Reviewerly/exHarmony huggingface dataset:
ByteGraph

Most products at ByteDance, e.g., TikTok, Douyin, and Toutiao, naturally generate massive amounts of graph data. To efficiently store, query and update massive graph data is challenging for the broad range of products at ByteDance with various performance requirements. We categorize graph workloads at ByteDance into three types: online analytical, transaction, and serving processing, where each workload has its own characteristics. Existing graph databases have different performance bottlenecks in handling these workloads and none can efficiently handle the scale of graphs at ByteDance. We developed ByteGraph to process these graph workloads with high throughput, low latency and high scalability. There are several key designs in ByteGraph that make it efficient for processing our workloads, including edge-trees to store adjacency lists for high parallelism and low memory usage, adaptive optimizations on thread pools and indexes, and geographic replications to achieve fault tolerance and availability. ByteGraph has been in production use for several years and its performance has shown to be robust for processing a wide range of graph workloads at ByteDance.
Augmented Geometric Distillation for Data-Free Incremental Person ReID

Incremental learning (IL) remains an open issue for Person Re-identification (ReID), where a ReID system is expected to preserve preceding knowledge while learning incrementally. However, due to the strict privacy licenses and the open-set retrieval setting, it is intractable to adapt existing class IL methods to ReID. In this work, we propose an Augmented Geometric Distillation (AGD) framework to tackle these issues. First, a general data-free incremental framework with dreaming memory is constructed to avoid privacy disclosure. On this basis, we reveal a “noisy distillation” problem stemming from the noise in dreaming memory, and further propose to augment distillation in a pairwise and cross-wise pattern over different views of memory to mitigate it. Second, for the open-set retrieval property, we propose to maintain feature space structure during evolving via a novel geometric way and preserve relationships between exemplars when representations drift. Extensive experiments demonstrate the superiority of our AGD to baseline with a margin of 6.0% mAP/7.9% R@1 and it could be generalized to class IL. Code is available here <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> †https://github.com/eddielyc/Augmented-Geometric-Distillation.
3DConvCaps: 3DUnet with Convolutional Capsule Encoder for Medical Image Segmentation

Convolutional Neural Networks (CNNs) have achieved promising results in medical image segmentation. However, CNNs require lots of training data and are incapable of handling pose and deformation of objects. Furthermore, their pooling layers tend to discard important information such as positions as well as CNNs are sensitive to rotation and affine transformation. Capsule network is a recent new architecture that has achieved better robustness in part-whole representation learning by replacing pooling layers with dynamic routing and convolutional strides, which has shown potential results on popular tasks such as digit classification and object segmentation. In this paper, we propose a 3D encoder-decoder network with Convolutional Capsule Encoder (called 3DConvCaps) to learn lower-level features (short-range attention) with convolutional layers while modeling the higher-level features (long-range dependence) with capsule layers. Our experiments on multiple datasets including iSeg-2017, Hippocampus, and Cardiac demonstrate that our 3D 3DConvCaps network considerably outperforms previous capsule networks and 3D-UNets. We further conduct ablation studies of network efficiency and segmentation performance under various configurations of convolution layers and capsule layers at both contracting and expanding paths. The implementation is available at: https://github.com/UARK-AICV/3DConvCaps
Utilization of Questionnaire Results Using Aspect-based Sentiment Analysis

Often, existing sentiment analysis determines whether a questionnaire is positive or negative. Unfortunately, this makes it challenging to utilize the negative and correct them. This study used descriptive questionnaires from customers who visited Japanese gastropubs and analyzed them to determine the correct polarity. We used existing sentiment analysis methods to see if the questionnaire was not classified as the correct polarity (positive or negative). Consequently, we found that the whole sentence of the questionnaire was still not always able to judge the content and polarity correctly. Therefore, we conducted an aspect-based sentiment analysis, characterized by a polarity evaluation of certain words in a sentence, rather than a polarity evaluation of the whole sentence. Negative expressions were extracted from the text to determine what was negative. From each expression, it was possible to see what was explicitly unsatisfactory. The extracted expressions were also grouped as aspects belonging to the same group before examining them individually. Subsequently, the extracted expressions were grouped into aspects, resulting in the percentage of each aspect. The results were then used to suggest what could be improved for Japanese gastropubs.
Learning Prior Feature and Attention Enhanced Image Inpainting

Many recent inpainting works have achieved impressive results by leveraging Deep Neural Networks (DNNs) to model various prior information for image restoration. Unfortunately, the performance of these methods is largely limited by the representation ability of vanilla Convolutional Neural Networks (CNNs) backbones. On the other hand, Vision Transformers (ViT) with self-supervised pre-training have shown great potential for many visual recognition and object detection tasks. A natural question is whether the inpainting task can be greatly benefited from the ViT backbone? However, it is nontrivial to directly replace the new backbones in inpainting networks, as the inpainting is an inverse problem fundamentally different from the recognition tasks. To this end, this paper incorporates the pre-training based Masked AutoEncoder (MAE) into the inpainting model, which enjoys richer informative priors to enhance the inpainting process. Moreover, we propose to use attention priors from MAE to make the inpainting model learn more long-distance dependencies between masked and unmasked regions. Sufficient ablations have been discussed about the inpainting and the self-supervised pre-training models in this paper. Besides, experiments on both Places2 and FFHQ demonstrate the effectiveness of our proposed model. Codes and pre-trained models are released in https://github.com/ewrfcas/MAE-FAR .
Human Action Recognition From Digital Videos Based on Deep Learning

With the development of closed-circuit television, video-based human motion recognition has made great progress. A large number of surveillance video footages have been archived. In this paper, we implement deep learning methods to resolve human action recognition problem. We propose a new method that combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) together, which is able to produce a better result after expansive and extensive experiments. The experimental results of this paper show that it is feasible to implement human action recognition through deep learning algorithms, the outcome is excellent. The CNN+LSTM method proposed in this paper can better recognize human actions, which is more efficient than general deep learning methods. In addition, in this paper, we compare the differences in the recognition results using deep learning methods.
Multi-Dimensional Generalized Integral Transform in the Weighted Spaces of Summable Functions

In this paper, we study a multi-dimensional generalized integral transformation. The functional and compositional properties of the integral transformation in spaces of summable functions are investigated. The scheme of study is similar to the process of constructing the theory of the H-transformation, in which the central place is given to the questions of bounded and one-to-one action of the corresponding integral operator in spaces of integrable functions with weight concentrated at zero and at infinity. Theory of the considered integral transformation in weighted spaces of summable functions is constructed.
Timelocked Bribing

A Hashed Time Lock Contract (HTLC) is a central concept in cryptocurrencies where some value can be spent either with the preimage of a public hash by one party (Bob) or after a timelock expires by another party (Alice). We present a bribery attack on HTLC's where Bob's hash-protected transaction is censored by Alice's timelocked transaction. Alice incentivizes miners to censor Bob's transaction by leaving almost all her value to miners in general. Miners follow (or refuse) this bribe if their expected payoff is better (or worse). We explore conditions under which this attack is possible, and how HTLC participants can protect themselves against the attack. Applications like Lightning Network payment channels and Cross-Chain Atomic Swaps use HTLC's as building blocks and are vulnerable to this attack. Our proposed solution uses the hashpower share of the weakest known miner to derive parameters that make these applications robust against this bribing attack.
C2AM Loss: Chasing a Better Decision Boundary for Long-Tail Object Detection

Long-tail object detection suffers from poor performance on tail categories. We reveal that the real culprit lies in the extremely imbalanced distribution of the classifier's weight norm. For conventional softmax cross-entropy loss, such imbalanced weight norm distribution yields ill conditioned decision boundary for categories which have small weight norms. To get rid of this situation, we choose to maxi-mize the cosine similarity between the learned feature and the weight vector of target category rather than the inner-product of them. The decision boundary between any two categories is the angular bisector of their weight vectors. Whereas, the absolutely equal decision boundary is sub-optimal because it reduces the model's sensitivity to vari-ous categories. Intuitively, categories with rich data diver-sity should occupy a larger area in the classification space while categories with limited data diversity should occupy a slightly small space. Hence, we devise a Category-Aware Angular Margin Loss (C2AM Loss) to introduce an adaptive angular margin between any two categories. Specif-ically, the margin between two categories is proportional to the ratio of their classifiers' weight norms. As a result, the decision boundary is slightly pushed towards the cat-egory which has a smaller weight norm. We conduct comprehensive experiments on LVIS dataset. C2AM Loss brings 4.9~5.2 AP improvements on different detectors and back-bones compared with baseline.
Efficient and Privacy-Preserving Spatial Keyword Similarity Query Over Encrypted Data

As a popular and practical query type in location-based services, the spatial keyword query has been extensively studied in both academia and industry. Meanwhile, with the growing demand for data privacy, many privacy-preserving spatial keyword query schemes have been proposed to deal with queries over encrypted data. However, none of the existing schemes preserve access pattern privacy, and the recent research illustrates that leaking such privacy may incur inference attacks and thus disclose sensitive information. In addition, most existing schemes only consider the boolean keyword search, which is not quite practical and flexible in real-world applications. To address the above issues, in this paper, we propose two privacy-preserving spatial keyword similarity query schemes that can preserve full and partial access pattern privacy, respectively. First, we present a basic privacy-preserving spatial keyword similarity query scheme (PPSKS) by integrating a secure set membership test (SSMT) technique with secure circuits. After that, to improve performance, we propose a tree-based scheme (PPSKS+) by employing a new index called FR-tree together with a predicate encryption technique that can encrypt FR-tree. Formal security analysis shows that: i) our proposed schemes can protect outsourced data, query requests, and query results; ii) our PPSKS scheme can hide full access patterns, while the PPSKS+ scheme preserves <inline-formula><tex-math notation="LaTeX">$m$</tex-math></inline-formula> -access pattern privacy. Extensive experiments are also conducted, and the results indicate that our tree-based PPSKS+ scheme is much more efficient, almost two orders of magnitude better than our linear search PPSKS scheme in performing queries.
A Privacy-Preserving Transparent Central Bank Digital Currency System Based on Consortium Blockchain and Unspent Transaction Outputs

There is rising global demand for the deployment of a central bank digital currency (CBDC) system to achieve financial stability.However, striking a balance between privacy, transparency, and auditability in such a system is technically difficult.We propose a CBDC system based on a consortium blockchain that adopts a privacy-preserving, transparent unspent transaction output (UTXO) model.The proposed system satisfies the travel rule of payment, unlike existing cryptocurrencies.Unlike the conventional UTXO approach, users use wallet-linked addresses for transactions rather than their actual wallet addresses.Each transacting address is generated using two keys: a random private key computed by the sender and the recipient's public key.The final private key is known only to the recipient, and it is required to spend the UTXO received using the address.Thus, each user holds only a single authorized public key and address, which eases regulatory compliance in the network without compromising anonymity and privacy.To manage the blockchain, the central bank and several certificate authorities execute the energy-efficient Clique consensus algorithm.Only the central bank supplies money to the network.A prototype of the system was implemented using Python-Flask, and it outperformed the state-of-the-art systems by providing a smaller transaction size (665 B) and lower verification time (9 ms).
Knowledge Graphs for Automated Driving

Automated Driving (AD) datasets, when used in combination with deep learning techniques, have enabled significant progress on difficult AD tasks such as perception, trajectory prediction and motion planning. These datasets represent the content of driving scenes as captured by various sensors, including cameras, RADAR, and LiDAR, along with 2D/3D annotations of traffic participants. Such datasets, however, often fail to capture and to represent the spatial, temporal, functional, and semantic relations between entities in a scene. This lack of knowledge leads to a shallow understanding of the true complexity and dynamics inherent in a driving scene. In this paper, we argue that a knowledge graph based representation of driving scenes, that provides a richer structure and semantics, will lead to further improvements in automated driving. Towards this goal, we developed a layered architecture and ontologies for specific automated driving datasets and a fundamental ontology of shared concepts. We also built knowledge graphs (KG) for three different AD datasets. We perform an analysis w.r.t. information contained in the AD KGs and outline how the additional semantic information contained in the KGs could improve the performance of different AD tasks. Moreover, example queries are provided to retrieve relevant information that can be exploited for augmenting the AD pipelines. All artefacts needed for reproducability purposes are provided via a Dropbox folder <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> shorturl.at/iwyCV - we will go through an internal approval process for making all artefacts publicly available. We removed our internal namespaces of reused ontologies, because of confidentiality and to provide self-contained ontologies. As the original datasets are under specific licences we can not publish the KGs themselves, but we provided the scripts to generate them.
Efficient and Accurate Damage Detector for Wind Turbine Blade Images

The damage of wind turbine blades is one of the main problems restricting wind power development.Object detection can identify the damaged regions and diagnose the damage types.To handle the high-resolution wind turbine blade images, this article presents a novel efficient, and accurate damage detector (EADD) for wind turbine blade images.The proposed method adopts Single Shot MultiBox Detector (SSD) as the detection framework and offers an improved ResNet as the backbone.Firstly, the improved ResNet backbone uses dense connection blocks consisting of factorized depth-wise separable bottleneck (FDSB) and feature aggregation module (FAM), which makes the damage detection model more lightweight and has a faster detection speed.Secondly, the bidirectional cross-scale feature pyramid (BiFPN) is introduced into the proposed method to use multi-scale features fully and have more feature expression.In addition, data pre-processing, exponential moving average (EMA) and label smooth methods are utilized to improve the accuracy and robustness of the model.The experimental results on the wind turbine blade damage detection dataset show that our proposed method can achieve the best trade-off between detection accuracy and computation time compared with other competitive methods.
FPGA Accelerated Post-Quantum Cryptography

Recent advancement in quantum information processing technology has led to the emergence of advanced cryptography in the post-quantum era. Next generation cryptographic techniques aim to be mathematically resistant against any known attacks related to quantum computing, and can be easily implemented on traditional hardware platforms. The National Institutes of Standards and Technology (NIST) has entered the fourth-round standardization process of post-quantum cryptography (PQC). Software implementations of PQC candidates have been widely investigated. Interests in domain-specific hardware acceleration of PQC algorithms have risen, in particular using field-programmable gate arrays (FPGAs). While conventional general-purpose hardware platforms have been used for PQC implementations, modern FPGAs promise software-hardware co-optimisation, deep pipeline parallelism and trivial support for custom-precision arithmetic. Therefore, the time is ripe for reviewing recent FPGA-based PQC implementations. This article first surveys state-of-the-art advances in PQC implementations on FPGAs, including fast arithmetic, algorithm-hardware codesign approaches and open-source PQC hardware projects, then gives a brief review of recent attacks on PQC algorithms and their hardware implementations. Finally, we summarise the challenges for hardware implementations along with potential research directions in this promising field.
Finding locally densest subgraphs

Finding the densest subgraph (DS) from a graph is a fundamental problem in graph databases. The DS obtained, which reveals closely related entities, has been found to be useful in various application domains such as e-commerce, social science, and biology. However, in a big graph that contains billions of edges, it is desirable to find more than one subgraph cluster that are not necessarily the densest, yet they reveal closely-related vertices. In this paper, we study the locally densest subgraph (LDS), a recently-proposed variant of DS. An LDS is a subgraph which is the densest among the "local neighbors". Given a graph G , a number of LDS's can be returned, which reflect different dense regions of G and thus give more information than DS. The existing LDS solution suffers from low efficiency. We thus develop a convex-programming-based solution that enables powerful pruning. Extensive experiments on seven real large graph datasets show that our proposed algorithm is up to four orders of magnitude faster than the state-of-the-art.
JoJoGAN: One Shot Face Stylization

A style mapper applies some fixed style to its input images (so, for example, taking faces to cartoons). This paper describes a simple procedure – JoJoGAN – to learn a style mapper from a single example of the style. JoJoGAN uses a GAN inversion procedure and StyleGAN’s style-mixing property to produce a substantial paired dataset from a single example style. The paired dataset is then used to fine-tune a StyleGAN. An image can then be style mapped by GAN-inversion followed by the fine-tuned StyleGAN. JoJoGAN needs just one reference and as little as 30 s of training time. JoJoGAN can use extreme style references (say, animal faces) successfully. Furthermore, one can control what aspects of the style are used and how much of the style is applied. Qualitative and quantitative evaluation show that JoJoGAN produces high quality high resolution images that vastly outperform the current state-of-the-art.
Radar Compound Jamming Cognition Based on a Deep Object Detection Network

This article proposes a deep-learning-based compound jamming cognition method to recognize, detect individual jamming elements, and estimate key parameters of them. The method first uses a time–frequency distribution (TFD) to characterize jamming in multiple dimensions (time, frequency, and energy) and then applies an object detection network to identify and locate jamming in the time–frequency domain. This article summarizes the types of jamming parameters and gives corresponding methods for estimating parameters. Unlike traditional studies, this article models jamming recognition as an object detection problem and applies a deep learning framework to find solutions. Therefore, the proposed method has better stability and robustness than conventional techniques, which solves the problem of feature selection caused by the lack of mapping relationship between jamming and features. Another advantage over conventional methods is the multijamming detection capability of this algorithm, which provides more information about individual elements of compound jamming. In terms of jamming parameter estimation, the proposed method makes full use of the geometric characteristics of TFDs, so it is more versatile than conventional methods based on analytical analysis. Simulations and experimental data are used to verify the effectiveness of the proposed method.
An Improved Algorithm for Wind Turbine Blade Defect Detection

With the increase in wind power generation, wind turbine blades require regular inspections to ensure they continue to operate safely.You only look once (YOLO) is one of the most widely used object detection algorithms and is easy to deploy into drone devices.To enhance the real-time detection of small target defects in wind turbine blades, this paper proposes an improved attention and feature balanced YOLO (AFB-YOLO) algorithm based on YOLOv5s.Specifically, AFB-YOLO improves the feature pyramid network by using weighted feature fusion and cross-scale connections.The improved feature pyramid network solves the problem that most previous feature pyramid networks treat all input features equally, and obtains more feature information.Furthermore, the coordinate attention (CA) module is introduced into the network to augment the representations of the objects of interest.Finally, the paper redesigned the loss function through efficient intersection over union (EIoU) loss to make the model obtain a better localization effect.The experimental results on the imagery of wind turbine blade defects indicate that our method shows significant gains in performance.The mean average precision (mAP50) of AFB-YOLO is 83.7% and the detection accuracy is improved by 4.0% compared to the original YOLOv5s model.The experiments in this paper demonstrate that AFB-YOLO is more effective and robust than state-of-theart detectors.