Reviewerly/exHarmony huggingface dataset:
Gamelan Land: A Multiplayer Virtual Reality Game based on a Social Presence Approach

A Multiplayer Virtual Reality Game (MVRG) was developed in order to support experiences in learning and playing orchestral music through a virtual world called the Gamelan Land (GL) game. The GL game was aimed as a virtual place to support interaction between musicians and Gamelan enthusiasts who are spread all over the world. It supports them carrying out Gamelan community activities in the real world into a virtual world through immersive virtual experiences. The story developed in the GL game reflects the real life of the Gamelan community. Oculus Quest 2 which is a Head Mounted Displays (HMD) device that can deliver highly immersive 3D environments was selected as the platform to run the Gamelan music MVRG provided in the GL game. The performance of the Gamelan music MVRG shows promising results. Improvement of audio and visual latency in playing music in orchestra is still a problem to be solved. The problem can be caused by differences in internet network speed and device specifications. So far, low poly 3D models can be an alternative solution. Moreover, although it still requires testing involving more users and longer time, the social presence approach in the GL game can be implemented well and has promising prospects for further development.
Artificial intelligence in the hotel industry in Slovakia

Artificial intelligence in the hotel industry streamlines processes, personalizes services, enhances customer service, and analyzes data. It helps reduce costs, increase security, and improve competitiveness. In the current technology-driven era, it is essential for efficient operations and customer satisfaction. The main goal of the presented article is to explore and analyze the application of artificial intelligence in the hotel industry and map the effects it brings to hotels and other accommodation facilities in Slovakia. Using a sociological survey method in the form of a questionnaire, this article analyzes the extent of artificial intelligence technology implementation in accommodation facilities in Slovakia and the effects it brings. Based on the results of our primary research, we can conclude that artificial intelligence is not widely adopted in the Slovak hotel industry to its full potential, considering its possibilities and utilization in various operational areas of accommodation facilities.
Polyform: A Versatile Architecture for Multi-DNN Execution via Spatial and Temporal Acceleration

Contemporary applications and cloud workloads often comprise multiple Deep Neural Network (Multi-DNN) models. These models exhibit significant variations in computation, memory, and communication characteristics. For such heterogeneous workloads, a static and rigid hardware accelerator can no longer provide efficient and high-performance execution. To this end, we propose a versatile accelerator, called Polyform, to support the concurrent execution of different DNN models with the goal of improving energy and performance efficiency. Specifically, Polyform features two unique designs from both hardware and scheduling standpoints. On the hardware level, we have designed a flexible interconnection network that facilitates the formation of multiple sub-accelerators. Our design allows for spatial resource partitioning, including bandwidth and computation, while also providing effective communication support for various parallelism choices. On the scheduling level, Polyform employs a novel two-stage Genetic Algorithm (GA) to explore and identify the optimal configurations such as task orders, partition size, dataflow styles (e.g., weight or output stationary), and bandwidth. Our simulation shows that Polyform achieves remarkable results compared to prior work, including up to 77.8% energy reduction and a 2.79× improvement in throughput as compared to prior work [1]–[3].
Exponential quantum speedup in simulating coupled classical oscillators<sup>*</sup>

We study the problem of simulating the time evolution of a system of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> classical coupled oscillators (e.g., 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> balls connected by springs) on a quantum computer. We map Newton’s equation for harmonic potentials to Schrödinger’s equation, such that the amplitudes of an $\mathcal{O}(n)$-qubit quantum state encode the momenta and displacements of the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> classical oscillators. Given oracle access to the masses and spring constants, we describe a quantum algorithm with query and time complexity poly (n) that solves this problem when certain parameters are polynomially bounded and the initial state is easy to prepare. As an example application, we apply our quantum algorithm to efficiently estimate the normalized kinetic energy of an oscillator at any time. We then show that any classical algorithm solving the same problem must make $2^{\Omega(n)}$ queries to the oracle and we also show that when the oracles are instantiated by poly (n)-size circuits, the problem is BQP-complete. Thus, our approach solves a potentially practical application with an exponential speedup over classical computers.
A Domain-Specific Language for Monitoring ML Model Performance

As machine learning (ML) starts to offer competitive advantages for an increasing number of application domains, many organisations invest in developing ML-enabled products. The development of these products poses unique challenges compared to traditional software engineering projects and requires the collaboration of people from different disciplines. This work focuses on alleviating some of these challenges related to implementing monitoring systems for deployed ML models. To this end, a domain-specific language (DSL) is developed that data scientists can use to declaratively define monitoring workflows. Complementary to the DSL, a runtime component is developed that implements the specified behaviour. This component is designed to be easily integrated with the rest of an organisation's ML platform and extended by software engineers that do not necessarily have experience with model-driven engineering. An evaluation of the proposed system that supports the validity of the approach is also presented.
An Ontological View on Types

Types are fundamental for modeling, being an essential construct in all major modeling languages. These include traditional conceptual modeling languages - such as Entity-Relationship models, UML class diagrams, or Object-Role-Modeling (ORM) specifications, and knowledge representation languages alike (e.g., the Web Ontology Language - OWL).
Detection of Linguistic Bad Smells in GRL Models: An NLP Approach

Goal-Oriented Requirements Engineering (GORE) plays a crucial role in facilitating effective communication between stakeholders in system development. Through the use of goal models, GORE provides a structured approach for eliciting, analyzing, and managing requirements from the perspective of stakeholders' goals and intentions. However, goal models are susceptible to poor practices, called also bad smells, that may hinder the effective communication and understanding among stakeholders, potentially leading to misinterpretations and inconsistencies in requirements. In particular, goal models are prone to linguistic bad smells encompassing a spectrum of anoma-lies such as unclear or ambiguous goal statements, conflicting or contradictory requirements, and instances of misspellings. Therefore, identifying and addressing linguistic bad smells in goal models is crucial for ensuring the quality and accuracy of goal models. In this paper, we define seventeen linguistic bad smells in goal models, classified into four categories: Syntax, Semantics, Pragmatics, and Complexity. Furthermore, we provide Natural Language Processing (NLP) based detection methods for twelve specific bad smells, which we have automated to target Textual GRL (TGRL) models. The proposed approach and tool are evaluated using two TGRL models achieving an F2-Score of 0.8.
Power Allocation and Data Assignment for Over-The-Air Distributed Learning

Recently, over-the-air computation is considered an efficient scheme for enormous data transmission in distributed learning and computing systems. Its performance is limited by the aggregation errors, which may be caused by noise, channel fading, and insufficient device power budgets. Inspired by gradient coding, this paper considers to leverage the computing abilities of the edge devices to reap a diversity gain and alleviate the effects of inadequate transmit power. The edge server divides the whole dataset into subsets and distributes them to edge devices by some data assignment scheme. The edge devices send the computation results simultaneously back to the edge server by over-the-air transmission. This paper jointly optimizes the data assignment and power allocation problems in over-the-air distributed learning systems to minimize the mean square error (MSE) of the aggregation data. Given the data assignment scheme, the power allocation problem is solved optimally by block coordinate descent (BCD) and grid search. Besides, some optimality conditions for data assignment are proved. Accordingly, a heuristic data assignment scheme is proposed. Numerical results show our proposed scheme outperforms existing works in terms of MSE and learning metrics.
Opportunities and challenges of computer aided diagnosis in new millennium: A bibliometric analysis from 2000 to 2023

Background: After entering the new millennium, computer-aided diagnosis (CAD) is rapidly developing as an emerging technology worldwide. Expanding the spectrum of CAD-related diseases is a possible future research trend. Nevertheless, bibliometric studies in this area have not yet been reported. This study aimed to explore the hotspots and frontiers of research on CAD from 2000 to 2023, which may provide a reference for researchers in this field. Methods: In this paper, we use bibliometrics to analyze CAD-related literature in the Web of Science database between 2000 and 2023. The scientometric softwares VOSviewer and CiteSpace were used to visually analyze the countries, institutions, authors, journals, references and keywords involved in the literature. Keywords burst analysis were utilized to further explore the current state and development trends of research on CAD. Results: A total of 13,970 publications were included in this study, with a noticeably rising annual publication trend. China and the United States are major contributors to the publication, with the United States being the dominant position in CAD research. The American research institutions, lead by the University of Chicago, are pioneers of CAD. Acharya UR, Zheng B and Chan HP are the most prolific authors. Institute of Electrical and Electronics Engineers Transactions on Medical Imaging focuses on CAD and publishes the most articles. New computer technologies related to CAD are in the forefront of attention. Currently, CAD is used extensively in breast diseases, pulmonary diseases and brain diseases. Conclusion: Expanding the spectrum of CAD-related diseases is a possible future research trend. How to overcome the lack of large sample datasets and establish a universally accepted standard for the evaluation of CAD system performance are urgent issues for CAD development and validation. In conclusion, this paper provides valuable information on the current state of CAD research and future developments.
Face Warping Deepfake Detection and Localization in a Digital Video using Transfer Learning Approach

Generative AI (GenAI) can generate high-resolution and complex content mimicking the creativity of humans, thereby benefiting industries such as gaming, entertainment, and product design. In recent times, AI-generated fake videos, commonly referred to as deepfakes, have become more commonplace and convincing. An additional deepfake technique, face warping, uses digital processing to noticeably distort shapes on a face. Tracking such warping in images and videos is crucial and preventing its use for destructive purposes. A technique is proposed for detecting and localizing face warped areas in video. The input video is extracted to perform various image pre-processing techniques that refine the video into a format that is more likely to classify the classes efficiently. Transfer learning is employed, and the pre-trained model is adopted to train using Convolutional Neural Network (CNN) with the source videos to identify face warping. Based on the experimental results, it was determined that the proposed model detects and localizes the warped areas of the face satisfactorily with an accuracy of 89.25%.
Evaluation of Machine Learning Approach for Sentiment Analysis using Yelp Dataset

Due to the abundance of text data representing public opinion, the Sentiment Analysis study is getting more and more important. Various techniques and methods have been proposed to address the issues. One of those techniques is deep learning algorithms which have been used to achieve great results in Natural Language Processing (NLP) applications. Sentiment Analysis is a part of NLP application that extracts emotional information from texts. In this study, we investigate the performance of sequence-based model, i.e., LSTM, compared with multi-layer perceptron Neural Network (NN) to classify the polarity of the text review based on negative or positive. The dataset used in this study is a restaurant review taken from the Yelp website. The dataset is trained using Word2vec word embedding to convert words contained in the dataset into numerical vector representation which is used as the deep learning model input. Based on the experiment results, it is shown that the LSTM model is outperformed compared to the multi-layer NN model. The best accuracy performance provided by LSTM model is 91%, whereas the best accuracy performance of multi-layer NN model is 76%.
Systematic Literature Review: Trends Literacy Numeration in Maritime Materials Teaching Materials in the Digital Era

Study This aim for do a Related literature review on trend literacy teaching materials numeration in the material maritime For child age early in the digital era. Research methods used _ in the study This is the SLR (Systematic Literature Review) method. Data collection was carried out with document articles that have been studied similarly. The articles used are 17 articles from some journals national and international indexed by Google Scholar Semantic Scholar and Scopus published in 2013-2023, based on a study found that teaching materials _ maritime form printed and digital teaching materials. Printed maritime teaching materials in the form of modules, comic media, learning models Webb, form songs, form book education characters, suitcase media literacy, and form products. Whereas teaching materials in digital form only One teaching material viz shaped book story digital illustration entitled Baso and Pinisi For increase culture maritime child. Therefore _ that's necessary done develop teaching materials _ maritime so that it can help children increase their literacy and numeracy related to culture maritime in children's age early in a way maximum. Study This expectation can give insight and knowledge for teachers and parents specifically material, so can increase inner teacher creativity in making teaching materials. Study This clarifies and explains research previously about trend teaching materials _ maritime For growing literacy numeracy child age early in the digital era. And based on searches carried out Not yet found a study with the same topic.
Reducing the Computational Cost of Transformers for Person Re-identification

Transformer-based visual technologies have witnessed remarkable progress in recent years, and person re-identification (ReID) is one of the active research areas that adopts transformers to improve the performance. However, a major challenge of applying transformers to ReID is the high computational cost, which hinders the real-time deployment of such methods. To address this issue, this paper proposes two simple yet effective techniques to reduce the computation of transformers for ReID. The first technique is to eliminate the invalid patches that do not contain any person information, thereby reducing the number of tokens fed into the transformer. Considering that computational complexity is quadratic with respect to input tokens, the second technique partitions the image into multiple windows, applies separate transformers to each window, and merges class tokens from each window, which can reduce the complexity of the self-attention mechanism. By combining these two techniques, our proposed method reduces the SOTA baseline model by 12.2% FLOPs, while slightly improving the rank-1 accuracy and only sacrificing 1.1% mAP on DukeMTMC-ReID dataset.
HYDRO: Optimizing Interactive Hybrid Images for Digital Signage Content

In modern society, digital signage installed in many large-scale facilities supports our daily life. However, with a limited screen size, it is difficult to provide different types of information for many viewers at varying distances from the screen simultaneously. Therefore, in this study, we extend existing research on the use of hybrid images for tiled displays. To facilitate smoother information selection, a new interactive display method is proposed that incorporates a touchactivated widget as a high-frequency part of the hybrid image; these widgets are novel in that they are more visible to the viewers near to the display. We develop an authoring tool that we call the Hybrid Image Display Resolutions Optimizer (HYDRO); it features two kinds of control functions by which to optimize the visibility of the touch-activated widgets in terms of placement and resolution. The effectiveness of the present method is shown empirically via a quantitative user study and an eye-tracking-based qualitative evaluation.
RealFuVSR: Feature Enhanced Real-World Video Super-Resolution

The recurrent recovery is one of the common methods for video super-resolution, which models the correlation between frames via hidden states. However, when we apply the structure to real-world scenarios, it leads to unsatisfactory artifacts. We found that, in the real-world video super-resolution training, the use of unknown and complex degradation can better simulate the degradation process of the real world. Based on this, we propose the RealFuVSR model, which simulates the real-world degradation and mitigates the artifacts caused by the video super-resolution. Specifically, we propose a multi-scale feature extraction module(MSF) which extracts and fuses features from multiple scales, it facilitates the elimination of hidden state artifacts. In order to improve the accuracy of hidden states alignment information, RealFuVSR use advanced optical flow-guided deformable convolution. Besides, cascaded residual upsampling module is used to eliminate the noise caused by the upsampling process. The experiment demonstrates that our RealFuVSR model can not only recover the high-quality video but also outperform the state-of-the-art RealBasicVSR and RealESRGAN models.
Learning Adequate Alignment and Interaction for Cross-Modal Retrieval

Cross-modal retrieval has attracted widespread attention in many cross-media similarity search applications, especially image-text retrieval in the fields of computer vision and natural language processing. Recently, visual and semantic embedding (VSE) learning has shown promising improvements on image-text retrieval tasks. Most existing VSE models employ two unrelated encoders to extract features, then use complex methods to contextualize and aggregate those features into holistic embeddings. Despite recent advances, existing approaches still suffer from two limitations: 1) without considering intermediate interaction and adequate alignment between different modalities, these models cannot guarantee the discriminative ability of representations; 2) existing feature aggregators are susceptible to certain noisy regions, which may lead to unreasonable pooling coefficients and affect the quality of the final aggregated features. To address these challenges, we propose a novel cross-modal retrieval model containing a well-designed alignment module and a novel multimodal fusion encoder, which aims to learn adequate alignment and interaction on aggregated features for effectively bridging the modality gap. Experiments on Microsoft COCO and Flickr30k datasets demonstrates the superiority of our model over the state-of-the-art methods.
Rethink Geographical Generalizability with Unsupervised Self-Attention Model Ensemble: A Case Study of OpenStreetMap Missing Building Detection in Africa

The recent advance of adapting pre-trained task-agnostic artificial intelligence (AI) models leads to great successes in downstream tasks via fine-tuning, or low-resource (i.e., few-shot and zero-shot) learning. However, when adapting such pre-trained AI models to geographical applications, it is still challenging to find the "sweet spot" of the model's generalizability and specializability (e.g., geographic generalizability v.s. spatial heterogeneity). For instance, a building detection task may require vision models with different parameters across different geographic areas of the world. In this paper, we rethink this interesting topic, namely Geographical Generalizability of GeoAI models, with a case study of detecting OpenStreetMap (OSM) missing buildings across different countries in sub-Saharan Africa. We consider a real-world scenario, in which we first train a Single-Shot Multibox Detection (SSD) base model for OSM missing building detection in Kakola, Tanzania, where a previous humanitarian mapping project of OSM was organized to map all possible buildings. Then we extrapolate this base model using Few-Shot Transfer Learning (FSTL) to a set of areas in the proximity of the test area in Cameroon. Here, we develop a Geographical Weighted Model Ensemble (GWME) method to improve Geographical Generalizability of GeoAI models. Moreover, we compare four unsupervised model ensemble weighting strategies: 1) Average weighting, 2) Image similarity weighting, 3) Geographical distance weighting, and 4) Self-attention-based weighting. Experiments show promising results of the proposed GWME method, which implicitly generates model weights from their location embedding and image feature embedding in an unsupervised manner. More specifically, the self-attention-based model ensemble achieves the highest performance. The results shed inspiring light on improving the generalizability and replicability of GeoAI models across geographic areas. Data and code are available at https://github.com/tum-bgd/GWME.