Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Federated Recurrent Neural Networks

We present RHODE, a novel system that enables privacy-preserving training of and prediction on Recurrent Neural Networks (RNNs) in a cross-silo federated learning setting by relying on multiparty homomorphic encryption. RHODE preserves the confidentiality of the training data, the model, and the prediction data; and it mitigates federated learning attacks that target the gradients under a passive-adversary threat model. We propose a packing scheme, multi-dimensional packing, for a better utilization of Single Instruction, Multiple Data (SIMD) operations under encryption. With multi-dimensional packing, RHODE enables the efficient processing, in parallel, of a batch of samples. To avoid the exploding gradients problem, RHODE provides several clipping approximations for performing gradient clipping under encryption. We experimentally show that the model performance with RHODE remains similar to non-secure solutions both for homogeneous and heterogeneous data distributions among the data holders. Our experimental evaluation shows that RHODE scales linearly with the number of data holders and the number of timesteps, sub-linearly and sub-quadratically with the number of features and the number of hidden units of RNNs, respectively. To the best of our knowledge, RHODE is the first system that provides the building blocks for the training of RNNs and its variants, under encryption in a federated learning setting.
Evolution of Winning Solutions in the 2021 Low-Power Computer Vision Challenge

We organized the 2021 IEEE Low-Power Computer Vision Challenge to advance state-of-the-art solutions in low-power computer vision. Here, we examine the winning teams’ development patterns and design decisions, focusing on their techniques to balance power consumption and accuracy to provide guidelines for future competitions.
Remote Sensing Image Object Detection Based on Improved YOLOv5

Aiming at the problems of complex background, high difficulty of small target detection and high miss detection rate in target detection of satellite remote sensing images, this paper proposes a multiscale target detection model based on YOLOv5 network with attention mechanism. —The feature extraction capability of the backbone network is enhanced by fusing efficient channel attention modules in the backbone network, and the detection head is decoupled and parallel convolution is used to perform classification and regression tasks separately to alleviate the conflict between classification and regression tasks. After experimental validation, the algorithm achieves 74.2% mAP and 64 FPS detection speed on Dior remote sensing dataset. experimental results show that the improved detection algorithm can effectively improve the detection capability of YOLOv5 for small and medium targets in remote sensing images and meet the real-time performance of detection.
Cauchy combination omnibus test for normality

Testing whether data are from a normal distribution is a traditional problem and is of great concern for data analyses. The normality is the premise of many statistical methods, such as t-test, Hotelling T2 test and ANOVA. There are numerous tests in the literature and the commonly used ones are Anderson-Darling test, Shapiro-Wilk test and Jarque-Bera test. Each test has its own advantageous points since they are developed for specific patterns and there is no method that consistently performs optimally in all situations. Since the data distribution of practical problems can be complex and diverse, we propose a Cauchy Combination Omnibus Test (CCOT) that is robust and valid in most data cases. We also give some theoretical results to analyze the good properties of CCOT. Two obvious advantages of CCOT are that not only does CCOT have a display expression for calculating statistical significance, but extensive simulation results show its robustness regardless of the shape of distribution the data comes from. Applications to South African Heart Disease and Neonatal Hearing Impairment data further illustrate its practicability.
Action Recognition System Based 5G Communication and Edge Computing for Service Robot

Action recognition is one of the most critical technologies for the robot to provide service excellently. In order to solve the problems of low accuracy, limited arithmetic power, and low intelligence of service robot's action recognition, an action recognition system based on 5G communication and edge computing is proposed. Communication and data interaction between robot and edge computing device is realized by using 5G routers and technologies such as NAT and Socket. A LightSlowfast network is designed for the service robot, which improves action recognition accuracy while reducing the computational effort. The experimental results show that the Light-Slowfast network achieves 72.9% accuracy on the HMDB51 dataset. The system performs well in practical tests, and the low latency of data transmission can satisfy the robot's demand for real-time action recognition.
Self-Supervised Meta-Path-Based Heterogeneous Graph Embedding for Recommender Systems

In recent years, significant progress has been made in research on recommender systems based on Heterogeneous Information Networks (HIN). However, most existing HIN-based recommender system methods rely on meta-path-based embedding models, which do not fully exploit the inherent homogeneous and contrastive difference information present in heterogeneous networks. In this study, we propose a method for learning representations by constructing graphs using both heterogeneous graph structures and homogeneous similarity graphs. We then apply contrastive loss to obtain embeddings that capture the differences between these two types of graphs. Our proposed recommendation method combines two perspectives through data embedding of the two types of graphs to train Graph Neural Networks (GNN). Specifically, we find that using primitive paths is the most effective way to directly embed heterogeneous graphs. Integrating and supplementing information rationally through realistic logic also makes sense. Additionally, supplementing data through similarity analogies between viewing sequences and users themselves is also meaningful. Through a twoview neighborhood selection process of logical relations and established facts, experiments show that our approach can improve HIN-based recommendation models.
Towards Accurate Crowd Counting Via Smoothed Dilated Convolutions and Transformer

Density-based methods have shown promising results on crowd counting. Many existing methods seek to extract multi-scale features by dilated convolutions, but always gridding artifacts plague dilated convolutions. In this work, we propose to solve the gridding artifacts via smooth dilated residual block (SDRB). The smoothed dilation technique adds separable and shared convolutions that provide dependency among feature maps. Moreover, we present a residual contextual transformer block (RCTB) for multi-scale feature generation. The RCTB enables the location and recognition of people on the pixel level. Finally, we corroborate the prediction accuracy and the generalization capability with extensive experimental support. Our model enjoys superior performance on three realistic and public benchmarks: JHU-CROWD++, ShanghaiTech, and FDST.
On Lattice Tilings of <i>ℤ<sup>n</sup> </i> by Limited Magnitude Error Balls <i>B</i>(<i>n</i>, 2, 1, 1)

Limited magnitude error model has applications in flash memory. In this model, a perfect code is equivalent to a tiling of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {Z}^{n}$ </tex-math></inline-formula> by limited magnitude error balls. In this paper, we give a complete classification of lattice tilings of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {Z}^{n}$ </tex-math></inline-formula> by limited magnitude error balls <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {B}(n,2,1,1)$ </tex-math></inline-formula> .
Privacy-Preserving Multi-User Outsourced Computation for Boolean Circuits

With the prevalence of outsourced computation, such as Machine Learning as a Service, protecting the privacy of sensitive data throughout the whole computation is a critical yet challenging task. The problem becomes even more tricky when multiple sources of input and/or multiple recipients of output are involved, who would encrypt/decrypt data using different keys. Considering many computation tasks demand binary operands and operations but there are only outsourced computation constructions for arithmetic calculations [1], in this paper, the authors propose a privacy-preserving outsourced computation framework for Boolean circuits. The proposed framework can protect sensitive data throughout the whole computation, i.e., input, output and all the intermediate values, ensuring privacy for general outsourced tasks. Moreover, it compresses the ciphertext domain of [1] and attains secure protocols for four logic gates (AND, OR, NOT, and XOR) which are the basic operations in Boolean circuits. With the proposed framework as a building block, a novel Privacy-preserved (encrypted) Bloom Filter and a Multi-keyword Searchable Encryption scheme under the multi-user setting are presented. Security proof and experimental results show that the proposal is reliable and practical.
ABAEKS: Attribute-Based Authenticated Encryption With Keyword Search Over Outsourced Encrypted Data

The widespread adoption of cloud computing and the exponential growth of data highlight the need for secure data sharing and querying. Attribute-based keyword search (ABKS) has emerged as an efficient means of searching encrypted data stored in the cloud. However, existing ABKS schemes incur high end-to-end delay and are vulnerable to quantum computer attacks and/or (insider) keyword guessing attacks (KGA). To address these vulnerabilities, this paper introduces a new concept called attribute-based authenticated encryption with keyword search (ABAEKS) and proposes an efficient ABAEKS scheme. Our ABAEKS has low end-to-end delay, and is resistant to both quantum computer attacks and (insider) KGA. In addition, we formalize the security model of ABAEKS system and prove its security in the random oracle model. Finally, we conduct a comprehensive performance evaluation of ABAEKS, and the experimental results show that our ABAEKS is computationally efficient and outperforms current state-of-the-art ABKS schemes.
LCA-Net: A Context-Aware Lightweight Network for Low-Illumination Image Enhancement

Low light image enhancement has made great progress owing to powerful deep representation learning. However, due to the introduction of a large number of parameters, the computation cost of a deep learning based method increases dramatically, which poses great challenges for computational resources, and results in difficult to deploy the method on conventional imaging instruments. To address this problems, this paper proposes a context-aware lightweight model for low illumination image enhancement (Simply termed as LCA-Net). In which, to achieve a brightness balance between the overall image and local details, a context based modeling technique for modeling the non-local dependency of image pixels is developed, and a contrast enhancement model is constructed. To avoid overexposure, Gaussian function is employed to re-establish the light distribution mapping between different image channels. Finally, to avoid the loss of high-frequency details caused by smoothing operations, the fast Fourier transform (FFT) loss is introduced to constrain the network training. Considerable evaluations show that the proposed method performs favorably against the compared counterparts. Moreover, extensive experiments on public datasets have shown that the evaluation metrics and performance of LCA-Net are superior to current state-of-the-art methods.
MoiréTag: Angular Measurement and Tracking with a Passive Marker

Stable, low-cost, and precise visual measurement of directional information has many applications in domains such as virtual and augmented reality, visual odometry, or industrial computer vision. Conventional approaches like checkerboard patterns require careful pre-calibration, and can therefore not be operated in snapshot mode. Other optical methods like autocollimators offer very high precision but require controlled environments and are hard to take outside the lab. Non-optical methods like IMUs are low cost and widely available, but suffer from high drift errors.
Diffusion Image Analogies

In this paper we present Diffusion Image Analogies—an example-based image editing approach that builds upon the concept of image analogies originally introduced by Hertzmann et al. [2001]. Given a pair of images that specify the intent of a specific transition, our approach enables to modify the target image in a way that it follows the analogy specified by this exemplar. In contrast to previous techniques which were able to capture analogies mostly on the low-level textural details our approach handles also changes in higher level semantics including transition of object domain, change of facial expression, or stylization. Although similar modifications can be achieved using diffusion models guided by text prompts [Rombach et al. 2022] our approach can operate solely in the domain of images without the need to specify the user’s intent using textual form. We demonstrate power of our approach in various challenging scenarios where the specified analogy would be difficult to transfer using previous techniques.
A Hybrid Generator Architecture for Controllable Face Synthesis

Modern data-driven image generation models often surpass traditional graphics techniques in quality. However, while traditional modeling and animation tools allow precise control over the image generation process in terms of interpretable quantities — e.g., shapes and reflectances — endowing learned models with such controls is generally difficult.
UCDN: A CenterNet-Based Dense Muti-scale Detection Fusion Net on Underwater Objects

Underwater object detection is an inevitable task with urgent practical importance in the field of economic marine life. Due to the special underwater optical environment, most of the existing common detection algorithms are not capable of providing ideal results for underwater objects. For this reason, this paper proposes a constructive CenterNet-based underwater object detection model, named UCDN, accompanied by a detection strategy with pervasive applicability. Specifically, a detailed fusion module is created with the goal of filtering out interfering information. Meanwhile, we propose an exclusive idea of dense scale linking to fuse multi-scale features as much as possible. More importantly, we fuse our detection network with the Frankle-McCann Retinex algorithm to detect more objects obscured by the environment without increasing the training consumption. In addition to this, an efficient automatic sample balancing strategy is proposed, which is well suited to our detection situation. Finally, we evaluate our algorithm on underwater image datasets. The experiment results showed that the precision (mAP) of UCDN reached 87.46% which was higher than existing state-of-the-art land-based detection algorithms and underwater detection algorithms.
SDCG: Silhouette-based Deep Clustering with GNN for Improved Graph Node Clustering

Graph Neural Networks (GNNs) are powerful tools for analyzing graph-structured data in various fields because of their great expressive power for graph data. They use a message-passing mechanism to update node embeddings, which are then used for tasks such as node classification and link prediction. Recently, node embeddings have also been used in research on graph node clustering, which aims to group similar nodes based on their features and graph topology. However, traditional methods for node clustering have a limitation in that GNNs only focus on generating node embeddings without considering the ultimate objective of clustering. To address this issue, a novel technique called "Deep Clustering" has been proposed, which integrates both node embedding and clustering stages. This requires defining a new loss function by simultaneously minimizing the GNN loss and the clustering loss. Our proposed loss function incorporates not only the distance within clusters but also the distance between clusters by applying the Silhouette coefficient, which enables us to achieve better clustering results. In this paper, we propose a Silhouette-based Deep Clustering with GNN (SDCG) to more effectively cluster nodes in a graph by iteratively training the embedding model to produce embedding vectors with improved clustering results. Through extensive experiments, we demonstrate that SDCG outperforms the conventional approach of performing embedding and clustering independently.
Calibrating Cybersecurity Experiments: Evaluating Coverage Analysis for Fuzzing Benchmarks

Computer science experimentation, whether it be for safety, reliability or cybersecurity, is an important part of scientific advancement. Evaluation of relative merits of various experiments typically requires well-calibrated benchmarks that can be used to measure the experimental results. This paper reviews current trends in using benchmarks in fuzzing experimental research for cybersecurity, specifically with metrics related to coverage analysis. Strengths and weaknesses of the current techniques are evaluated and suggestions for improving the current approaches are proposed. The end goal is to convince researchers that benchmarks for experimentation must be well documented, archived and calibrated so that the community knows how well the tools and techniques perform with respect to the possible maximum in the benchmark.
The Strategy of Digital Twin Convergence Service based on Metavers

The Advanced and radical development of IT technology and artificial intelligence technology have made it possible to develop advanced services Digital Twin, Metaverse, Metatwin-verse, etc using Artificial Intelligence(AI). The results induced from AI present the correct solution when AI performs accurate study and analysis. Specifically, real situations reflecting complex relationships between objects, results from real situations have to be adaptive to convergence situations and then it should be possible to draw conclusions and make decisions that are not limited to specific situations. So, it is essential to conduct AI based study and analysis by considering these real world characteristics to provide digital twin services based on metaverse. Recently, there are many studies on Graph Neural Network(GNN) and services applied to GNN for learning the relationship between objects detected in real situations. Accordingly, this paper proposes a metaverse-based Digital Twin Convergence Service(DTCS) including spatial elements strategy that is possible to draw accurate conclusions in a changing convergence situation. DTCS is able to conduct causal reasoning and association learning between objects considering directions and distances change characteristics between objects and this is possible to make correct solution and decision making in the process of simulation and analysis of digital twin. In that DTCS proceeds by considering distance and changing angle between objects, this overcomes the limitation of existing GNN which only considers the degree of association or assigns the same parameters to connected objects. DTCS would be possible to expand the advanced services of Metatwinverse in that it is possible to have robust learning based conclusions in real-time changing convergence situations.
Towards an Adversarial Machine Learning Framework in Cyber-Physical Systems

The applications of machine learning (ML) in cyber-physical systems (CPS), such as the smart energy grid has increased significantly. While ML technology can be integrated into CPS, the security risk of ML technology has to be considered. In particular, adversarial examples provide inputs to a ML model with intentionally attached perturbations (noise) that could pose the model to make incorrect decisions. Perturbations are expected to be small or marginal so that adversarial examples could be invisible to humans, but can significantly affect the output of ML models. In this paper, we design a taxonomy to provide the problem space for investigating the adversarial example generation techniques based on state-of-the-art literature. We propose a three-dimensional framework containing three dimensions for adversarial attack scenarios (i.e., black-box, white-box, and gray-box), target type, and adversarial examples generation methods (gradient-based, score-based, decision-based, transfer- based, and others). Based on the designed taxonomy, we systematically review the existing research efforts on adversarial ML in representative CPS (i.e., transportation, healthcare, and energy). Furthermore, we provide one case study to demonstrate the impact of adversarial examples of attacks on a smart energy CPS deployment. The results indicate that the accuracy can decrease significantly from 92.62% to 55.42% with a 30% adversarial sample injection. Finally, we discuss potential countermeasures and future research directions for adversarial ML.
Classification of Multilingual Medical Documents using Deep Learning

Due to a large number of documents available on the web, operations such as finding a set of information contained in a document has become a difficult task, especially with multilingual documents. Hence the necessity to have performance tools for finding, organizing and classifying information. A variety of classification methods are proposed to resolve this kind of problem but these techniques suffer from limits such as the loss of information, and the loss of relations between words that affects the effectiveness and the performance of the classification process. So, this paper attempts to support the idea of multilingual document classification, especially in the biomedical domain using a new approach, based on deep learning. The key idea is to generate a new conceptual representation of textual multilingual medical documents to facilitate the classification task. In this context, a deep learning technique will be exploited for a good representation. To show the feasibility of our approach, we implemented a system related to a domain that attracts more and more attention from the data mining community: the biomedical domain. An experimental study is performed, using documents extracted from the biomedical benchmark corpus, called Oshumed, which contains documents distributed by different categories.
Research and Application of Content-based Image Hash Retrieval Algorithm

In order to store and manage a large amount of image information, we built a database retrieval system and studied and applied content-based image retrieval algorithms. We found that the difference hashing algorithm performs better than the perceptual hashing algorithm, the mean hashing algorithm and the histogram feature extraction algorithm in the scenario of handling a large number of images. Finally, we applied the difference-value hashing algorithm to a database retrieval system for a film festival to achieve in storing and retrieving a large amount of film materials.