Reviewerly/exHarmony huggingface dataset:
On Enhancing the Performance of Inter-Cloud Data Transfers

The research presented in this paper concerns the transfer of data in the form of requests from a compute cloud to a storage cloud using a buffer at the compute system. Different buffering techniques from literature have been shown to improve performance either when the system is subjected to a high load or a low load, but not both. This paper discusses the performance of a threshold based and a machine learning based adaptive data transfer methods that dynamically change the buffering technique at runtime to offer an improvement in performance at all loads. The performance results of a proof-of-concept prototype are discussed for both the threshold based and machine learning based adaptive methods.
Enhancing Out-of-Domain Detection for Speech Spoofing Countermeasure via Supervised Contrastive Learning

High-performance anti-spoofing countermeasures (CMs) have been widely used to protect automatic speaker verification systems by identifying and filtering spoofing speech. However, their performance degrades severely when confronted with out-of-domain (OOD) samples. To solve this issue, recent papers investigate the strategy that a CM can opt for abstention when it is not confident about the decision. In this paper, we develop an effective approach to enhance the performance of CMs with abstention. Specifically, we introduce supervised contrastive learning to group samples into known classes more tightly and employ data augmentation to enhance the diversity of known samples. The experiments are conducted on ASV spoof 2019 logical access corpus and another test set consisting of different OOD samples from other databases. With our scheme, CMs with abstention can achieve an equal error rate (EER) of 1.84 % on the LA test set and 0.86 % on the other test set. These results demonstrate that CMs trained with our approach show better OOD detection performance and can make more confident decisions.
Machine Learning-Based Context Space Theory

Situation awareness of human and artificial agents can be improved by the recognition and adequate representation of real-life situations. The lack of easy-understandable, easy-to-use, and effective computational models of situations hindered the adoption and diffusion of situation awareness approaches in modern human-machine systems. Context Space Theory is a context awareness approach that uses a geometric metaphor to provide integrated mechanisms for representing contexts and situations. A drawback of this approach is the expert-based definition of context and situation spaces. This process can be time-consuming and expensive. In this paper, we propose a novel approach, namely Machine Learning-based Context Space Theory, which adopts machine learning techniques and, in particular, decision trees, to semi-automatically define context spaces and situation spaces with a data-driven approach. A case study related to the monitoring and control of the Covid-19 pandemic in Italy is proposed to demonstrate the feasibility and benefits of the proposed approach.
CFS: Character Feature Summarization Model for Real-time End-to-end Text Spotting

Most real-time end-to-end text spotting methods employ sequence models as their recognition heads. However, these models generate characters one by one, which is inefficient when there are many characters. To solve this problem, we propose a Character Feature Summarization (CFS) Model, which can predict fixed-length characters in parallel, regardless of length. Specifically, we propose a Character Feature Summarization Module (CFSM) consisting of a Global Feature Capture and a Historical Feature Summarizer to extract and summarize global character features, enabling getting characters by simple linear prediction. We use Multi-stage Testing, cascading multiple CFSMs to obtain multi-stage summarized global character features to obtain several predictions for better convergence. The Result Selector is used to select the most likely result. Experiments on the Total-Text dataset show that CFS achieves a 3.53% improvement on the "Full" while being 3.6 times faster than ABCNet v2’s head.
Client-based differential privacy federated learning

Deep learning provides better personalized services by training specific models through massive amounts of data. However, due to the problem of gradient leakage during model training, the original data uploaded by the users is restored and privacy leakage occurs. In order to prevent data leakage, this paper introduces a federated learning method to deal with the privacy issues brought by multi-user joint modeling. Gradients generated by the user’s local model training are uploaded to the aggregation server without being trained directly using the original user data. Under such a framework setting, the users’ original data still has a certain risk of being leaked. In order to strengthen the protection of users’ privacy, the training process is encrypted by combining the differential privacy mechanism and the federated learning system. The model parameters are stochastic to ensure that they cannot be acquired by adversaries. By adding Gaussian mechanism and Laplace mechanism, the influence of differential privacy on the convergence of federated learning model is analyzed. The Laplace mechanism is a strict definition of differential privacy, while the Gaussian mechanism is a relaxed definition and allows adding less noise for privacy protection. The simulation results show that both mechanisms can achieve good model convergence effect and verify that differential privacy can produce better privacy protection effect with lower communication cost.
Mitigating Forgetting in Continual Learning via Contrasting Semantically Distinct Augmentations

Online continual learning (OCL) aims to enable model learning from a non-stationary data stream to continuously acquire new knowledge as well as retain the learnt one. Under the constraints of having limited system size and computational cost, in which the main challenge comes from the “catastrophic forgetting” issue - the inability to well remember the learnt knowledge while learning the new ones. With the specific focus on the class-incremental OCL scenario, i.e. OCL for classification, the recent advance incorporates the contrastive learning technologies for learning more generalised feature representation to achieve the state-of-the-art performance but is still unable to fully resolve the catastrophic forgetting. In this paper, we follow the strategy of adopting contrastive learning but further introduce the semantically distinct augmentation technique, in which it leverages strong augmentation to generate more data samples, and we show that considering these samples semantically different from their original classes (thus being related to the out-of-distribution samples) in the contrastive learning mechanisms contributes to alleviate forgetting and facilitate model stability. Moreover, in addition to contrastive learning, the typical classification mechanism and objective (i.e. softmax classifier and cross-entropy loss) are included in our model design for utilising the label information, but particularly equipped with a sampling strategy to tackle the tendency of favouring the new classes (i.e. model bias towards the recently learnt classes). Upon conducting extensive experiments on CIFAR-10, CIFAR-100, and Mini-Imagenet datasets, our proposed method is shown to achieve superior performance against various baselines.
Air Target Threat Assessment Method Based on Variable Weight Cloud Bayesian Network

In this paper, an air Air target threat assessment method based on a variable weight cloud Bayesian network (VWCBN) is proposed, which addresses the qualitative issue of air target threat levels, as most of the existing threat assessment results in focus on quantitative analysis. The proposed method enables high, medium, and low qualitative decision-making for air target threat levels. Firstly, a Bayesian network model that incorporates the attribute of air threat is constructed, assessing the threat level of air targets. Secondly, the cloud model is introduced to the Bayesian network, using it to represent the probability of correlation between nodes in the network. Then, by combining the battlefield situation information, using an improved variable weight method with Gaussian expression, the weights of target attributes are determined. Finally, based on the correlation probability and target attribute weight, the cloud model operation rules are utilized to obtain the decision of the air target threat level. Simulation results demonstrate that the proposed VWCBN method can effectively assess target threats, obtain air target threat level decisions, and further improve the utilization of battlefield information.
FlyTransformer: A Cross-Modal Fusion Policy for UAV End-to-End Trajectory Planning

The ability to perform efficient trajectory planning is crucial for UAV to carry out tasks autonomously. However, existing research on UAV trajectory planning often employs the cascade process method that involves high-precision maps, real-time positioning and path planning. These methods have limitations such as high computational complexity and time delay, which hinder the efficiency of trajectory planning. End-to-end trajectory planning methods offer a promising solution to this problem. As the core of these end-to-end methods, perception-end plays a decisive role in trajectory planning. But current multimodal fusion of perception is only post-fusion, lacks intermediate feature-level fusion and lacks attention to global visuospatial information. To solve these problems, we propose a new network architecture called FlyTransformer, which fuses the proprioceptive state and visual perception in feature-level for end-to-end trajectory planning. And the key visuospatial information can be attentioned in this architecture. We evaluate our method in forest and cuboid scenarios and their corresponding outdoor scenarios. The results show that FlyTransformer outperforms other baseline algorithms in terms of efficiency and performance.
Exploring a Self-Attentive Multilayer Cross-Stacking Fusion Model for Video Salient Object Detection

As an effective measure to capture the object of interest in video sequence, video salient object detection (VSOD) requires the processing of information from spatial-motion modalities, although plenty of traditional VSOD models were dedicated to developing efficient spatial and motion features to obtain salient objects of global consistency, the highly redundant spatial information brought by consecutive identical objects will inevitably reduce the generalization ability of these VSOD model. Although exploring the integration of spatial and motion information can improve the inter-frame correlation of salient objects to some extent, previous models tend to focus only on simple spatio-temporal fusion, which can also lead to the generation of redundant information, resulting in poor detection performance. Therefore, it is necessary to focus on effectively fusing the feature information of different modalities to eliminate the effect of redundant information. In this research, we proposed a self-attentive multilayer cross-stacking fusion based VSOD model, which productively extracts the multimodal features for two-way information transfer, fully utilizes the spatial and temporal knowledge to complement each other, and refines the cross-stacking of the interacted information and spatial features for local and global saliency optimization. As a result, the redundant spatial information can be largely eliminated, reducing the misidentification of salient objects due to blurred backgrounds or moving objects, and adaptively activating more weights of the salient object to achieve globally consistent saliency. Comprehensive experiments on four publicly available VSOD datasets demonstrated that the model had superior performance compared to the latest multiple VSOD models.
Research Advanced in 2D Multi-Object Tracking based on Deep Learning

Multi-object tracking is one of the most important research orientation of computer vision, which has a broad applications in the fields of sports game data analysis, video monitoring, pedestrian behavior analysis, and automatic driving. With the rapid-developed convolutional neural network, deep learning-based multi-object tracking methods have made significant progress. In this research, the current multi-object tracking methods are classified, analyzed and summarized through comprehensive and thorough research. Firstly, the definition of multi-object tracking, research background and application advantages of deep learning are introduced. Next, 2D multi-object tracking and 3D multi-object tracking as well as their differences are briefly introduced. Then, different methods in 2D multi-object tracking are explored in detail. The commonly used evaluation metrics and datasets are also introduced, as well as the comparison of the experimental results of different methods on the datasets. Finally, the advantages and disadvantages of multi-object tracking methods are analyzed and the future development direction is prospected. The development of multi-object tracking is of great significance for enhancing social security as well as promoting technological progress.
Weighted Edge Sharpening Filtering for Upscaled Content in Adaptive Resolution Coding

Reference Picture Resampling (RPR) is an essential tool for quickly adapting to varying network conditions. It enables a resolution change without the introduction of an intra random access point (IRAP). This feature is particularly crucial in real-time transmission scenarios and has proven effective for compressing high-resolution pictures. However, a significant challenge of Reference Picture Resampling is the loss of high-frequency information in the downscaling operation. This results in blurred pictures after upscaling, which significantly affects the viewer experience. To address this issue, we propose an additional enhancement step after upscaling. The proposed enhancement method involves a locally weighted adaptive filter specifically designed for edge sharpening. Through the application of local weighting, we can avoid the common problems associated with linear high-pass filters. Picture-wise content adaptivity helps in handling different scene characteristics. The proposed method is implemented into the enhanced compression model 8.0 (ECM) and achieves performance gains for the joint video experts team (JVET) common testing conditions for reference picture resampling (RPR), with a Bjøntegaard delta-rate (BD-rate) reduction of -7.27%, -0.57%, and -0.32% for the Y, Cb, and Cr channels, respectively.
A Study on the Development of a Teaching–Learning Model for Good Listening Using Scenario-Based Education in Korean Language Education

Communication competency is an important core competency in a future society. To develop communication competency, an attitude of listening to others is necessary, and this importance should be further emphasized. A literature review was conducted to identify problems in existing listening education for good listeners. As a result, the problems of Korean language and listening education were as follows: 1) absence of a listening practice model, 2) imbalance in role rotation for mutual transaction, 3) instrumental perspective for problem-solving, and 4) lack of specific situational context. To complement these problems, a scenario-based listening teaching and learning model that emphasizes situational context is proposed. The design principle is as follows: 1) providing a teaching and learning environment where listening can occur, 2) providing scenarios containing specific situational contexts, 3) selecting the degree of self-disclosure for learners to immerse themselves in the situation, 4) analyzing and reflecting on the discourse text, and 5) learner activities and presenting feedback from the instructor who observed and investigated.
Uso de la Inteligencia Artificial para la traducción de lenguajes de señas: una revisión sistemática de literatura

Introduction: in this systematic literature review, the use of artificial intelligence in sign language translation for people with hearing and speech loss was analyzed. This review aims to identify the results of the application of artificial intelligence to sign language translation. Method: 462 articles, original and conference papers in SCOPUS, until June 2023, and relying on a selection process based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, of which 26 studies met exclusion and inclusion criteria. Convolutional Neural Network (CNN) was the most widely implemented machine learning technique in the selected studies. Results: Many sign language systems were tested with various artificial intelligence algorithms and datasets on different continents to create new models and improve sign language translation accuracy. An increasing use of neural networks to achieve better sign language translation efficiency was identified, achieving results ranging from 90 % to 100 % accuracy. Conclusions: The application of artificial intelligence has greatly excelled in the field of Computer Science and has significantly improved the accuracy of sign language translation which has led to lower communication barriers between natural persons and persons with communication disabilities.
A two-branch remote sensing image dehazing network based on hierarchical feature interaction and enhanced receptive field

è¿å¹´æ¥ï¼æ·±åº¦å­¦ä¹ çå»é¾æ¹æ³å¨å¾åå»é¾é¢ååå¾äºæ¾èçæç»©ãç¶èï¼å¤§å¤æ°åºäºUåç½ç»çå»é¾æ¹æ³å°ç¼ç å±ç¹å¾ç´æ¥ä¼ éå°å¯¹åºè§£ç å±ï¼ç¼ºä¹æµ å±åæ·±å±ç¹å¾ä¹é´çä¿¡æ¯äº¤äºãæ­¤å¤ï¼åºäºéUåç½ç»çå»é¾æ¹æ³å­å¨æåéåéé®é¢ï¼æ æ³ææçå©ç¨ä¸ä¸æä¿¡æ¯ãä»èå¯¼è´è¿äºæ¹æ³å¨åºæ¯å°ºåº¦ååè¾å¤§çé¥æå¾åå»é¾ä¸­æ æ³åå¾çæ³ææãä¸ºæ­¤ï¼æ¬ææåºäºä¸ç§å±çº§ç¹å¾äº¤äºä¸å¢å¼ºæåéçååæ¯é¥æå¾åå»é¾ç½ç»ï¼è¯¥æ¹æ³å å«å±çº§ç¹å¾äº¤äºå­ç½åå¤å°ºåº¦ä¿¡æ¯æåå­ç½ãå ¶ä¸­ï¼å±çº§ç¹å¾äº¤äºå­ç½å©ç¨å±çº§ç¹å¾äº¤äºèåæ¨¡åï¼éå±çå¨æµ å±ç¹å¾ä¸­å¼å ¥è¯­ä¹ä¿¡æ¯ï¼æ·±å±ç¹å¾ä¸­å¼å ¥ç©ºé´ç»èä¿¡æ¯ï¼ä»èå¢å¼ºç¼ç å±ä¸­ä¸åå±çº§ç¹å¾ä¹é´çä¿¡æ¯äº¤äºãå¤å°ºåº¦ä¿¡æ¯æåå­ç½å©ç¨å¤å°ºåº¦æ®å·®ç©ºæ´å·ç§¯æ¨¡åï¼èåä¸åæåéçç¹å¾ï¼ä»èè·åå¯¹äºé¥æå¾åå»é¾è³å ³éè¦çä¸ä¸æä¿¡æ¯ãå¨ä¸¤ä¸ªå ¬å¼æ°æ®éä¸çå®éªç»æè¡¨æï¼æ¬ææåºçå»é¾æ¹æ³ç¸æ¯ç°æç9ç§ä¼ç§çå»é¾ç®æ³ï¼åå¾äºæå¥½çå®¢è§è¯ä»·ææ åè§è§ææã
Development and Evaluation of a Mobile App to Support Reading Education

We report on the design, development and evaluation of an app to support teachers in the teaching of reading at primary level. The app was developed with two main features: book levelling, where a book photograph, OCR and a simple machine learning model was used to suggest the appropriate reading age; and pedagogical tips, giving the teacher suggestions for how to structure reading sessions with learners. We conducted evaluation with in- and pre-service teachers in three Districts in KwaZulu-Natal using survey and focus group sessions. Participants found the app useful for planning and delivering teaching. The pedagogical advice was more widely used than the reading age prediction function. Barriers to accessibility were encountered in terms of device signal and compatibility. We discuss the outcome of the prototype development and evaluation and how the app might be integrated into digital support environments for literacy educators.
Could large language models estimate valence of words? A small ablation study

Large language models (LLMs) saw substantial development in recent years. Although trained with broad-range corpora, LLMs have been shown to display capabilities such as quantitative sentiment analysis without the need for further fine tuning. In this study, we performed a small ablation study to evaluate the performance of 3 off-the-shelf LLMs in the task of assigning ratings of hedonic valence to words: GPT-3.5 in chat mode, and GPT-3 and Bloom in completion mode. The models were operated via their public APIs, using prompts engineered to request emojis and ratings of valence in a 9-point scale to represent each of 140 words drawn from a large dataset rated by humans. Prompts were designed to demand the ratings from an adult, with modifiers average or overly positive employed to assess their effects on the results. All linear regressions between the LLM outputs and the human ratings had p-value. The 95% confidence intervals of the slopes include 1.0 for “adult” and “average adult”, except for the model Bloom. These simulacra responded, albeit with limitations, tovalenceofwords andtomodifiersintheprompt.
Phylogenetic Inference on Limnonectes kuhlii Complex in Java and Sumatra Reveals Significant Novel Diversity

The fanged frog, Limnonectes kuhlii, exemplifies the complexity of cryptic species. Though originally described from Java, subsequent studies indicate that L. kuhlii encompasses multiple lineages, suggesting the presence of several undescribed species. Suspecting the existence of multiple undescribed species within the L. kuhlii complex in Sumatra, we collected 17 specimens from Java and Sumatra. We extracted the 12S, tRNAval, and 16S mitochondrial DNA from these specimens for phylogenetic analysis using Maximum Likelihood and Bayesian Inference methods and for estimating time divergence. Our findings uncovered two novel diversity within the L. kuhlii complex both in Sumatra and Java. Furthermore, our estimation of time divergence suggests that the diversification of L. kuhlii in Java and Sumatra was influenced by geological and climatic events, including landmass emergence and sea-level fluctuations. In conclusion, our research provides critical insights into the evolutionary complexity and diversity of the L. kuhlii complex in Sumatra and Java. While our molecular evidence suggests novel diversity, further comprehensive morphological studies are imperative to confirm and describe these potential new species. Furthermore, additional sampling across its range and deeper investigations integrating both molecular and morphological data are crucial. Keywords: fanged frogs, Limnonectes kuhlii, Limnonectes sisikdagu, phylogenetics.
Uma Abordagem para Geração e Visualização de Regras de Associação de Acesso a Conteúdos de Portal de Notícias

This paper aims to develop and visually association rules through a data mining project approach, using a data set of history navigation contents of an online news portal of a Brazilian magazine. The rules found allow us to understand users and subscribers access trends sequences through frequent items by applying the Apriori algorithm. To achieve this objective, the concepts of exploratory data analysis – EDA, scientific data visualization, knowledge discovery in databses – KDD and association rules were used. The result is a dataset and and visualization using parallel bar diagrams for the association rules found which bring together the main features of publications to frequently read together in sequence.