the whole process are specified. In the conclusion, some of the research subjects that appear open, scarce, or require further elaboration were remarked on as well. It is anticipated this study to contribute to the efforts toward leveraging color in barcodes.
Poisoning-Attack Detection Using an Auto-encoder for Deep Learning Models

Modern Deep Learning DL models can be trained in various ways, including incremental learning. The idea is that a user whose model has been trained on his own data will perform better on new data. The model owner can share its model with other users, who can then train it on their data and return it to the model owner. However, these users can perform poisoning attacks PA by modifying the model's behavior in the attacker's favor. In the context of incremental learning, we are interested in detecting a DL model for image classification that has undergone a poisoning attack. To perform such attacks, an attacker can, for example, modify the labels of some training data, which is then used to fine-tune the model in such a way that the attacked model will incorrectly classify images similar to the attacked images, while maintaining good classification performance on other images. As a countermeasure, we propose a poisoned model detector that is capable of detecting various types of PA attacks. This technique exploits the reconstruction error of a machine learning-based auto-encoder AE trained to model the distribution of the activation maps from the second-to-last layer of the model to protect. By analyzing AE reconstruction errors for some given inputs, we demonstrate that a PA can be distinguished from a fine-tuning operation that can be used to improve classification performance. We demonstrate the performance of our method on a variety of architectures and in the context of a DL model for mass cancer detection in mammography images.
Web-Based Rapid Application Development (RAD) for Marketing of Ende Lio Traditional Bond Motif Woven Fabric

The marketing of Ende Lio traditional bond motif ikat woven fabric predominantly relies on conventional market stalls. However, with the growing demand for diverse products derived from ikat weaving, such as garments, bags, tunics, and accessories, there is a need for a more efficient marketing strategy. This study aims to implement Rapid Application Development (RAD) and design a web-based marketing system for the Marketing of Ende Lio Traditional Bond Motif Woven Fabric to enhance accessibility and increase public awareness of the products. The objectives of this research include developing an intuitive and interactive web-based platform for marketing Ende Lio's woven ikat fabrics, improving the efficiency of the marketing process, ensuring a seamless user experience, and evaluating the system's impact on sales and customer satisfaction. The RAD methodology, renowned for its incremental and time-constrained software development approach, was employed. The design process involved the use of Use Case Diagrams, and unit testing was conducted to validate the functionality and performance of the application.
The Network Load Balancer in Decentrilized Systems

This article explores the implementation of network load balancing in decentralized systems using OpenWrt, Quality of Service (QoS), and traffic balancing techniques. The increasing demand for high-quality net- work services and the surge in network traffic requires the adoption of more efficient load-balancing methods to main- tain network performance. This paper discusses the use of OpenWrt, an open-source firmware for network routers, to configure and manage network traffic. The article also covers the implementation of QoS and traffic balancing techniques to optimize network performance and reduce network congestion. The study employs iperf3 to evaluate network performance and demonstrates the effectiveness of the proposed network load-balancing approach. The index terms include OpenWrt, QoS, balancing, traffic, and ip- erf3.
A new methodology for assessing SAR despeckling filters

&lt;p&gt;Supervised learning requires labeled data to train models and then make predictions from new input data. Deep Learning (DL) methods require immense amounts of training data and processing power to provide reasonable results. In computer vision applications, and more specifically in despeckling SAR (Synthetic Aperture Radar) images, due to the speckle content, there is no ground truth available. To test the performances of despeckling filters, the common approach is tocorrupt synthetic images with a suitable speckle model and then, after filtering, well-known metrics are obtained. Then, filters are tested on actual SAR data, and specific metrics for SAR are evaluated. However, even the most elaborated speckle models are far from accounting for the complex mechanisms related to SAR images. In this paper, a methodology to design a realistic dataset to overcome these limitations is proposed. Actual SAR images of the same scene but acquired with the same sensor on different dates are downloaded from one of the available satellite platforms. Images are properly co-registered and averaged to get a ground truth-like reference image to objectively evaluate the performance of a despeckling method. To show the benefits of the proposed methodology, an on-the-shelf deep learning approach is used to filter the data, and compared with the standard approach using synthetic corrupted images with a speckle model. The final validation on actual SAR data not included in the training phase validates the proposed dataset. From the results shown, it is recommended to test filters on the proposed more realistic dataset and abandon the usual approach.&lt;/p&gt;
Exploring a Novel Mexican Sign Language Lexicon Video Dataset

In Mexico, the incorporation of deaf people into education has been lacking since only 14% of the deaf population in the age group between 3 and 29 years access education with the support of a hearing aid. Additionally, those who have been incorporated frequently face inappropriate educational strategies which poorly develop the use of Mexican Sign Language (MSL) and therefore academical success and opportunities for insertion in the workplace are difficult. This research explores a novel mexican sign language lexicon video dataset containing the dynamical gestures most frequently used by MSL. Each gesture consists of a set of different versions of videos under uncontrolled conditions. MX-ITESO-100 data set is composed of a lexicon of 100 gestures and 5,000 videos from three participants with different grammatical elements. Additionally, the data set is evaluated in a two-step neural network model with an accuracy greater than 99%. and thus serves as a benchmark for future training of machine learning models in computer vision systems. Finally, this research provides an inclusive environment within society and organizations in particular for people with hearing impairment.
Addressing the Limitations of News Recommendation System: Incorporating User Demographic for Enhanced Personalization

News recommendation schemes utilize features of the news itself and information about users to suggest and recommend relevant news items to the users towards the interest they have. However, the effectiveness of the existing news recommendation scheme is limited in the occurrence of new user cold start problems. Therefore, we designed a news recommender system using hybrid approaches to address new user cold start problems to ease and suggest more related news articles for new users. To achieve the objective mentioned above, user demographic data with a hybrid recommendation system that contains the scheme of both content-based and collaborative filtering approaches is proposed. To evaluate the effectiveness of the proposed model, an extensive experiment is conducted using a dataset of news articles with user rating value and user demographic data. The performance of the proposed model is done by two ways of experiment. So, the performance of the proposed model performs around 68.05% of Precision, 42.46% of Recall and 52.1% of the average F1 score for the experiment based on individual user similarity in the system. And also performs around 93.75% of precision, 40.25% of recall and 56.31% F1-score for the similarity of users based on the similarity of users within the same category which is better than the first experiment.
Are Large Language Models a Threat to Digital Public Goods? Evidence from Activity on Stack Overflow

Large language models like ChatGPT efficiently provide users with information about various topics, presenting a potential substitute for searching the web and asking people for help online. But since users interact privately with the model, these models may drastically reduce the amount of publicly available human-generated data and knowledge resources. This substitution can present a significant problem in securing training data for future models. In this work, we investigate how the release of ChatGPT changed human-generated open data on the web by analyzing the activity on Stack Overflow, the leading online Q\&A platform for computer programming. We find that relative to its Russian and Chinese counterparts, where access to ChatGPT is limited, and to similar forums for mathematics, where ChatGPT is less capable, activity on Stack Overflow significantly decreased. A difference-in-differences model estimates a 16\% decrease in weekly posts on Stack Overflow. This effect increases in magnitude over time, and is larger for posts related to the most widely used programming languages. Posts made after ChatGPT get similar voting scores than before, suggesting that ChatGPT is not merely displacing duplicate or low-quality content. These results suggest that more users are adopting large language models to answer questions and they are better substitutes for Stack Overflow for languages for which they have more training data. Using models like ChatGPT may be more efficient for solving certain programming problems, but its widespread adoption and the resulting shift away from public exchange on the web will limit the open data people and models can learn from in the future.
Effect Handlers for Programmable Inference

Inference algorithms for probabilistic programming are complex imperative programs with many moving parts. Efficient inference often requires customising an algorithm to a particular probabilistic model or problem, sometimes called inference programming. Most inference frameworks are implemented in languages that lack a disciplined approach to side effects, which can result in monolithic implementations where the structure of the algorithms is obscured and inference programming is hard. Functional programming with typed effects offers a more structured and modular foundation for programmable inference, with monad transformers being the primary structuring mechanism explored to date.
Blockchain based Licensed Spectrum Fair Distribution Method Towards 6G-envisioned Communications

Spectrum distribution is a classical licensed spectrum accessing method in mobile communication networks. The licensed idle spectrum resources are authorized and distributed from spectrum owners to mobile users. However, the exponential growth of user capacity brings excessive load pressure on the traditional centralized network architecture. As lack of sufficient supervision and penalty measures, dishonest behaviors of spectrum owners and spectrum users will lead to the unfair status in the distribution process. As a result, the honest participants’ interest will be harmed. As an important supporting infrastructure of Internet of things technology, 6G cannot completely follow the existing spectrum distribution method. Towards 6G network spectrum distribution, an blockchain based licensed spectrum fair distribution method is proposed. A lightweight consensus mechanism named as proof of trust (PoT) is applied to reduce computational power consumption and consensus time overhead. We deploy the method on the Ethereum test chain, theoretical analysis and experimental results demonstrate the fairness, effectiveness and security of the method.
Readers Theater in Virtual Reality

Appropriate techniques for promoting reading fluency are difficult to implement in the classroom. There is little time to provide students with individualized feedback on reading aloud or to moti-vate them to do so. In this context, VR can be beneficial for learning, to provide individualized feedback and for increasing learners&amp;#039; engagement. Studies analyzing established methods of lan-guage learning in school in VR seem to be lacking so far. Therefore, this study project was the first to analyze the acceptance of students towards the implementation of a concept of reading fluency training with students at a secondary school in VR. The study method was developed based on the Technology Acceptance Model. The VR environment developed is web-based and provides indi-vidual and collaborative opportunities for training reading fluency, giving, and receiving feed-back, and deepening content understanding of the reading texts. To analyze the acceptance of the VR environment, 5 guided interviews were conducted. The results show that despite technical challenges with the VR environment, students accepted and appreciated the reading fluency training in VR. The integration of established concepts of reading fluency training in foreign lan-guage classrooms has great potential as additional added value can be created to address the challenges of face-to-face instruction.
P-TransUNet: an improved parallel network for medical image segmentation

Abstract Deep learning-based medical image segmentation has made great progress over the past decades. Scholars have proposed many novel transformer-based segmentation networks to solve the problems of building long-range dependencies and global context connections in convolutional neural networks (CNNs). However, these methods usually replace the CNN-based blocks with improved transformer-based structures, which leads to the lack of local feature extraction ability, and these structures require a huge number of data for training. Moreover, those methods did not pay attention to edge information, which is essential in medical image segmentation. To address these problems, we proposed a new network structure, called P-TransUNet. This network structure combines the designed efficient P-Transformer and the fusion module, which extract distance-related long-range dependencies and local information respectively and produce the fused features. Besides, we introduced edge loss into training to focus the attention of the network on the edge of the lesion area to improve segmentation performance. Extensive experiments across four tasks of medical image segmentation demonstrated the effectiveness of P-TransUNet, and showed that our network outperforms other state-of-the-art methods.
Tourism embraces blockchain towards the smart tourism era

Blockchain, also known as Distributed Ledger Technology (DLT), has attracted a considerable lot of attention from academics and industry professionals. When it comes to the tourism industry, the exploitation of blockchain technology could result in significant gains. Among the possible uses are digital identity management, tokenization of frequent flyer programs, accommodation, and transportation. After conducting a comprehensive review of the scientific literature published in this field over the past five years, we identify the most significant blockchain-enabled services and uses of this technology within the context of smart tourism. We analyze the benefits of blockchain technology for the travel industry, local economies, and tourists, laying the groundwork for future studies. Moreover, we present a novel context-aware mobile application that uses artificial intelligence and blockchain to create a hybrid cyber-smart tourist application.
Comparative Assessment of Colon Cancer Classification Using Diverse Deep Learning Approaches

Colon cancer is a general form of avoidable cancer, which is also widely spread across the globe. It is also a leading cancer and considered as big killer among all kinds of cancers. In recent times, significant advances are developed in treatment field of this frequently causing disease. In this research several deep learning techniques namely convolutional neural network (CNN), recurrent neural network (RNN), transfer learning, AlexNet and GoogLeNet are compared for colon cancer classification. Pre-processing is conducted utilizing median filter for removing noises from an input colon cancer image. The filtered image is then segmented using SegNet, which is utilized to segment the affected portions. Finally, classification of colon cancer is conducted employing various deep learning approaches like CNN, RNN, transfer learning, AlexNet and GoogLeNet. The comparative assessment showed GoogLeNet as the best classifier for colon cancer classification with maximal values of accuracy as 94.165, sensitivity as 97.589 and specificity as 87.359 respectively for 60% training data.
Particle Origins for String Vibrations

Considering the origin of the particles an approach has been provided to show the origin of Bosons, Fermions along with supersymmetry (SUSY) from string vibrations in g \(\ge\) 1 genus of a hypercomplex manifold through \(\eta\) X \(\eta\) matrix for the generator \(\nabla\)(p,q).
An Effective Method for Computing the Least-Cost Path Using a Multi-Resolution Raster Cost Surface Model

Calculating the least-cost path (LCP) is a fundamental operation in raster-based geographic information systems (GIS). The LCP is applied to raster cost surfaces, in which it determines the most cost-effective path. Increasing the raster resolution results in a longer computation time to obtain LCP. This paper proposes a method for calculating the LCP using a multi-resolution raster cost surface model to enhance computational performance for large-scale grids. The original raster cost surface is progressively downsampled to generate grids of decreasing resolutions. Subsequently, the path is determined on the low-resolution raster. By performing operations such as filtering directional points and mapping path points, the final path on the high-resolution raster can be obtained. The method enables a parallel computation of paths. Therefore, it significantly improves the efficiency for synthetic raster cost surfaces with continuous or discrete characteristics, as well as for raster cost surfaces generated from real terrain datasets, while also providing an end-to-end path output. The experiments show that 80% of the results are very close to the original LCP, and the accuracy of the remaining paths falls within an acceptable range. At the same time, our method greatly improves the efficiency of path solving on a large-scale raster, fulfilling practical application requirements.
A Gloss Based Translation From European Portuguese to Portuguese Sign Language

Deaf people use sign language to communicate using only their vision (observing hands, arms, body, face) which leads to the need for efficient translators to help communicate with them. This paper presents a Hybrid Portuguese to Portuguese Sign Language Glosses Translator, comprising a Rule-Based and a Neural translator. This hybrid translator, named Glossifica, achieved BLEU (BiLingual Evaluation Understudy) scores of around 70%, a very respectable result, been affected negatively by the small size of the training corpus of the Neural Machine Translator part. However, our training corpus, already having about 6 thousand translations, and growing with the help of Portuguese Sign Language users. Thus, the first results, obtained using our hybrid translator, are quite promising.