人纹理感知是多感官输入的加权平均值：视觉和触觉。当视觉传感机制提取全局特征时，触觉机制通过提取本地特征来补充它。文献中缺乏耦合的视觉效果数据集是研究类似于人类质地知觉的多模式融合策略的挑战。本文介绍了一个视觉数据集，可扩大现有的触觉数据集。我们提出了一种新型的深层融合体系结构，该融合体使用四种类型的融合策略融合了视觉和触觉数据：求和，串联，最大程度和注意力。我们的模型仅在触觉（SVM -92.60％）和仅视觉（FENET -50-50-85.01％）体系结构方面显示出显着的性能改进（97.22％）。在几种融合技术中，注意引导的体系结构可提高分类的精度。我们的研究表明，类似于人类纹理感知，提出的模型选择了两种方式（视觉和触觉）的加权组合，从而导致表面粗糙度分类的精度较高。它选择最大化视觉模态失败的触觉方式的重量，反之亦然。

Information overloading requires the need for summarizers to extract salient information from the text. Currently, there is an overload of dialogue data due to the rise of virtual communication platforms. The rise of Covid-19 has led people to rely on online communication platforms like Zoom, Slack, Microsoft Teams, Discord, etc. to conduct their company meetings. Instead of going through the entire meeting transcripts, people can use meeting summarizers to select useful data. Nevertheless, there is a lack of comprehensive surveys in the field of meeting summarizers. In this survey, we aim to cover recent meeting summarization techniques. Our survey offers a general overview of text summarization along with datasets and evaluation metrics for meeting summarization. We also provide the performance of each summarizer on a leaderboard. We conclude our survey with different challenges in this domain and potential research opportunities for future researchers.

本文介绍了基于可见性的移动机器人的\传感器\的数学模型。提供类似于针对计算机视觉的针孔摄像机模型的目的，介绍的模型有望提供有用的，理想化的与任务相关信息的特征，可以从其输出或观察值中推断出来。可能的任务包括当在未知环境中部署移动机器人时导航，本地化和映射。这些模型可以在传统的深度传感器之间进行直接比较，并突出显示触摸传感可能与飞行时间或视觉传感器互换的案例，并表征触摸传感提供的独特优势。这些模型包括接触检测，压缩，负载轴承和挠度。该结果可以作为移动机器人传感器融合系统创新触摸传感器设计的基本构建块。

长表质疑应答（LFQA）任务要求将相关的文件检索到查询，使用它们形成段落长度答案。尽管LFQA建模相当大，但基本问题妨碍了其进度：i）火车/验证/测试数据集重叠，ii）缺少自动度量标准和III）在检索的文档中产生的答案不会“接地”。这项工作解决了这些关键瓶颈的每一个，有助于自然语言推理/生成（NLI / NLG）方法和指标，使其减轻重大进展。

随着移动计算和网络技术的快速增长，令人反感的语言在社交网络平台上变得更加普遍。由于本地语言的令人反感语言识别对于中等社交媒体内容至关重要，因此在本文中，我们使用三种Dravidian语言，即Malayalam，Tamil和Kannada，这些语言遭到资源。我们在EACL 2021的Fire 2020- Hasoc-DravidiancodeMix和Dravidianlangtech提供了一个评估任务，旨在提供一个比较不同方法对此问题的框架。本文介绍了数据创建，定义任务，列出参与系统，并讨论各种方法。

社交媒体平台上的滥用内容的增长增加对在线用户的负面影响。对女同性恋，同性恋者，跨性别或双性恋者的恐惧，不喜欢，不适或不疑虑被定义为同性恋/转铁症。同性恋/翻译语音是一种令人反感的语言，可以总结为针对LGBT +人的仇恨语音，近年来越来越受到兴趣。在线同性恋恐惧症/ Transphobobia是一个严重的社会问题，可以使网上平台与LGBT +人有毒和不受欢迎，同时还试图消除平等，多样性和包容性。我们为在线同性恋和转鸟以及专家标记的数据集提供了新的分类分类，这将允许自动识别出具有同种异体/传递内容的数据集。我们受过教育的注释器并以综合的注释规则向他们提供，因为这是一个敏感的问题，我们以前发现未受训练的众包注释者因文化和其他偏见而诊断倡导性的群体。数据集包含15,141个注释的多语言评论。本文介绍了构建数据集，数据的定性分析和注册间协议的过程。此外，我们为数据集创建基线模型。据我们所知，我们的数据集是第一个已创建的数据集。警告：本文含有明确的同性恋，转基因症，刻板印象的明确陈述，这可能对某些读者令人痛苦。

Quadruped robots are currently used in industrial robotics as mechanical aid to automate several routine tasks. However, presently, the usage of such a robot in a domestic setting is still very much a part of the research. This paper discusses the understanding and virtual simulation of such a robot capable of detecting and understanding human emotions, generating its gait, and responding via sounds and expression on a screen. To this end, we use a combination of reinforcement learning and software engineering concepts to simulate a quadruped robot that can understand emotions, navigate through various terrains and detect sound sources, and respond to emotions using audio-visual feedback. This paper aims to establish the framework of simulating a quadruped robot that is emotionally intelligent and can primarily respond to audio-visual stimuli using motor or audio response. The emotion detection from the speech was not as performant as ERANNs or Zeta Policy learning, still managing an accuracy of 63.5%. The video emotion detection system produced results that are almost at par with the state of the art, with an accuracy of 99.66%. Due to its "on-policy" learning process, the PPO algorithm was extremely rapid to learn, allowing the simulated dog to demonstrate a remarkably seamless gait across the different cadences and variations. This enabled the quadruped robot to respond to generated stimuli, allowing us to conclude that it functions as predicted and satisfies the aim of this work.

Searching long egocentric videos with natural language queries (NLQ) has compelling applications in augmented reality and robotics, where a fluid index into everything that a person (agent) has seen before could augment human memory and surface relevant information on demand. However, the structured nature of the learning problem (free-form text query inputs, localized video temporal window outputs) and its needle-in-a-haystack nature makes it both technically challenging and expensive to supervise. We introduce Narrations-as-Queries (NaQ), a data augmentation strategy that transforms standard video-text narrations into training data for a video query localization model. Validating our idea on the Ego4D benchmark, we find it has tremendous impact in practice. NaQ improves multiple top models by substantial margins (even doubling their accuracy), and yields the very best results to date on the Ego4D NLQ challenge, soundly outperforming all challenge winners in the CVPR and ECCV 2022 competitions and topping the current public leaderboard. Beyond achieving the state-of-the-art for NLQ, we also demonstrate unique properties of our approach such as gains on long-tail object queries, and the ability to perform zero-shot and few-shot NLQ.

Machine Translation (MT) system generally aims at automatic representation of source language into target language retaining the originality of context using various Natural Language Processing (NLP) techniques. Among various NLP methods, Statistical Machine Translation(SMT). SMT uses probabilistic and statistical techniques to analyze information and conversion. This paper canvasses about the development of bilingual SMT models for translating English to fifteen low-resource Indian Languages (ILs) and vice versa. At the outset, all 15 languages are briefed with a short description related to our experimental need. Further, a detailed analysis of Samanantar and OPUS dataset for model building, along with standard benchmark dataset (Flores-200) for fine-tuning and testing, is done as a part of our experiment. Different preprocessing approaches are proposed in this paper to handle the noise of the dataset. To create the system, MOSES open-source SMT toolkit is explored. Distance reordering is utilized with the aim to understand the rules of grammar and context-dependent adjustments through a phrase reordering categorization framework. In our experiment, the quality of the translation is evaluated using standard metrics such as BLEU, METEOR, and RIBES

We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 26 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. This dataset is the largest ever collection of lidar sensor data and supports self-supervised learning and the emerging task of point cloud forecasting. Finally, the Motion Forecasting Dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene. Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category. In all three datasets, each scenario contains its own HD Map with 3D lane and crosswalk geometry - sourced from data captured in six distinct cities. We believe these datasets will support new and existing machine learning research problems in ways that existing datasets do not. All datasets are released under the CC BY-NC-SA 4.0 license.