Reviewerly/exHarmony huggingface dataset:
The method of reuse of knowledge in the form of logical equations

The subject of research is the processes of building and replenishing knowledge bases, the processes of knowledge reuse and the creation of software systems based on knowledge bases, the interpretation of knowledge is one of the approaches to their repeated application, which consists in deriving new knowledge based on the available facts in the knowledge base. The purpose of the research is to develop a method of knowledge reuse by solving logical equations of finite predicates for a certain subject area. The tasks were performed: to investigate descriptive approaches to logical modeling of the subject area, which make it possible to reuse the knowledge given by the system of logical equations within the algebra of finite predicates, to develop a method of replenishing the knowledge base in the form of predicate equations by extracting variables from logical systems, where equations represent complex logical connections between discrete features of objects or processes. The methods were used to solve the listed problems: the algebra of finite predicates, quantifier operations with predicates for the interpretation of knowledge. The results were obtained: descriptive methods of knowledge reuse were considered; a method of replenishing the knowledge base in the form of logical equations is proposed simplify the further use of implicit connections between discrete features; an example of replenishment of the knowledge base of the medical field is considered, which makes it possible to reuse knowledge given implicitly. Conclusions: the proposed method makes it possible to replenish the knowledge base in the form of logic equations by adding predicate equations connecting separate sets of discrete variables that are of interest to a scientist or a practitioner. The method of replenishing the knowledge base is based on adding new simplified equations; simplified predicate equations model logical patterns implicitly contained in the main knowledge base; an experimental study was conducted
SkyGAN: Realistic Cloud Imagery for Image‐based Lighting

Abstract Achieving photorealism when rendering virtual scenes in movies or architecture visualizations often depends on providing a realistic illumination and background. Typically, spherical environment maps serve both as a natural light source from the Sun and the sky, and as a background with clouds and a horizon. In practice, the input is either a static high‐resolution HDR photograph manually captured on location in real conditions, or an analytical clear sky model that is dynamic, but cannot model clouds. Our approach bridges these two limited paradigms: a user can control the sun position and cloud coverage ratio, and generate a realistically looking environment map for these conditions. It is a hybrid data‐driven analytical model based on a modified state‐of‐the‐art GAN architecture, which is trained on matching pairs of physically‐accurate clear sky radiance and HDR fisheye photographs of clouds. We demonstrate our results on renders of outdoor scenes under varying time, date and cloud covers. Our source code and a dataset of 39 000 HDR sky images are publicly available at https://github.com/CGGMFF/SkyGAN .
Craniofacial and Cervical Aesthetic Surgical Outcome Analysis Using Visual Similarity Metrics

Head and neck (face) operations focused on augmenting or changing a person’s facial characteristics have seen notable breakthroughs in plastic surgery. For medical experts and patients seeking such therapies, the careful assessment of results and efficacy in head and neck plastic surgery is of utmost importance. This study article offers an in-depth examination of several image similarity measures specially designed for assessing face alterations brought on by cosmetic surgery treatments. The proposed evaluation methodology incorporates specialized measurements, including the Facial Symmetry Index (FSI), Facial Proportion Index (FPI), Euclidean Distance of Facial Landmarks (EDFL), and Perceptual Hashing (PH), providing a precise and reliable assessment of surgical results. The field of head and neck plastic surgery has increased patient satisfaction by combining these cutting-edge measures, according to medical experts and people.
Real-Time GPU-Accelerated Driver Assistance System

We present an extensive driver assistance system capable of executing two essential tasks. The first module is used in assisting the driver with road safety alerts (RSA); it scans the road environment and detects any significant entities including but not limited to vehicles, pedestrians, and traffic lights. Further, alerts are issued when the estimated physical distance from detected entities is less than a set threshold. For this module, we also propose the usage of a compute-accelerated Swin Transformer model and evaluate its efficacy against other state-of-the-art models by considering relevant metrics like inference time and mAP. The second module pertains to driver alertness detection (DAD) for identifying signs of fatigue. It scans the driver’s face and monitors a live video feed to ensure that the driver shows no signs of micro-sleep. When either module detects a behavioural anomaly, it will alert the driver with text-based messages and non-disruptive audio messages. We propose such a state-of-the-art safety system being integrated into the advanced driver assistance systems (ADAS’s) seen in modern vehicles.
Virtual Reality Training System Using an Autonomy Agent for Learning Hospitality Skills of a Retail Store

Training systems are well known as one practical usage for Virtual Reality. Previous works of hospitality services have mainly focused on some situations which target customers who are face-to-face or whose position is located at their seats in a store. However, there are some types of customer service businesses, such as retail stores, which should serve customers who patrol a store to find desired products. In this study, we developed a VR training system in which customer agents autonomously form a path based on the location of product shelves in the VR store and patrol those paths. We aim to facilitate learning some skills for the hospitality service of a retail store, such as understanding the entire store’s perspective, serving customers in need, and not obstructing their paths. Customer agents patrol paths and interact with products by finite state machine system. If agents have an interest during patrolling, they look for shoppers (i.e., trainees) to ask about the location of products. In this paper, we described the autonomy agent and serving systems for trainees in detail.
Analysis of Early Literacy Practices Based on Social Inclusion in Palembang

The purpose of this study is to explore children's literacy activities at home that involve parenting in Indonesia from a social inclusion perspective. Design/methods/approach – This study uses a qualitative research method. Analyzing the results of interviews conducted on four families in Palembang, Indonesia, and using a critical theory perspective as the analysis. Findings – In the setting of Early Childhood Education and Care (ECEC), literacy practices are not only carried out at school but also in the home and community. Based on the findings that we conducted on four families in Palembang, the practice of early literacy at home is very diverse. The practice of early literacy at home is also still experiencing problems, especially in the provision of media that supports children's literacy interests, especially in the fields of reading and numeracy. Research implications and limitations – Thus, it is necessary to synergize the role of families, institutions, and also the government in an effort to support the practice of early literacy based on social inclusion. Practical implications – so as to realize the interest in children's literacy in this country better
Directional intensified feature description using tertiary filtering for augmented reality tracking

Abstract Augmented Reality (AR) is applied in almost every field, and a few, but not limited, are engineering, medical, gaming and internet of things. The application of image tracking is inclusive in all these mentioned fields. AR uses image tracking to localize and register the position of the user/AR device for superimposing the virtual image into the real-world. In general terms, tracking the image enhance the users’ experience. However, in the image tracking application, establishing the interface between virtual realm and the physical world has many shortcomings. Many tracking systems are available, but it lacks in robustness and efficiency. The robustness of the tracking algorithm, is the challenging task of implementation. This study aims to enhance the users’ experience in AR by describing an image using Directional Intensified Features with Tertiary Filtering. This way of describing the features improve the robustness, which is desired in the image tracking. A feature descriptor is robust, in the sense that it does not compromise, when the image undergoes various transformations. This article, describes the features based on the Directional Intensification using Tertiary Filtering (DITF). The robustness of the algorithm is improved, because of the inherent design of Tri-ocular, Bi-ocular and Dia-ocular filters that can intensify the features in all required directions. The algorithm’s robustness is verified with respect to various image transformations. The oxford dataset is used for performance analysis and validation. DITF model is designed to achieve the repeatability score of illumination-variation , blur changes and view-point variation, as 100%, 100% and 99% respectively. The comparative analysis has been performed in terms of precision and re-call. DITF outperforms the state-of-the-art descriptors, namely, BEBLID, BOOST, HOG, LBP, BRISK and AKAZE. An Implementation of DITF source code is available in the following GitHub repository: github.com/Johnchristopherclement/Directional-Intensified-Feature-Descriptor.
A Federated Learning Enabled Edge-Based Green Internet of Things System: A Short Survey

In today’s world, the importance of the Green Internet of Things (GIoT) in the transformed sustainable smart cities cannot be overstated. For a variety of applications, the GIoT may make use of advanced machine learning (ML) methodologies. However, owing to high processing costs and privacy issues, centralized ML-based models are not a feasible option for the large data kept at a single cloud server and created by multiple devices. In such circumstances, edge-based computing may be used to increase the privacy of GIoT networks by bringing them closer to users and decentralizing them without requiring a central authority in such circumstances. Nonetheless, enormous amounts of data are stored in a distribution mechanism, and managing them for application purposes remains a difficulty. Hence, federated learning (FL) is one of the most promising solutions for bringing learning to end devices through edge computing without sharing private data with a central server. Therefore, the paper proposes a federated learning-enabled edge-based GIoT system, which seeks to improve the communication strategy while lowering liability in terms of energy management and data security for data transmission. The proposed model uses FL to produce feature values for data routing, which could aid in sensor training for identifying the best routes to edge servers. Furthermore, combining FL-enabled edge-based techniques simplifies security solutions while also allowing for a more efficient computing system. The experimental results show an improved performance against existing models in terms of network overhead, route interruption, energy consumption, and end-to-end delay, route interruption.
Effective Fine-tuning Method for Tibetan Low-resource Dialect Speech Recognition

Tibetan is a distinctive and culturally rich language spoken by millions of people across the Tibetan Plateau and surrounding regions. Exploring the application of speech recognition technology to Tibetan has special significance for preserving language diversity and fostering cultural integration. Moreover, Tibetan comprises a multitude of distinct dialects, which present a hurdle for reusing speech recognition models. In low-resource dialect tasks, conventional approaches endeavor to transfer well-trained models from linguistically akin languages to the target. However, recent studies have shown that an indiscriminate fine-tuning of all parameters may disrupt the feature extractor of the pre-trained model, leading to catastrophic forgetting. This paper introduces an innovative fine-tuning method grounded in model adaptation. Aimed at training automatic speech recognition (ASR) models within the constraints of limited training data and cross-dialect transfer, our novel approach refines a select group of language-specific parameters, leading to robust performance. These parameters, signified by a sparse binary mask identical to the model, circumvent the need for additional parameters. Experiments conducted on two downstream low-resource Tibetan languages show that our proposed methodology outperforms the traditional fine-tuning and adapter based fine-tuning.
Black-box Lossless Fragile Watermarking Based on Hidden Space Search for DNN Integrity Authentication

A lossless and fragile watermarking scheme based on black-box with hidden space search is proposed for DNN. This is a publicized remote verification scheme, any authorized party can generate and verify the watermark of the target model without knowing the internal situation of the model and only by the model ground-truth input and output results. Specifically, we generate some sensitive samples near the decision boundary of the target model and exploit these samples as the fingerprints of the model. Once these target models are attacked or tampered to a certain extent, the labels corresponding to these sensitive samples will be flipped, thus accomplishing the purpose of authentication. Experimental results show that the proposed scheme is sensitive to the some current common model attack schemes, and only a small number of sensitive samples are needed to efficiently identify whether the watermarked model is intact or not.
DisC-VC: Disentangled and F<sub>0</sub>-Controllable Neural Voice Conversion

Voice conversion is a task to convert a non-linguistic feature of a given utterance. Since nuance of speech strongly depends on its pitch pattern, in some applications, it would be desirable to keep the original rise/fall pitch pattern while changing the speaker identity. Some of the existing methods address this problem by either using a source-filter model or developing a neural network that takes an F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> pattern as input to the model. Although the latter approach can achieve relatively high sound quality compared to the former one, there is no consideration for discrepancy between the target and generated F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> patterns in its training process. In this paper, we propose a new variational-autoencoder-based voice conversion model accompanied by an auxiliary network, which ensures that the conversion result correctly reflects the specified F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> /timbre information. We show the effectiveness of the proposed method by objective and subjective evaluations.
DETR with Additional Object Instance-Specific Features for Encoder

This paper focuses on the process of developing from a convolutional neural network (CNN)-based target detection method to a transformer-based DETR paradigm-based target detection method. DETR adopts a Transformer-based end-to-end detection method, and it does not use the traditional anchor box and non-maximum suppression by transforming target detection into a set prediction problem. DETR has shown competitive results on public datasets and brought new ideas and methods to the field of object detection. We observed that DETR and DETE-like models include backbone and encoder that have same effect on the image, that is, they both did the same feature extraction function. We propose to add additional embedding module, which represents the full class information, and establishes global attention between feature tokens to provide prior knowledge for the extractor.
A Sense of Self for Power Side-Channel Signatures: Time-Series Side-Channel Disassembler (TSD) and Integrity Monitor (TSD-IM)

Systems are often designed with a presumption of trust, despite their microelectronics and software-foundations being inherently untrustworthy. This foundational issue can lead to undetected and unmitigated cyber-attacks occurring below or between implemented security measures. To overcome this, a side-channel analysis-based approach is proposed to monitor ground truth of instruction execution at microcontroller clock-cycle fidelity. The foundation of this approach, Time-series Side-channel Disassembler (TSD) is evaluated against an ATmega328P at 16 MHz using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. TSD's average instruction classification accuracy exceeds 99.5%, surpassing the performance of current side-channel disassemblers while being the first to achieve complete disassembly of the ATmega328P's AVR instruction set. TSD is then extended for use as an embedded integrity monitor (TSD-IM), using One-Class Support Vector Machine with Stochastic Gradient Descent (OCSVM-SGD), resulting in perfect discrimination between normal and anomalous cases, for a notional Point of Sale (PoS) application. TSD and TSD-IM advance state of the art for side-channel disassembly and embedded security monitoring research in open literature. Design and implementation details are provided to support future work and reproducibility within the community.
A Method for Estimating Time to Collision from Local Expansion Rate of Moving Images Obtained Directly Using the Spatio-Temporal Differentiation

This study attempts to estimate Time To Collision (TTC), the time between camera and obstacle collisions, using only a monocular camera. The TTC can be calculated from the expansion rate of the moving image. The spatio-temporal differential method was used to estimate the expansion rate of the video image. It is assumed that a part of the moving image holds a local model of the velocity field and that the motion parameters within this locality are constant. Instead of estimating the expansion rate via the velocity vectors, the expansion rate was estimated directly from the image by assuming the local model that takes the expansion rate into account. Experimental results showed the estimated TTC decreased on average by about 1.25 s per 1 s decrease in the actual TTC when the actual TTC was between 2 s and 10 s, although there is an offset between the actual TTC and the estimated TTC.
Machine Learning Based Action Recognition with Modular CNN

When building models for action recognition, 3D convolutional neural networks (CNNs) are commonly used. However, 3D CNNs also increase the model parameters significantly. We propose two methods, image segmentation and dynamic sampling learning to reduce network parameters and required memory access. Using image segmentation to keep the location of the action and remove the background of each image reduces the size of the feature map. Dynamic sampling learning allows the model to learn from low sampling rates without adding additional parameters, and to maintain performance while reducing the number of images. In order to implement the overall model in hardware for edge devices, we limit the kernel sizes of the 2D convolution layers and 3D convolution layers in the model to only 3x3 and 3x3x3 respectively. We perform experiments on HMDB51 [1] and UCF101 [2] datasets respectively with our proposed model. The accuracy of our proposed method achieve 7.2% and 5.9% reduction compared with DS-GRU2021 [3]. However, the number of parameters of our model is 30% fewer and execution speed x180 faster than DS-GRU2021 [3].
A Transformer-Based Framework for Tiny Object Detection

This paper proposes a fully transformer-based method for building an end-to-end model dedicated to tiny object detection. Our approach eliminates the components which are difficult to be designed in detecting tiny objects, such as anchor generation and non-maximum suppression. Additionally, we address the issue of receptive fields for tiny objects in convolutional neural networks through self-attention. The model named Swin-Deformable DEtection TRansformer (SD DETR) integrates Swin Transformer [1] and Deformable DETR [2]. Furthermore, we have introduced architectural enhancements and optimized the loss function to improve the model’s ability in detecting tiny objects. Experimental results on the AI-TOD [3] dataset demonstrate that SD DETR achieves 10.9 AP for very tiny objects with only 2 to 4 pixels, showcasing a significant improvement of +1.2 AP compared to the current state-of-the-art model. The code is available at https://github.com/kai271828/SD-DERT
A Novel LRKS-WSQoS Model for Web Service Quality Estimation Using Machine Learning-Based Linear Regression and Kappa Methods

Web services are used as the building blocks for IT applications. There are several competing elements to consider when choosing a reliable Internet company. The goal is to divide potential services into several groups based on end users’ preferences and taking into account each service’s distinctive qualities. Our method will look at service characters in terms of highest quality using the kappa statistics value. The kappa statistic methodology is the most effective machine learning technique for evaluating service quality while taking into account a variety of quality attribute values, commonly known as QoS characteristics. For each and every service, our method calculates the classification accuracy score. The kappa static value (KV), a parametric value obtained using a non-leaner model, is used to evaluate a service’s performance using a logistic regression-based accuracy model. The accuracy of each service’s balance is then assessed.
Application of InceptionV3, SqueezeNet, and VGG16 Convoluted Neural Networks in the Image Classification of Oral Squamous Cell Carcinoma: A Cross-Sectional Study

Background Artificial intelligence (AI) is a rapidly emerging field in medicine and has applications in diagnostics, therapeutics, and prognostication in various malignancies. The present study was conducted to analyze and compare the accuracy of three deep learning neural networks for oral squamous cell carcinoma (OSCC) images. Materials and methods Three hundred and twenty-five cases of OSCC were included and graded histologically by two grading systems. The images were then analyzed using the Orange data mining tool. Three neural networks, viz., InceptionV3, SqueezeNet, and VGG16, were used for further analysis and classification. Positive predictive value, negative predictive value, specificity, sensitivity, area under curve (AUC), and accuracy were estimated for each neural network. Results Histological grading by Bryne's yielded significantly stronger inter-observer agreement. The highest accuracy was found for the classification of poorly differentiated squamous cell carcinoma images irrespective of the network used. Other values were variegated. Conclusion AI could serve as an adjunct for improvement in theragnostics. Further research is required to achieve the modification of mining tools for greater predictive values, sensitivity, specificity, AUC, accuracy, and security. Bryne's grading system is warranted for the better application of AI in OSCC image analytics.