Reviewerly/exHarmony huggingface dataset:
Python And Opencv For Sign Language Recognition

Hearing-impaired people cannot communicate with normal people easily. Most people are not aware of sign language recognition. To support this, machine learning and CV can be used to create an impact on the impaired. This can be improved into automatic editors, in which the person can easily understand the sign language of the impaired people by just using hand sign recognition. In non-verbal communication, hand gesture always being an important mode of communication and it plays a vital role to bridge gap between deaf and dumb people. Several sign language recognition systems have been developed but the systems are not flexible and cost-effective. Physically challenged people can express their emotions and feelings through sign language. In this paper, we develop a sign detector that can detect signs and numbers and also other signs that are used in sign language with the help of OpenCV and Keras modules in python. By using this technology, we can understand what they want to convey through sign language which is not a common language to communicate with people. OpenCV and Keras of python are the modules used to achieve our work and the proposed work proved to be a user-friendly approach to communication by using Python language to recognize sign languages for hearing-impaired persons.
Experimental super-Heisenberg quantum metrology with indefinite gate order

The precision of quantum metrology is widely believed to be restricted by the Heisenberg limit, corresponding to a root mean square error that is inversely proportional to the number of independent processes probed in an experiment, N. In the past, some proposals have challenged this belief, for example, using nonlinear interactions among the probes. However, these proposals turned out to still obey the Heisenberg limit with respect to other relevant resources, such as the total energy of the probes. Here we present a photonic implementation of a quantum metrology protocol surpassing the Heisenberg limit by probing two groups of independent processes in a superposition of two alternative causal orders. Each process creates a phase-space displacement, and our setup is able to estimate a geometric phase associated with two sets of N displacements with an error that falls quadratically with N. Our results only require a single-photon probe with an initial energy that is independent of N. Using a superposition of causal orders outperforms every setup where the displacements are probed in a definite order. Our experiment features the demonstration of indefinite causal order in a continuous-variable system, and opens up the experimental investigation of quantum metrology setups boosted by indefinite causal order. Normally, quantum operations are thought of as being applied in a particular order, but it is possible to create superpositions of different orders. An experiment now demonstrates this indefinite causal order may give an advantage for quantum sensing.
Refinement of image quality in panoramic radiography using a generative adversarial network

We aimed to develop and assess the clinical usefulness of a generative adversarial network (GAN) model for improving image quality in panoramic radiography.Panoramic radiographs obtained at Yonsei University Dental Hospital were randomly selected for study inclusion (n = 100). Datasets with degraded image quality (n = 400) were prepared using four different processing methods: blur, noise, blur with noise, and blur in the anterior teeth region. The images were distributed to the training and test datasets in a ratio of 9:1 for each group. The Pix2Pix GAN model was trained using pairs of the original and degraded image datasets for 100 epochs. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were obtained for the test dataset, and two oral and maxillofacial radiologists rated the quality of clinical images.Among the degraded images, the GAN model enabled the greatest improvement in those with blur in the region of the anterior teeth but was least effective in improving images exhibiting blur with noise (PSNR, 36.27 > 32.74; SSIM, 0.90 > 0.82). While the mean clinical image quality score of the original radiographs was 44.6 out of 46.0, the highest and lowest predicted scores were observed in the blur (45.2) and noise (36.0) groups.The GAN model developed in this study has the potential to improve panoramic radiographs with degraded image quality, both quantitatively and qualitatively. As the model performs better in refining blurred images, further research is required to identify the most effective methods for handling noisy images.
Unsupervised Domain Adaptation for Image Classification and Object Detection Using Guided Transfer Learning Approach and JS Divergence

Unsupervised domain adaptation (UDA) is a transfer learning technique utilized in deep learning. UDA aims to reduce the distribution gap between labeled source and unlabeled target domains by adapting a model through fine-tuning. Typically, UDA approaches assume the same categories in both domains. The effectiveness of transfer learning depends on the degree of similarity between the domains, which determines an efficient fine-tuning strategy. Furthermore, domain-specific tasks generally perform well when the feature distributions of the domains are similar. However, utilizing a trained source model directly in the target domain may not generalize effectively due to domain shift. Domain shift can be caused by intra-class variations, camera sensor variations, background variations, and geographical changes. To address these issues, we design an efficient unsupervised domain adaptation network for image classification and object detection that can learn transferable feature representations and reduce the domain shift problem in a unified network. We propose the guided transfer learning approach to select the layers for fine-tuning the model, which enhances feature transferability and utilizes the JS-Divergence to minimize the domain discrepancy between the domains. We evaluate our proposed approaches using multiple benchmark datasets. Our domain adaptive image classification approach achieves 93.2% accuracy on the Office-31 dataset and 75.3% accuracy on the Office-Home dataset. In addition, our domain adaptive object detection approach achieves 51.1% mAP on the Foggy Cityscapes dataset and 72.7% mAP on the Indian Vehicle dataset. We conduct extensive experiments and ablation studies to demonstrate the effectiveness and efficiency of our work. Experimental results also show that our work significantly outperforms the existing methods.
Embodiment and Personalization for Self-Identification with Virtual Humans

Our work investigates the impact of virtual human embodiment and personalization on the sense of embodiment (SoE) and self-identification (SI). We introduce preliminary items to query self-similarity (SS) and self-attribution (SA) with virtual humans as dimensions of SI. In our study, 64 participants successively observed personalized and generic-looking virtual humans, either as embodied avatars in a virtual mirror or as agents while performing tasks. They reported significantly higher SoE and SI when facing personalized virtual humans and significantly higher SoE and SA when facing embodied avatars, indicating that both factors have strong separate and complimentary influence on SoE and SI.
A Large-Scale Study of Proxemics and Gaze in Groups

Scholars who study nonverbal behavior have focused an incredible amount of work on proxemics, how close people stand to one another, and mutual gaze, whether or not they are looking at one another. Moreover, many studies have demonstrated a correlation between gaze and distance, and so-called equilibrium theory posits that people modulate gaze and distance to maintain proper levels of nonverbal intimacy. Virtual reality scholars have also focused on these two constructs, both for theoretical reasons, as distance and gaze are often used as proxies for psychological constructs such as social presence, and for methodological reasons, as head orientation and body position are automatically produced by most VR tracking systems. However, to date, the studies of distance and gaze in VR have largely been conducted in laboratory settings, observing behavior of a small number of participants for short periods of time. In this experimental field study, we analyze the proxemics and gaze of 232 participants over two experimental studies who each contributed up to about 240 minutes of tracking data during eight weekly 30-minute social virtual reality sessions. Participants' non-verbal behaviors changed in conjunction with context manipulations and over time. Interpersonal distance increased with the size of the virtual room; and both mutual gaze and interpersonal distance increased over time. Overall, participants oriented their heads toward the center of walls rather than to corners of rectangularly-aligned environments. Finally, statistical models demonstrated that individual differences matter, with pairs and groups maintaining more consistent differences over time than would be predicted by chance. Implications for theory and practice are discussed.
Ubiq-Genie: Leveraging External Frameworks for Enhanced Social VR Experiences

This paper describes the Ubiq-Genie framework for integrating external frameworks with the Ubiq social VR platform. The proposed architecture is modular, allowing for easy integration of services and providing mechanisms to offload computationally intensive processes to a server. To showcase the capabilities of the framework, we present two prototype applications: 1) a voice- and gesture-controlled texture generation method based on Stable Diffusion 2.0 and 2) an embodied conversational agent based on ChatGPT. This work aims to demonstrate the potential of integrating external frameworks into social VR for the creation of new types of collaborative experiences.
CaV3: Cache-assisted Viewport Adaptive Volumetric Video Streaming

Volumetric video (VV) recently emerges as a new form of video application providing a photorealistic immersive 3D viewing experience with 6 degree-of-freedom (DoF), which empowers many applications such as VR, AR, and Metaverse. A key problem therein is how to stream the enormous size VV through the network with limited bandwidth. Existing works mostly focused on predicting the viewport for a tiling-based adaptive VV streaming, which however only has quite a limited effect on resource saving. We argue that the content repeatability in the viewport can be further leveraged, and for the first time, propose a client-side cache-assisted strategy that aims to buffer the repeatedly appearing VV tiles in the near future so as to reduce the redundant VV content transmission. The key challenges exist in three aspects, including (1) feature extraction and mining in 6 DoF VV context, (2) accurate long-term viewing pattern estimation and (3) optimal caching scheduling with limited capacity. In this paper, we propose CaV3, an integrated cache-assisted viewport adaptive VV streaming framework to address the challenges. CaV3 employs a Long-short term Sequential prediction model (LSTSP) that achieves accurate short-term, mid-term and long-term viewing pattern prediction with a multi-modal fusion model by capturing the viewer's behavior inertia, current attention, and subjective intention. Besides, CaV3 also contains a contextual MAB-based caching adaptation algorithm (CCA) to fully utilize the viewing pattern and solve the optimal caching problem with a proved upper bound regret. Compared to existing VV datasets only containing single or co-located objects, we for the first time collect a comprehensive dataset with sufficient practical unbounded 360° scenes. The extensive evaluation of the dataset confirms the superiority of CaV3, which outperforms the SOTA algorithm by 15.6%-43% in viewport prediction and 13%-40% in system utility.
Learning Heavily-Degraded Prior for Underwater Object Detection

Underwater object detection suffers from low detection performance because the distance and wavelength dependent imaging process yield evident image quality degradations such as haze-like effects, low visibility, and color distortions. Therefore, we commit to resolving the issue of underwater object detection with compounded environmental degradations. Typical approaches attempt to develop sophisticated deep architecture to generate high-quality images or features. However, these methods are only work for limited ranges because imaging factors are either unstable, too sensitive, or compounded. Unlike these approaches catering for high-quality images or features, this paper seeks transferable prior knowledge from detector-friendly images. The prior guides detectors removing degradations that interfere with detection. It is based on statistical observations that, the heavily degraded regions of detector-friendly (DFUI) and underwater images have evident feature distribution gaps while the lightly degraded regions of them overlap each other. Therefore, we propose a residual feature transference module (RFTM) to learn a mapping between deep representations of the heavily degraded patches of DFUI- and underwater-images, and make the mapping as a heavily degraded prior (HDP) for underwater detection. Since the statistical properties are independent to image content, HDP can be learned without the supervision of semantic labels and plugged into popular CNN-based feature extraction networks to improve their performance on underwater object detection. Without bells and whistles, evaluations on URPC2020 and UODD show that our methods outperform CNN-based detectors by a large margin. Our method with higher speeds and less parameters still performs better than transformer-based detectors. Our code and DFUI dataset can be found in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/xiaoDetection/Learning-Heavily-Degraed-Prior</uri> .
Internet of Things for Earthquake Early Warning Systems: A Performance Comparison Between Communication Protocols

Earthquake Early Warning Systems (EEWSs) characterize seismic events in real time and estimate the expected ground motion amplitude in specific areas to send alerts before the destructive waves arrive. Together with the reliability of the results, the rapidity with which an EEWS can detect an earthquake becomes a focal point for developing efficient seismic node networks. Internet of Things (IoT) architectures can be used in EEWSs to expand a seismic network and acquire data even from low-cost seismic nodes. However, the latency and the total alert time introduced by the adopted communication protocols should be carefully evaluated. This study proposes an IoT solution based on the message queue-telemetry transport protocol for the waveform transmission acquired by seismic nodes and presents a performance comparison between it and the most widely used standard in current EEWSs. The comparison was performed in evaluation tests where different seismic networks were simulated using a dataset of real earthquakes. This study analyzes the phases preceding the earthquake detection, showing how the proposed solution detects the same events of traditional EEWSs with a total alert time of approximately 1.6 seconds lower.
FeQA: Fusion and enhancement of multi-source knowledge on question answering

In the question answering task, we usually need to reason the answer according to the question. Question answering tasks mostly use the pretrained language model to obtain the semantic embedding of questions and choices to predict answers, while the pretrained language model cannot accurately represent the potential relationship between entities in the question. Therefore, researchers introduce knowledge graph to realize the reasoning from question entity to answer entity. However, the limitation of knowledge graph lies in the lack of background information of entities, which may lead to wrong reasoning. To solve the above problems, a new question answering system model FeQA is proposed, which adopts large-scale pretrained language model and knowledge graph. The former uses dual-attention mechanism to enhance the semantics of questions by using Wiktionary and other question answering datasets, while the latter uses graph neural network to infer entities. During the interaction of two modal knowledge, the former provides the basis for the reasoning of nodes in the latter, and the latter provides structured knowledge for the former. After several reasoning iterations, the final answer is obtained by using the knowledge of the two modes. The experimental results on the CommonsenseQA and OpenBookQA datasets show that the performance of this model is better than that of the baseline models. Ablation experiments show that the components and knowledge sources included in this model play an important role in the effect of question and answering task. Extended experiments show the model has good application capability.
Orthonormal product quantization network for scalable face image retrieval

Existing deep quantization methods provided an efficient solution for large-scale image retrieval. However, the significant intra-class variations, like pose, illumination, and expressions in face images, still pose a challenge. In light of this, face image retrieval requires sufficiently powerful learning metrics, which are absent in current deep quantization works. Moreover, to tackle the growing unseen identities in the query stage, face image retrieval drives more demands regarding model generalization and scalability than general image retrieval tasks. This paper integrates product quantization with orthonormal constraints into an end-to-end deep learning framework to effectively retrieve face images. Specifically, we propose a novel scheme that uses predefined orthonormal vectors as codewords to enhance the quantization informativeness and reduce codewords’ redundancy. A tailored loss function maximizes discriminability among identities in each quantization subspace for both the quantized and original features. An entropy-based regularization term is imposed to reduce the quantization error. Experiments are conducted on four commonly-used face datasets under both seen and unseen identity retrieval settings. Our method outperforms all the compared state-of-the-art under both settings. The proposed orthonormal codewords consistently boost both models’ standard retrieval performance and generalization ability, demonstrating the superiority of our method for scalable face image retrieval.
Sorting Red and Green Chilies by Digital Image Processing

The purpose of this research is to help save electricity within organizations and workplaces that use computers. Due to the behavior of general users today, they do not give importance to saving electricity. This work has developed an automatic computer shutdown system. with image processing via a webcam by using image processing techniques (image processing) applied to a webcam by using a webcam to capture the user on the computer screen. Then the images are analyzed and examined with the principles of image processing with the face detection technique using the algorithm of Haar -like features to detect human faces and ROI. define a specific area of interest. The system will then check the amount of inactivity on the screen. or does not show the face image within 10 minutes, the system will automatically turn off the computer. with image processing. This work can make it easier to turn off computers when not in use. and helps to save electricity for the organization or agencies as well.
Practicality analysis of utilizing text-based CAPTCHA vs. graphic-based CAPTCHA authentication

CAPTCHA as "Completely Automated Public Turing test to tell Computers and Humans Apart" is becoming an essential tool to help reduce many automated security authentication attacks. This research focused on studying differences running text-based CAPTCHA vs. graphical-based CAPTCHA in a utilization applicable dominant practicality manner. The ordinary text-based CAPTCHA works simple to prevent automated submissions as thought of being relatively easy to exploit. On the other hand, graphic-based CAPTCHA can be more preferred from users side, but can be providing some complexities making clear tradeoff analysis need between its usability and security. Even though graphic-based CAPTCHA has been generally considered as improvement of text-based CAPTCHA with respect to security, its usage is still not common, raising a practicality gap needing some search for comparing the two methods side by side comprehensively involving usability applicability and cultural preference beside security. In this regard, this research contributes towards filling the gap in knowledge running thorough local experimentations for finding different CAPTCHA performance tradeoffs in terms of real statistical humanoid possibilities of practicality easiness, repetition secrecy, and configuration solving timing, that can be used as basis for conducting further techno improvement human-oriented research.
Activities-based virtual reality experience for better audience engagement

The benefits of virtual reality have definitely expanded beyond the gaming industry to a variety of other businesses. From virtual conferences to virtual exhibitions, the MICE (Meetings, Incentives, Conferences, and Exhibitions) industry has several applications for virtual reality. Researchers and experts should pay more attention to the virtual reality applications in this market segment. This study's objectives were to 1) create a virtual reality experience for Thailand's Ministry of Digital Economy and Society to serve as an international exhibition showcase by incorporating virtual activities, 2) evaluate the efficiency of the virtual reality experience based on the activities and audience engagement, and 3) investigate the virtual reality experience's audience satisfaction. This virtual immersive experience is the outcome of using Tilt Brush to build a virtual spatial playground centered on three topics: the rural broadband internet project, the cybersecurity act 2019 & the personal data protection act 2019, and awareness of fake news. The virtual reality experience was on show at Digital Thailand Big bang 2019, which was held at BITEC International Exhibition in Bangkok, Thailand from October 28 to October 31, 2019. The investigation utilized mixed-method research, consisting of a quantitative technique based on observation and a qualitative technique based on questionnaires. A convenience sample of 126 cases was obtained from viewers aged seven and up who participated in the virtual reality experience. The virtual experience required the audience to engage in four sorts of physical activity: looking, walking, performing a specific physical action, and locating an object. Observation forms, questionnaires, and examinations were utilized to collect data. The findings indicate that the virtual reality experience, while entertaining the audience, encourages learning through physical activities. These activities contribute to audience engagement and, ultimately, comprehension of the subject. The majority of the audience was able to accurately answer test questions, indicating that not only did they engage the presentation through active participation, but they also retained its content. This project is a case study of how virtual reality can be used to increase audience participation during an exhibition.