Reviewerly/exHarmony huggingface dataset:
Understanding Pragmatics as a Way to Practice Natural Communication Skills in Various Contexts of Interactions

Students’ problems in using English to communicate need to be handled seriously. Teaching English is no more teaching the knowledge of English only but also the use of context. This library research elaborates on the importance of pragmatics for EFL students to improve their communication skills in various contexts of interaction and to strengthen the importance of pragmatic competence for non-native English teachers in teaching natural English communication. Documentation is the collecting technique using a checklist and fieldnote instruments. The primary sources are theories about pragmatics and teaching communication from books and published journals. In order to obtain correct and precise results in analyzing data, a content analysis technique is used. The data analysis procedure covers determining the design, which is linkage analysis, finding main data, and theories about pragmatics from books, then finding contextual knowledge from published research related to pragmatics and teaching English communication, and finally, writing and interpreting the linkage to describe the results. The findings show that pragmatics should not limitedly act as classroom instructions but as competence to be constantly developed and practised for students or teachers. Pragmatics should be seen from its whole aspect in order to communicate naturally and contextually. It can also be concluded that opportunities for pragmatic practice should be provided to achieve the goal of teaching English, that is, the ability to use English in various contexts of interaction.
An application of stereo matching algorithm based on transfer learning on robots in multiple scenarios

Abstract Robot vision technology based on binocular vision has significant potential for development in various fields, including 3D scenes and reconstruction, target detection, autonomous driving, and other fields. To date, current binocular vision methods used in robotics engineering suffer from high costs, complex algorithms, and low reliability of the generated disparity map in multiple scenarios. Robots require a cost-effective algorithm with cross-domain generalization capabilities for multiple scenarios. To address these issues, a cross-domain stereo matching algorithm for binocular vision based on transfer learning was proposed in this paper, named Cross-Domain Adaptation and Transfer Learning Network (Ct-Net), which has shown valuable results in multiple robot scenes. First, this paper introduces a General Feature Extractor (GFE) to extract rich general feature information for domain adaptive stereo matching tasks. Then, a feature adapter is used to adapt the general features to the stereo matching network. Furthermore, a Domain Adaptive Cost Optimization Module (DACOM) was designed to optimize the matching cost. A disparity score prediction module was also embedded to adaptively adjust the search range of disparity and optimize the cost distribution. The overall framework was trained using a phased strategy, and ablation experiments were conducted to verify the effectiveness of the training strategy. On KITTI 2015 benchmark, compared with the prototype PSMNet, the 3PE − f g of Ct-Net in all regions and non-occluded regions decreased by 19.3% and 21.1% respectively. On the Middlebury dataset, the 2PE of Ct-Net achieved comparable results on all samples. The quantitative and qualitative results obtained from Middlebury, Apollo, and other datasets demonstrate that Ct-Net significantly improves the cross-domain performance of stereo matching. Stereo matching experiments in real-world scenarios have shown that it can effectively address visual tasks in multiple robot scenes.
Analisa Strategi Implementasi Green Computing pada ITS NU Sriwijaya Sumatera Selatan

&#x0D; Educational activities are inseparable from the use of Information and Communication Technology (ICT). ICT is not only limited to supporters but also a competitive advantage in the world of education. In addition to the convenience gained from the use of ICT devices there is an environmentally unfriendly effect produced by the influence of energy consumption used during the operation of ICT equipment. To overcome this problem, it is important that there are efforts and strategies that must be carried out in order to achieve Green Computing in daily operations at the ITS NU Sriwijaya. In this study, the authors analyzed strengths, weaknesses, opportunities, and risks on campus using a SWOT analyst. The results showed that the situation of ITS NU Sriwijaya is now in a grow position. The strategies developed include SO (Strength-Opportunitiy) making strategies that use power to seize opportunities, namely: using ICT devices with a long service life, virtualization, increasing the use of laptops compared to computers, developing E-Learning-based learning systems, expanding ICT infrastructure as needed, and developing the quality of human resources through training and workshops around ICT.
Zero-shot Temporal Relation Extraction with ChatGPT

The goal of temporal relation extraction is to infer the temporal relation between two events in the document. Supervised models are dominant in this task. In this work, we investigate ChatGPT's ability on zero-shot temporal relation extraction. We designed three different prompt techniques to break down the task and evaluate ChatGPT. Our experiments show that ChatGPT's performance has a large gap with that of supervised methods and can heavily rely on the design of prompts. We further demonstrate that ChatGPT can infer more small relation classes correctly than supervised methods. The current shortcomings of ChatGPT on temporal relation extraction are also discussed in this paper. We found that ChatGPT cannot keep consistency during temporal inference and it fails in actively long-dependency temporal inference.
Supersymmetric Quantum Mechanics, multiphoton algebras and coherent states

The multiphoton algebras for one-dimensional Hamiltonians with infinite discrete spectrum, and for their associated kth-order SUSY partners are studied. In both cases, such an algebra is generated by the multiphoton annihilation and creation operators, as well as by Hamiltonians which are functions of an appropriate number operator. The algebras obtained turn out to be polynomial deformations of the corresponding single-photon algebra previously studied. The Barut-Girardello coherent states, which are eigenstates of the annihilation operator, are obtained and their uncertainty relations are explored by means of the associated quadratures.
CamDiff: Camouflage Image Augmentation via Diffusion Model

The burgeoning field of camouflaged object detection (COD) seeks to identify objects that blend into their surroundings. Despite the impressive performance of recent models, we have identified a limitation in their robustness, where existing methods may misclassify salient objects as camouflaged ones, despite these two characteristics being contradictory. This limitation may stem from lacking multi-pattern training images, leading to less saliency robustness. To address this issue, we introduce CamDiff, a novel approach inspired by AI-Generated Content (AIGC) that overcomes the scarcity of multi-pattern training images. Specifically, we leverage the latent diffusion model to synthesize salient objects in camouflaged scenes, while using the zero-shot image classification ability of the Contrastive Language-Image Pre-training (CLIP) model to prevent synthesis failures and ensure the synthesized object aligns with the input prompt. Consequently, the synthesized image retains its original camouflage label while incorporating salient objects, yielding camouflage samples with richer characteristics. The results of user studies show that the salient objects in the scenes synthesized by our framework attract the user's attention more; thus, such samples pose a greater challenge to the existing COD models. Our approach enables flexible editing and efficient large-scale dataset generation at a low cost. It significantly enhances COD baselines' training and testing phases, emphasizing robustness across diverse domains. Our newly-generated datasets and source code are available at https://github.com/drlxj/CamDiff.
SGL: Structure Guidance Learning for Camera Localization

Camera localization is a classical computer vision task that serves various Artificial Intelligence and Robotics applications. With the rapid developments of Deep Neural Networks (DNNs), end-to-end visual localization methods are prosperous in recent years. In this work, we focus on the scene coordinate prediction ones and propose a network architecture named as Structure Guidance Learning (SGL) which utilizes the receptive branch and the structure branch to extract both high-level and low-level features to estimate the 3D coordinates. We design a confidence strategy to refine and filter the predicted 3D observations, which enables us to estimate the camera poses by employing the Perspective-n-Point (PnP) with RANSAC. In the training part, we design the Bundle Adjustment trainer to help the network fit the scenes better. Comparisons with some state-of-the-art (SOTA) methods and sufficient ablation experiments confirm the validity of our proposed architecture.
Learning Transferable Pedestrian Representation from Multimodal Information Supervision

Recent researches on unsupervised person re-identification~(reID) have demonstrated that pre-training on unlabeled person images achieves superior performance on downstream reID tasks than pre-training on ImageNet. However, those pre-trained methods are specifically designed for reID and suffer flexible adaption to other pedestrian analysis tasks. In this paper, we propose VAL-PAT, a novel framework that learns transferable representations to enhance various pedestrian analysis tasks with multimodal information. To train our framework, we introduce three learning objectives, \emph{i.e.,} self-supervised contrastive learning, image-text contrastive learning and multi-attribute classification. The self-supervised contrastive learning facilitates the learning of the intrinsic pedestrian properties, while the image-text contrastive learning guides the model to focus on the appearance information of pedestrians.Meanwhile, multi-attribute classification encourages the model to recognize attributes to excavate fine-grained pedestrian information. We first perform pre-training on LUPerson-TA dataset, where each image contains text and attribute annotations, and then transfer the learned representations to various downstream tasks, including person reID, person attribute recognition and text-based person search. Extensive experiments demonstrate that our framework facilitates the learning of general pedestrian representations and thus leads to promising results on various pedestrian analysis tasks.
FALQU: Finding Answers to Legal Questions

This paper presents a new test collection for Legal IR, FALQU: Finding Answers to Legal Questions, where questions and answers were obtained from Law Stack Exchange (LawSE), a Q&A website for legal professionals, and others with experience in law. Much in line with Stack overflow, Law Stack Exchange has a variety of questions on different topics such as copyright, intellectual property, and criminal laws, making it an interesting source for dataset construction. Questions are also not limited to one country. Often, users of different nationalities may ask questions about laws in different countries and expertise. Therefore, questions in FALQU represent real-world users' information needs thus helping to avoid lab-generated questions. Answers on the other side are given by experts in the field. FALQU is the first test collection, to the best of our knowledge, to use LawSE, considering more diverse questions than the questions from the standard legal bar and judicial exams. It contains 9880 questions and 34,145 answers to legal questions. Alongside our new test collection, we provide different baseline systems that include traditional information retrieval models such as TF-IDF and BM25, and deep neural network search models. The results obtained from the BM25 model achieved the highest effectiveness.
MoMo: A shared encoder Model for text, image and multi-Modal representations

We propose a self-supervised shared encoder model that achieves strong results on several visual, language and multimodal benchmarks while being data, memory and run-time efficient. We make three key contributions. First, in contrast to most existing works, we use a single transformer with all the encoder layers processing both the text and the image modalities. Second, we propose a stage-wise training strategy where the model is first trained on images, then jointly with unimodal text and image datasets and finally jointly with text and text-image datasets. Third, to preserve information across both the modalities, we propose a training pipeline that learns simultaneously from gradient updates of different modalities at each training update step. The results on downstream text-only, image-only and multimodal tasks show that our model is competitive with several strong models while using fewer parameters and lesser pre-training data. For example, MoMo performs competitively with FLAVA on multimodal (+3.1), image-only (+1.1) and text-only (-0.1) tasks despite having 2/5th the number of parameters and using 1/3rd the image-text training pairs. Finally, we ablate various design choices and further show that increasing model size produces significant performance gains indicating potential for substantial improvements with larger models using our approach.
Understanding Causality with Large Language Models: Feasibility and Opportunities

We assess the ability of large language models (LLMs) to answer causal questions by analyzing their strengths and weaknesses against three types of causal question. We believe that current LLMs can answer causal questions with existing causal knowledge as combined domain experts. However, they are not yet able to provide satisfactory answers for discovering new knowledge or for high-stakes decision-making tasks with high precision. We discuss possible future directions and opportunities, such as enabling explicit and implicit causal modules as well as deep causal-aware LLMs. These will not only enable LLMs to answer many different types of causal questions for greater impact but also enable LLMs to be more trustworthy and efficient in general.
WildRefer: 3D Object Localization in Large-scale Dynamic Scenes with Multi-modal Visual Data and Natural Language

We introduce the task of 3D visual grounding in large-scale dynamic scenes based on natural linguistic descriptions and online captured multi-modal visual data, including 2D images and 3D LiDAR point clouds. We present a novel method, WildRefer, for this task by fully utilizing the appearance features in images, the location and geometry features in point clouds, and the dynamic features in consecutive input frames to match the semantic features in language. In particular, we propose two novel datasets, STRefer and LifeRefer, which focus on large-scale human-centric daily-life scenarios with abundant 3D object and natural language annotations. Our datasets are significant for the research of 3D visual grounding in the wild and has huge potential to boost the development of autonomous driving and service robots. Extensive comparisons and ablation studies illustrate that our method achieves state-of-the-art performance on two proposed datasets. Code and dataset will be released when the paper is published.
Semantic Feature Verification in FLAN-T5

This study evaluates the potential of a large language model for aiding in generation of semantic feature norms - a critical tool for evaluating conceptual structure in cognitive science. Building from an existing human-generated dataset, we show that machine-verified norms capture aspects of conceptual structure beyond what is expressed in human norms alone, and better explain human judgments of semantic similarity amongst items that are distally related. The results suggest that LLMs can greatly enhance traditional methods of semantic feature norm verification, with implications for our understanding of conceptual representation in humans and machines.
Best Practices for 2-Body Pose Forecasting

The task of collaborative human pose forecasting stands for predicting the future poses of multiple interacting people, given those in previous frames. Predicting two people in interaction, instead of each separately, promises better performance, due to their body-body motion correlations. But the task has remained so far primarily unexplored. In this paper, we review the progress in human pose forecasting and provide an in-depth assessment of the single-person practices that perform best for 2-body collaborative motion forecasting. Our study confirms the positive impact of frequency input representations, space-time separable and fully-learnable interaction adjacencies for the encoding GCN and FC decoding. Other single-person practices do not transfer to 2-body, so the proposed best ones do not include hierarchical body modeling or attention-based interaction encoding. We further contribute a novel initialization procedure for the 2-body spatial interaction parameters of the encoder, which benefits performance and stability. Altogether, our proposed 2-body pose forecasting best practices yield a performance improvement of 21.9% over the state-of-the-art on the most recent ExPI dataset, whereby the novel initialization accounts for 3.5%. See our project page at https://www.pinlab.org/bestpractices2body
Erosion Attack: Harnessing Corruption To Improve Adversarial Examples

Although adversarial examples pose a serious threat to deep neural networks, most transferable adversarial attacks are ineffective against black-box defense models. This may lead to the mistaken belief that adversarial examples are not truly threatening. In this paper, we propose a novel transferable attack that can defeat a wide range of black-box defenses and highlight their security limitations. We identify two intrinsic reasons why current attacks may fail, namely data-dependency and network-overfitting. They provide a different perspective on improving the transferability of attacks. To mitigate the data-dependency effect, we propose the Data Erosion method. It involves finding special augmentation data that behave similarly in both vanilla models and defenses, to help attackers fool robustified models with higher chances. In addition, we introduce the Network Erosion method to overcome the network-overfitting dilemma. The idea is conceptually simple: it extends a single surrogate model to an ensemble structure with high diversity, resulting in more transferable adversarial examples. Two proposed methods can be integrated to further enhance the transferability, referred to as Erosion Attack (EA). We evaluate the proposed EA under different defenses that empirical results demonstrate the superiority of EA over existing transferable attacks and reveal the underlying threat to current robust models. Codes will be publicly available.
Monocular 2D and 3D human pose estimation review

Human pose estimation has been a significant task in computer vision in the last few decades and had a wide variety of applications in the scientific field and in people’s daily life. People usually use cameras to obtain RGB images or videos to compose an integrated dataset and then use different deep learning methods or some other combinations of methods to try to estimate the proper posture of the human body. Although scientists have thought of various kinds of technical ways to estimate human pose, they still cannot achieve 100 percent accuracy because of some unavoidable factors, for example, the complex environmental changes of everyday life, flexible body, and a variety of body shapes make prediction accuracy more difficult because it affects the confirmation of key points, and limitations of some methods. For better research, this review will focus on advanced technical methods, datasets, and metrics for 3D and 2D human pose estimation with single or multiple individuals.
Malicious URL Detection Using Machine Learning

Malicious Uniform Resource Locators (URLs), or malicious websites, are one of the most common threats to web security. They host unwanted content (spam, malware, inappropriate ads, scams, etc.) Your visit to this website may have been prompted by emails, advertisements, web searches or links from other websites. Either way, the user must click on the malicious URL. The growing prevalence of phishing, spam, and malware has led to a strong need for a reliable solution that can classify and identify malicious URLs. In this paper, we address malicious URL detection as a binary classification problem and evaluate the performance of several known machine-learning classifiers. Key Words: Malicious URLs, Machine Learning, Phishing, Spam, Malware, Fraud. Key Words: Malicious URLs, Machine Learning, Phishing, Spam, M alware, Fraud.
Extension to Block and Customize Websites in the Browser to Isolate the User from Piracy Websites, Fraudulent Risk, Online Gambling, and Social Media Addiction

Website-blocking browser extensions are tools that users can install in their web browsers to block access to certain websites or content. This extension is a tool that can be installed on web browsers to restrict access to the websites like social media platforms, piracy websites, online fraudulent risk websites, gambling websites, and websites that are unsafe to the users. Users can temporarily block social media, piracy, and other dangerous websites with this extension. A website blocking extension is designed to help users limit their exposure to distracting or harmful websites while browsing the internet. This extension allows users to manually turn on and off to block or restrict access to social media sites, online shopping platforms, piracy websites, and adult content websites. When a user tries to open a website on their blocklist, the extension redirects to a different website and shows them a 404 error message. Website blocking extensions are often used by people who want to improve their focus on studies and productivity or reduce internet usage. Parents can protect their children from unsafe online content. Key Words: Extension, Websites, Browser, social media, Piracy, Fraud.
A Survey on Approaches for Recognition of Dravidian Tulu Script

Handwritten script recognition is a interesting task that has been widely researched in the arena of computer vision and image processing. Tulu language is a Dravidian language spoken in coastal regions of Southern part of India. Recognition of Tulu handwritten scripts is important for preserving cultural heritage, historical documents, and facilitating communication among native speakers. This survey paper provides an overview of the existing tactics for recognition of Tulu scripts. We present a comprehensive review of the literature on various methods such as feature extraction, segmentation, classification, and deep learning techniques used for Tulu script recognition. The paper also discusses the challenges associated with Tulu script recognition and highlights the future research directions in this area. Key Words: Tulu, Handwritten script recognition, Feature extraction, Segmentation, Classification, Deep learning