Reviewerly/exHarmony huggingface dataset:
GPR-Net: Multi-view Layout Estimation via a Geometry-aware Panorama Registration Network

We present a room layout estimation framework that jointly learns wide baseline panorama registration and layout estimation given a pair of 360° panoramas. To effectively tackle the wide baseline registration problem, we introduce a novel end-to-end supervised Geometry-aware Panorama Registration Network or GPR-Net that exploits the layout geometry and computes fine-grained correspondences on the layout boundary, instead of the global pixel-space. GPR-Net consists of two main parts. The geometry transformer learns a set of 1D horizon features sampled on the panorama. These 1D feature maps encode geometric cues describing the ceiling-wall and floor-wall layout boundaries, and the correspondence and co-visibility between layout boundaries. These learned geometric cues are further used for direct regression of relative pose (translation and rotation) with a pose transformer. The final layout is then obtained by registering the two layouts using the estimated pose and taking the union of the two individual layouts derived from the estimated layout boundary maps. Experimental results indicate that our method achieves state-of-the-art performance in both panorama registration and layout estimation on a large-scale indoor panorama dataset ZInD [3]. Our code is available online <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
PRB-FPN+: Video Analytics for Enforcing Motorcycle Helmet Laws

We present a video analytic system for enforcing motorcycle helmet regulation as a participation to the AI City Challenge 2023 [18] Track 5 contest. The advert of powerful object detectors enables real-time localization of the road users and even the ability to determine if a motorcyclist or a rider is wearing a helmet. Ensuring road safety is important, as the helmets can effectively provide protection against severe injuries and fatalities. However, monitoring and enforcing helmet compliance is challenging, given the large number of motorcyclists and limited visual input such as occlusions. To address these challenges, we propose a novel two-step approach. First, we introduce the PRB-FPN+, a state-of-the-art detector that excels in object localization. We also explore the benefits of deep supervision by incorporating auxiliary heads within the network, leading to enhanced performance of our deep learning architectures. Second, we utilize an advanced tracker named SMILEtrack to associate and refine the target track-lets. Comprehensive experimental results demonstrate that the PRB-FPN+ outperforms the state-of-the-art detectors on MS-COCO. Our system achieved a remarkable rank of 8 on the AI City Challenge 2023 [18] Track 5 Public Leader-board. Code implementation is available at: https://github.com/NYCU-AICVLab/AICITY_2023_Track5.
Name your style: text-guided artistic style transfer

Image style transfer has attracted widespread attention in the past years. Despite its remarkable results, it requires additional style images available as references, making it less flexible and inconvenient. Using text is the most natural way to describe the style. Text can describe implicit abstract styles, like styles of specific artists or art movements. In this work, we propose a text-driven style transfer (TxST) that leverages advanced image-text encoders to control arbitrary style transfer. We introduce a contrastive training strategy to effectively extract style descriptions from the image-text model (i.e., CLIP), which aligns stylization with the text description. To this end, we also propose a novel cross-attention module to fuse style and content features. Finally, we achieve an arbitrary artist-aware style transfer to learn and transfer specific artistic characters such as Picasso, oil painting, or a rough sketch. Extensive experiments demonstrate that our approach outperforms the state-of-the-art methods. Moreover, it can mimic the styles of one or many artists to achieve attractive results, thus highlighting a promising future direction.
SB-VQA: A Stack-Based Video Quality Assessment Framework for Video Enhancement

In recent years, several video quality assessment (VQA) methods have been developed, achieving high performance. However, these methods were not specifically trained for enhanced videos, which limits their ability to predict video quality accurately based on human subjective perception. To address this issue, we propose a stack-based framework for VQA that outperforms existing state-of-the-art methods on VDPVE, a dataset consisting of enhanced videos.In addition to proposing the VQA framework for enhanced videos, we also investigate its application on professionally generated content (PGC). To address copyright issues with premium content, we create the PGCVQ dataset, which consists of videos from YouTube. We evaluate our proposed approach and state-of-the-art methods on PGCVQ, and provide new insights on the results. Our experiments demonstrate that existing VQA algorithms can be applied to PGC videos, and we find that VQA performance for PGC videos can be improved by considering the plot of a play, which highlights the importance of video semantic understanding.
Multi-Attention Transformer for Naturalistic Driving Action Recognition

To detect the start time and end time of each action in an untrimmed video in the Track 3 of AI City Challenge, this paper proposes a powerful network architecture, Multi-Attention Transformer. The previous methods extract features by setting a fixed sliding window whitch means a fixed time interval, and predict the start and end times of the action. We believe that adopting a series of fixed windows will corrupt the video feature containing contextual information. So we present a Multi-Attention transformer module which combines the local window attention and global attention to fix this problem. The method equipped with features provided by VideoMAE achieved a score of 66.34. Then use the time correction module to improve the score to 67.23 on validation set A2. Finally, we have achieved third place on Track3 A2 dataset of the AI City Challenge 2023. Our code is available at: https://github.com/wolfworld6/Aicity2023-Track3.
THE PLACE OF AXIOLOGICAL AND DEONTOLOGICAL COMPETENCES IN THE STRUCTURE OF PROFESSIONAL TRAINING OF IT SPECIALISTS

The article analyses the main elements of the phenomenon of axiological and deontological competences of a modern IT specialist. One of the fundamental elements of axiological and deontological competence is substantiated, namely knowledge, which plays a fundamental role of the deontological aspect in the professional training of future IT specialists. It is emphasized that the cognitive components of the studied competence also include skills and abilities. The author analyses the Ukrainian and foreign practice of relevant research, identifies a variety of interpretations of the selected components of the axiological and deontological competencies of a modern IT specialist.&#x0D; Thus, the purpose of the article is to provide a reasoned allocation of the place of axiological and deontological competencies in the structure of professional training of a modern IT specialist, which, in turn, should be integrated into the educational process for specific desired learning outcomes of an IT specialist. In particular, the importance of life skills, which are focused on personal development, and flexible professional skills is substantiated for the further quality educational process of training IT specialists. The integration of such elements of axiological and deontological competence will help in the development of appropriate training programmes, the use of various teaching methods, and the stimulation of personal development. The integration of skills based on axiological and deontological knowledge and skills is an area for further research.&#x0D; Life skills and flexible professional skills play an important role in the training of IT professionals and the educational process. Here are a few reasons for their importance: personal development, communication and networking, adaptation to change, creativity and innovation. Thus, the article indicates the prospects of integrating the studied components of the phenomenon of formation of value and deontological competence in the process of professional training of future IT specialists.
Internet of Things for Healthcare: Evaluate User’s Acceptance and Sustainability During Pandemics

The prolonged pandemic has put a burden on the world's healthcare systems, public services, and economy. With lockdowns and working from home, it has additionally put us to the test in every way possible. With increasing cases and innovation in healthcare industry, COVID-19 testing, building health, and remote device maintenance, IoT technology opens up new possibilities and can help us develop a new normal. The potential acceptance of this type of new technology is a very challenging issue and has been studied by many authors. Further, many authors have looked into the difficult topic of how these new technologies might be accepted in the future. In order to identify influential writers and their points of view, a thorough literature review was done in the field of technological acceptance. This current research considered technology acceptance model (TAM) and diffusion of technology (DoI) model to measure the user's acceptance of IoT for healthcare.
Dynamic virtual target guidance algorithm for path following control of a 4WD4WS mobile robot

This paper presents a method of applying a combination of the dynamic virtual target guidance algorithm and sliding mode control (SMC) to control a four-wheel-drive/four-wheel-steer mobile robot (4WD4WS) to follow the trajectory with small curvature, based on visual information captured by a single camera. A kinematic model of four-wheel-drive/four-wheel-steer mobile robot were also present, the proposed controller ensure stability and robustness when operating under uncertainties and disturbances. The process of synthesizing control laws is strictly mathematically guaranteed. The simulation in Matlab shows the research results visually.
Leveraging Information Technology for Enhanced Information Quality and Managerial Performance

This study aims to analyze and understand the contribution of information technology and its impact on information quality and management performance in the digital printing industry. The approach used in this study is causality between variables, while the sampling technique used is a census method, which means that the entire population is a target sample of 52 respondents who are business leaders. To measure the variables in this study, variable indicators consisting of a five-level Likert scale have been used, while the analysis method used to demonstrate the hypothesis is SmartPLS Software. The results of this study find that information technology has a positive contribution to improving information quality and management performance in the digital printing industry. The results show that improving information quality and management performance can be achieved by using new information technology and computer-based telecommunications. It was also found that the quality of information has a positive and significant effect on improving managerial performance. This implies that to improve the management performance of the digital printing industry, information quality support is required so that the decisions taken are effective and relevant to achieving management performance and maintaining business continuity.
Explaining Decisions of Black-Box Models Using BARBE

Machine learning models are ubiquitous today in most application domains and are often taken for granted. While integrated into many systems, oftentimes even unnoticed by the user, these powerful models frequently remain as black-boxes. They are black-boxes because while they are powerful predictive models, it is commonly the case that one cannot understand the decision-making process behind their predictions. Even if we understand the inner workings of a learning algorithm building a predictive model, the mechanism during inference is more often than not obscure. How can we trust that a certain prediction from a model is correct? How can we trust that the model is making reasonable predictions in general? Debugging a predictive model is unworkable in the absence of explanations. We propose herein a new framework, called BARBE, a model-independent explainer, that learns a surrogate rule-based model on data labeled by the black-box. BARBE makes use of an interpretable associative classifier to create a rule-based model that provides various explanations, including salient features, associations between features, and rule-based representations. Our experimental analysis illustrates the effectiveness of BARBE in generating rule-based explanations for both numerical and text data, when compared to state-of-the-art explainers. Our study demonstrates the faithfulness of BARBE to black-box models. The text-based explanations generated by BARBE are more meaningful to show the fidelity and trustworthiness of the explanation.
Error scaling laws for kernel classification under source and capacity conditions

Abstract In this manuscript we consider the problem of kernel classification. While worst-case bounds on the decay rate of the prediction error with the number of samples are known for some classifiers, they often fail to accurately describe the learning curves of real data sets. In this work, we consider the important class of data sets satisfying the standard source and capacity conditions, comprising a number of real data sets as we show numerically. Under the Gaussian design, we derive the decay rates for the misclassification (prediction) error as a function of the source and capacity coefficients. We do so for two standard kernel classification settings, namely margin-maximizing support vector machines and ridge classification, and contrast the two methods. We find that our rates tightly describe the learning curves for this class of data sets, and are also observed on real data. Our results can also be seen as an explicit prediction of the exponents of a scaling law for kernel classification that is accurate on some real datasets.
Using Virtual Reality Video Development as Educational and Tourist Promotional Media

Maintaining tourism during a pandemic is a very tough challenge. Policy restrictions limit access and mobility by the government area until state regulations are lifted. The impact on sector tourist experiences are significant, because there is no activity in many destination tours. Indonesia and Thailand have similar destination tour cultures and heritage character histories , i.e. artifacts detailing the legacy and glory of Buddha. The Jago temple and the Grand Palace in Thailand are interestinng educational tours to learn about past civilizations . The appreciation activity for common artifacts that is conducted with a method visit to the location must be replaced with another way. Excellent and sophisticated metaverse technology can be utilized to present ambience and a natural feel of the location. The purpose of this study is to produce helpful virtual reality video products to educate the public about the artifacts in temple Jago in Malang and the Grand Palace artifacts in Thailand as an alternative educational media tour. The method was developed by implementing the ADDIE model. The purpose of this study is as following : (1) produce access in visual forms and shape the atmosphere of destination tours, (2) create access information especially on the culture of products and artifacts, (3) design educational media in website/video/multimedia format or in any other possible format to replace the atmosphere of visit travel, (4) produce deep media video forms or photographs to become products that can raise audience interaction, (5) produce the validity of related data with product artifacts in Jago temple culture and the Grand Palace of Thailand.&#x0D; Keywords: artifact Grand Palace, Candi Jago artifact, VR temple
Jordanian Women Entrepreneurs and the Role of Social Media: The Road to Empowerment

Objectives: This study aims to investigate the factors that have influenced women entrepreneurs to use social media to run their businesses. Additionally, it explores how social media has influenced and affected women entrepreneurs' businesses and examines the social changes in their lives instigated by using social media for business purposes.&#x0D; Methods: The study adopts a qualitative-inductive approach and utilizes semi-structured interviews with 30 women entrepreneurs from various industries in three main cities in Jordan.&#x0D; Results: The paper highlights that social media platforms have provided women with varying degrees of empowerment. The degree of empowerment differs among women and is associated with their motivations for entrepreneurship. Women who started their businesses with opportunity motivations and used social media as their primary platform are found to be more empowered compared to women who engaged in necessity entrepreneurship. The research findings confirm that female entrepreneurs have experienced positive effects by using social media as their primary business platform. Embracing social media has presented new marketing opportunities, leading to significant economic gains.&#x0D; Conclusions: This study supports the notion that the status of women's entrepreneurship in Jordan is rapidly increasing, particularly due to the presence of social media platforms. Women adopt different titles and descriptions to gain control over their lives, often referred to as empowerment, which is achieved through access to financial resources, education, and confidence. This empowerment subsequently manifests as entrepreneurship.
Obstacle Detection in Infrared Navigation for Blind People and Mobile Robots

The paper is a continuation of the authors’ work intended for infrared navigation for blind people and mobile robots. This concerns the detection of obstacles in the person’s or mobile robot’s trajectory, in particular, the detection of corners. The temperature distribution of a building’s internal wall near a corner has been investigated. Due to geometry, more heat will be transferred by conduction so that inside the building, the temperature on the wall will be decreasing towards a corner. The problem will be investigated theoretically and numerically, and the results are confirmed by experimental measurements. The purpose of this research is to help blind people by equipping them with a small infrared camera that warns them when they are approaching a corner inside a building. The same aim is addressed to mobile robots.
A Joint Extraction Model for Entity Relationships Based on Span and Cascaded Dual Decoding

The entity–relationship joint extraction model plays a significant role in entity relationship extraction. The existing entity–relationship joint extraction model cannot effectively identify entity–relationship triples in overlapping relationships. This paper proposes a new joint entity–relationship extraction model based on the span and a cascaded dual decoding. The model includes a Bidirectional Encoder Representations from Transformers (BERT) encoding layer, a relational decoding layer, and an entity decoding layer. The model first converts the text input into the BERT pretrained language model into word vectors. Then, it divides the word vectors based on the span to form a span sequence and decodes the relationship between the span sequence to obtain the relationship type in the span sequence. Finally, the entity decoding layer fuses the span sequences and the relationship type obtained by relation decoding and uses a bi-directional long short-term memory (Bi-LSTM) neural network to obtain the head entity and tail entity in the span sequence. Using the combination of span division and cascaded double decoding, the overlapping relations existing in the text can be effectively identified. Experiments show that compared with other baseline models, the F1 value of the model is effectively improved on the NYT dataset and WebNLG dataset.
Digital Twins For Topology Density Map Analysis

Properly analyzing spatiotemporal patterns is of paramount importance, especially in urban planning. In this paper, we introduce two digital twins to support the analysis of spatiotemporal data associated with urban topologies. In particular, both tools visually encode temporal changes in density maps constrained by a network. We introduce their software architectures and discuss their use in urban planning usage scenarios.
Using Small UAS for STEM Education: Introducing Robotics and Mechatronics with Drones

Several global developments point to importance of STEM-related education and studies.Every industry sector faces challenges in the recruiting of qualified personnel, due to retiring employees and general economic growth.Also, several domains, foremost automotive and aviation are undergoing a tremendous shift towards electrification to achieve sustainability.Hence, sparking the interest in STEM studies, education and vocational training cannot start early enough.This paper introduces the benefits of the educational use of sUAS by identifying core benefits in the three domains of learning -cognitive, affective, psycho-motor.Preliminary data and survey results from from several science, technology, engineering and math (STEM) drone providers worldwide will be used to identify and underline the benefits of this educational concepts.
The use of ICT in the education of students with dyslexia

In this article, a systematic study of the research literature is carried out regarding the use of Information and Communication Technology (ICT) and its applications as means of supporting and assisting reading for students with dyslexia. The main objective was to examine the use and effectiveness of this type of intervention in serving the learning needs of students with dyslexia. For this purpose, a bibliographic review of relevant research was conducted. The results of the research showed that the different forms of ICT, such as computers, educational platforms, specific software programs, electronic games and various multimedia applications, are able to be used both as a means of early prevention of the risk of dyslexia in children and in enhancing the reading skills of dyslexic children. Probably the positive effect of ICT on students with dyslexia is related to children's familiarity with it, recommending a particularly pleasant learning environment for children. In the future, further research is needed regarding the use of ICT in the diagnosis and management of dyslexia.
Simple and Effective Unsupervised Speech Translation

The amount of labeled data to train models for speech tasks is limited for most languages, however, the data scarcity is exacerbated for speech translation which requires labeled data covering two different languages.To address this issue, we study a simple and effective approach to build speech translation systems without labeled data by leveraging recent advances in unsupervised speech recognition, machine translation and speech synthesis, either in a pipeline approach, or to generate pseudo-labels for training end-to-end speech translation models.Furthermore, we present an unsupervised domain adaptation technique for pre-trained speech models which improves the performance of downstream unsupervised speech recognition, especially for low-resource settings.Experiments show that unsupervised speech-to-text translation outperforms the previous unsupervised state of the art by 3.2 BLEU on the Libri-Trans benchmark, on CoVoST 2, our best systems outperform the best supervised end-to-end models (without pre-training) from only two years ago by an average of 5.0 BLEU over five X-En directions.We also report competitive results on MuST-C and CVSS benchmarks.