Reviewerly/exHarmony huggingface dataset:
DenseHashNet: A Novel Deep Hashing for Medical Image Retrieval

With the wide application of imaging modalities such as X-ray and Computed Tomography (CT) in clinical practice, Content-based Medical Image Retrieval (CBMIR) has become a current research hotspot. Related studies have shown that hash-based image retrieval algorithms can retrieve relevant images faster and more accurately than traditional image retrieval methods. Therefore, in this paper, we propose a novel deep hashing method for medical image retrieval, called DenseHashNet. Specifically, we first use DenseNet to extract the original image features, and introduce the Spatial Pyramid Pooling (SPP) layer after the last Dense Block so that features at different scales can be extracted and multi-scale features fused with information from multiple regions. Then, the output of the SPP layer is subjected to Power-Mean Transformation (PMT) operation to enhance the nonlinearity of the model and improve the performance of the model. Finally, we map the output of PMT to hash codes through fully connected layers. Experimental results show that our method achieves better performance, compared with some representative methods.
Trace Controlled Text to Image Generation

Text to Image generation is a fundamental and inevitable challenging task for visual linguistic modeling. The recent surge in this area such as DALL $$\cdot $$ E has shown breathtaking technical breakthroughs, however, it still lacks a precise control of the spatial relation corresponding to semantic text. To tackle this problem, mouse trace paired with text provides an interactive way, in which users can describe the imagined image with natural language while drawing traces to locate those they want. However, this brings the challenges of both controllability and compositionality of the generation. Motivated by this, we propose a Trace Controlled Text to Image Generation model (TCTIG), which takes trace as a bridge between semantic concepts and spatial conditions. Moreover, we propose a set of new technique to enhance the controllability and compositionality of generation, including trace guided re-weighting loss (TGR) and semantic aligned augmentation (SAA). In addition, we establish a solid benchmark for the trace-controlled text-to-image generation task, and introduce several new metrics to evaluate both the controllability and compositionality of the model. Upon that, we demonstrate TCTIG’s superior performance and further present the fruitful qualitative analysis of our model.
A Probabilistic Programming Idiom for Active Knowledge Search

In this paper, we derive and implement a probabilistic programming idiom for the problem of acquiring new knowledge about an environment. The idiom is implemented utilizing a modern probabilistic programming language. We demonstrate the utility of this idiom by implementing an algorithm for the specific problem of active mapping and robot exploration. Finally, we evaluate the functionality of the implementation through an extensive simulation study utilizing the HouseExpo dataset.
A Practical Blockchain-Based Maintenance Record System for Better Aircraft Security

Public transportation not only has a high utilization rate but also has a high density of population on-board. Keeping these facilities safe-to-use and well-functioned is the highest priority since it is heavily relevant to public safety. A malfunctioning may cause an accident ranged from slight to severe, even fatal. In particular, civil aviation (aircraft) is one of the public transportation industries that requires the highest safety standards. However, several accidents on aircraft maintenance system have revealed that there is an emerging need to secure the record integrity and traceability. Motivated by this observation and supported by an airline company, in this work, we propose and implement AirChain, a blockchain-based aircraft maintenance record system, in which the data can be stored in a way that is resistant to tampering but is easy to access. In the evaluation, we examine the system performance in the aspects of storage growth and processing time. The results indicate the viability and practicability of our system.
Exploiting Spatial-Temporal Behavior Patterns for Fraud Detection in Telecom Networks

Fraud detection in telecom network is a crucial problem that threatens users' privacy and property security. In recent years, fraudsters adopt more advanced camouflage strategies to avoid being detected by traditional algorithms. To deal with these new types of fraud, it is necessary to analyze the integrated spatial-temporal features, which are rarely involved in existing literature. In this paper, we propose a novel fraud detection model based on the intertwined spatial-temporal patterns of user behaviors. Specifically, we first introduce the extension of statistical and interactive features to dynamic call patterns, and build a probabilistic model to simulate users' call behaviors. Then the sequential patterns reflecting users' own behaviors are obtained by the mixture Hidden Markov Models, and the structural patterns reflecting the collaboration between users in the telecom network are obtained by the attention-based Graph-SAGE model. Finally, our model outputs a fraud score for each user to detect potential fraudsters. We conduct extensive experiments on a real-world telecom dataset. The experimental results demonstrate that our intertwined spatial-temporal call patterns can effectively represent user behavior and improve the accuracy of fraud detection compared with state-of-the-art methods. The results also validate the efficiency and the interpretability of our model.
Augmented reality (AR) posture training for manual material handling: iterative design, evaluation, and recommendation for AR interface

Manual material handling (MMH) tasks can pose risks of musculoskeletal disorders for workers, which makes posture training programs essential. The ultimate goal of this research was to design and evaluate an augmented reality (AR) based training platform, namely the virtual instructor application (VIA). Specifically, the usability problems revealed from the preliminary version (VIA-1) were addressed and enhanced features were implemented in the current version (VIA-2). The usability of VIA-2 was assessed by ten individuals with experience of MMH. Participants explored VIA-2 while performing a series of predefined tasks. A usability questionnaire was administered, and a brief interview was conducted. Results showed that VIA-2 had increased usability compared to VIA-1. While some visual content display issues occurred, a future minor refurnish can address these problems. Finally, the iterative design and evaluation scheme can be applied to the future design of AR-based training programs and evaluation protocols.
Learning Instance-Specific Adaptation for Cross-Domain Segmentation

We propose a test-time adaptation method for cross-domain image segmentation. Our method is simple: Given a new unseen instance at test time, we adapt a pre-trained model by conducting instance-specific BatchNorm (statistics) calibration. Our approach has two core components. First, we replace the manually designed BatchNorm calibration rule with a learnable module. Second, we leverage strong data augmentation to simulate random domain shifts for learning the calibration rule. In contrast to existing domain adaptation methods, our method does not require accessing the target domain data at training time or conducting computationally expensive test-time model training/optimization. Equipping our method with models trained by standard recipes achieves significant improvement, comparing favorably with several state-of-the-art domain generalization and one-shot unsupervised domain adaptation approaches. Combining our method with the domain generalization methods further improves performance, reaching a new state of the art. Our project page is https://yuliang.vision/InstCal/ .
Forward-Secure Encryption with Fast Forwarding

Forward-secure encryption (FSE) allows communicating parties to refresh their keys across epochs, in a way that compromising the current secret key leaves all prior encrypted communication secure. We investigate a novel dimension in the design of FSE schemes: fast-forwarding (FF). This refers to the ability of a stale communication party, that is "stuck" in an old epoch, to efficiently "catch up" to the newest state, and frequently arises in practice. While this dimension was not explicitly considered in prior work, we observe that one can augment prior FSEs—both in symmetric- and public-key settings—to support fast-forwarding which is sublinear in the number of epochs. However, the resulting schemes have disadvantages: the symmetric-key scheme is a security parameter slower than any conventional stream cipher, while the public-key scheme inherits the inefficiencies of the HIBE-based forward-secure PKE. To address these inefficiencies, we look at the common real-life situation which we call the bulletin board model, where communicating parties rely on some infrastructure—such as an application provider—to help them store and deliver ciphertexts to each other. We then define and construct FF-FSE in the bulletin board model, which addresses the above-mentioned disadvantages. In particular,
SAR Image Data Augmentation via Residual and Attention-Based Generative Adversarial Network for Ship Detection

In recent years, generative adversarial networks (GANs) have been successfully applied to generate the SAR images. However, due to the fact that it is more difficult to generate the images than to distinguish the real or fake, GANs usually suffer from the problems of unstable training and mode collapse. As such, a residual and attention-based generative adversarial network (RAGAN) is proposed for SAR data augmentation. Firstly, the directional bounding box is used as a constraint in the RAGAN to limit the position of ship in the generated SAR image, which can be further set as the annotation of the SAR image for ship detection directly. After that, inspired by the residual and attention learning, a residual and attention block (RABlock) and a transposed RABlock (TRABlock) are designed to improve the generator of the RAGAN, thus preventing the whole model from gradient vanishing and suppressing the effects of speckle noise and background to enhance the quality of the generated SAR images. Experimental results on the HRSID data set demonstrate the effectiveness of our RAGAN model in SAR data augmentation for ship detection.
Sectum: Accurate Latency Prediction for TEE-hosted Deep Learning Inference

As the security issue of cloud-offloaded Deep Learning (DL) inference is drawing increasing attention, running DL inference in Trusted Execution Environments (TEEs) has become a common practice. Latency prediction of TEE-hosted DL model inference is essential for many scenarios, such as DNN model architecture searching with a latency constraint or layer scheduling in model-parallelism inference. However, existing solutions fail to address the memory over-commitment issue in resource-constrained environments inside TEEs.This paper presents Sectum, an accurate latency predictor for DL inference inside TEE enclaves. We first perform a synthetic empirical study to analyze the relationship between inference latency and memory occupation. Sectum predicts inference latency following a two-stage design based on some critical observations. First, Sectum uses a Graph Neural Network (GNN)-based model to detect whether a given model would trigger memory over-commitment in TEEs. Then, combining operator-level latency modeling with linear regression, Sectum could predict the latency of a model. To evaluate Sectum, we design a large dataset that contains the latency information of over 6k CNN models. Our experiments demonstrate that Sectum could achieve over 85% ±10% accuracy of latency prediction. To our knowledge, Sectum is the first method to predict TEE-hosted DL inference latency accurately.
Information Systems Security Threats and Vulnerabilities: A Case of the Institute of Accountancy Arusha (IAA)

All modern computer users need to be concerned about information system security (individuals and organisations).Many businesses established various security structures to protect information system security from harmful occurrences by implementing security procedures, processes, policies, and information system security organisational structures to ensure data security.Despite all the precautions, information security remains a disaster in Tanzania's learning institutions.The fundamental issue appears to be a lack of awareness of crucial information security factors.Various companies have different security issues due to differences in ICT infrastructure, implementations, and usage.The study focuses on identifying information system security threats and vulnerabilities in public higher learning institutions in Tanzania, particularly the Institute of Accountancy Arusha (IAA).The study involved all employees of IAA, academics, and other supporting staff, which totalled 302, and the sample size was 170.The study utilised a descriptive research design, where the quantitative methodology was used through a five-point Likert scale questionnaire, and found that key factors that affect the security of information systems at IAA include human factors, policy-related issues, work environment and demographic factors.The study proposed regular awareness and training programs; an increase in women's awareness of information system security; proper policy creation and reviews every 4 years; promote actions that lessen information system security threats and vulnerabilities, and the creation of information system security policy documents independently from ICT policy.
A Mongolian Handwritten Word Images Generation Approach Based on Generative Adversarial Networks

The particular word formation manner of the Mongolian language makes its vocabulary reach millions. It is tough to manually collect a Mongolian handwritten word images dataset covering all words. In order to realize the automatic generation of Mongolian handwritten word images, an approach based on generative adversarial networks has been proposed in this paper, which is called Mongolian Generator (MG). The proposed MG generates Mongolian handwritten word images with better writing details and more accurate text content. Also, it can generate handwritten word images based on a specific writing style or text content. Moreover, the perceptual adversarial loss has been integrated into the MG, which guarantees that the generated handwritten word images appear more realistic. The experimental results indicate that the performance of the proposed MG outperforms several baselines and state-of-the-art models.
Parametric Dimension Reduction by Preserving Local Structure

We extend a well-known dimension reduction method, t-distributed stochastic neighbor embedding (t-SNE), from non-parametric to parametric by training neural networks. The main advantage of a parametric technique is the generalization of handling new data, which is beneficial for streaming data visualization. While previous parametric methods either require a network pre-training by the restricted Boltzmann machine or intermediate results obtained from the traditional non-parametric t-SNE, we found that recent network training skills can enable a direct optimization for the t-SNE objective function. Accordingly, our method achieves high embedding quality while enjoying generalization. Due to mini-batch network training, our parametric dimension reduction method is highly efficient. For evaluation, we compared our method to several baselines on a variety of datasets. Experiment results demonstrate the feasibility of our method. The source code is available at https://github.com/a07458666/parametric_dr.
Mixed Reality for Engineering Design Review Using Finite Element Analysis

Mixed Reality (MR) provides a unique environment for engineers to visualize a virtual CAD model's structural characteristics within a real physical environment. Existing implementations have explored conducting structural design reviews in virtual environments. However, more can be done to explore conducting virtual structural reviews on assemblies using MR. A MR-based application is developed, for engineers to conduct structural design reviews with support for complete assemblies and visualizing adjacent structural behaviors, using Finite Element Analysis (FEA). A preliminary evaluation study conducted an engineering design review with participants in either MR or on a conventional desktop computer, measuring the usability, sense of flow, and workload. The pilot study results suggest that MR is an intuitive platform with higher absorption of activity and flow experience, despite incurring a higher physical demand than the desktop environment. Based on the findings, we discuss the current potential and challenges faced when conducting design reviews within a MR environment, suggest improvements that can be made when designing MR-based engineering tools, and discuss the significant benefits that MR can offer over traditional desktop monitor based applications.
A sampling-based approach for efficient clustering in large datasets

We propose a simple and efficient clustering method for high-dimensional data with a large number of clusters. Our algorithm achieves high-performance by evaluating distances of datapoints with a subset of the cluster centres. Our contribution is substantially more efficient than k-means as it does not require an all to all comparison of data points and clusters. We show that the optimal solutions of our approximation are the same as in the exact solution. However, our approach is considerably more efficient at extracting these clusters compared to the state-of-the-art. We compare our approximation with the exact k-means and alternative approximation approaches on a series of standardised clustering tasks. For the evaluation, we consider the algorithmic complexity, including number of operations to convergence, and the stability of the results. An efficient implementation of the algorithm is available online.
Adaptive Correction of Landmark for Visual Homing in Mobile Vehicles

This paper presents an optimized visual homing method based on Average Landmark Vector (ALV), termed Adaptive Correction of Landmark (ACoL), which exhibits good homing performance and robustness in both static and dynamic indoor environments. The salient features of this paper are: (1) Inherent constraints of visual homing are analyzed in detail to reveal the limitations of existing ALV-based methods. (2) Instead of removing mismatched landmarks or artificially assigning fixed weights to landmarks, a novel solution is proposed by adaptively evaluating a reasonable weight for each landmark to account for contribution to the output. (3) Computation time of the proposed method is particularly concerned to enable the vehicle to perform real-time homing tasks in actual scenarios. (4) Simulations and experiments demonstrate the superiority of the proposed method under both static and dynamic conditions. Our method focuses on improving a vehicle's homing ability in terms of landmark matching, landmark distribution and homing direction. Comparative studies with other ALV-based methods demonstrate that our method generalizes well to various complex and challenging indoor scenarios.
Cluster Analysis of Low-Dimensional Medical Concept Representations from Electronic Health Records

The study of existing links among different types of medical concepts can support research on optimal pathways for the treatment of human diseases. Here, we present a clustering analysis of medical concept learned representations generated from MIMIC-IV, an open dataset of de-identified digital health records. Patient’s trajectory information were extracted in chronological order to generate +500k sequence-like data structures, which were fed to a word2vec model to automatically learn concept representations. As a result, we obtained concept embeddings that describe diagnostics, procedures, and medications in a continuous low-dimensional space. A quantitative evaluation of the embeddings shows the significant power of the extracted embeddings on predicting exact labels of diagnoses, procedures, and medications for a given patient trajectory, achieving top-10 and top-30 accuracy over 47% and 66%, respectively, for all the dimensions evaluated. Moreover, clustering analyses of medical concepts after dimensionality reduction with t-SNE and UMAP techniques show that similar diagnoses (and procedures) are grouped together matching the categories of ICD-10 codes. However, the distribution by categories is not as evident if PCA or SVD are employed, indicating that the relationships among concepts are highly non-linear. This highlights the importance of non-linear models, such as those provided by deep learning, to capture the complex relationships of medical concepts.
Lightweight Industrial Image Classifier Based on Federated Few-Shot Learning

Image classification using convolutional neural networks (CNNs) is critical for broader industrial applications like defect detection. To protect sensitive data during the industrial process, increasing institutions are highly interested in training CNN classifiers collaboratively with federated learning (FL). However, the existing FL solutions cannot address the sample deficiency and heterogeneous learning resource issues at different practical institutions. In this article, we present a federated lightweight relation network (FLRN), a lightweight industrial image classifier based on our federated few-shot learning (FFSL) architecture. Results of extensive experiments considering different real-world FFSL scenarios indicate that, unlike the state-of-the-art few-shot learning method relation network (RN), the FLRN performs well on not only FL participants with mutually isolated classes of samples but also external institutions with limited samples from unseen classes. Compared to the RN with the predominating FedAvg-based FL deployment, the FLRN manages to achieve as low as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{29.6}\times$</tex-math></inline-formula> less client–cloud communication, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{5.2}\times$</tex-math></inline-formula> less computation, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{22.0}\times$</tex-math></inline-formula> less storage costs of clients.