Reviewerly/exHarmony huggingface dataset:
Chebyshev Polynomials for Efficient Gaussian Process Computation

Gaussian processes (GP) are becoming more and more popular way to solve statistics and machine learning problems. One of the reasons is the increase in computational power that can handle the inherent computational problem for GP models. Still, in the case of big data, the computational burden can be impractical. For this reason, various approximation methods are developed.In our work, we would like to present an alternative to the internal approximation by using the properties of Chebyshev polynomials. The idea is to calculate the GP model only at Chebyshev nodes and use the property of transforming function values in them to Chebyshev coefficients giving a solution to the original problem. In our research, we propose our version of the algorithm and test it on cases of various functions.
Efficient Aerial Image Object Detection with Imaging Condition Decomposition

Object detection in aerial images faces domain adaptive challenges, such as changes in shooting height, viewing angle, and weather. These changes constitute a large number of fine-grained domains that place greater demands on network’s generalizability. To tackle these challenges, we propose a submodule named Fine-grained Feature Disentanglement which decomposes image features into domain-invariant and domain-specific using practical imaging condition parameters. The composite feature can improve the domain generalization and single domain accuracy compared to the conventional fine-grained domain detection method. The proposed algorithm is compared with state-of-the-art fine-grained domain detectors on the UAVDT and VisDrone datasets. The results show that it achieves an average detection precision improvement of 5.7 and 2.4, respectively.
Multi-Scale Variational Graph AutoEncoder for Graph Disentanglement Learning

Graph Convolutional Neural Networks are serving as a common network to learn complex relationships and extract deep features for downstream tasks. The existing graph-based autoencoders take an encoder with fixed adjacency matrix to learn latent representations in the real graph and adopt a decoder to reconstruct the adjacency matrix for self-supervised learning. However, the relations are heterogeneous in the real world, but entangled together because of many latent factors. In this paper, we propose a novel graph framework, termed multi-scale variational graph autoencoder for graph disentanglement learning, which disentangles the input graph into several factor graphs and learns latent representations. Then, we present a decoder to infer mixed relations from latent representations. In addition, we introduce β-VAE framework to maintain diversity of latent representations. Generally, different metrics demonstrate our model’s advantaged performance on both downstream tasks and graph-level disentanglement tasks. Our model outperforms the state-of-the-art methods, especially on the ZINC dataset and other datasets, for example those related to bioinformatics graphs, which are featured by practical node representations.
Adapted Lightweight MobileNet for Tire Pattern Classification

This paper presents with deep neural network model MOBIADAPT which has been specifically developed for tire pattern classification on mobile devices to provide with critical clues in crime investigations. Understanding that only low-end mobile devices are normally present during real-life situations, the proposed solution is designed to be both lightweight and effective. Such a solution is achieved starting with the choice of backbone which is both highly efficient and provides optimal accuracy. To achieve this goal, the state-of-the-art image classification model MobileNet [1] was chosen due to its excellent generalization capability and high accuracy while remaining lightweight. The model was further optimized for tire pattern classification by applying the concept of Adaptation. This technique simplifies the model architecture while maintaining high accuracy, thus making it more suitable for deployment on mobile devices.After adaptation the complexity of the initial backbone was reduced >10x to only have about ~53K parameters while maintaining the accuracy to stand over 99% in classifying the test dataset containing tire patterns images.
Mitigating Dataset Bias in Image Captioning Through Clip Confounder-Free Captioning Network

The dataset bias has been identified as a major challenge in image captioning. When the image captioning model predicts a word, it should consider the visual evidence associated with the word, but the model tends to use contextual evidence from the dataset bias and results in biased captions, especially when the dataset is biased toward some specific situations. To solve this problem, we approach from the causal inference perspective and design a causal graph. Based on the causal graph, we propose a novel method named C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Cap which is CLIP confounder-free captioning network. We use the global visual confounder to control the confounding factors in the image and train the model to produce debiased captions. We validate our proposed method on MSCOCO benchmark and demonstrate the effectiveness of our method. https://github.com/yeonju7kim/C2Cap
Long-Tailed Federated Learning Via Aggregated Meta Mapping

One major problem concerned in federated learning is data non-IIDness. Existing federated learning methods to deal with non-IID data generally assume that the data is globally balanced. However, real-world multi-class data tends to exhibit long-tail distribution. Therefore, we propose a new federated learning method called Federated Aggregated Meta Mapping (FedAMM) to address the joint problem of non-IID and global long-tailed data in a federated learning scenario. FedAMM assigns different weights to the local training samples by trainable loss-weight mapping in a meta-learning manner. To deal with data non-IIDness and global long-tail, the meta loss-weight mappings are aggregated on the server to acquire global long-tail distribution knowledge implicitly. We further propose an asynchronous meta updating mechanism to reduce the communication cost for meta-learning training. Experiments show that FedAMM outperforms the state-of-the-art federated learning methods.
Physics-Informed Deep Deblurring: Over-parameterized vs. Under-parameterized

Image deblurring is a classic problem in inverse computational imaging, where the blur is characterized by a kernel, known as the point spread function (PSF). For a complicated PSF, the resulting blurred image is usually incomprehensible to the human eye, such as in the case of lensless images. In this paper, we design and compare the reconstruction performance of under-parameterized and over-parameterized networks for the inverse imaging problem of lensless image reconstruction. We perform an untrained iterative reconstruction against a physics-informed loss function that requires knowledge of the forward imaging process. We conduct an extensive performance evaluation using multiple evaluation metrics and explore the obtained results contrastively by identifying the strengths and weaknesses of under-parameterization. Also, we provide reconstructions obtained using our custom PSF captured with a random diffuser.
Jointly Pre-training with Coarsely Labeled Ultrasound Images for Breast Tumor Classification

The incidence of breast cancer has been increasing in recent years, and ultrasound imaging has shown to be effective in early detection and treatment. Diagnosing breast cancer from ultrasound images with common deep learning methods usually requires a large-scale number of training data with accurate labels, which is challenging in reality. Many clinical images are labeled based on the physicians’ preliminary assessment without the biopsy verification, which leads to coarsely labeled data. Directly utilizing such a coarsely labeled clinical dataset to train a fully supervised model would not bring satisfactory accuracy. In this paper, we propose a joint self-supervised learning and coarse-label supervision learning network to overcome the limitations of the small dataset and coarse labels. Furthermore, we also propose to refine the model with a limited number of data with biopsy verified labels. We conducted extensive experiments on the collected dataset. The results show our method outperforms the state-of-the-art methods.
HER2-Sish Histopathology Image Classification Using Deep Neural Networks

The status of the human epidermal growth factor receptor 2 (HER2) gene amplification is an important marker for assessing the efficacy of clinical treatments for breast cancer. This article discusses the application of deep learning to classify HER2-SISH (silver-enhanced in situ hybridization) pathological images and identifies their HER2/Chr17 amplification status. We used four pre-trained models for classifying the cases into either amplified or non-amplified: two models from the convolutional neural networks, CNNs (DenseNet, and MobileNet), and two transformer models (Vision Transformer, and Data-Efficient Image Transformers). Apart from these single models, we also built two ensemble models by concatenating the transformer and CNN architectures to observe their performances. A private dataset obtained from our collaborating hospital is used in this project, with several preprocessing techniques applied to the raw images prior to feeding the models. Promising results are reported with ViT emerged as the best performing model with a high accuracy of 87.48%, with 92.93% recall in detecting amplified HER2-SISH samples.
AI-based Green Energy Saving Solutions for IDC

In recent years, with the accelerated development of social informatization, digital economy has gradually become the core force of economic growth in various countries. As the carrier for the digital economy, the number of IDCs is also increasing day by day, and their construction volume and scale are expanding. Energy consumption and carbon emissions are growing rapidly as IDCs require large amounts of electricity to run servers, storage, backup, cooling systems and other infrastructure. IDCs are facing serious challenges of energy saving and greenhouse gas emission. How to achieve green, low-carbon and high-quality development is of particular concern. This paper summarizes and classifies all the current green energy-saving technologies in IDCs, introduces AI-based energy-saving solutions for IDC cooling systems in detail, compares and analyzes the energy-saving effects of AI energy-saving technologies and traditional energy-saving technologies, and points out the advantages of AI energy-saving solutions applied in green IDCs.
Dataset-Level Directed Image Translation for Cross-Domain Crowd Counting

Most crowd counting methods rely on a large amount of manually labeled data to train a supervised model. With the availability of synthetic dataset, one way to alleviate the scarcity of large-scale dataset is to use an image-to-image translation method to adapt synthetic data for training. However, previous methods focus on adapting local visual feature of the image, which leads to distorted and blurry translation results. In this paper, we propose a novel CLIP-guided image-to-image translation method, based on the observation that synthetic and real images can be easily separated in CLIP’s embedding space. We make use of the difference between two domains in the CLIP-space as a consistent guide to train an image translator. Then a crowd counting model is trained using images translated from synthetic data by the translator. Experiments on real-world crowd counting datasets demonstrate the effectiveness of the proposed method which enables the crowd counting model to achieve a state-of-the-art performance.
Trans-ARPG: Automated ICD Coding method based on Adversarial Reinforcement Path Generation and Transformer mechanism

The International Classification of Diseases (ICD) is a vital tool used in clinical and health management, providing codes for disease classification. The use of deep learning techniques to automatically extract valuable information from medical records and assist in coding has gained significant attention due to the increasing volume of medical data and the advancement of precision medicine research. However, current coding methods face challenges such as the large candidate space for disease coding and imbalanced code distribution. This study focuses on these challenges and proposes a hierarchical ICD automatic coding method. By introducing a Transformer-based hierarchical path propagation mechanism, the study effectively captures the relationships between disease codes at different hierarchical levels and reduces the candidate space for coding. Experimental results demonstrate the method’s efficacy in information extraction and coding improvement.
Improving Hyper-Network-Based Knowledge Graph Completion with Linear Transformation

Knowledge graph is a data structure with strong expression ability, which has been widely used in both academic and production fields, but most of existing knowledge graphs suffer from incompleteness, various methods like ConvE, Hyper and InteraceE have been proposed to tackle with this problem. Combining with hyper-network-based convolution and linear transformation of entities, a knowledge graph completion method named HyperL is proposed in this paper, HyperL not only inherits the excellent information fusion and expression ability of convolution transformation, but also enhanced the expression ability of entities with linear transformation. The proposed method is evaluated on three standard datasets including FB15k-237, WN18RR and YAGO3-10, and the performances are compared with other benchmark methods. Experimental results shows that the MRR of HyperL on FB15k-237, WN18RR and YAG03 can reach 0.360, 0.468, 0.552, respectively, which indicate that HyperL is and effective knowledge graph completion method.
Persona and Contextual Semantic Embeddings for Entity Alignment

Entity alignment is to identify entities from different KGs that are equivalent in the real world. The complexity of neighborhood relations and structural heterogeneity remain major obstacles to entity alignment. To tackle the challenges, our proposed PCSE utilizes three components to form a new framework that projects persona embedding of the entity on the hyperplane based on the translation model and graph attention network. It employs BERT to obtain the name and description semantics of the entity and applies a message propagation mechanism to aggregate the context information. This strategy can produce a cognitive expression of the entity, which abstracts the whole picture of it. The experimental results on three datasets demonstrate that our proposed model outperforms existing baselines.
SGM-based Refined Hints for Stereo Matching Generalization Improvement leveraging Gaussian Policy

Stereo matching has been a hot research topic in 3D reconstruction. Although great progress has been made thanks to deep learning technique, the performance still suffers a significant drop when dealing with a new domain. Recently, LiDAR and Radar guidance strategies are explored to alleviate the generalization problem. However, they rely on extra depth sensor cues. In this paper, a novel SGM-based refined hints guided stereo matching paradigm is developed, which does not require corresponding depth sensor data. The proposed method includes disparity hints acquisition and disparity hints guided stereo matching. The former utilizes a divide-and-conquer technique to detect, replace, and refine SGM-based disparity to ensure the uniformity and density of hints, while the latter leverages the Gaussian policy to incorporate the location information of hints to enhance the effective matching, and thus improve the out-of-domain performance. Extensive experiments show a significant generalization improvement ($e.g$., from 4.78 to 1.03 average errors on the KITTI 2015 dataset).
Conv-based Temporal Sets Prediction for Next-basket Recommendation

Next-basket recommendation (NBR) aims to recommend a set of items to a consumer according to past shopping carts. NBR essientially a more general and complex form than sequential recommendation which recommends the next item according to the historical records. However, the majority of recent literature fails to simultaneously consider the two aspects: 1) complex order-invariant relation of multiple elements within a set; 2) intangible order-variant user appetite in users’ historical interactions. To overcome the barrier, we propose a novel Conv-based Temporal Sets Prediction model (dubbed ConvTSP). We contrive a Conv-based Relationship Learning module to capturing the cross-relation between items in each basket. Besides, we integrate sequential-level dynamic user appetite and individual-level static user appetite into a fused user appetite representation. Extensive experiments and ablation studies are done to show how effective ConvTSP is.
Research on Time-series Visualization Methods for Scientific and Technical Consulting Graph Data

The existing open-source knowledge visualization components do not support knowledge graph temporal visualization exploration capabilities and cannot meet the demand of analyzing the dynamic evolutionary changes of industries in science and technology consulting scenarios. In this paper, to address the above problems, we propose a knowledge graph-based temporal visualization component construction and optimization method to build a graph visualization component that supports temporal dynamic exploration based on existing open-source knowledge visualization components to address the demand for industry insight analysis in science and technology consulting graph analysis services. At the same time, a data processing and graph rendering optimization strategy is proposed to improve the overall task execution efficiency in response to the problems of low rendering efficiency and high computational resource occupation rate in the time-series visualization exploration scenario.
Local Texture Complexity Guided Adversarial Attack

Extensive research revealed that deep neural networks are vulnerable to adversarial examples. In addition, recent studies have demonstrated that convolutional neural networks tend to recognize the texture (high-frequency components) rather than the shape (low-frequency components) of images. Thus, crafting adversarial perturbation in the frequency domain is proposed to enhance the attack strength. However, these methods either will increase the perceptibility of adversarial examples to the human visual system (HVS) or increase the computational effort in generating adversarial examples. To generate adversarial examples with better imperceptibility while consuming less computational effort, we propose an adversarial attack method to construct adversarial examples in the frequency domain with guidance from the local texture complexity of the image. Experiments on ImageNet and CIFAR-10 show that the proposed method is effective in generating adversarial examples imperceptible to the HVS.
Implementation of FPGA Cipher Card Supporting SR-IOV

Virtualization technology has brought various information security issues to cloud computing while promoting its development. The most effective way to cope with this problem is private data encryption in virtualization environment. In this paper, Field Programmable Gate Array (FPGA) cipher card supporting Single Root I/O Virtualization (SR-IOV) are designed. Cipher card can be considered as Physical Function (PF) device, which can enable and manager up to four Virtual Function (VF) devices. VF device is equipped with SHA-256 and AES algorithm functions and key management functions. The experimental results show that VF device produces measured transfer speeds up to 6.91GB/s ( DMA read) and 6.32GB/s (DMA write), respectively, which has the same data transmission performance as the physical device. Heterogeneous computing architecture for implementation of SHA-256 and AES cryptographic algorithms are also designed in this paper, which reduce the development difficulty of FPGA logic and improve the system overall efficiency. The system can meet the encryption requirements of different tenants for private data and improve the data security.
Eye-gesture-based multi-context interaction

Eye gestures have been acknowledged as a crucial real-time input channel for human-computer interaction, especially for people with disabilities who lack fine motor skills. However, existing eye-gesture-based interactions mainly rely on gaze duration, which can cause visual fatigue and is not user-friendly for those with disabilities. To address these issues, we propose acceptable interactive paths based on eye gestures in multiple contexts, such as reading and selection, to assist impaired individuals in interacting efficiently and naturally in their daily work. In the selection context, cursor movement is accomplished by fixation position, while single and double clicks of the left mouse button are executed through blink frequency. In the reading context, page scrolling is controlled by the vertical coordinates of the fixation position. In experiments, we set up reading and selection contexts to evaluate and compare the constructed interaction paths with the existing system. The experimental results demonstrate that the interaction paths proposed in this paper are efficient and natural.
Bidirectional field-steering and atomic steering induced by a magnon mode in a qubit-photon system

This paper investigates the cavity-magnon steering and qubit-qubit steering of a hybrid quantum system consisting of a single-mode magnon, a two-qubit state, and a single-mode cavity field in the presence of their damping rates. The temporal wave vector of the system is obtained for the initial maximally entangled two-qubit state and initial vacuum state of the magnon and cavity modes. Additionally, the mathematical inequalities for obtaining the cavity-magnon steering and qubit-qubit steering are introduced. The findings reveal that steering between the magnon and cavity is asymmetric, while steering between the two qubits is symmetric in our system. Increasing the atom-field coupling improves steering from magnon to field, while reducing steering between the two qubits. Moreover, increasing magnon-field coupling enhances and elevates the lower bounds of qubit-qubit steering. Further, adding the damping rates causes deterioration of the cavity-magnon steering and qubit-qubit steering. However, the steering persistence is slightly greater when damping originates from the cavity field rather than the magnon modes based on the coupling parameters.