Reviewerly/exHarmony huggingface dataset:
Noniterative Generalized Camera Model for Near-Central Camera System

This paper proposes a near-central camera model and its solution approach. 'Near-central' refers to cases in which the rays do not converge to a point and do not have severely arbitrary directions (non-central cases). Conventional calibration methods are difficult to apply in such cases. Although the generalized camera model can be applied, dense observation points are required for accurate calibration. Moreover, this approach is computationally expensive in the iterative projection framework. We developed a noniterative ray correction method based on sparse observation points to address this problem. First, we established a smoothed three-dimensional (3D) residual framework using a backbone to avoid using the iterative framework. Second, we interpolated the residual by applying local inverse distance weighting on the nearest neighbor of a given point. Specifically, we prevented excessive computation and the deterioration in accuracy that may occur in inverse projection through the 3D smoothed residual vectors. Moreover, the 3D vectors can represent the ray directions more accurately than the 2D entities. Synthetic experiments show that the proposed method can achieve prompt and accurate calibration. The depth error is reduced by approximately 63% in the bumpy shield dataset, and the proposed approach is noted to be two digits faster than the iterative methods.
Unsupervised domain adaptation for semantic segmentation via cross-region alignment

Semantic segmentation requires a lot of training data, which necessitates costly annotation. There have been many studies on unsupervised domain adaptation (UDA) from one domain to another, e.g., from computer graphics to real images. However, there is still a gap in accuracy between UDA and supervised training on native domain data. It is arguably attributable to the class-level misalignment between the source and target domain data. To cope with this, we propose a method that applies adversarial training to align two feature distributions in the target domain. It uses a self-training framework to split the image into two regions (i.e., trusted and untrusted), which form two distributions to align in the feature space. We term this approach cross-region adaptation (CRA) to distinguish it from the previous methods of aligning different domain distributions, which we call cross-domain adaptation (CDA). CRA can be applied after any CDA method. Experimental results show that this always improves the accuracy of the combined CDA method.
Searchable encryption over encrypted speech retrieval scheme in cloud storage

Aiming at the privacy and security issues of sensitive speech data in cloud storage, and how to achieve efficient retrieval with privacy-preserving for encrypted speech data in cloud storage, a searchable encryption over encrypted speech retrieval scheme in cloud storage was proposed. Firstly, the data owner encrypts the original speech data adopting symmetric encryption algorithm Lorenz chaotic mapping. Secondly, the encrypted speech data is uploaded to the cloud server for storage, and meanwhile the Mel frequency cepstrum coefficient (MFCC) features of original speech are extracted as the input of the convolutional neural network (CNN) to execute the deep semantic feature extraction, which is taken as the speech keywords. Finally, the extracted speech keywords are encrypted and stored in the cloud. When the authorized user sends a retrieval request, the trained CNN is utilized to extract the keywords of the speech to be retrieved, and the search trapdoor is generated and sent to the cloud server. In the process of retrieval, the Euclidean distance is used to match the encrypted keywords with the search trapdoor. The theoretical analysis and experimental results show that the proposed scheme has higher security and retrieval accuracy, and can be suitable for the encrypted storage of speech data and efficient secure retrieval.
Model Fitness and Predictive Accuracy in Linear Mixed-Effects Models with Latent Clusters

In clustered data, observations within a cluster show similarity between themselves because they share common features different from observations in the other clusters. In a given population, different clustering may surface because correlation may occur across more than one dimension. The existing multilevel analysis techniques of the primal linear mixed-effect models are limited to natural clusters which are often not realistic to capture in real-life situations. Therefore, this paper proposes dual linear mixed models (DLMMs) for modeling unobserved latent clusters when such are present in data sets to yield appreciable gains in model fitness and predictive accuracy. The methodology explored the development and analysis of the dual linear mixed models (DLMMs) based on the derived latent clusters from the natural clusters using multivariate cluster analysis. A published data set on political analysis was used to demonstrate the efficiency of the proposed models. The proposed DLMMs have yielded minimum values of the models' assessment criteria (Akaike information criterion, Bayesian information criterion, and root mean squared error), and hence, outperformed the classical PLMMs in terms of model fitness and predictive accuracy.
Recognition Method for Electronic Component Signals Based on LR-SMOTE and Improved Random Forest Algorithm

&lt;div&gt;Loose particles are a major problem affecting the performance and safety of aerospace electronic components. The current particle impact noise detection (PIND) method used in these components suffers from two main issues: data collection imbalance and unstable machine-learning-based recognition models that lead to redundant signal misclassification and reduced detection accuracy. To address these issues, we propose a signal identification method using the limited random synthetic minority oversampling technique (LR-SMOTE) for unbalanced data processing and an optimized random forest (RF) algorithm to detect loose particles. LR-SMOTE expands the generation space beyond the original SMOTE oversampling algorithm, generating more representative data for underrepresented classes. We then use an RF optimization algorithm based on the correlation measure to identify loose particle signals in balanced data. Our experimental results demonstrate that the LR-SMOTE algorithm has a better data balancing effect than SMOTE, and our optimized RF algorithm achieves an accuracy of over 96% for identifying loose particle signals. The proposed method can also be popularized in the field of loose particle detection for large-scale sealing equipment and other various areas of fault diagnosis based on sound signals.&lt;/div&gt;
SpaceX Mag

Low-vision users interact with smartphones via screen magnifiers, which uniformly magnify raw screen pixels, including whitespace and user interface (UI) elements. Screen magnifiers thus occlude important contextual information, such as visual cues, from the user's viewport. This requires low-vision users to pan over the occluded portions and mentally reconstruct the context, which is cumbersome, tiring, and mentally demanding. Prior work aimed to address these usability issues with screen magnifiers by optimizing the representation of UI elements suitable for low-vision users or by magnifying whitespace and non-whitespace content (e.g., text, graphics, borders) differently. This paper combines both techniques and presents SpaceXMag, an optimization framework that automatically reduces whitespace within a smartphone app, thereby packing more information within the current magnification viewport. A study with 11 low-vision users indicates that, with a traditional screen magnifier, the space-optimized UI is more usable and saves at least 28.13% time for overview tasks and 42.89% time for target acquisition tasks, compared to the original, unoptimized UI of the same app. Furthermore, our framework is scalable, fast, and automatable. For example, on a public dataset containing 16, 566 screenshots of different Android apps, it saves approximately 47.17% of the space (area) on average, with a mean runtime of around 1.44 seconds, without requiring any human input. All are indicative of the promise and potential of SpaceXMag for low-vision screen magnifier users.
Multi-Range Mixed Graph Convolution Network for Skeleton-Based Action Recognition

Skeleton-based action recognition is a long-standing task in computer vision which aims to distinguish different human actions by identifying their unique characteristic patterns in the input data. Most of the existing Graph Convolutional Network (GCN) models developed for this task primarily model the skeleton graph as either directed or undirected. Furthermore, these models also restrict the receptive field in the temporal domain to a fixed range which significantly inhibits their expressive power. Therefore, we propose a mixed graph network comprising both directed and undirected graph networks with a multi-range temporal module called MMGCN. In this way, the model can benefit from the different interpretations of the same action by the different graphs. Moreover, the multi-range temporal module enhances the model's expressive power as it can choose the appropriate receptive field for each layer, thus allowing the model to dynamically adapt to the input data. With this lightweight MMGCN model, we further show that deep learning models can learn the underlying patterns in the data and model large receptive fields without additional semantics or high model complexity. Finally, this model achieved state-of-the-art results on three benchmark datasets namely NTU RGB+D, NTU RGB+D 120 and Northwestern-UCLA despite its low model complexity thus proving its effectiveness.
SmartASL

Sign language builds up an important bridge between the d/Deaf and hard-of-hearing (DHH) and hearing people. Regrettably, most hearing people face challenges in comprehending sign language, necessitating sign language translation. However, state-of-the-art wearable-based techniques mainly concentrate on recognizing manual markers (e.g., hand gestures), while frequently overlooking non-manual markers, such as negative head shaking, question markers, and mouthing. This oversight results in the loss of substantial grammatical and semantic information in sign language. To address this limitation, we introduce SmartASL, a novel proof-of-concept system that can 1) recognize both manual and non-manual markers simultaneously using a combination of earbuds and a wrist-worn IMU, and 2) translate the recognized American Sign Language (ASL) glosses into spoken language. Our experiments demonstrate the SmartASL system's significant potential to accurately recognize the manual and non-manual markers in ASL, effectively bridging the communication gaps between ASL signers and hearing people using commercially available devices.
Contributing Factors in Electronic Signature Misuse: A Case Study of Software Engineering Students

The use of electronic signatures was still dominated by a scan of a written signature non-certified signatures onto an image attached to a document. It causes the potential for misuse of electronic signatures to persist. Opportunities to misuse the electronic signatures were still possible, so organizations need to understand factors can encourage individual behavior to abuse electronic signatures. As the largest population within the university environment, students became the biggest risk holder for the misuse of electronic signatures. This research was motivated to reveal the contributing factors that may trigger students to misuse the electronic signatures. This research aims to identify the factors that contribute to the misuse of electronic signatures through these variables: Punishment Severity and Certainty (PSC), Subject Norm (SNO), Cybersecurity Skill (CYS), Attitude (ATT), Social Desirability Pressure (SDP), and Technical Control (TCO). This research performed the data collection in Software Engineering major in a university using 25 research instruments in five points of Likert-scale. After collecting 81 students, this research found Cyber Security Skill (CYS) and Punishment Severity and Certainty (PSC) as factors contributing to electronic signature misuse, while other variables were not proven statistically. Since PSC has proven as inhibiting factors, this research claimed that the existence of sanctions is sufficient to frighten students into not abusing the lecturer's electronic signature.
Research on web service composition selection based on QoS metrics

Aiming at the hot and difficult issues in Web services and service composition, this paper analyzes and studies the selection of Web services and service composition from the functional and non functional attributes of Web services, and mainly makes the following work and innovation. First of all, the functional attributes of Web services are studied, Web services on various service provision platforms are collected, and Web services are classified. Secondly, aiming at the shortcomings of existing Web service QoS attribute evaluation models, an improved method is proposed, which introduces a comprehensive evaluation mechanism of variable weight vector. Based on the constant weight comprehensive evaluation method, the state variable weight vector is established to dynamically adjust the attribute weights of various service QoS indicators, so as to improve the accuracy and objectivity of the Web service QoS attribute evaluation. Finally, in view of the defects and deficiencies of traditional algorithms in the selection of Web service composition, the particle swarm optimization algorithm with linearly decreasing inertia weight and learning factor is introduced, which better balances the self cognition and social learning ability of particles, and improves the search speed and global search ability of particles. And a large number of experiments are carried out to compare it with the traditional algorithm, which verifies the effectiveness and superiority of the improved particle swarm optimization algorithm.
Sentiment Analysis of Twitter Data on the Tourism Industry During the Covid-19 Pandemic

In most countries, the tourist sector becomes one of the significant factors for economic change. When it comes to Sri Lanka, the country's economy has grown primarily due to the tourism sector. Recently, the Covid-19 pandemic phase has had a significant negative impact on Sri Lanka's tourism industry. If we could recognize these problems, we would be able to prepare the tourism sector for future pandemics and ensure its stability. Studying peoples' opinions is crucial for taking the necessary measures, and Twitter is one of the greatest places to do this. This study suggests a model for categorizing tweets about the tourism sector during Covid-19 in Sri Lanka into four categories: positive, negative, commercial, and neutral. For this study, take into account 18980 tweets that were gathered on Twitter between the 2020 and 2022 time period. After selecting the 6257 tweets by prepossessing, the study extracts the feature vectors and result compares them using three different machine learning algorithms, including Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Artificial Neural Networks (ANN). These results in a forecast paradigm for sentiment analysis of the movement of the tourism industry. To build a model, 67% of training data sets and 33% of testing data sets were used. The findings showed that LSTM, ANN and SVM accuracy was 85%, 91%, and 76% respectively. Additionally, the study takes into account the evaluation results of precision, recall, f-measure, and error values. The ANN algorithm provides the greatest results for sentiment analysis for monitoring Sri Lanka's tourism industry movements, according to the final results.
Stack Data Protection Mechanism for LLVM Intermediate Representation

Stack overflow attack has long history. Recently, it has spawned a number of senior stack overflow ways, such as Return Oriented Programming (ROP). The traditional way can defend against most stack overflow attacks, but cannot protect against this advanced ROP stack overflow attacks, so it is necessary to implement stack data protection against ROP attacks. The work of this paper is to realize the protection of stack overflow attacks (including ROP and other senior stack overflow attacks), our experiments are deployed in LLVM framework. We write a pass which instruments the intermediate represention to split the stack into the safe stack and the unsafe stack. stack into the safe stack and the unsafe stack and the critical data in stack is protected, effectively prevent the destruction of non-control data, can play a certain degree of memory data privacy protection.
Fair Contract Signing Model Based on Blockchain and VES Algorithm

The Verifiably Encrypted Signature (VES) algorithm is a common method for solving the fair exchange of digital signatures. It uses an offline trusted third party (TTP) as an adjudicator, playing a role similar to a guarantor. However, the fairness of signature exchange in traditional methods depends on the neutrality of the adjudicator, and there is a possibility that the adjudicator and a party may collude to commit fraud. To address this issue, this paper designs a fair signature exchange scheme without the TTP based on blockchain and VES algorithm, thus solving the problem of the trust crisis with adjudicator. At the same time, current blockchain smart contract do not support the generation and verification of the VES. As a result, this paper introduces the blockchain oracle to delegate these operations to off-chain execution. Finally, we achieve a closed-loop solution for a Fair Contract Signing model, this scheme utilizes smart contracts and blockchain oracle to complete all aspects of digital signature exchange.
Platform-agnostic Model to Detect Sinophobia on Social Media

Although the boom of social media in the past decade has enabled the creation, distribution, and consumption of information at a remarkable rate, it has also led to the growth of different forms of online abuse. Since the outbreak of COVID-19, hate against Chinese or Sinophobia has increased significantly in real world as well as on online platforms making it necessary to design ways to combat it. In this paper, we design a platform-agnostic model to detect Sinophobic content on social media websites automatically. We use pre-trained word embeddings with several machine learning classifiers to detect Sinophobia on three platforms---Parler, Reddit, and Twitter. Our results demonstrate that the BERT model shows the best performance among all the models by achieving an accuracy of 98.51% on Parler, 95.36% on Reddit, and 88.12% on Twitter datasets.
A Survey on Conversational Search and Applications in Biomedicine

This paper aims to provide a radical rundown on Conversational Search (ConvSearch), an approach to enhance the information retrieval (IR) method where users engage in a dialogue for the information-seeking tasks. In this survey, we predominantly focused on the human interactive characteristics of the ConvSearch systems, highlighting the operations of the action modules, likely the retrieval system, question-answering, and recommender system. We labeled various ConvSearch research problems in knowledge bases, natural language processing, and dialogue management systems with action modules. We further categorized the framework to ConvSearch, and the application is directed toward biomedical and healthcare fields for the utilization of clinical social technology. Finally, we conclude by talking through the challenges and issues of ConvSearch, particularly in Bio-Medicine. Our main aim is to provide an integrated and unified vision of the ConvSearch components from different fields, which benefit the information-seeking process in healthcare systems.
Fine-tuning XLM-R for the Detection of Sinhala Hate Speech Content on Twitter and Youtube

Hate speech disseminated through social media platforms triggered multiple intense incidents in Sri Lanka during the past few years. Regardless of the gravity of these incidents, existing policies and automated algorithms used by social media platforms failed to detect hate speech content in the Sinhala language. Further, only a few studies had been conducted to detect Sinhala hate speech due to the limited datasets available. In this paper, a new hate speech dataset consisting of 4168 annotated text data samples extracted from Twitter and Youtube platforms has been introduced. Further, five XLM-R <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">base</inf> based fine-tuning strategies have been evaluated for Sinhala hate speech classification using the newly introduced Twitter-Youtube dataset and publicly available Kaggle dataset. XLMR <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">base</inf> model fine-tuned with bidirectional RNN layer has outperformed all the other fine-tuning strategies for both datasets. Further, XLM-R <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">base</inf> -RNN models have achieved a 91.2 F1-score for the Kaggle dataset and an 88.3 F1-score TwitterYoutube dataset.
Twitter Sentiment Analysis and Topic Modeling for Online Learning

The COVID-19 pandemic has caused entirely new transitions in the worldwide higher education sectors. Online education has gained popularity due to its ability to provide more flexible access to information and teaching at any time and from any location. Students and faculty members can connect to their institution’s online portals and engage in virtual educational activities through the use of live environments provided by electronic learning management systems. Despite the fact that modern technology actively facilitates these online sessions, students’ active involvement remains a difficulty that has been addressed in prior studies. Using the Valence Aware Dictionary for Sentiment Reasoning (VADER) model, the sentiment of roughly 220,000 tweets was calculated to identify people’s opinions toward online learning by using sentiments as positive, negative, and neutral. The topic modeling was utilized to uncover some hidden themes and to define the narrative and direction of the online learning related topics. Topic modeling also helped to detect inter-cluster similar terms and assess the flow of information from a group of similar perspectives. In this study, one text feature extraction TF-IDF with LDA model was utilized for topic modeling and an optimum number of topics were extracted from the corpus that are mostly addressed in the tweets. Finally, a cutting-edge deep learning and machine learning classification model was applied to the dataset with varying epoch sizes to predict and classify the emotions of the participants and proposed a new machine learning approach to evaluate the dataset related to online learning. Multiple parameters, such as accuracy, validation loss, validation accuracy, etc., have been used to evaluate the deep learning model.