Reviewerly/exHarmony huggingface dataset:
Detecting Humans in Search and Rescue Operations Based on Ensemble Learning

Deep learning is a sub field of machine learning that focuses on training artificial neural networks to learn and make predictions or decisions without being explicitly programmed.In this paper, we present a convolutional neural network-based model for the detection of humans in aerial images of mountain landscapes acquired by unmanned aerial vehicles (UAVs) used in search and rescue operations..By using drones in SAR applications, it is desirable to minimize the cost and time spent on SAR operations.In this paper, we present a convolution neural network-based model for the detection of humans in aerial images of mountain landscapes acquired by unmanned aerial vehicles (UAVs) used in search and rescue operations.Detection of humans in aerial images remains a complex task due to various challenges such as pose and scale variations of humans, low visibility, camouflaged environment, adverse weather conditions, motion blur, and high-resolution aerial images.Due to imaging from high altitudes, in most high-resolution aerial images captured by UAVs, only 0.1 to 0.2 percentage of the image represents humans.To solve the problem of low coverage of the object of interest in high resolution aerial images, we propose to implement a deep learning-based object detection model.In this paper, we propose a novel method for the detection of humans in aerial images based on the Efficient DET architecture and ensemble learning.The method has been validated on the HERIDAL image datasets.By implementing the proposed methodologies, we achieved an map of 95:11%.To the best of our knowledge, this is the highest accuracy result for human detection on the HERIDAL datasets.
Mediation Model of Service Quality and Behavioural Intention to Use of Artificial Intelligence Security Technology in UAE

This study created and evaluated a mediation model which allowsthe role of essential artificial intelligence (AI) in mediating the connection between service quality and behavioural intent to use AI security features in the United Arab Emirates.The primary objective is to improve the standards for customer service in the UAE's artificial intelligence security industry. The data to developed the model was derived from 389 valid questionnaires form the questionnaire survey. The data was screened and cleaned before uploaded in Smart-PLS software to developing and assessing the model.Based on the assessment on the model, it was found that the most fundamental form of artificial intelligence exerts a mediating effect to some extent, on the connectionthat exists between service quality and behavioural intention in terms of the application of Al security technology.The coefficient and t-value point to a substantial indirect relationship between the quality of the service and the intention to use artificial intelligence. This relationship is shown to be indirect rather than direct. It is possible to draw the conclusion that improved service quality raises people's likelihood of intending to use AI security technologies. This is due to the fact that the contribution of such technologies to improved job performance, as well as the convenience with which such technologies can be utilised, raises people's awareness of the perceived value of such technologies.
Directive Speech Acts of Teacher and Students in Online Teaching Learning Interactions at SMP Ignatius Global School Palembang

Purpose: This research aimed to discuss the form and function of the directive speech acts of teacher and students in online teaching and learning interactions at SMP Ignatius Global School (IGS) Palembang. Methodology: The method used was descriptive qualitative, and the data source contained directive speech acts of teacher and students in online teaching at SMP IGS. Results: Based on the results, there were forms and functions of teacher and students’ directive speech acts. Furthermore, transactions were found in the discourse of classroom interactions. The forms of teacher directive speech acts in online teaching and learning interactions were requertives, questions, requirements, prohibitives, permissive, and advisories. Meanwhile, the forms of students’ directive speech acts are requertives, questions, and advisories. Limitations: This research is limited to discussing only the directive speech acts of teacher and students in teaching and learning interactions. Contribution: This research can contribute to the interaction of students and teacher regarding the directive speech acts in online teaching and learning interactions.
Analysing the Readability Level of the Short Stories Used in the IEEL Course

The short story as a literary work has been ubiquitously used to teach language and literature. However, not all short stories are appropriate and suitable to be used in the language-learning classroom. One of the aspects that should be considered in choosing short stories to teach language and literature is the readability level. The matching of appropriate text difficulty level to the readers’ reading ability is crucial to inculcate an interest in reading and elicit comprehension. Derived from this rationale, this study aimed to investigate the readability level of short stories used in the Introduction to Educational English Literature Course (IEEL). Moreover, this study also attempted to analyse the students’ perceptions of the short stories assigned to them to read in the IEEL course. In measuring the readability level, document analysis utilizing the Flesch-Kincaid Readability Formula was employed. To strengthen the data and ensure triangulation, 5 participants were purposely selected to be interviewed to explore their perspectives after reading those short stories. The results showed that 3 short stories namely The Birthday Party, The Dead Men’s Path, and Turning Thirty were categorized as standard (63.1), fairly easy (74.4), and easy level (83.7) respectively. Lastly, further inquiries through semi-structured interviews found that the 5 participants were reported to have positive viewpoints after reading those short stories.
Analysis of Early Literacy Practices Based on Social Inclusion in Palembang

Purpose – 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 with four parents and teachers in Palembang, Indonesia, and using a critical theory perspective as the basis for 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 parents and teachers 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/limitations – From an early age, it is important to synergize the roles of families, institutions, and the government in an effort to support the practice of early literacy based on social inclusion. Practical implications – This study has deep implications for realizing the interest in children's literacy in this country. Originality/value – his study contributes in the form of understanding literacy practices in early childhood education. Paper type Research paper
Korea Army’s Value as a Way to Character Education for Military Personnel

Purpose: This paper intends to identify the core values of the Republic of Korea Army (ROKA) and the foundation for character education. The ROKA aims to win the ground war as the central force of national defense.&#x0D; Method: In order to accomplish the research purpose, the researcher critically reviewed the literature related to the Korea Army value and character education including policy reports, army papers, and academic articles.&#x0D; Results: The ROKA is trying to establish a new military culture and value system to responds to changes from the internal and external environment and to educate the so-called MZ generation soldiers who are majority of the military personnel. Value refers to a resource that satisfies an individual's needs in a subjective sense, and a hierarchy occurs according to an individual's tendency or desire. If these values persist for a long time, they will also have a hierarchy at the social level, too. In addition, an organizational culture is formed in the direction of pursuing the values even in the military organizations.&#x0D; Conclusion: In this respect, this paper presents the values that the ROKA pursues, the organizational culture formed around these values, and the directions of character education system to imbue the core values.
Real-Time Detection of Robotic Traffic in Online Advertising

Detecting robotic traffic at scale on online ads needs an approach that is scalable, comprehensive, precise, and can rapidly respond to changing traffic patterns. In this paper we describe SLIDR or SLIce-Level Detection of Robots, a real-time deep neural network model trained with weak supervision to identify invalid clicks on online ads. We ensure fairness across different traffic slices by formulating a convex optimization problem that allows SLIDR to achieve optimal performance on individual traffic slices with a budget on overall false positives. SLIDR has been deployed since 2021 and safeguards advertiser campaigns on Amazon against robots clicking on ads on the e-commerce site. We describe some of the important lessons learned by deploying SLIDR that include guardrails that prevent updates of anomalous models and disaster recovery mechanisms to mitigate or correct decisions made by a faulty model.
Using Google Forms as a tool to review and evaluate English grammar knowledge at Hong Bang International University

Learning outcomes assessment plays a significant role in teaching and learning, not only does it motivate learners but also provide information about how newly transmitted knowledge has been absorbed by learners. It is obviously true that both teachers and students require to adjust the content, teaching and learning methods to achieve the set goals. Using the Google Forms tool, teachers can easily assess students understanding in English Grammar classes at HIU. This study aims to explore the opinions of HIU students about the Google Form tool in reviewing and testing English Grammar knowledge. The research team used qualitative and quantitative methods to collect data from 94 first-year students of the English Language Department of Hong Bang International University. Specific methods for data collection are observations, interviews, and online surveys. Initial results show that the majority of students have positive comments about this assessment tool such as creating excitement, motivation, ease of use, receiving instant scores and feedback;in addition , students can self-test many times, which helps them memorize the content quickly. However, there are also a few students who believe that this tool is dependent on technology, limited to some types of exercises, and does not comprehensively foster students' creative and application skills.
Towards Accelerating Data Intensive Application's Shuffle Process Using SmartNICs

Emerging SmartNIC creates new opportunities to offload application-level computation into the networking layer. Shuffle, the all-to-all data exchange process, is a critical building block for network communication in distributed data-intensive applications and can potentially benefit from SmartNICs. In this paper, we develop SmartShuffle, which accelerates the data-intensive application's shuffle process by offloading computation tasks into the SmartNIC devices. SmartShuffle supports offloading both low-level network functions, including data partitioning and network transport, and high-level computation tasks, including filtering, aggregation, and sorting. SmartShuffle adopts a coordinated offload architecture to make sender-side and receiver-side SmartNICs jointly contribute to the computation offload. SmartShuffle manages the computation and memory constraints on the device using liquid offloading, which dynamically migrates computations between the host CPU and the SmartNIC at runtime. We prototype SmartShuffle on the Stingray SoC SmartNICs and plug it into Spark. Our evaluation shows that SmartShuffle outperforms Spark, and Spark RDMA by up to 40% on TPC-H.
RWEN-TTS: Relation-Aware Word Encoding Network for Natural Text-to-Speech Synthesis

With the advent of deep learning, a huge number of text-to-speech (TTS) models which produce human-like speech have emerged. Recently, by introducing syntactic and semantic information w.r.t the input text, various approaches have been proposed to enrich the naturalness and expressiveness of TTS models. Although these strategies showed impressive results, they still have some limitations in utilizing language information. First, most approaches only use graph networks to utilize syntactic and semantic information without considering linguistic features. Second, most previous works do not explicitly consider adjacent words when encoding syntactic and semantic information, even though it is obvious that adjacent words are usually meaningful when encoding the current word. To address these issues, we propose Relation-aware Word Encoding Network (RWEN), which effectively allows syntactic and semantic information based on two modules (i.e., Semantic-level Relation Encoding and Adjacent Word Relation Encoding). Experimental results show substantial improvements compared to previous works.
Hierarchical Text Classification as Sub-hierarchy Sequence Generation

Hierarchical text classification (HTC) is essential for various real applications. However, HTC models are challenging to develop because they often require processing a large volume of documents and labels with hierarchical taxonomy. Recent HTC models based on deep learning have attempted to incorporate hierarchy information into a model structure. Consequently, these models are challenging to implement when the model parameters increase for a large-scale hierarchy because the model structure depends on the hierarchy size. To solve this problem, we formulate HTC as a sub-hierarchy sequence generation to incorporate hierarchy information into a target label sequence instead of the model structure. Subsequently, we propose the Hierarchy DECoder (HiDEC), which decodes a text sequence into a sub-hierarchy sequence using recursive hierarchy decoding, classifying all parents at the same level into children at once. In addition, HiDEC is trained to use hierarchical path information from a root to each leaf in a sub-hierarchy composed of the labels of a target document via an attention mechanism and hierarchy-aware masking. HiDEC achieved state-of-the-art performance with significantly fewer model parameters than existing models on benchmark datasets, such as RCV1-v2, NYT, and EURLEX57K.
Online Noisy Continual Relation Learning

Recent work for continual relation learning has achieved remarkable progress. However, most existing methods only focus on tackling catastrophic forgetting to improve performance in the existing setup, while continually learning relations in the real-world must overcome many other challenges. One is that the data possibly comes in an online streaming fashion with data distributions gradually changing and without distinct task boundaries. Another is that noisy labels are inevitable in real-world, as relation samples may be contaminated by label inconsistencies or labeled with distant supervision. In this work, therefore, we propose a novel continual relation learning framework that simultaneously addresses both online and noisy relation learning challenges. Our framework contains three key modules: (i) a sample separated online purifying module that divides the online data stream into clean and noisy samples, (ii) a self-supervised online learning module that circumvents inferior training signals caused by noisy data, and (iii) a semi-supervised offline finetuning module that ensures the participation of both clean and noisy samples. Experimental results on FewRel, TACRED and NYT-H with real-world noise demonstrate that our framework greatly outperforms the combinations of the state-of-the-art online continual learning and noisy label learning methods.
Taming Continuous Posteriors for Latent Variational Dialogue Policies

Utilizing amortized variational inference for latent-action reinforcement learning (RL) has been shown to be an effective approach in Task-oriented Dialogue (ToD) systems for optimizing dialogue success.Until now, categorical posteriors have been argued to be one of the main drivers of performance. In this work we revisit Gaussian variational posteriors for latent-action RL and show that they can yield even better performance than categoricals. We achieve this by introducing an improved variational inference objective for learning continuous representations without auxiliary learning objectives, which streamlines the training procedure. Moreover, we propose ways to regularize the latent dialogue policy, which helps to retain good response coherence. Using continuous latent representations our model achieves state of the art dialogue success rate on the MultiWOZ benchmark, and also compares well to categorical latent methods in response coherence.
Prompting Neural Machine Translation with Translation Memories

Improving machine translation (MT) systems with translation memories (TMs) is of great interest to practitioners in the MT community. However, previous approaches require either a significant update of the model architecture and/or additional training efforts to make the models well-behaved when TMs are taken as additional input. In this paper, we present a simple but effective method to introduce TMs into neural machine translation (NMT) systems. Specifically, we treat TMs as prompts to the NMT model at test time, but leave the training process unchanged. The result is a slight update of an existing NMT system, which can be implemented in a few hours by anyone who is familiar with NMT. Experimental results on several datasets demonstrate that our system significantly outperforms strong baselines.
Transferable Post-hoc Calibration on Pretrained Transformers in Noisy Text Classification

Recent work has demonstrated that pretrained transformers are overconfident in text classification tasks, which can be calibrated by the famous post-hoc calibration method temperature scaling (TS). Character or word spelling mistakes are frequently encountered in real applications and greatly threaten transformer model safety. Research on calibration under noisy settings is rare, and we focus on this direction. Based on a toy experiment, we discover that TS performs poorly when the datasets are perturbed by slight noise, such as swapping the characters, which results in distribution shift. We further utilize two metrics, predictive uncertainty and maximum mean discrepancy (MMD), to measure the distribution shift between clean and noisy datasets, based on which we propose a simple yet effective transferable TS method for calibrating models dynamically. To evaluate the performance of the proposed methods under noisy settings, we construct a benchmark consisting of four noise types and five shift intensities based on the QNLI, AG-News, and Emotion tasks. Experimental results on the noisy benchmark show that (1) the metrics are effective in measuring distribution shift and (2) transferable TS can significantly decrease the expected calibration error (ECE) compared with the competitive baseline ensemble TS by approximately 46.09%.
Learning towards Selective Data Augmentation for Dialogue Generation

As it is cumbersome and expensive to acquire a huge amount of data for training neural dialog models, data augmentation is proposed to effectively utilize existing training samples. However, current data augmentation techniques on the dialog generation task mostly augment all cases in the training dataset without considering the intrinsic attributes between different cases. We argue that not all cases are beneficial for augmentation task, and the cases suitable for augmentation should obey the following two attributes: (1) low-quality (the dialog model cannot generate a high-quality response for the case), (2) representative (the case should represent the property of the whole dataset). Herein, we explore this idea by proposing a Selective Data Augmentation framework (SDA) for the response generation task. SDA employs a dual adversarial network to select the lowest quality and most representative data points for augmentation in one stage. Extensive experiments conducted on two publicly available datasets, i.e., DailyDialog and OpenSubtitles, show that our framework can improve the response generation performance with respect to various metrics
Feature-Level Debiased Natural Language Understanding

Natural language understanding (NLU) models often rely on dataset biases rather than intended task-relevant features to achieve high performance on specific datasets. As a result, these models perform poorly on datasets outside the training distribution. Some recent studies address this issue by reducing the weights of biased samples during the training process. However, these methods still encode biased latent features in representations and neglect the dynamic nature of bias, which hinders model prediction. We propose an NLU debiasing method, named debiasing contrastive learning (DCT), to simultaneously alleviate the above problems based on contrastive learning. We devise a debiasing, positive sampling strategy to mitigate biased latent features by selecting the least similar biased positive samples. We also propose a dynamic negative sampling strategy to capture the dynamic influence of biases by employing a bias-only model to dynamically select the most similar biased negative samples. We conduct experiments on three NLU benchmark datasets. Experimental results show that DCT outperforms state-of-the-art baselines on out-of-distribution datasets while maintaining in-distribution performance. We also verify that DCT can reduce biased latent features from the model's representation.
Structured Case-Based Reasoning for Inference-Time Adaptation of Text-to-SQL Parsers

Inference-time adaptation methods for semantic parsing are useful for leveraging examples from newly-observed domains without repeated fine-tuning. Existing approaches typically bias the decoder by simply concatenating input-output example pairs (cases) from the new domain at the encoder’s input in a Seq-to-Seq model. Such methods cannot adequately leverage the structure of logical forms in the case examples. We propose StructCBR, a structured case-based reasoning approach, which leverages subtree-level similarity between logical forms of cases and candidate outputs, resulting in better decoder decisions. For the task of adapting Text-to-SQL models to unseen schemas, we show that exploiting case examples in a structured manner via StructCBR offers consistent performance improvements over prior inference-time adaptation methods across five different databases. To the best of our knowledge, we are the first to attempt inference-time adaptation of Text-to-SQL models, and harness trainable structured similarity between subqueries.