Reviewerly/exHarmony huggingface dataset:
Corpus Processing of Multi-Word Discourse Markers for Advanced Learners

Aim/Purpose. The most crucial aspects of teaching a foreign language to more advanced learners are building an awareness of discourse modes, how to regulate discourse, and the pragmatic properties of discourse components. However, in different languages, the connections and structure of discourse are ensured by different linguistic means which makes matters complicated for the learner. Background. By uncovering regularities in a foreign language and comparing them with patterns in one’s own tongue, the corpus research method offers the student unique opportunities to acquire linguistic knowledge about discourse markers. This paper reports on an investigation of the functions of multi-word discourse markers. Methodology. In our research, we combine the alignment model of the phrase-based statistical machine translation and manual treatment of the data in order to examine English multi-word discourse markers and their equivalents in Lithuanian and Hebrew translations by researching their changes in translation. After establishing the full list of multi-word discourse markers in our generated parallel corpus, we research how the multi-word discourse markers are treated in translation. Contribution. Creating a parallel research corpus to identify multi-word expressions used as discourse markers, analyzing how they are translated into Lithuanian and Hebrew, and attempting to determine why the translators made the choices add value to corpus-driven research and how to manage discourse. Findings. Our research proves that there is a possible context-based influence guiding the translation to choose a particle or other lexical item integration in Lithuanian or Hebrew translated discourse markers to express the rhetorical domain which could be related to the so-called phenomenon of “over-specification.” Recommendations for Practitioners. The comparative examination of discourse markers provides language instructors and translators with more specific information about the roles of discourse markers. Recommendations for Researchers. Understanding the multifunctionality of discourse markers provides new avenues for discourse marker application in translation research. Impact on Society. The current study may be a useful method to strengthen students’ language awareness and analytic skills and is particularly important for students specializing in English philology or translation. Beyond the empirical research, an extensive parallel data resource has been created to be openly used. Future Research. It should be noted that the observed phenomenon of “over-specification” could be analyzed further in future research.
INVESTIGATING PRIMARY SCHOOL STUDENTS’ GRAMMATICAL ERRORS IN WRITING: DULAY’S SURFACE STRATEGY TAXONOMY

This study was carried out to identify different types of grammatical errors made by primary school students since some previous studies only conducted the same research with high school students as the participants. This study used a descriptive qualitative research design. The participants were 40 fourth-grade students from SD Muslim Cendekia Batu. Students' writing products were used as the instrument. The data were analyzed, identified and classified based on Dulay's (1982) surface strategy taxonomy. The data show that the students made 79 grammatical errors, with errors of omission accounting for 25 items (31.6%), 21 (26.5%) errors in addition, 26 errors (32.7%) in misformation, and 7 (9.2%) errors in misordering. The findings reveal that students lack grammar mastery; therefore, they frequently create their own rules in writing a text. To summarize, the student's understanding of the use of English structure remained low. The researcher's recommendations for future researchers are to focus more on the sources of the students' errors and to identify and determine the appropriate strategies to overcome students’ grammatical errors.
ANALYSIS OF ENGLISH TEXTBOOK FOR FIFTH GRADE ELEMENTARY SCHOOL

The purpose of this study is to evaluate the English language textbook from Ministry of Education and Culture (MOEC) for grade 5 Elementary School Level, entitled “My Next Word 5”. As an evaluation method, content-analysis was used in this study. To evaluate this book, the researchers adapt the instrument from Minister of Education and Culture Regulation Number 8 of 2016 and the book evaluation from Cunnings worth (1995); (1) Materials, (2) Language (3) Materials presenting (4) Graphic. As a result of the evaluation, it was found that the textbook is suitable for learners in materials, language i.e., there was a topic that relevant with the curriculum, the content of the book is more practical so that it can improve students’ skills, there was reflection table for student on the last page in every page. This book meets the criteria, but there are a few things that could be improved, such as the presentation of culture that is balanced with the culture of the language being studied.
ONE DAY ONE ARTICLE (ODOA): HABITUATING READING COMPREHENSION SKILLS

This article explores quality habits that can be taught to non-English department students to improve their reading comprehension skills in English-language scientific literature. By using the theory and practice of reading comprehension, students are expected to be able to get used to reading scientific writings in English at a certain stage and time. This paper was developed using a qualitative approach based on data from grounded research that explores phenomena based on inductive studies which include reading theory, reading activity, and reading habit. This study found that students can increase their knowledge by understanding aspects of activities that become routine. These activities are inherent in the quantity and quality of reading activities. The results of this paper have implications for complex literacy mastery in supporting students' higher-order thinking skills (HOTS) as an academic need in the Society 5.0 era. This article proves that there is no barrier for non-English department students to be able to master reading skills from English texts by making quality reading habits.
AN ANALYSIS OF NON-ENGLISH STUDENTS’ UNDERSTANDING IN USING PRONOUNS: CORRECT AND ERROR PRONOUNS

This study was to investigate the non – English students’ error in using pronouns at STAKat Negeri Pontianak. The participant of this research were 40 students of seconds semester in academic year 2022. This research used descriptive research to gather the information and analyzed the result of the test. The result of the data gathered from the test that was given to the students through Google Form. Based on the analysis, the students wrote 53,98% correct answer and 46.02% of the students still made errors in using pronouns. There were 4.4% errors were found in the used of subjective pronouns, 13.90% errors were found in the used of objective pronouns, 12.81% errors were found in the used of possessive pronouns, and 16.56% errors were found in the used of reflexive pronouns. The lowest errors were found in the used of subjective pronouns, and the highest error was found in the used of reflexive pronouns.Keywords: Non – English Students, Errors, Pronouns, clearly, professionally
Pedestrian and Vehicle Detection Based on Pruning YOLOv4 with Cloud-Edge Collaboration

Nowadays, the rapid development of edge computing has driven an increasing number of deep learning applications deployed at the edge of the network, such as pedestrian and vehicle detection, to provide efficient intelligent services to mobile users.However, as the accuracy requirements continue to increase, the components of deep learning models for pedestrian and vehicle detection, such as YOLOv4, become more sophisticated and the computing resources required for model training are increasing dramatically, which in turn leads to significant challenges in achieving effective deployment on resource-constrained edge devices while ensuring the high accuracy performance.For addressing this challenge, a cloud-edge collaboration-based pedestrian and vehicle detection framework is proposed in this paper, which enables sufficient training of models by utilizing the abundant computing resources in the cloud, and then deploying the well-trained models on edge devices, thus reducing the computing resource requirements for model training on edge devices.Furthermore, to reduce the size of the model deployed on edge devices, an automatic pruning method combines the convolution layer and BN layer is proposed to compress the pedestrian and vehicle detection model size.Experimental results show that the framework proposed in this paper is able to deploy the pruned model on a real edge device, Jetson TX2, with 6.72 times higher FPS.Meanwhile, the channel pruning reduces the volume and the number of parameters to 96.77% for the model, and the computing amount is reduced to 81.37%.
DEVELOPMENT OF PADLET-BASED ENGLISH LEARNING MEDIA ON RECOUNT TEXT MATERIAL

This research is aimed at producing product in the form of material content developed in a web format in the form of a material page and assignment page to make students easily in the learning process. This study used the Research and Development (R&amp;D) method with the 4D model as a guide for developing an effective product. The research procedure is to determine the learning requirements, design the required learning media, developed the learning media that had been designed, and disseminate the result of the product developed to students. The research results obtained in this study were the Padlet media feasibility test which scored 92.30% in the “Very Eligible” category for media validation and 93% in the “Very Eligible” category for material development. This development research showed that this Padlet media could involve students actively in the learning process.Keywords: Learning Media; Padlet; Recount Text
No Free Lunch: The Hazards of Over-Expressive Representations in Anomaly Detection

Anomaly detection methods, powered by deep learning, have recently been making significant progress, mostly due to improved representations. It is tempting to hypothesize that anomaly detection can improve indefinitely by increasing the scale of our networks, making their representations more expressive. In this paper, we provide theoretical and empirical evidence to the contrary. In fact, we empirically show cases where very expressive representations fail to detect even simple anomalies when evaluated beyond the well-studied object-centric datasets. To investigate this phenomenon, we begin by introducing a novel theoretical toy model for anomaly detection performance. The model uncovers a fundamental trade-off between representation sufficiency and over-expressivity. It provides evidence for a no-free-lunch theorem in anomaly detection stating that increasing representation expressivity will eventually result in performance degradation. Instead, guidance must be provided to focus the representation on the attributes relevant to the anomalies of interest. We conduct an extensive empirical investigation demonstrating that state-of-the-art representations often suffer from over-expressivity, failing to detect many types of anomalies. Our investigation demonstrates how this over-expressivity impairs image anomaly detection in practical settings. We conclude with future directions for mitigating this issue.
Adversarial Attacks on Leakage Detectors in Water Distribution Networks

Many Machine Learning models are vulnerable to adversarial attacks: There exist methodologies that add a small (imperceptible) perturbation to an input such that the model comes up with a wrong prediction. Better understanding of such attacks is crucial in particular for models used in security-critical domains, such as monitoring of water distribution networks, in order to devise counter-measures enhancing model robustness and trustworthiness. We propose a taxonomy for adversarial attacks against machine learning based leakage detectors in water distribution networks. Following up on this, we focus on a particular type of attack: an adversary searching the least sensitive point, that is, the location in the water network where the largest possible undetected leak could occur. Based on a mathematical formalization of the least sensitive point problem, we use three different algorithmic approaches to find a solution. Results are evaluated on two benchmark water distribution networks.
Read, look and detect: Bounding box annotation from image-caption pairs

Various methods have been proposed to detect objects while reducing the cost of data annotation. For instance, weakly supervised object detection (WSOD) methods rely only on image-level annotations during training. Unfortunately, data annotation remains expensive since annotators must provide the categories describing the content of each image and labeling is restricted to a fixed set of categories. In this paper, we propose a method to locate and label objects in an image by using a form of weaker supervision: image-caption pairs. By leveraging recent advances in vision-language (VL) models and self-supervised vision transformers (ViTs), our method is able to perform phrase grounding and object detection in a weakly supervised manner. Our experiments demonstrate the effectiveness of our approach by achieving a 47.51% recall@1 score in phrase grounding on Flickr30k Entities and establishing a new state-of-the-art in object detection by achieving 21.1 mAP 50 and 10.5 mAP 50:95 on MS COCO when exclusively relying on image-caption pairs.
SAM-helps-Shadow:When Segment Anything Model meet shadow removal

The challenges surrounding the application of image shadow removal to real-world images and not just constrained datasets like ISTD/SRD have highlighted an urgent need for zero-shot learning in this field. In this study, we innovatively adapted the SAM (Segment anything model) for shadow removal by introducing SAM-helps-Shadow, effectively integrating shadow detection and removal into a single stage. Our approach utilized the model's detection results as a potent prior for facilitating shadow detection, followed by shadow removal using a second-order deep unfolding network. The source code of SAM-helps-Shadow can be obtained from https://github.com/zhangbaijin/SAM-helps-Shadow.
$FastDoc$: Domain-Specific Fast Pre-training Technique using Document-Level Metadata and Taxonomy

As the demand for sophisticated Natural Language Processing (NLP) models continues to grow, so does the need for efficient pre-training techniques. Current NLP models undergo resource-intensive pre-training. In response, we introduce $FastDoc$ (Fast Pre-training Technique using Document-Level Metadata and Taxonomy), a novel approach designed to significantly reduce computational demands. $FastDoc$ leverages document metadata and domain-specific taxonomy as supervision signals. It involves continual pre-training of an open-domain transformer encoder using sentence-level embeddings, followed by fine-tuning using token-level embeddings. We evaluate $FastDoc$ on six tasks across nine datasets spanning three distinct domains. Remarkably, $FastDoc$ achieves remarkable compute reductions of approximately 1,000x, 4,500x, 500x compared to competitive approaches in Customer Support, Scientific, and Legal domains, respectively. Importantly, these efficiency gains do not compromise performance relative to competitive baselines. Furthermore, reduced pre-training data mitigates catastrophic forgetting, ensuring consistent performance in open-domain scenarios. $FastDoc$ offers a promising solution for resource-efficient pre-training, with potential applications spanning various domains.
Using Foundation Models to Detect Policy Violations with Minimal Supervision

Foundation models, i.e. large neural networks pre-trained on large text corpora, have revolutionized NLP. They can be instructed directly (e.g. (arXiv:2005.14165)) - this is called hard prompting - and they can be tuned using very little data (e.g. (arXiv:2104.08691)) - this technique is called soft prompting. We seek to leverage their capabilities to detect policy violations. Our contributions are: We identify a hard prompt that adapts chain-of-thought prompting to policy violation tasks. This prompt produces policy violation classifications, along with extractive explanations that justify the classification. We compose the hard-prompts with soft prompt tuning to produce a classifier that attains high accuracy with very little supervision; the same classifier also produces explanations. Though the supervision only acts on the classifications, we find that the modified explanations remain consistent with the (tuned) model's response. Along the way, we identify several unintuitive aspects of foundation models. For instance, adding an example from a specific class can actually reduce predictions of that class, and separately, the effects of tokenization on scoring etc. Based on our technical results, we identify a simple workflow for product teams to quickly develop effective policy violation detectors.
Circular Rectifiction of 3D Video and Efficient Modification of 3D-HEVC

Video acquired from multiple cameras located along a line is often rectified to video virtually obtained from cameras with ideally parallel optical axes collocated on a single plane and principal points on a line. Such an approach simplifies video processing including depth estimation and compression. Nowadays, for many application video, like virtual reality or virtual navigation, the content is often acquired by cameras located nearly on a circle or on a part of that. Therefore, we introduce new operation of circular rectification that results in multiview video virtually obtained from cameras located on an ideal arc and with optical axes that are collocated on a single plane and they intersect in a single point. For the circularly rectified video, depth estimation and compression are simplified. The standard 3DHEVC codec was designed for rectified video and its efficiency is limited for video acquired from cameras located on an arc. Therefore, we developed a 3-D HEVC codec modified in order to compress efficiently circularly rectified video. The experiments demonstrate its better performance than for the standard 3D-HEVC codec.
Towards Arabic Multimodal Dataset for Sentiment Analysis

Multimodal Sentiment Analysis (MSA) has recently become a centric research direction for many real-world applications. This proliferation is due to the fact that opinions are central to almost all human activities and are key influencers of our behaviors. In addition, the recent deployment of Deep Learning-based (DL) models has proven their high efficiency for a wide range of Western languages. In contrast, Arabic DL-based multimodal sentiment analysis (MSA) is still in its infantile stage due, mainly, to the lack of standard datasets. In this paper, our investigation is twofold. First, we design a pipeline that helps building our Arabic Multimodal dataset leveraging both state-of-the-art transformers and feature extraction tools within word alignment techniques. Thereafter, we validate our dataset using state-of-the-art transformer-based model dealing with multimodality. Despite the small size of the outcome dataset, experiments show that Arabic multimodality is very promising
AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder

The recently introduced Segment Anything Model (SAM) combines a clever architecture and large quantities of training data to obtain remarkable image segmentation capabilities. However, it fails to reproduce such results for Out-Of-Distribution (OOD) domains such as medical images. Moreover, while SAM is conditioned on either a mask or a set of points, it may be desirable to have a fully automatic solution. In this work, we replace SAM's conditioning with an encoder that operates on the same input image. By adding this encoder and without further fine-tuning SAM, we obtain state-of-the-art results on multiple medical images and video benchmarks. This new encoder is trained via gradients provided by a frozen SAM. For inspecting the knowledge within it, and providing a lightweight segmentation solution, we also learn to decode it into a mask by a shallow deconvolution network.
Improving Non-autoregressive Translation Quality with Pretrained Language Model, Embedding Distillation and Upsampling Strategy for CTC

Non-autoregressive approaches aim to improve the inference speed of translation models, particularly those that generate output in a one-pass forward manner. However, these approaches often suffer from a significant drop in translation quality compared to autoregressive models. This paper introduces a series of innovative techniques to enhance the translation quality of Non-Autoregressive Translation (NAT) models while maintaining a substantial acceleration in inference speed. We propose fine-tuning Pretrained Multilingual Language Models (PMLMs) with the CTC loss to train NAT models effectively. Furthermore, we adopt the MASK insertion scheme for up-sampling instead of token duplication, and we present an embedding distillation method to further enhance performance. In our experiments, our model outperforms the baseline autoregressive model (Transformer \textit{base}) on multiple datasets, including WMT'14 DE$\leftrightarrow$EN, WMT'16 RO$\leftrightarrow$EN, and IWSLT'14 DE$\leftrightarrow$EN. Notably, our model achieves better performance than the baseline autoregressive model on the IWSLT'14 En$\leftrightarrow$De and WMT'16 En$\leftrightarrow$Ro datasets, even without using distillation data during training. It is worth highlighting that on the IWSLT'14 DE$\rightarrow$EN dataset, our model achieves an impressive BLEU score of 39.59, setting a new state-of-the-art performance. Additionally, our model exhibits a remarkable speed improvement of 16.35 times compared to the autoregressive model.
Two-Stage Holistic and Contrastive Explanation of Image Classification

The need to explain the output of a deep neural network classifier is now widely recognized. While previous methods typically explain a single class in the output, we advocate explaining the whole output, which is a probability distribution over multiple classes. A whole-output explanation can help a human user gain an overall understanding of model behaviour instead of only one aspect of it. It can also provide a natural framework where one can examine the evidence used to discriminate between competing classes, and thereby obtain contrastive explanations. In this paper, we propose a contrastive whole-output explanation (CWOX) method for image classification, and evaluate it using quantitative metrics and through human subject studies. The source code of CWOX is available at https://github.com/vaynexie/CWOX.
Vista-Morph: Unsupervised Image Registration of Visible-Thermal Facial Pairs

For a variety of biometric cross-spectral tasks, Visible-Thermal (VT) facial pairs are used. However, due to a lack of calibration in the lab, photographic capture between two different sensors leads to severely misaligned pairs that can lead to poor results for person re-identification and generative AI. To solve this problem, we introduce our approach for VT image registration called Vista Morph. Unlike existing VT facial registration that requires manual, hand-crafted features for pixel matching and/or a supervised thermal reference, Vista Morph is completely unsupervised without the need for a reference. By learning the affine matrix through a Vision Transformer (ViT)-based Spatial Transformer Network (STN) and Generative Adversarial Networks (GAN), Vista Morph successfully aligns facial and non-facial VT images. Our approach learns warps in Hard, No, and Low-light visual settings and is robust to geometric perturbations and erasure at test time. We conduct a downstream generative AI task to show that registering training data with Vista Morph improves subject identity of generated thermal faces when performing V2T image translation.