Reviewerly/exHarmony huggingface dataset:
Spatial-temporal Fusion Network for Fast Video Shadow Detection

Existing video shadow detectors often need postprocessing or additional input to perform better, thereby degrading their video shadow detection speed. In this work, we present a novel spatial-temporal fusion network (STF-Net), which can eﬃciently detect shadows in videos with real-time speed (30FPS) and postprocessing-free. Our STF-Net is based solely on an attention-based spatial-temporal fusion block, equipping with recurrence and CNNs entirely. Experimental results on ViSha validation dataset show that our network exceeds state-of-the-art methods quantitatively and qualitatively.
A Novel Clustering-Based Image Inpainting Model Using the Loermgan Algorithm

The process of recovering the damaged or missing areas from an image utilizing information as of known portions is termed Image Inpainting. To refurbish the damaged image into a new one alike an actual image, numerous sophisticated methodologies have been established to date. Nevertheless, in the case of images with a larger missing region, these models are not effective in addressing the problem. Likewise, these methodologies are ineffective towards the edge. Therefore, by utilizing the Log of Exponent Rule Generative Adversarial Network algorithm, a novel clustering-centric image inpainting system has been proposed here. Initially, two significant steps, namely (i) noise removal, and (ii) Contrast Enhancement (CE), are performed to pre-process the input images. After that, by utilizing the Adaptive Max One-Sided Box Filter (AMOSBF) algorithm, the pre-processed images’ edges are well-preserved. Then, the most needed features are extracted as of the edge preserved images. Next, by employing Supremum Distance Fast Density Peaks Clustering Algorithm (SDFDPCA), the features being extracted are clustered. Next, the proposed model, termed Log of Exponent Rule Mish Generative Adversarial Network (LOERMGAN), which reconstructs the actual images effectively, is fed with the clustered features and also the masked image and the mask itself. In this research, the openly accessible datasets termed ADE20k, Paris, and Places2 are utilized. Subsequently, the outcomes obtained are analogized with the prevailing methodologies. The experiential outcomes displayed that the proposed model outshines the other prevailing methodologies by effectively reconstructing images.
WLA-Net: A Whole New Light-weight Architecture For Visual Task

In this paper, we introduce WLA-Net, a whole new convolutional networks that have smaller parameters and FLOPs model. WLA-Net are based on a cross architecture that uses mechanism of attention and Residual block to build light deep neural networks. While improving the classification accuracy, the parameters of model is reduced, make the model more lightweight and improving resource utilization. A lightweight convolution module is designed in the network that can perform image classification tasks accurately and efficiently while introducing a module that large Convolution attention to improve image classification accuracy. In addition, an new AttentionModule is proposed, which mines information aggregations in the channel direction as much as possible to extract more efficient depth features. It can effectively fuse the features of the channels in the image to obtain higher accuracy. At the same time, a new residual structure is designed to fuse the information between feature channels to make it more closely related. The image classification accuracy of the model is verified on the large natural images datasets. Experimental results show that the proposed method has SOTA performance.
Semantic-assisted Unified Network for Feature Point Extraction and Matching

Feature point matching between two images is an essential part of 3D reconstruction, augmented reality, panorama stitching, etc. The quality of the initial feature point matching stage greatly affects the overall performance of a system. We present a unified feature point extraction-matching method, making use of semantic segmentation results to constrain feature point matching. To integrate high-level semantic information into feature points efficiently, we propose a unified feature point extraction and matching network, called SP-Net, which can detect feature points and generate feature descriptors simultaneously and perform feature point matching with accurate outcomes. Compared with previous works, our method can extract multi-scale context of the image, including shallow information and high-level semantic information of the local area, which is more stable when handling complex conditions such as changing illumination or large viewpoint. In evaluating the feature-matching benchmark, our method shows superior performance over the state-of-art method. As further validation, we propose SP-Net++ as an extension for 3D reconstruction. The experimental results show that our neural network can obtain accurate feature point positioning and robust feature matching to recover more cameras and get a well-shaped point cloud. Our semantic-assisted method can improve the stability of feature points as well as specific applicability for complex scenes.
A Data Classifier Based on Maximum Likelihood Evidential Reasoning Rule

In Dempster–Shafer evidence theory (DST), some classical evidence combination rules can be used to fuse the multiple pieces of evidence, respectively abstracted from different attributes (features) so as to increase the accuracy of multiattribute classification decision making. However, most of them have not yet considered the interdependence among multiple pieces of evidence. The newly proposed maximum likelihood evidential reasoning (MAKER) rule measures such ubiquitous interdependence by introducing correlation factors into evidence combination. Hence, this paper designs a MAKER-based classifier to mine more correlation information for data classification. Finally, some numerical analysis (classification) experiments are carried out using five popular benchmark databases from the University of California, Irvine (UCI) to illustrate that the refined measure for evidence interdependence can aggregate the fused probability (belief degree) into real class label of a sample and further improve classification accuracy.
MixUp based Cross-Consistency Training for Named Entity Recognition

Named Entity Recognition (NER) is one of the first stages in deep natural language understanding. The state-of-the-art deep NER models are dependent on high-quality and massive datasets. Also, the NER tasks require token-level labels. For this reason, there is a problem that annotating many sentences for the NER tasks is time-consuming and expensive. To solve this problem, many prior studies have been conducted to use the auto annotated weakly labeled data. However, the weakly labeled data contains a lot of noises that are obstructive to the training of NER models. We propose to use MixUp and cross-consistency training (CCT) together as a strategy to use weakly labeled data for NER tasks. In this study, the proposed method stems from the idea that MixUp, which was recently considered the data augmentation strategy, hinders the NER model training. Inspired by this point, we propose to use MixUp as a perturbation of cross-consistency training for NER. Experiments conducted on several NER benchmarks demonstrate the proposed method achieves improved performance compared to employing only a few human-annotated data.
An Effective Implementation of Detection and Retrieval Property of Episodic Memory

A deep understanding of the brain can lead to significant breakthroughs in Artificial Intelligence. Many researchers concentrate their efforts on simulating the human mind to comprehend its complexities better. With the intention of better understanding the episodic memory aspect of the human mind, we propose a deep learning model to implement the detection and retrieval properties of human episodic memory, a part of long-term memory. A model based on LSTM and CNN is proposed, which follows the architectural methodology of Rosenblatt’s experiential memory model. A comparison of detection efficiency and accuracy and the proposed model’s retrieval property with a recently suggested method demonstrate its effectiveness and superiority.
Incremental Learning of Classification models in Deep Learning

research-article Share on Incremental Learning of Classification models in Deep Learning Authors: Atharv Nagarikar CDAC Pune, Project Engineer, India CDAC Pune, Project Engineer, India 0000-0003-4511-2498View Profile , Rahul Singh Dangi CDAC Pune, Senior Technical Officer, India CDAC Pune, Senior Technical Officer, India 0000-0002-2437-5996View Profile , Samrit Kumar Maity CDAC Pune, Joint Director, India CDAC Pune, Joint Director, India 0000-0002-4050-9624View Profile , Ashish Kuvelkar CDAC Pune, Senior Director, India CDAC Pune, Senior Director, India 0000-0002-1179-3400View Profile , Sanjay Wandhekar CDAC Pune, Senior Director and HOD, India CDAC Pune, Senior Director and HOD, India 0000-0001-9618-4411View Profile Authors Info & Claims ICAAI '22: Proceedings of the 6th International Conference on Advances in Artificial IntelligenceOctober 2022 Pages 56–60https://doi.org/10.1145/3571560.3571568Published:12 January 2023Publication History 0citation9DownloadsMetricsTotal Citations0Total Downloads9Last 12 Months9Last 6 weeks9 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 AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
GLAF: Global-and-Local Attention Flow Model for Question Answering

Question answering is one of the well-studied tasks in the natural language processing(NLP) community, which aims to secure an answer span from a given document and query. Previous attempts decomposed this task into two subtask, i.e., understanding the semantic information of the given document and query, then finding a reasonable textual span within the document as the corresponding answer. However, one of the major drawbacks of the previous works is lack of extracting sufficient semantics that is buried within the input. To alleviate the issue above, in this paper, we propose a global-local attention flow model to take advantage of the semantic features from different aspects and reduce the redundancy of model encoder. Experimental results on the SQUAD dataset shows that our model outperforms the baseline models, which proves the effectiveness of the proposed method.
Proxy-based Metric Learning for Emotion Recognition

Emotion Recognition (ER) is an essential research area of ​​natural language processing that can be applied to various fields. Texts in the fields of health care, marketing, and psychological counseling take various forms, and it is very important from a business point of view to find the emotions inherent in these texts. Recently, ER using text embeddings generated through a pre-trained language model with a large corpus was performed. However, since the embeddings are generalized to various domains, there is a limitation to directly using them for ER. In this study, to overcome the limitation, we propose a method that modifies generalized embeddings to emotional embeddings by performing proxy-based metric learning. In the proposed method, we fine-tuned the pre-trained language model by using proxy-anchor loss so that embeddings represent emotion appropriately. Previous studies only added linear classifiers. But, it is possible to capture emotional relationships between data by using proxy-based metric learning. In this study, we conducted ER experiments with benchmark datasets. The experimental result shows that the proposed method achieves better performance than the baseline and creates emotion-specific embeddings.
A semantic real-time activity recognition system for sequential procedures in vocational learning

In various areas of study, standard established procedures are critical for the successful accomplishment of a kinaesthetic task. Such standard procedures are important in various industries like engineering and health. This study makes a case for the development of intelligent activity monitoring systems for learning purposes through a proof of concept in first-aid training. Minor accidents such as simple cuts, bruises and minor burns are frequently treated without the need of emergency medical services. However, an incorrect first-aid procedure may lead to medical complications. This study aims to aid a learner to train how to perform a first-aid procedure for treating a wound through real-time monitoring, instructions and feedback. We propose a three-phase system where fast object detection, activity recognition in a temporal dimension and sequencing are used to semantically understand leaner actions. The You Only Look Once (YOLOv5) was used in phase 1 to detect multiple objects like wounds and bandages and Mediapipe to detect hand landmarks. Each class was assigned a different threshold for more accurate detections. The object detection model achieved a mean Average Precision (mAP) of 72.74% on the validation set and was subsequently used in a temporal manner to recognize an action. This temporal method to recognize the action of applying pressure over a wound, achieved an F1-Score of 91.67%. The method using an ontology-based technique to recognize the action of applying a bandage, achieved an F1-Score of 90.91%. The optimum distance from camera was found to be the actor placed at a position where the arm of the wounded actor occupies a significant portion of the viewport, whilst the optimum camera angle was found to be 110°. The created sequencing algorithm was tested using three different scenarios with the aid of a number of participants. The overall accuracy was 83.33%, wherein the result highlights that the algorithm is able to identify the sequence being conducted even with minimal movement involved during bandage application. The proposed system has high prospects of addressing challenges in a real-world environment.
Conjuring Fusion Laws via Relational Calculus

Wadler demonstrated that several laws, called “free theorems”, about polymorphic functions can be derived from their types alone. Free theorems form the foundations of many program transformations, including function fusions particularly. Walder's approach requires systematic but cumbersome reasoning, which makes it difficult for non-experts to apply. To alleviate this problem, Voigtländer proposed a conjuring lemma. This lemma derives free theorems from an expression containing polymorphic functions but whose type does not contain polymorphic type variables. Unfortunately, it cannot derive function fusion laws. In this work, we generalize the conjuring lemma on the basis of Bird and de Moor's relational calculus. This calculus uses non-deterministic computations for uniformly describing and thereby relating specifications and implementations. To make the generalized conjuring lemma easier to use, we develop simpler corollaries by borrowing intuitions from the worker/wrapper transformations. The effectiveness of our approach is demonstrated through experiments with the derivation of three well-known fusion laws. While the original conjuring lemma cannot derive any of them, the generalization can systematically derive all.
Automatic Foucault test method based on transverse cutting at two different axial positions near the foci

Foucault knife-edge method is an irreplaceable qualitative testing method in the early stage of optical processing, but it needs to accurately place the knife-edge at the focus of the mirror surface to be measured for testing. In view of the complex and difficult calibration process of the knife-edge instrument and the inability to perform quantitative testing efficiently, here the Foucault knife-edge testing method is improved by digitizing and automating it. The knife-edge instrument is first placed on the motorized precision translation stage for two programmed cutting movement perpendicular to the optical axis with controlled axial spacing. And the normal vector of each point on the mirror surface to be measured is solved through the knife-edge transverse position relationship for the bright and dark transition of the corresponding point on a series of knife-edge shadow grams obtained by two cuts. Then the automation and quantification of knife-edge method can be realized. The experimental results of this method are compared with the interferometer testing results, which have good consistency and high accuracy. This method does not require high calibration accuracy for the knife-edge instrument and can perform rapid and efficient testing. It provides a testing method with good performance for automatic and quantitative testing in the early stage of optical processing.
Design Considerations for a Sustainable Scholarly Big Data Service

The advancement of web programming techniques, such as Ajax and jQuery, and datastores, such as Apache Solr and Elasticsearch, have made it much easier to deploy small to medium scale web-based search engines. However, developing a sustainable search engine that supports scholarly big data services is still challenging often because of limited human resources and financial support. Such scenarios are typical in academic settings or small businesses. Here, we showcase how four key design decisions were made by trading-off competing factors such as performance, cost, and efficiency, when developing the Next Generation CiteSeerX (NGX), the successor of CiteSeerX, which was a pioneering digital library search engine that has been serving academic communities for more than two decades. This work extends our previous work in Wu et al. (2021) and discusses design considerations of infrastructure, web applications, indexing, and document filtering. These design considerations can be generalized to other web-based search engines with a similar scale that are deployed in small business or academic settings with limited resources.
“If you can’t beat them, join them”: A Word Transformation based Generalized Skip-gram for Embedding Compound Words

While a class of data-driven approaches has been shown to be effective in embedding words of languages that are relatively simple as per inflections and compounding characteristics (e.g. English), an open area of investigation is ways of integrating language-specific characteristics within the framework of an embedding model. Standard word embedding approaches, such as word2vec, Glove etc. embed each word into a high dimensional dense vector. However, these approaches may not adequately capture the inherent linguistic phenomenon namely that of word compounding. We propose a stochastic word transformation based generalization of the skip-gram algorithm, which seeks to potentially improve the representation of the compositional compound words by leveraging information from the contexts of their constituents. Our experiments show that addressing the compounding effect of a language as a part of the word embedding objective outperforms existing methods of compounding-specific post-transformation based approaches on word semantics prediction and word polarity prediction tasks.
Topic-Mono-BERT: A Joint Retrieval-Clustering System for Retrieving Overview Passages

For most queries, the set of relevant documents spans multiple subtopics. Inspired by the neural ranking models and query-specific neural clustering models, we develop Topic-Mono-BERT which performs both tasks jointly. Based on text embeddings of BERT, our model learns a shared embedding that is optimized for both tasks. The clustering hypothesis would suggest that embeddings which place topically similar text in close proximity will also perform better on ranking tasks. Our model is trained with the Wikimarks approach to obtain training signals for relevance and subtopics on the same queries.
Automating License Rule Generation to Help Maintain Rule-based OSS License Identification Tools

Many license identification tools have been proposed to support OSS reuse. License identification tools automatically identify OSS licenses declared in source files. Ninka is one of the most accurate license identification tools. Because OSS licenses are often newly created or inherited, rules built into license identification tools need to be created and updated on a regular basis. However, when a large number of unknown licenses are detected in large OSS products, it is not easy to manually create new rules. In our previous studies, we proposed a method for clustering license statements that Ninka determined to be unknown. In this paper, we propose a method to automatically generate license rules from the clustered license statements. Our approach further filters the license statements from the created clusters to extract sequential patterns and converts the extracted patterns into regular expressions. We conducted conduct a case study where our method is applied to 1, 821, 3, 561 and 2, 838 unknown license statement files respectively collected from FreeBSD v10.3.0, Linux Kernel v4.4.6, and Debian v7.8.0, to confirm the usefulness of our method. As a result, we confirmed that our method successfully generated license rules that take into consideration the orthographical variants and that our method also efficiently identified licenses with a small number of license rules. Furthermore, we found that adding the license rules generated by our method to Ninka improves the licensing rule performance.
Light-SRNet: a lightweight dual-attention feature fusion network for hand-drawn sketch recognition

Free-hand sketches play a vital role in graphically portraying ideas and concepts in image recognition systems. Most recently proposed learning-based sketch recognition methods have achieved marked progress in recognition accuracy, but they rarely optimize the use of the sparsity features of sketch images. Although several attention-based sketch recognition models have been presented, they endure complex computations and large model sizes. To address these challenges, we present a lightweight convolutional neural network called Light-SRNet based on a dual-attention mechanism to improve the accuracy of sketch recognition while retaining its lightweight nature. In the proposed model, we introduced both the spatial and channel attention mechanisms into the feature extraction network to highlight more discriminative feature representations to enhance its powerful sketch recognition ability. We compared the proposed Light-SRNet with its competitors on the TU-Berlin dataset, Sketchy dataset, and QuickDrawExtended dataset. Extensive experimental results show that Light-SRNet achieves a recognition accuracy of 73.14%, which is comparable to other similar sketch recognition techniques, while requiring only about a quarter of the model parameters.
Understanding Pitfalls and Opportunities of Applying Heuristic Evaluation Methods to VR Training Systems: An Empirical Study

The usability of virtual reality (VR) training applications is crucial for their success, but examining the usability in the early development stages remains challenging. A realistic and plausible solution would be revisiting and reconciling Heuristics Evaluation (HE) methods among the most widely used usability inspection methods in the human-computer interaction (HCI) domain. While research on studying and using HE methods is growing within the VR domain, few studies have considered the novel VR environment challenges new requirements for fitting HE methods to the context and applying them effectively. To this end, we conducted a user study with 14 evaluators using the standard HE methods to complete two HE sessions for a VR training application. We identified five critical challenges that evaluators encountered in the HE process by observing and interviewing them. Based on our findings, we discuss the importance of considering an easy-to-use heuristic set, how we can facilitate the HE procedures in the VR context, and the opportunities for developing HE-supporting tools.