Reviewerly/exHarmony huggingface dataset:
Multiscale anchor box and optimized classification with faster R‐CNN for object detection

Abstract For the two‐stage object detector as a faster region‐convolutional neural network (Faster R‐CNN), upgrading the accuracy of object recognition depends on the proposal box, which is generated by the region proposal algorithms. Due to the limitations of the anchor setting of Faster RCNN, the size of the proposal box generated by the region proposal network (RPN) used is large, which would easily cause a great number of overflows in the sliding search. To improve the accuracy of object detection and remit the overflow problem of the anchor box, multi‐scale anchor box and moving overflow anchor box strategies are introduced here. Then, to increase the positive sample range of the foreground, the hierarchical weight cross entropy classification function is set for binary classification in the RPN network. These strategies could improve the accuracy of object detection. The experimental result achieves 76.2% AP on the Pascal VOC 2007(VOC 07) dataset, which is 2.7% higher than the Faster R‐CNN. The result of the Pascal VOC 2012(VOC 12) test, we achieve 75.6% AP , is improved by 2.5% compared with the Faster R‐CNN.
Reliability-Aware Prediction via Uncertainty Learning for Person Image Retrieval

Current person image retrieval methods have achieved great improvements in accuracy metrics. However, they rarely describe the reliability of the prediction. In this paper, we propose an Uncertainty-Aware Learning (UAL) method to remedy this issue. UAL aims at providing reliability-aware predictions by considering data uncertainty and model uncertainty simultaneously. Data uncertainty captures the “noise” inherent in the sample, while model uncertainty depicts the model’s confidence in the sample’s prediction. Specifically, in UAL, (1) we propose a sampling-free data uncertainty learning method to adaptively assign weights to different samples during training, down-weighting the low-quality ambiguous samples. (2) we leverage the Bayesian framework to model the model uncertainty by assuming the parameters of the network follow a Bernoulli distribution. (3) the data uncertainty and the model uncertainty are jointly learned in a unified network, and they serve as two fundamental criteria for the reliability assessment: if a probe is high-quality (low data uncertainty) and the model is confident in the prediction of the probe (low model uncertainty), the final ranking will be assessed as reliable. Experiments under the risk-controlled settings and the multi-query settings show the proposed reliability assessment is effective. Our method also shows superior performance on three challenging benchmarks under the vanilla single query settings. The code is available at: https://github.com/dcp15/UAL .
Neighborhood Collective Estimation for Noisy Label Identification and Correction

Learning with noisy labels (LNL) aims at designing strategies to improve model performance and generalization by mitigating the effects of model overfitting to noisy labels. The key success of LNL lies in identifying as many clean samples as possible from massive noisy data, while rectifying the wrongly assigned noisy labels. Recent advances employ the predicted label distributions of individual samples to perform noise verification and noisy label correction, easily giving rise to confirmation bias. To mitigate this issue, we propose Neighborhood Collective Estimation, in which the predictive reliability of a candidate sample is re-estimated by contrasting it against its feature-space nearest neighbors. Specifically, our method is divided into two steps: 1) Neighborhood Collective Noise Verification to separate all training samples into a clean or noisy subset, 2) Neighborhood Collective Label Correction to relabel noisy samples, and then auxiliary techniques are used to assist further model optimization. Extensive experiments on four commonly used benchmark datasets, i.e., CIFAR-10, CIFAR-100, Clothing-1M and Webvision-1.0, demonstrate that our proposed method considerably outperforms state-of-the-art methods.
Toward Teachable Autotelic Agents

Autonomous discovery and direct instruction are two distinct sources of learning in children but education sciences demonstrate that mixed approaches such as assisted discovery or guided play result in improved skill acquisition. In the field of artificial intelligence (AI), these extremes, respectively, map to autonomous agents learning from their own signals and interactive learning agents fully taught by their teachers. In between should stand teachable autonomous agents (TAA): agents that learn from both internal and teaching signals to benefit from the higher efficiency of assisted discovery. Designing such agents will enable real-world nonexpert users to orient the learning trajectories of agents toward their expectations. More fundamentally, this may also be a key step to build agents with human-level intelligence. This article presents a roadmap toward the design of teachable autonomous agents from a developmental AI perspective. Building on developmental psychology and education sciences, we start by identifying key features enabling assisted discovery processes in child–tutor interactions. This leads to the production of a checklist of features that future TAAs will need to demonstrate. The checklist allows us to precisely pinpoint the various limitations of current reinforcement learning agents and to identify the promising first steps toward TAAs. It also shows the way forward by highlighting key research directions toward the design or autonomous agents that can be taught by ordinary people via natural pedagogy.
Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-Ahead Forward Ones

While significant progress has been made in deep video denoising, it remains very challenging for exploiting historical and future frames. Bidirectional recurrent networks (BiRNN) have exhibited appealing performance in several video restoration tasks. However, BiRNN is intrinsically offline because it uses backward recurrent modules to propagate from the last to current frames, which causes high latency and large memory consumption. To address the offline issue of BiRNN, we present a novel recurrent network consisting of forward and look-ahead recurrent modules for unidirectional video denoising. Particularly, look-ahead module is an elaborate forward module for leveraging information from near-future frames. When denoising the current frame, the hidden features by forward and look-ahead recurrent modules are combined, thereby making it feasible to exploit both historical and near-future frames. Due to the scene motion between non-neighboring frames, border pixels missing may occur when warping look-ahead feature from near-future frame to current frame, which can be largely alleviated by incorporating forward warping and proposed border enlargement. Experiments show that our method achieves state-of-the-art performance with constant latency and memory consumption. Code is avaliable at https://github.com/nagejacob/FloRNN .
Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark Dataset

In recent years, real image super-resolution (SR) has achieved promising results due to the development of SR datasets and corresponding real SR methods. In contrast, the field of real video SR is lagging behind, especially for real raw videos. Considering the superiority of raw image SR over sRGB image SR, we construct a real-world raw video SR (Real-RawVSR) dataset and propose a corresponding SR method. We utilize two DSLR cameras and a beam-splitter to simultaneously capture low-resolution (LR) and high-resolution (HR) raw videos with 2 $$\times $$ , 3 $$\times $$ , and 4 $$\times $$ magnifications. There are 450 video pairs in our dataset, with scenes varying from indoor to outdoor, and motions including camera and object movements. To our knowledge, this is the first real-world raw VSR dataset. Since the raw video is characterized by the Bayer pattern, we propose a two-branch network, which deals with both the packed RGGB sequence and the original Bayer pattern sequence, and the two branches are complementary to each other. After going through the proposed co-alignment, interaction, fusion, and reconstruction modules, we generate the corresponding HR sRGB sequence. Experimental results demonstrate that the proposed method outperforms benchmark real and synthetic video SR methods with either raw or sRGB inputs. Our code and dataset are available at https://github.com/zmzhang1998/Real-RawVSR .
Entity Type Prediction Leveraging Graph Walks and Entity Descriptions

The entity type information in Knowledge Graphs (KGs) such as DBpedia, Freebase, etc. is often incomplete due to automated generation or human curation. Entity typing is the task of assigning or inferring the semantic type of an entity in a KG. This paper presents GRAND, a novel approach for entity typing leveraging different graph walk strategies in RDF2vec together with textual entity descriptions. RDF2vec first generates graph walks and then uses a language model to obtain embeddings for each node in the graph. This study shows that the walk generation strategy and the embedding model have a significant effect on the performance of the entity typing task. The proposed approach outperforms the baseline approaches on the benchmark datasets DBpedia and FIGER for entity typing in KGs for both fine-grained and coarse-grained classes. The results show that the combination of order-aware RDF2vec variants together with the contextual embeddings of the textual entity descriptions achieve the best results.
Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain Adaptation

The prime challenge in unsupervised domain adaptation (DA) is to mitigate the domain shift between the source and target domains. Prior DA works show that pretext tasks could be used to mitigate this domain shift by learning domain invariant representations. However, in practice, we find that most existing pretext tasks are ineffective against other established techniques. Thus, we theoretically analyze how and when a subsidiary pretext task could be leveraged to assist the goal task of a given DA problem and develop objective subsidiary task suitability criteria. Based on this criteria, we devise a novel process of sticker intervention and cast sticker classification as a supervised subsidiary DA problem concurrent to the goal task unsupervised DA. Our approach not only improves goal task adaptation performance, but also facilitates privacy-oriented source-free DA i.e. without concurrent source-target access. Experiments on the standard Office-31, Office-Home, DomainNet, and VisDA benchmarks demonstrate our superiority for both single-source and multi-source source-free DA. Our approach also complements existing non-source-free works, achieving leading performance.
Learning Instance and Task-Aware Dynamic Kernels for Few-Shot Learning

Learning and generalizing to novel concepts with few samples (Few-Shot Learning) is still an essential challenge to real-world applications. A principle way of achieving few-shot learning is to realize a model that can rapidly adapt to the context of a given task. Dynamic networks have been shown capable of learning content-adaptive parameters efficiently, making them suitable for few-shot learning. In this paper, we propose to learn the dynamic kernels of a convolution network as a function of the task at hand, enabling faster generalization. To this end, we obtain our dynamic kernels based on the entire task and each sample, and develop a mechanism further conditioning on each individual channel and position independently. This results in dynamic kernels that simultaneously attend to the global information whilst also considering minuscule details available. We empirically show that our model improves performance on few-shot classification and detection tasks, achieving a tangible improvement over several baseline models. This includes state-of-the-art results on four few-shot classification benchmarks: mini-ImageNet, tiered-ImageNet, CUB and FC100 and competitive results on a few-shot detection dataset: MS COCO-PASCAL-VOC.
Unsupervised Domain Adaptation for One-Stage Object Detector Using Offsets to Bounding Box

Most existing domain adaptive object detection methods exploit adversarial feature alignment to adapt the model to a new domain. Recent advances in adversarial feature alignment strives to reduce the negative effect of alignment, or negative transfer, that occurs because the distribution of features varies depending on the category of objects. However, by analyzing the features of the anchor-free one-stage detector, in this paper, we find that negative transfer may occur because the feature distribution varies depending on the regression value for the offset to the bounding box as well as the category. To obtain domain invariance by addressing this issue, we align the feature conditioned on the offset value, considering the modality of the feature distribution. With a very simple and effective conditioning method, we propose OADA (Offset-Aware Domain Adaptive object detector) that achieves state-of-the-art performances in various experimental settings. In addition, by analyzing through singular value decomposition, we find that our model enhances both discriminability and transferability.
Research on Innovative Design of Traditional Clothing Handicraft under Computer Big Data Technology

This design implements a traditional clothing handicraft recognition system based on computer big data technology model. Tensor Flow is used to train a custom network, and weight parameters are fixed-point processing. The ARM side OpenCV is used for image preprocessing, and the FPGA side CNNIP is used for real-time recognition. The spatial attention mechanism is introduced into the output feature map of the VGG16 feature extraction network to increase the attention to the clothing area in the image. A bilinear pooling method with a single feature path is designed. While reducing the number of parameters and computation, two operations, global average pooling and global max max pooling, are used to fully my fine-grained features. This system can accurately identify and display pictures of traditional clothing crafts in real time, with an accuracy rate of 92.39%. Under the operating frequency of 100MHz, the image processing speed can reach 1.361ms per frame, and the power consumption is only 0. 53W.
IT Infrastructure and Perceived Ease of Use to Increase E-Learning Adoption

The pandemic in 2020 known as Covid-19 had an impact on independent learning at home. Learning in university ultimately needs to be done online because of face-to-face restrictions. However, online learning activities ultimately affect the level of student motivation in learning. Examining the implementation of learning system on the university, the purpose of this study is to evaluate e-Iearning adoption on student psychological motivation through IT infrastructure support and perceived ease of use. This research examines a model with the goal of student psychological motivation, so that a study was carried out on students with a total of 149 respondents. The research method was carried out by quantitative surveys and research data obtained from questionnaires. Research with path analysis was conducted to evaluate the research model. In testing the research model, a study was carried out through the evaluation of research hypotheses. The findings in the model test results it is known that IT infrastructure and perceived ease of use are important for student regarding adoption to online learning. Another finding was conveyed that e-Iearning adoption can increase students' psychological motivation. The research findings focus on the mediation model of e-Iearning adoption; furthermore for universities, this model can be used as input on the implementation of online learning. Model adaptation aims to increase student motivation in dealing with online learning.
Tax-Loss Harvesting with Cryptocurrencies

We describe the landscape of taxation in the crypto markets, especially that concerning U.S. taxpayers, and examine how recent increases in tax scrutiny have led to changes in trading behavior by crypto traders. We predict under a simple theoretical framework and then empirically document that increased tax scrutiny leads crypto investors to utilize legal tax planning with tax-loss harvesting as an alternative to non-compliance. In particular, domestic traders increase tax-loss harvesting following the increase in tax scrutiny, and U.S. exchanges exhibit a significantly greater amount of wash trading. Additional findings suggest that broad-based and targeted changes in tax scrutiny can differentially affect crypto traders' preference for U.S.-based exchanges. We also discuss other gray areas for tax regulation related to new crypto assets such as Non-Fungible Tokens and Decentralized Finance protocols that further highlight the importance of coordinating tax policy and other regulations.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Decentralized autonomous organizations review, importance, and applications

Blockchain technology has the potential to provide a secure digital ledger for tracking financial transactions across the internet. Which is projected to serve as the conceptual foundation for “Distributed autonomous organizations (DAOs), were launched to destabilize the existing hierarchical management structure and drastically cut enterprises' communication, management, and cooperation costs. Meanwhile, DAOs continue to confront other obstacles, including security and privacy concerns, an unknown legal position, etc. Cyber Movement Organizations (CMOs) on the Web and Distributed Artificial Intelligence (DAI) can all be considered embryonic forms of DAOs, laying the groundwork for their emergence. This article covers the literature on decentralized autonomous organizations, and a brief of its governance challenges, besides the linkages between DAOs, the new term of Metaverse, Non-Fungible Tokens, and blockchains to demonstrate their importance and potential in the future.
Grounding Hindsight Instructions in Multi-Goal Reinforcement Learning for Robotics

This paper focuses on robotic reinforcement learning with sparse rewards for natural language goal representations. An open problem is the sample-inefficiency that stems from the compositionality of natural language, and from the grounding of language in sensory data and actions. We address these issues with three contributions. We first present a mechanism for hindsight instruction replay utilizing expert feedback. Second, we propose a seq2seq model to generate linguistic hindsight instructions. Finally, we present a novel class of language-focused learning tasks. We show that hindsight instructions improve the learning performance, as expected. In addition, we also provide an unexpected result: We show that the learning performance of our agent can be improved by one third if, in a sense, the agent learns to talk to itself in a self-supervised manner. We achieve this by learning to generate linguistic instructions that would have been appropriate as a natural language goal for an originally unintended behavior. Our results indicate that the performance gain increases with the task-complexity.
On Assessing Vulnerabilities of the 5G Networks to Adversarial Examples

The use of artificial intelligence and machine learning is recognized as the key enabler for 5G mobile networks which would allow service providers to tackle the network complexity and ensure security, reliability and allocation of the necessary resources to their customers in a dynamic, robust and trustworthy way. Dependability of the future generation networks on accurate and timely performance of its artificial intelligence components means that disturbance in the functionality of these components may have negative impact on the entire network. As a result, there is an increasing concern about the vulnerability of intelligent machine learning driven frameworks to adversarial effects. In this study, we evaluate various adversarial example generation attacks against multiple artificial intelligence and machine learning models which can potentially be deployed in future 5G networks. First, we describe multiple use cases for which attacks on machine learning components are conceivable including the models employed and the data used for their training. After that, attack algorithms, their implementations and adjustments to the target models are summarised. Finally, the attacks implemented for the aforementioned use cases are evaluated based on deterioration of the objective functions optimised by the target models.
Super-Resolution 3D Human Shape from a Single Low-Resolution Image

We propose a novel framework to reconstruct super-resolution human shape from a single low-resolution input image. The approach overcomes limitations of existing approaches that reconstruct 3D human shape from a single image, which require high-resolution images together with auxiliary data such as surface normal or a parametric model to reconstruct high-detail shape. The proposed framework represents the reconstructed shape with a high-detail implicit function. Analogous to the objective of 2D image super-resolution, the approach learns the mapping from a low-resolution shape to its high-resolution counterpart and it is applied to reconstruct 3D shape detail from low-resolution images. The approach is trained end-to-end employing a novel loss function which estimates the information lost between a low and high-resolution representation of the same 3D surface shape. Evaluation for single image reconstruction of clothed people demonstrates that our method achieves high-detail surface reconstruction from low-resolution images without auxiliary data. Extensive experiments show that the proposed approach can estimate super-resolution human geometries with a significantly higher level of detail than that obtained with previous approaches when applied to low-resolution images. https://marcopesavento.github.io/SuRS/ .
Implementing Grover Oracle for Lightweight Block Ciphers Under Depth Constraints

Grover’s search algorithm allows a quantum attack against block ciphers by searching for an n-bit secret key in time $$O(2^{n/2})$$ . In the PQC standardization process, NIST defined the security categories by imposing the upper bound on the depth of the quantum circuit of the Grover oracle. In this work, we study quantum key search attacks on lightweight block ciphers under depth constraints. We design optimized quantum circuits for GIFT, SKINNY, and SATURNIN and enumerate the quantum resources to implement the Grover oracle in terms of the number of qubits, Clifford+T gates, and circuit depth. We also give the concrete cost of Grover oracle for these ciphers in both the gate-count and depth-times-width cost models. We then present the cost estimates of Grover-based key search attacks on these ciphers under NIST’s depth constraints. We also release Q# implementations of the full Grover oracle for all the variants of GIFT, SKINNY, and SATURNIN to automatically reproduce our quantum resource estimates.