Reviewerly/exHarmony huggingface dataset:
ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Spotting

Scene text spotting is of great importance to the computer vision community due to its wide variety of applications. Recent methods attempt to introduce linguistic knowledge for challenging recognition rather than pure visual classification. However, how to effectively model the linguistic rules in end-to-end deep networks remains a research challenge. In this paper, we argue that the limited capacity of language models comes from 1) implicit language modeling; 2) unidirectional feature representation; and 3) language model with noise input. Correspondingly, we propose an autonomous, bidirectional and iterative ABINet++ for scene text spotting. First, the autonomous suggests enforcing explicitly language modeling by decoupling the recognizer into vision model and language model and blocking gradient flow between both models. Second, a novel bidirectional cloze network (BCN) as the language model is proposed based on bidirectional feature representation. Third, we propose an execution manner of iterative correction for the language model which can effectively alleviate the impact of noise input. Additionally, based on an ensemble of the iterative predictions, a self-training method is developed which can learn from unlabeled images effectively. Finally, to polish ABINet++ in long text recognition, we propose to aggregate horizontal features by embedding Transformer units inside a U-Net, and design a position and content attention module which integrates character order and content to attend to character features precisely. ABINet++ achieves state-of-the-art performance on both scene text recognition and scene text spotting benchmarks, which consistently demonstrates the superiority of our method in various environments especially on low-quality images. Besides, extensive experiments including in English and Chinese also prove that, a text spotter that incorporates our language modeling method can significantly improve its performance both in accuracy and speed compared with commonly used attention-based recognizers. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/FangShancheng/ABINet-PP</uri> .
Densely connected convolutional transformer for single image dehazing

Image Dehazing is an important low-level vision task that aims to remove the haze from an image. In this paper, we proposed Densely Connected Convolutional Transformer (DCCT) for single image dehazing. DCCT is an efficient architecture that combines the multi-head Performer with the local dependencies. To prevent loss of information between features at different levels, we propose a learnable connection layer that is used to fuse features at different levels across the entire architecture. We guide the training of DCCT through a joint loss considering a supervised metric learning approach that allows us to consider both negative and positive features for a multi-image perceptual loss. We validate the design choices and the effectiveness of the proposed DCCT through ablation studies. Through comparison with the representative techniques, we establish that the proposed DCCT is highly competitive with the state of the art.
Instance Space Analysis for Algorithm Testing: Methodology and Software Tools

Instance Space Analysis (ISA) is a recently developed methodology to (a) support objective testing of algorithms and (b) assess the diversity of test instances. Representing test instances as feature vectors, the ISA methodology extends Rice’s 1976 Algorithm Selection Problem framework to enable visualization of the entire space of possible test instances, and gain insights into how algorithm performance is affected by instance properties. Rather than reporting algorithm performance on average across a chosen set of test problems, as is standard practice, the ISA methodology offers a more nuanced understanding of the unique strengths and weaknesses of algorithms across different regions of the instance space that may otherwise be hidden on average. It also facilitates objective assessment of any bias in the chosen test instances and provides guidance about the adequacy of benchmark test suites. This article is a comprehensive tutorial on the ISA methodology that has been evolving over several years, and includes details of all algorithms and software tools that are enabling its worldwide adoption in many disciplines. A case study comparing algorithms for university timetabling is presented to illustrate the methodology and tools.
BBNSF: Blockchain-Based Novel Secure Framework Using RP2-RSA and ASR-ANN Technique for IoT Enabled Healthcare Systems

The wearable healthcare equipment is primarily designed to alert patients of any specific health conditions or to act as a useful tool for treatment or follow-up. With the growth of technologies and connectivity, the security of these devices has become a growing concern. The lack of security awareness amongst novice users and the risk of several intermediary attacks for accessing health information severely endangers the use of IoT-enabled healthcare systems. In this paper, a blockchain-based secure data storage system is proposed along with a user authentication and health status prediction system. Firstly, this work utilizes reversed public-private keys combined Rivest-Shamir-Adleman (RP2-RSA) algorithm for providing security. Secondly, feature selection is completed by employing the correlation factor-induced salp swarm optimization algorithm (CF-SSOA). Finally, health status classification is performed using advanced weight initialization adapted SignReLU activation function-based artificial neural network (ASR-ANN) which classifies the status as normal and abnormal. Meanwhile, the abnormal measures are stored in the corresponding patient blockchain. Here, blockchain technology is used to store medical data securely for further analysis. The proposed model has achieved an accuracy of 95.893% and is validated by comparing it with other baseline techniques. On the security front, the proposed RP2-RSA attains a 96.123% security level.
Joint learning method with teacher–student knowledge distillation for on-device breast cancer image classification

The deep learning models such as AlexNet, VGG, and ResNet achieved a good performance in classifying the breast cancer histopathological images in BreakHis dataset. However, these models are not practically appropriate due to their computational complexity and too many parameters; as a result, they are rarely utilized on devices with limited computational resources. This paper develops a lightweight learning model based on knowledge distillation to classify the histopathological images of breast cancer in BreakHis. This method employs two teacher models based on VGG and ResNext to train two student models, which are similar to the teacher models in development but have fewer deep layers. In the proposed method, the adaptive joint learning approach is adopted to transfer the knowledge in the final-layer output of a teacher model along with the feature maps of its middle layers as the dark knowledge to a student model. According to the experimental results, the student model designed by ResNeXt architecture obtained the recognition rate 97.09% for all histopathological images. In addition, this model has ∼69.40 million fewer parameters, ∼0.93 G less GPU memory use, and 268.17 times greater compression rate than its teacher model. While in the student model the recognition rate merely dropped down to 1.75%. The comparisons indicated that the student model had a rather acceptable outputs compared with state-of-the-art methods in classifying the images of breast cancer in BreakHis.
Applications of Ontology in the Internet of Things: A Systematic Analysis

Ontology has been increasingly implemented to facilitate the Internet of Things (IoT) activities, such as tracking and information discovery, storage, information exchange, and object addressing. However, a complete understanding of using ontology in the IoT mechanism remains lacking. The main goal of this research is to recognize the use of ontology in the IoT process and investigate the services of ontology in IoT activities. A systematic literature review (SLR) is conducted using predefined protocols to analyze the literature about the usage of ontologies in IoT. The following conclusions are obtained from the SLR. (1) Primary studies (i.e., selected 115 articles) have addressed the need to use ontologies in IoT for industries and the academe, especially to minimize interoperability and integration of IoT devices. (2) About 31.30% of extant literature discussed ontology development concerning the IoT interoperability issue, while IoT privacy and integration issues are partially discussed in the literature. (3) IoT styles of modeling ontologies are diverse, whereas 35.65% of total studies adopted the OWL style. (4) The 32 articles (i.e., 27.83% of the total studies) reused IoT ontologies to handle diverse IoT methodologies. (5) A total of 45 IoT ontologies are well acknowledged, but the IoT community has widely utilized none. An in-depth analysis of different IoT ontologies suggests that the existing ontologies are beneficial in designing new IoT ontology or achieving three main requirements of the IoT field: interoperability, integration, and privacy. This SLR is finalized by identifying numerous validity threats and future directions.
Superposition Meet-in-the-Middle Attacks: Updates on Fundamental Security of AES-like Hashing

The Meet-in-the-Middle approach is one of the most powerful cryptanalysis techniques, demonstrated by its applications in preimage attacks on the full MD4, MD5, Tiger, HAVAL, and Haraka-512 v2 hash functions, and key recovery of the full block cipher KTANTAN. The success relies on the separation of a primitive into two independent chunks, where each active cell of the state is used to represent only one chunk or is otherwise considered unusable once mixed. We observe that some of such cells are linearly mixed and can be as useful as the independent ones. This leads to the introduction of superposition states and a whole suite of accompanied techniques, which we incorporate into the MILP-based search framework proposed by Bao et al. at EUROCRYPT 2021 and Dong et al. at CRYPTO 2021, and find applications on a wide range of AES-like hash functions and block ciphers.
Boosting 3D Object Detection by Simulating Multimodality on Point Clouds

This paper presents a new approach to boost a single-modality (LiDAR) 3D object detector by teaching it to sim-ulate features and responses that follow a multi-modality (LiDAR-image) detector. The approach needs LiDAR-image data only when training the single-modality detector, and once well-trained, it only needs LiDAR data at inference. We design a novel framework to realize the approach: re-sponse distillation to focus on the crucial response samples and avoid most background samples; sparse-voxel distillation to learn voxel semantics and relations from the esti-mated crucial voxels; a fine-grained voxel-to-point distillation to better attend to features of small and distant objects; and instance distillation to further enhance the deep-feature consistency. Experimental results on the nuScenes dataset show that our approach outperforms all SOTA LiDAR-only 3D detectors and even surpasses the baseline LiDAR-image detector on the key NDS metric, filling ~72% mAP gap be-tween the single- and multi-modality detectors.
A Simple Single-Scale Vision Transformer for Object Detection and Instance Segmentation

This work presents a simple vision transformer design as a strong baseline for object localization and instance segmentation tasks. Transformers recently demonstrate competitive performance in image classification. To adopt ViT to object detection and dense prediction tasks, many works inherit the multistage design from convolutional networks and highly customized ViT architectures. Behind this design, the goal is to pursue a better trade-off between computational cost and effective aggregation of multiscale global contexts. However, existing works adopt the multistage architectural design as a black-box solution without a clear understanding of its true benefits. In this paper, we comprehensively study three architecture design choices on ViT – spatial reduction, doubled channels, and multiscale features – and demonstrate that a vanilla ViT architecture can fulfill this goal without handcrafting multiscale features, maintaining the original ViT design philosophy. We further complete a scaling rule to optimize our model's trade-off on accuracy and computation cost / model size. By leveraging a constant feature resolution and hidden size throughout the encoder blocks, we propose a simple and compact ViT architecture called Universal Vision Transformer (UViT) that achieves strong performance on COCO object detection and instance segmentation benchmark. Our code is available at https://github.com/tensorflow/models/tree/master/official/projects/uvit .
Federated Learning Enabled Credit Priority Task Processing for Transportation Big Data

Due to the epidemic COVID-19 spread and Intelligent Transportation System (ITS) development, investigators are now to conduct their research over the generated Transportation Big Data (TBD) in many critical areas, such as medical supplies, food supplies, as well as logistics supplies. At present, Vehicular Edge Computing (VEC) is an emerging paradigm to integrate resources from vehicles, road-site units, base stations, and cloud center to promote the performance of TBD tasks scheduling and running. In this paper, we design a three-layered TBD task processing architecture with a federated learning mechanism for credit priority-based task scheduling and running. In our design, we consider the efficiency of task offloading and misbehavior attack problems simultaneously. We propose a vehicular federated learning framework combined with Multi-Layer Perceptron (MLP) credit measurement, which can preserve the privacy of vehicles and obtain the related features for vehicular credit prediction. We also propose a task offloading algorithm to solve the optimization problem for credit priority task offloading between edge computing servers and vehicles. The proposed solution can prioritize tasks and assign sufficient resources for reliable and active task requesters. Experimental results expose that the proposed mechanism outperforms the state-of-the-art solutions when considering efficiency and attack simultaneously for TBD tasks scheduling and running.
Multi-query Video Retrieval

Retrieving target videos based on text descriptions is a task of great practical value and has received increasing attention over the past few years. Despite recent progress, imperfect annotations in existing video retrieval datasets have posed significant challenges on model evaluation and development. In this paper, we tackle this issue by focusing on the less-studied setting of multi-query video retrieval, where multiple descriptions are provided to the model for searching over the video archive. We first show that multi-query retrieval task effectively mitigates the dataset noise introduced by imperfect annotations and better correlates with human judgement on evaluating retrieval abilities of current models. We then investigate several methods which leverage multiple queries at training time, and demonstrate that the multi-query inspired training can lead to superior performance and better generalization. We hope further investigation in this direction can bring new insights on building systems that perform better in real-world video retrieval applications (Code is available at https://github.com/princetonvisualai/MQVR ).
OCSampler: Compressing Videos to One Clip with Single-step Sampling

Videos incorporate rich semantics as well as redundant information. Seeking a compact yet effective video representation, e.g., sample informative frames from the entire video, is critical to efficient video recognition. There have been works that formulate frame sampling as a sequential decision task by selecting frames one by one according to their importance. In this paper, we present a more efficient framework named OCSampler, which explores such a representation with one short clip. OCSampler designs a new paradigm of learning instance-specific video condensation policies to select frames only in a single step. Rather than picking up frames sequentially like previous methods, we simply process a whole sequence at once. Accordingly, these policies are derived from a light-weighted skim network together with a simple yet effective policy network. Moreover, we extend the proposed method with a frame number budget, enabling the framework to produce correct predictions in high confidence with as few frames as possible. Experiments on various benchmarks demonstrate the effectiveness of OCSampler over previous methods in terms of accuracy and efficiency. Specifically, it achieves 76.9% mAP and 21.7 GFLOPs on ActivityNet with an impressive throughput: 123.9 Video/s on a single TITAN Xp GPU.
A Survey on Underwater Computer Vision

Underwater computer vision has attracted increasing attention in the research community due to the recent advances in underwater platforms such as of rovers, gliders, autonomous underwater vehicles (AUVs) , and the like, that now make possible the acquisition of vast amounts of imagery and video for applications such as biodiversity assessment, environmental monitoring, and search and rescue. Despite growing interest, underwater computer vision is still a relatively under-researched area, where the attention in the literature has been paid to the use of computer vision techniques for image restoration and reconstruction, where image formation models and image processing methods are used to recover colour corrected or enhanced images. This is due to the notion that these methods can be used to achieve photometric invariants to perform higher-level vision tasks such as shape recovery and recognition under the challenging and widely varying imaging conditions that apply to underwater scenes. In this paper, we review underwater computer vision techniques for image reconstruction, restoration, recognition, depth, and shape recovery. Further, we review current applications such as biodiversity assessment, management and protection, infrastructure inspection and AUVs navigation, amongst others. We also delve upon the current trends in the field and examine the challenges and opportunities in the area.
From 5G to beyond: Passive optical network and multi-access edge computing integration for latency-sensitive applications

A recent paradigm shift in support of 5G-and-beyond (5GB), Human-to-Machine/Robot (H2M/R), and the Tactile Internet has resulted in a surge of latency-sensitive applications being delivered across communication networks. These applications in conjunction with exponential growth in connected devices, have pushed for architectural and capacity changes in both wired and wireless networks. Passive optical networks (PONs) have evolved in terms of their capacity and function over the years and are now supporting both fixed as well as x-haul solutions to deliver cost-efficient, low-latency and high-bandwidth applications to end-users. In combination with multi-access edge computing (MEC), high computational tasks of applications can be offloaded to servers much closer to end-users further reducing the network latency. To this end, this paper presents a comprehensive review of the capabilities of integrated PON-MECs in delivering emerging latency-sensitive applications. Specific use case scenarios benefiting from integrated PON-MECs are also detailed. Shared learning through a federated PON-MEC framework to expedite resource allocation decisions is discussed and finally, the time-savings arising from implementing the proposed framework is quantified, showing further improvements in network latency performance.
<i>N</i>-Electron Valence Perturbation Theory with Reference Wave Functions from Quantum Computing: Application to the Relative Stability of Hydroxide Anion and Hydroxyl Radical

Quantum simulations of the hydroxide anion and hydroxyl radical are reported, employing variational quantum algorithms for near-term quantum devices. The energy of each species is calculated along the dissociation curve, to obtain information about the stability of the molecular species being investigated. It is shown that simulations restricted to valence spaces incorrectly predict the hydroxyl radical to be more stable than the hydroxide anion. Inclusion of dynamical electron correlation from nonvalence orbitals is demonstrated, through the integration of the variational quantum eigensolver and quantum subspace expansion methods in the workflow of N-electron valence perturbation theory, and shown to correctly predict the hydroxide anion to be more stable than the hydroxyl radical, provided that basis sets with diffuse orbitals are also employed. Finally, we calculate the electron affinity of the hydroxyl radical using an aug-cc-pVQZ basis on IBM's quantum devices.
Reevaluating Remote Library Storage in the Digital Age: A Comparative Study

Despite the popularity of mobile and digital technologies, physical collections still have high user demand in academic libraries and grow continuously. With renovation projects providing more studying and collaboration space, efforts to relocate materials to storage facilities are necessary for major libraries. With the change in student information needs and preferences, it is essential to reinvestigate their behavior and preferences for the use of physical resources and remote storage items. An online survey was conducted in a major international comprehensive university in Hong Kong, China, to explore these questions. Statistical tests were also performed to compare the difference between undergraduate and postgraduate students, as well as those with versus those without experience requesting items from storage.
Use of Metaverse Technology in Education Domain

Metaverse is the digital mirror of the physical world where users are connected as individual avatars. The purpose of the virtual surface where it should gather and mirror actual-time global statistics and also inquire for immersion is to link the physical and digital worlds. Users’ physical inputs may be used to train artificial intelligence (AI) systems to provide client users with highly customized offerings. This technology offers an appropriate answer in the sphere of education and training by using its process. By using Metaverse extended reality, education sector will be changed drastically. This study outlined the required principles and approaches for transforming the education sector utilizing the Metaverse. Qualitative method has been used for analyzing. In addition, ten key techniques based on Metaverse’s quality education standards have been outlined. This research will impact the whole education sector by visualizing contents, virtual campus, 3D simulation, and remote quality education which is accessible for all including underdeveloped countries. Additionally, the anticipated developments in the education industry have been highlighted. Finally, a model of classroom has been proposed using Mozilla Hubs platform, which can be used for group discussion, seminar, thesis meeting, presentation and many more which will bring changes to education system eventually. The main purpose of our research is to improve education sector using Metaverse by engaging more students from developing and under-developing countries, as well as providing more facilities and quality education.