Reviewerly/exHarmony huggingface dataset:
A Study on Ancient Temple Structural Elements Recognition Using Genetic Algorithm

The systematic and scientific study of the lifestyle and culture of earlier peoples is known as archaeology.The Indian history of archaeology spans from the 19th century to the present status, it includes the region's history investigated by a wide variety of archaeologists.They don't have any such authentic digital methods to be quoted in research.Manual Recognition of ancient temple structural elements is quite difficult to recognize, has archaeologists face many complex problems because they don't have any automated Recognition method.Recognition is helpful for archaeologists to get more detailed information of ancient temples which is very important for further research.Thus, in this work it is proposed to develop automated method for Recognition ancient temple structural elements (vimana & pillars) for further archaeological research purpose.The proposed method extracts Genetic programming evolved spatial descriptor and classifies the temple structural elements visited by archaeologists based on linear discriminant method [LDA].The proposed method is divided into 3 main phases: pre-processing, genetic programming evolution and Recognition.The Generalized Co-Occurrence Matrix is used in the pre-processing phase to change images into a format that genetic programming systems may use to process them.The second stage produces the best spatial descriptor to date in the form of a programme that is based on fitness Utilizing LDA, the Fitness is determined.Once the program's output has been received, it can be used for Recognition.Experimental results shows, it demonstrates relatively high accuracy in Recognizing both vimana(gopura) & pillars of different temples.The proposed method is implemented in MATLAB.These results will play very significant role in identification of temple architecture, which in-turn helps in conservation and reconstruction of temples.The proposed methodology will give 98.8% accuracy in pillars recognition and 98.4% accuracy in vimana recognition.
Dual Contrastive Learning for Efficient Static Feature Representation in Sequential Recommendations

Static user and item features constitute important information to be taken into account in the recommendation process. However, as these features are usually sparse and of large-vocabulary, existing deep learning-based methods typically construct large tables of high-dimensional feature embeddings, which is inefficient in terms of memory storage and is computationally problematic. On the other hand, while product quantization-based methods have been proposed to compress latent embeddings, they usually come at the cost of compromising recommendation performance due to the restrictive expressive power, as feature correlations and user-item interactions are not properly captured in the compression process. To address these issues, we propose a novel Dual Contrastive Learning method to generate low-dimensional discrete static feature representations that significantly reduce memory storage and computational complexity, while simultaneously producing superior recommendation performance. Extensive offline experiments on three large-scale industrial datasets demonstrate that our proposed model significantly outperforms the selected baselines. In addition, we conducted an online A/B test at Alibaba and show that the proposed model significantly improves the average video streaming time, while reducing the size of the feature embedding table by 90% over the currently deployed system.
Human Action Recognition (HAR) Classification Using MediaPipe and Long Short-Term Memory (LSTM)

Human Action Recognition is an important research topic in Machine Learning and Computer Vision domains. One of the proposed methods is a combination of MediaPipe library and Long Short-Term Memory concerning the testing accuracy and training duration as indicators to evaluate the model performance. This research tried to adapt proposed LSTM models to implement HAR with image features extracted by MediaPipe library. There would be a comparison between LSTM models based on their testing accuracy and training duration. This research was conducted under OSEMN methods (Obtain, Scrub, Explore, Model, and iNterpret). The dataset was preprocessed Weizmann dataset with data preprocessing and data augmentation implementations. Video features extracted by MediaPipe: Pose was used in training and validation processes on neural network models focusing on Long Short-Term Memory layers. The processes were finished by model performance evaluation based on confusion matrices interpretation and calculations of accuracy, error rate, precision, recall, and F1score. This research yielded seven LSTM model variants with the highest testing accuracy at 82% taking 10 minutes and 50 seconds of training duration.
A New Method for Graph-Based Representation of Text in Natural Language Processing

Natural language processing is still an emerging field in machine learning. Access to more and more data sets in textual form, new applications for artificial intelligence and the need for simple communication with operating systems all simultaneously affect the importance of natural language processing in evolving artificial intelligence. Traditional methods of textual representation, such as Bag-of-Words, have some limitations that result from the lack of consideration of semantics and dependencies between words. Therefore, we propose a new approach based on graph representations, which takes into account both local context and global relationships between words, allowing for a more expressive textual representation. The aim of the paper is to examine the possibility of using graph representations in natural language processing and to demonstrate their use in text classification. An innovative element of the proposed approach is the use of common cliques in graphs representing documents to create a feature vector. Experiments confirm that the proposed approach can improve classification efficiency. The use of a new text representation method to predict book categories based on the analysis of its content resulted in accuracy, precision, recall and an F1-score of over 90%. Moving from traditional approaches to a graph-based approach could make a big difference in natural language processing and text analysis and could open up new opportunities in the field.
Multi-Action Dialog Policy Learning from Logged User Feedback

Multi-action dialog policy (MADP), which generates multiple atomic dialog actions per turn, has been widely applied in task-oriented dialog systems to provide expressive and efficient system responses. Existing MADP models usually imitate action combinations from the labeled multi-action dialog samples. Due to data limitations, they generalize poorly toward unseen dialog flows. While reinforcement learning-based methods are proposed to incorporate the service ratings from real users and user simulators as external supervision signals, they suffer from sparse and less credible dialog-level rewards. To cope with this problem, we explore to improve MADPL with explicit and implicit turn-level user feedback received for historical predictions (i.e., logged user feedback) that are cost-efficient to collect and faithful to real-world scenarios. The task is challenging since the logged user feedback provides only partial label feedback limited to the particular historical dialog actions predicted by the agent. To fully exploit such feedback information, we propose BanditMatch, which addresses the task from a feedback-enhanced semi-supervised learning perspective with a hybrid learning objective of SSL and bandit learning. BanditMatch integrates pseudo-labeling methods to better explore the action space through constructing full label feedback. Extensive experiments show that our BanditMatch improves MADPL over the state-of-the-art methods by generating more concise and informative responses. The source code and the appendix of this paper can be obtained from https://github.com/ShuoZhangXJTU/BanditMatch.
LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfinding

We propose a novel complete algorithm for multi-agent pathfinding (MAPF) called lazy constraints addition search for MAPF (LaCAM). MAPF is a problem of finding collision-free paths for multiple agents on graphs and is the foundation of multi-robot coordination. LaCAM uses a two-level search to find solutions quickly, even with hundreds of agents or more. At the low-level, it searches constraints about agents' locations. At the high-level, it searches a sequence of all agents' locations, following the constraints specified by the low-level. Our exhaustive experiments reveal that LaCAM is comparable to or outperforms state-of-the-art sub-optimal MAPF algorithms in a variety of scenarios, regarding success rate, planning time, and solution quality of sum-of-costs.
RenewNAT: Renewing Potential Translation for Non-autoregressive Transformer

Non-autoregressive neural machine translation (NAT) models are proposed to accelerate the inference process while maintaining relatively high performance. However, existing NAT models are difficult to achieve the desired efficiency-quality trade-off. For one thing, fully NAT models with efficient inference perform inferior to their autoregressive counterparts. For another, iterative NAT models can, though, achieve comparable performance while diminishing the advantage of speed. In this paper, we propose RenewNAT, a flexible framework with high efficiency and effectiveness, to incorporate the merits of fully and iterative NAT models. RenewNAT first generates the potential translation results and then renews them in a single pass. It can achieve significant performance improvements at the same expense as traditional NAT models (without introducing additional model parameters and decoding latency). Experimental results on various translation benchmarks (e.g., 4 WMT) show that our framework consistently improves the performance of strong fully NAT methods (e.g., GLAT and DSLP) without additional speed overhead.
Universal Information Extraction as Unified Semantic Matching

The challenge of information extraction (IE) lies in the diversity of label schemas and the heterogeneity of structures. Traditional methods require task-specific model design and rely heavily on expensive supervision, making them difficult to generalize to new schemas. In this paper, we decouple IE into two basic abilities, structuring and conceptualizing, which are shared by different tasks and schemas. Based on this paradigm, we propose to universally model various IE tasks with Unified Semantic Matching (USM) framework, which introduces three unified token linking operations to model the abilities of structuring and conceptualizing. In this way, USM can jointly encode schema and input text, uniformly extract substructures in parallel, and controllably decode target structures on demand. Empirical evaluation on 4 IE tasks shows that the proposed method achieves state-of-the-art performance under the supervised experiments and shows strong generalization ability in zero/few-shot transfer settings.
Disentangled CVAEs with Contrastive Learning for Explainable Recommendation

Modern recommender systems are increasingly expected to provide informative explanations that enable users to understand the reason for particular recommendations. However, previous methods struggle to interpret the input IDs of user--item pairs in real-world datasets, failing to extract adequate characteristics for controllable generation. To address this issue, we propose disentangled conditional variational autoencoders (CVAEs) for explainable recommendation, which leverage disentangled latent preference factors and guide the explanation generation with the refined condition of CVAEs via a self-regularization contrastive learning loss. Extensive experiments demonstrate that our method generates high-quality explanations and achieves new state-of-the-art results in diverse domains.
Hierarchical Event Grounding

Event grounding aims at linking mention references in text corpora to events from a knowledge base (KB). Previous work on this task focused primarily on linking to a single KB event, thereby overlooking the hierarchical aspects of events. Events in documents are typically described at various levels of spatio-temporal granularity. These hierarchical relations are utilized in downstream tasks of narrative understanding and schema construction. In this work, we present an extension to the event grounding task that requires tackling hierarchical event structures from the KB. Our proposed task involves linking a mention reference to a set of event labels from a subevent hierarchy in the KB. We propose a retrieval methodology that leverages event hierarchy through an auxiliary hierarchical loss. On an automatically created multilingual dataset from Wikipedia and Wikidata, our experiments demonstrate the effectiveness of the hierarchical loss against retrieve and re-rank baselines. Furthermore, we demonstrate the systems' ability to aid hierarchical discovery among unseen events. Code is available at https://github.com/JefferyO/Hierarchical-Event-Grounding
Generalized Category Discovery with Decoupled Prototypical Network

Generalized Category Discovery (GCD) aims to recognize both known and novel categories from a set of unlabeled data, based on another dataset labeled with only known categories. Without considering differences between known and novel categories, current methods learn about them in a coupled manner, which can hurt model's generalization and discriminative ability. Furthermore, the coupled training approach prevents these models transferring category-specific knowledge explicitly from labeled data to unlabeled data, which can lose high-level semantic information and impair model performance. To mitigate above limitations, we present a novel model called Decoupled Prototypical Network (DPN). By formulating a bipartite matching problem for category prototypes, DPN can not only decouple known and novel categories to achieve different training targets effectively, but also align known categories in labeled and unlabeled data to transfer category-specific knowledge explicitly and capture high-level semantics. Furthermore, DPN can learn more discriminative features for both known and novel categories through our proposed Semantic-aware Prototypical Learning (SPL). Besides capturing meaningful semantic information, SPL can also alleviate the noise of hard pseudo labels through semantic-weighted soft assignment. Extensive experiments show that DPN outperforms state-of-the-art models by a large margin on all evaluation metrics across multiple benchmark datasets. Code and data are available at https://github.com/Lackel/DPN.
Compressed Heterogeneous Graph for Abstractive Multi-Document Summarization

Multi-document summarization (MDS) aims to generate a summary for a number of related documents. We propose HGSum — an MDS model that extends an encoder-decoder architecture to incorporate a heterogeneous graph to represent different semantic units (e.g., words and sentences) of the documents. This contrasts with existing MDS models which do not consider different edge types of graphs and as such do not capture the diversity of relationships in the documents. To preserve only key information and relationships of the documents in the heterogeneous graph, HGSum uses graph pooling to compress the input graph. And to guide HGSum to learn the compression, we introduce an additional objective that maximizes the similarity between the compressed graph and the graph constructed from the ground-truth summary during training. HGSum is trained end-to-end with the graph similarity and standard cross-entropy objectives. Experimental results over Multi-News, WCEP-100, and Arxiv show that HGSum outperforms state-of-the-art MDS models. The code for our model and experiments is available at: https://github.com/oaimli/HGSum.
STAGE: Span Tagging and Greedy Inference Scheme for Aspect Sentiment Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) has become an emerging task in sentiment analysis research, aiming to extract triplets of the aspect term, its corresponding opinion term, and its associated sentiment polarity from a given sentence. Recently, many neural networks based models with different tagging schemes have been proposed, but almost all of them have their limitations: heavily relying on 1) prior assumption that each word is only associated with a single role (e.g., aspect term, or opinion term, etc. ) and 2) word-level interactions and treating each opinion/aspect as a set of independent words. Hence, they perform poorly on the complex ASTE task, such as a word associated with multiple roles or an aspect/opinion term with multiple words. Hence, we propose a novel approach, Span TAgging and Greedy infErence (STAGE), to extract sentiment triplets in span-level, where each span may consist of multiple words and play different roles simultaneously. To this end, this paper formulates the ASTE task as a multi-class span classification problem. Specifically, STAGE generates more accurate aspect sentiment triplet extractions via exploring span-level information and constraints, which consists of two components, namely, span tagging scheme and greedy inference strategy. The former tag all possible candidate spans based on a newly-defined tagging set. The latter retrieves the aspect/opinion term with the maximum length from the candidate sentiment snippet to output sentiment triplets. Furthermore, we propose a simple but effective model based on the STAGE, which outperforms the state-of-the-arts by a large margin on four widely-used datasets. Moreover, our STAGE can be easily generalized to other pair/triplet extraction tasks, which also demonstrates the superiority of the proposed scheme STAGE.
Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy Gradient

Cooperative multi-agent policy gradient (MAPG) algorithms have recently attracted wide attention and are regarded as a general scheme for the multi-agent system. Credit assignment plays an important role in MAPG and can induce cooperation among multiple agents. However, most MAPG algorithms cannot achieve good credit assignment because of the game-theoretic pathology known as centralized-decentralized mismatch. To address this issue, this paper presents a novel method, Multi-Agent Polarization Policy Gradient (MAPPG). MAPPG takes a simple but efficient polarization function to transform the optimal consistency of joint and individual actions into easily realized constraints, thus enabling efficient credit assignment in MAPPG. Theoretically, we prove that individual policies of MAPPG can converge to the global optimum. Empirically, we evaluate MAPPG on the well-known matrix game and differential game, and verify that MAPPG can converge to the global optimum for both discrete and continuous action spaces. We also evaluate MAPPG on a set of StarCraft II micromanagement tasks and demonstrate that MAPPG outperforms the state-of-the-art MAPG algorithms.
Q-functionals for Value-Based Continuous Control

We present Q-functionals, an alternative architecture for continuous control deep reinforcement learning. Instead of returning a single value for a state-action pair, our network transforms a state into a function that can be rapidly evaluated in parallel for many actions, allowing us to efficiently choose high-value actions through sampling. This contrasts with the typical architecture of off-policy continuous control, where a policy network is trained for the sole purpose of selecting actions from the Q-function. We represent our action-dependent Q-function as a weighted sum of basis functions (Fourier, Polynomial, etc) over the action space, where the weights are state-dependent and output by the Q-functional network. Fast sampling makes practical a variety of techniques that require Monte-Carlo integration over Q-functions, and enables action-selection strategies besides simple value-maximization. We characterize our framework, describe various implementations of Q-functionals, and demonstrate strong performance on a suite of continuous control tasks.
Structured light 3-D sensing for scenes with discontinuous reflectivity: error removal based on scene reconstruction and normalization

Structured light-based 3-D sensing technique reconstructs the 3-D shape from the disparity given by pixel correspondence of two sensors. However, for scene surface containing discontinuous reflectivity (DR), the captured intensity deviates from its actual value caused by the non-ideal camera point spread function (PSF), thus generating 3-D measurement error. First, we construct the error model of fringe projection profilometry (FPP). From which, we conclude that the DR error of FPP is related to both the camera PSF and the scene reflectivity. The DR error of FPP is hard to be alleviated because of unknown scene reflectivity. Second, we introduce single-pixel imaging (SI) to reconstruct the scene reflectivity and normalize the scene with scene reflectivity "captured" by the projector. From the normalized scene reflectivity, pixel correspondence with error opposite to the original reflectivity is calculated for the DR error removal. Third, we propose an accurate 3-D reconstruction method under discontinuous reflectivity. In this method, pixel correspondence is first established by using FPP, and then refined by using SI with reflectivity normalization. Both the analysis and the measurement accuracy are verified under scenes with different reflectivity distributions in the experiments. As a result, the DR error is effectively alleviated while taking an acceptable measurement time.
Approximating Full Conformal Prediction at Scale via Influence Functions

Conformal prediction (CP) is a wrapper around traditional machine learning models, giving coverage guarantees under the sole assumption of exchangeability; in classification problems, a CP guarantees that the error rate is at most a chosen significance level, irrespective of whether the underlying model is misspecified. However, the prohibitive computational costs of full CP led researchers to design scalable alternatives, which alas do not attain the same guarantees or statistical power of full CP. In this paper, we use influence functions to efficiently approximate full CP. We prove that our method is a consistent approximation of full CP, and empirically show that the approximation error becomes smaller as the training set increases; e.g., for 1,000 training points the two methods output p-values that are
TC-DWA:Text Clustering with Dual Word-Level Augmentation

The pre-trained language models, e.g., ELMo and BERT, have recently achieved promising performance improvement in a wide range of NLP tasks, because they can output strong contextualized embedded features of words. Inspired by their great success, in this paper we target at fine-tuning them to effectively handle the text clustering task, i.e., a classic and fundamental challenge in machine learning. Accordingly, we propose a novel BERT-based method, namely Text Clustering with Dual Word-level Augmentation (TCDWA). To be specific, we formulate a self-training objective and enhance it with a dual word-level augmentation technique. First, we suppose that each text contains several most informative words, called anchor words, supporting the full text semantics. We use the embedded features of anchor words as augmented data, which are selected by ranking the norm-based attention weights of words. Second, we formulate an expectation form of word augmentation, which is equivalent to generating infinite augmented features, and further suggest a tractable approximation of Taylor expansion for efficient optimization. To evaluate the effectiveness of TCDWA, we conduct extensive experiments on several benchmark text datasets. The results demonstrate that TCDWA consistently outperforms the state-of-the-art baseline methods. Code available: https://github.com/BoCheng-96/TC-DWA.