Reviewerly/exHarmony huggingface dataset:
Soft Hindsight Experience Replay

Efficient learning in the environment with sparse rewards is one of the most important challenges in Deep Reinforcement Learning (DRL). In continuous DRL environments such as robotic arms control, Hindsight Experience Replay (HER) has been shown an effective solution. However, due to the brittleness of deterministic methods, HER and its variants typically suffer from a major challenge for stability and convergence, which significantly affects the final performance. This challenge severely limits the applicability of such methods to complex real-world domains. To tackle this challenge, in this paper, we propose Soft Hindsight Experience Replay (SHER), a novel approach based on HER and Maximum Entropy Reinforcement Learning (MERL), combining the failed experiences reuse and maximum entropy probabilistic inference model. We evaluate SHER on Open AI Robotic manipulation tasks with sparse rewards. Experimental results show that, in contrast to HER and its variants, our proposed SHER achieves state-of-the-art performance, especially in the difficult HandManipulation tasks. Furthermore, our SHER method is more stable, achieving very similar performance across different random seeds.
ONLINE LEARNING EFFECTIVENESS: THROUGH DIGITAL LITERACY AND TEACHER’S PROFESSIONAL COMPETENCE

&lt;p class="JurnalASSETSABSTRAK"&gt;&lt;strong&gt;ABSTRACT&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;span style="vertical-align: inherit;"&gt;&lt;span style="vertical-align: inherit;"&gt;This study aims to measure and analyze the effect of digital literacy and teachers' professional competence on the effectiveness of online learning. This research is quantitative research with a quantitative explanatory approach using a questionnaire. The research sample was 190 students of 11th-grade Financial Accounting using the purposive sampling technique. The data analysis used is multiple linear regression. The results showed that (1) digital literacy had a positive effect on the effectiveness of online learning, and (2) teachers' professional competence had a positive effect on the effectiveness of online learning. Teachers can use the findings in this study to increase the effectiveness of online learning. Teachers must master the material of learning and utilize technology to provide interactive online learning. Interactive online learning will help students improve problem-solving skills, critical thinking abilities, and adaptability.&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p class="JurnalASSETSABSTRAK"&gt;&lt;strong&gt;&lt;em&gt;ABSTRAK&lt;/em&gt;&lt;/strong&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;span style="vertical-align: inherit;"&gt;&lt;span style="vertical-align: inherit;"&gt;&lt;em&gt;Penelitian ini bertujuan untuk mengukur dan menganalisis pengaruh literasi digital dan kompetensi profesional guru terhadap efektivitas pembelajaran daring. Penelitian ini penelitian kuantitatif dengan pendekatan eksplanatif menggunakan kuesioner. Pengambilan sampel menggunakan teknik purposive sampling sejumlah 190 siswa kelas XI Akuntansi Keuangan dan Lembaga. Analisis data yang digunakan adalah regresi linier berganda. Hasil penelitian menunjukkan bahwa (1) literasi digital berpengaruh positif terhadap efektivitas pembelajaran daring, dan (2) kompetensi profesional guru berpengaruh positif terhadap efektivitas pembelajaran daring. Temuan dalam penelitian ini dapat digunakan oleh guru dalam upaya meningkatkan efektivitas pembelajaran daring dimana guru harus mampu menguasai materi secara menyeluruh dan memanfaatkan teknologi untuk menyediakan pembelajaran daring yang nyaman dan interaktif. Pembelajaran daring yang interaktif akan membantu siswa memiliki keterampilan pemecahan masalah, kemampuan berpikir kritis, dan kemampuan beradaptasi di kalangan siswa.&lt;/em&gt;&lt;br /&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;
Let’s Chat: Understanding User Expectations in Socialbot Interactions

This paper analyzes data from the 2021 Amazon Alexa Prize Socialbot Grand Challenge 4, in order to better understand the differences between human-computer interactions (HCI) in a socialbot setting and conventional humanto-human interactions.We find that because socialbots are a new genre of HCI, we are still negotiating norms to guide interactions in this setting.We present several notable patterns in user behavior toward socialbots, which have important implications for guiding future work in the development of conversational agents.
Amsqr at SemEval-2022 Task 4: Towards AutoNLP via Meta-Learning and Adversarial Data Augmentation for PCL Detection

This paper describes the use of AutoNLP techniques applied to the detection of patronizing and condescending language (PCL) in a binary classification scenario.The proposed approach combines meta-learning, in order to identify the best performing combination of deep learning architectures, with the synthesis of adversarial training examples; thus boosting robustness and model generalization.A submission from this system was evaluated as part of the first subtask of SemEval 2022 -Task 4 and achieved an F1 score of 0.57%, which is 16 percentage points higher than the RoBERTa baseline provided by the organizers.
OPDAI at SemEval-2022 Task 11: A hybrid approach for Chinese NER using outside Wikipedia knowledge

This article describes the OPDAI submission to SemEval-2022 Task 11 on Chinese complex NER.First, we explore the performance of model-based approaches and their ensemble, finding that fine-tuning the pre-trained Chinese RoBERTa-wwm model with word semantic representation and contextual gazetteer representation performs best among single models.However, the model-based approach performs poorly on test data because of low-context and unseen-entity cases.Then, we extend our system into two stages: (1) generating entity candidates by using neural model, soft-templates and Wikipedia lexicon.(2) predicting the final entity results within a feature-based rank model.For the evaluation, our best submission achieves an F 1 score of 0.7954 and attains the third-best score in the Chinese sub-track.
Autoclassify Software Defects Using Orthogonal Defect Classification

Software systems have become an integral part of all the organizations. These systems are performing many critical operations. A defect in these systems affects the product quality and the software development process. Prediction of the impact category of these defects helps in improving defect management process as well as taking correct decisions to fix defects. Orthogonal defect classification is a popular model for classifying defects and it provides an in-depth analysis of the defects. In this study, we proposed an auto classify approach to classify the defects into impact categories as defined by Orthogonal Defect Classification (ODC). Bag of words, term frequency-inverse document frequency and word embedding have been used to represent the textual data into numeric vectors. For experimental work, we have used 4,096 reports form three NoSQL databases. We have trained and tested the proposed autoclassify approach using Support Vector Machine (SVM) and Random Forest Classifier (RFC). We achieved maximum accuracy 94% and 85.99% using SVM and RFC respectively.
How do people talk about images? A study on open-domain conversations with images.

This paper explores how humans conduct conversations with images by investigating an open-domain image conversation dataset, Im-ageChat.We examined the conversations with images from the perspectives of image relevancy and image information.We found that utterances/conversations are not always related to the given image, and conversation topics diverge within three turns about half of the time.Besides image objects, more comprehensive non-object image information is also indispensable.After inspecting the causes, we suggested that understanding the overall scenario of image and connecting objects based on their high-level attributes might be very helpful to generate more engaging open-domain conversations when an image is presented.We proposed enriching the image information with image caption and object tags based on our analysis.With our proposed image + features, we improved automatic metrics including BLEU and Bert Score, and increased the diversity and image-relevancy of generated responses to the strong SOTA baseline.The result verifies that our analysis provides valuable insights and could facilitate future research on open-domain conversations with images.
ValCAT: Variable-Length Contextualized Adversarial Transformations Using Encoder-Decoder Language Model

Adversarial texts help explore vulnerabilities in language models, improve model robustness, and explain their working mechanisms.However, existing word-level attack methods trap in a one-to-one attack pattern, i.e., only a single word can be modified in one transformation round, and they ignore the interactions between several consecutive words.In this paper, we propose ValCAT, a black-box attack framework that misleads the language model by applying variable-length contextualized transformations to the original text.Compared to wordlevel methods, ValCAT expands the basic units of perturbation from single words to spans composed of multiple consecutive words, enhancing the perturbation capability.Experiments show that our method outperforms state-of-theart methods in terms of attack success rate, perplexity, and semantic similarity on several classification tasks and inference tasks.The comprehensive human evaluation demonstrates that ValCAT has a significant advantage in ensuring the fluency of the adversarial examples and achieves better semantic consistency.We release the code at https://github.com/linerxliner/ValCAT.
A Study of the Tasks and Models in Machine Reading Comprehension

To provide a survey on the existing tasks and models in Machine Reading Comprehension (MRC), this report reviews: 1) the dataset collection and performance evaluation of some representative simple-reasoning and complex-reasoning MRC tasks; 2) the architecture designs, attention mechanisms, and performance-boosting approaches for developing neural-network-based MRC models; 3) some recently proposed transfer learning approaches to incorporating text-style knowledge contained in external corpora into the neural networks of MRC models; 4) some recently proposed knowledge base encoding approaches to incorporating graph-style knowledge contained in external knowledge bases into the neural networks of MRC models. Besides, according to what has been achieved and what are still deficient, this report also proposes some open problems for the future research.
Sequence Classification via LCS

Computing a longest common subsequence of two given sequences is a fundamental problem in several fields of computer science. While there exist dynamic programming algorithms that can solve the problem in polynomial-time, their running time is considered too high for some practical applications. In this contribution we propose a method for comparing two sequences that is based on (1) a combinatorial algorithm that computes a window constrained longest common subsequence and (2) a genetic algorithm. We present experiments on synthetic datasets that show that the method is able to return solutions close to the length of a longest compute common subsequence. Moreover, the method is faster than the dynamic programming algorithm for the longest common subsequence problem.
Shareable Representations for Search Query Understanding

Understanding search queries is critical for shopping search engines to deliver a satisfying customer experience. Popular shopping search engines receive billions of unique queries yearly, each of which can depict any of hundreds of user preferences or intents. In order to get the right results to customers it must be known queries like "inexpensive prom dresses" are intended to not only surface results of a certain product type but also products with a low price. Referred to as query intents, examples also include preferences for author, brand, age group, or simply a need for customer service. Recent works such as BERT have demonstrated the success of a large transformer encoder architecture with language model pre-training on a variety of NLP tasks. We adapt such an architecture to learn intents for search queries and describe methods to account for the noisiness and sparseness of search query data. We also describe cost effective ways of hosting transformer encoder models in context with low latency requirements. With the right domain-specific training we can build a shareable deep learning model whose internal representation can be reused for a variety of query understanding tasks including query intent identification. Model sharing allows for fewer large models needed to be served at inference time and provides a platform to quickly build and roll out new search query classifiers.
Reducing Selection Bias in Counterfactual Reasoning for Individual  Treatment Effects Estimation

Counterfactual reasoning is an important paradigm applicable in many fields, such as healthcare, economics, and education. In this work, we propose a novel method to address the issue of \textit{selection bias}. We learn two groups of latent random variables, where one group corresponds to variables that only cause selection bias, and the other group is relevant for outcome prediction. They are learned by an auto-encoder where an additional regularized loss based on Pearson Correlation Coefficient (PCC) encourages the de-correlation between the two groups of random variables. This allows for explicitly alleviating selection bias by only keeping the latent variables that are relevant for estimating individual treatment effects. Experimental results on a synthetic toy dataset and a benchmark dataset show that our algorithm is able to achieve state-of-the-art performance and improve the result of its counterpart that does not explicitly model the selection bias.
Context-Aware Dynamic Feature Extraction for 3D Object Detection in  Point Clouds

Varying density of point clouds increases the difficulty of 3D detection. In this paper, we present a context-aware dynamic network (CADNet) to capture the variance of density by considering both point context and semantic context. Point-level contexts are generated from original point clouds to enlarge the effective receptive filed. They are extracted around the voxelized pillars based on our extended voxelization method and processed with the context encoder in parallel with the pillar features. With a large perception range, we are able to capture the variance of features for potential objects and generate attentive spatial guidance to help adjust the strengths for different regions. In the region proposal network, considering the limited representation ability of traditional convolution where same kernels are shared among different samples and positions, we propose a decomposable dynamic convolutional layer to adapt to the variance of input features by learning from local semantic context. It adaptively generates the position-dependent coefficients for multiple fixed kernels and combines them to convolve with local feature windows. Based on our dynamic convolution, we design a dual-path convolution block to further improve the representation ability. We conduct experiments with our Network on KITTI dataset and achieve good performance on 3D detection task for both precision and speed. Our one-stage detector outperforms SECOND and PointPillars by a large margin and achieves the speed of 30 FPS.
Deep Image Deraining Via Intrinsic Rainy Image Priors and Multi-scale  Auxiliary Decoding

Different rain models and novel network structures have been proposed to remove rain streaks from single rainy images. In this work, we bring attention to the intrinsic priors and multi-scale features of the rainy images, and develop several intrinsic loss functions to train a CNN deraining network. We first study the sparse priors of rainy images, which have been verified to preserve unbroken edges in image decomposition. However, its mathematical formulation usually leads to an intractable solution, we propose quasi-sparsity priors to decrease complexity, so that our network can be trained under the supervision of sparse properties of rainy images. Quasi-sparsity supervises network training in different gradient domain which is still ill-posed to decompose a rainy image into rain layer and background layer. We develop another $L_1$ loss based on the intrinsic low-value property of rain layer to restore image contents together with the commonly-used $L_1$ similarity loss. Multi-scale features are further explored via a multi-scale auxiliary decoding structure to show which kinds of features contribute the most to the deraining task, and the corresponding multi-scale auxiliary loss improves the deraining performance further. In our network, more efficient group convolution and feature sharing are utilized to obtain an one order of magnitude improvement in network running speed. The proposed deraining method performs favorably against state-of-the-art deraining approaches.
OSL Shaders for RenderMan

Share on OSL Shaders for RenderMan Author: Mitch Prater Laika, United States of America Laika, United States of AmericaView Profile Authors Info & Claims SIGGRAPH '22: ACM SIGGRAPH 2022 Educator's ForumJuly 2022 Article No.: 13Pages 1–100https://doi.org/10.1145/3532724.3535604Published:26 July 2022Publication History 0citation71DownloadsMetricsTotal Citations0Total Downloads71Last 12 Months71Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Online Grounding of Symbolic Planning Domains in Unknown Environments

If a robotic agent wants to exploit symbolic planning techniques to achieve some goal, it must be able to properly ground an abstract planning domain in the environment in which it operates. However, if the environment is initially unknown by the agent, the agent needs to explore it and discover the salient aspects of the environment necessary to reach its goals. Namely, the agent has to discover: (i) the objects present in the environment, (ii) the properties of these objects and their relations, and finally (iii) how abstract actions can be successfully executed. The paper proposes a framework that aims to accomplish the aforementioned perspective for an agent that perceives the environment partially and subjectively, through real value sensors (e.g., GPS, and on-board camera) and can operate in the environment through low level actuators (e.g., move forward of 20 cm). We evaluate the proposed architecture in photo-realistic simulated environments, where the sensors are RGB-D on-board camera, GPS and compass, and low level actions include movements, grasping/releasing objects, and manipulating objects. The agent is placed in an unknown environment and asked to find objects of a certain type, place an object on top of another, close or open an object of a certain type. We compare our approach with a state of the art method on object goal navigation based on reinforcement learning, showing better performances.
Risk-Aware Guidance of a Fixed-Wing UAV using Neural Network Model Predictive Control

This paper presents a guidance algorithm for fixed-wing Unmanned Aerial Vehicles (UAVs) that accounts for risk wrt people at ground in case of failure of the vehicle. Model Predictive Control is used along with neural networks to predict online the ground risk probability associated to future trajectories. Guidance inputs are computed in this way for the UAV to follow a reference path while ensuring a given level of safety for the mission, despite flight conditions that may differ from mission preparation (wind, altitude, speed). Computation time concerns are accounted for in the design of the algorithm with the objective to facilitate a possible future implementation on board of an UAV. More precisely, neural networks are used for fast risk prediction, as well as systematic search for resolution of the MPC problem corresponding to the risk avoidance component of the control. Simulation results are proposed to illustrate the proposed approach.
Research on the Application of Object Detection in the Installation of Spacer Bars

Abstract The correct installation of spacer bars is an important part of ensuring people’s daily life. This paper studies and implements a spacer bar installation confirmation system based on a deep learning object detection algorithm, which makes up for the deficiencies in the installation. Among the object detection algorithms, Faster-RCNN has higher detection accuracy, is more accurate than one-stage and can detect multi-scale and small objects. Through historical use, it is found that Faster-RCNN is excellent in detecting multi-class image sets tasks. For personal data sets, only fine-tuning is needed to achieve better results, and it also has a fast detection rate. Therefore, this paper is based on the Faster-RCNN algorithm to realize the detection and confirmation of the plug and rubber during the installation of the spacer bar.
An Ensemble Learning and Swarm Based Segmentation Framework for Video Event Recognition

Video event recognition aims to recognize the spatiotemporal visual patterns of events from videos.In recent years, event recognition has attracted growing interest from both academia and industry.Recognizing events in surveillance videos is still quite challenging, largely due to the tremendous intra class variations of events caused by visual appearance differences, target motion variations, viewpoint change and temporal variability.The existing system designed an extreme learning machine and action recognition algorithm for generalized maximum clique problem in video event recognition.The implemented system designed an enhanced ensemble deep learning and swarm based segmentation framework for video event recognition.The presented ensemble framework in that not only decreases the information loss and overfitting problems caused by single models.Initially, a video frames are taken as an input and most salient information extract from it.The VLAD for feature encoding is utilized for feature encoding.The segmentation process is done with the help of Random Inertia Weight based Particle Swarm Optimization (RIWPSO) of successive frames are exploited for pattern matching in a simple feature space.Thereafter, an Ensemble Learning (EL) is developed based on the performance of each SVM and Elman Recurrent Neural Network (ERNN) classifier on each feature set.Thus the simulation results demonstrate the effectiveness of the implemented enhanced ensemble deep learning technique for video event recognition compare to the existing methods.