Reviewerly/exHarmony huggingface dataset:
Towards Continual Social Network Identification

Social networks have become most widely used channels for sharing images and videos, and discovering the social platform of origin of multimedia content is of great interest to the forensics community. Several techniques address this problem, however the rapid development of new social platforms, and the deployment of updates to existing ones, often render forensic tools obsolete shortly after their introduction. This effectively requires constant updating of methods and models, which is especially cumbersome when dealing with techniques based on neural networks, as trained models cannot be easily fine-tuned to handle new classes without drastically reducing the performance on the old ones – a phenomenon known as catastrophic forgetting. Updating a model thus often entails retraining the network from scratch on all available data, including that used for training previous versions of the model. Continual learning refers to techniques specifically designed to mitigate catastrophic forgetting, thus making it possible to extend an existing model requiring no or a limited number of examples from the original dataset. In this paper, we investigate the potential of continual learning techniques to build an extensible social network identification neural network. We introduce a simple yet effective neural network architecture for Social Network Identification (SNI) and perform extensive experimental validation of continual learning approaches on it. Our results demonstrate that, although Continual SNI remains a challenging problem, catastrophic forgetting can be significantly reduced by only retaining a fraction of the original training data.
Vehicle Target Detection Method for Wide-Area SAR Images Based on Coarse-Grained Judgment and Fine-Grained Detection

The detection of vehicle targets in wide-area Synthetic Aperture Radar (SAR) images is crucial for real-time reconnaissance tasks and the widespread application of remote sensing technology in military and civilian fields. However, existing detection methods often face difficulties in handling large-scale images and achieving high accuracy. In this study, we address the challenges of detecting vehicle targets in wide-area SAR images and propose a novel method that combines coarse-grained judgment with fine-grained detection to overcome these challenges. Our proposed vehicle detection model is based on YOLOv5, featuring a CAM attention module, CAM-FPN network, and decoupled detection head, and it is strengthened with background-assisted supervision and coarse-grained judgment. These various techniques not only improve the accuracy of the detection algorithms, but also enhance SAR image processing speed. We evaluate the performance of our model using the Wide-area SAR Vehicle Detection (WSVD) dataset. The results demonstrate that the proposed method achieves a high level of accuracy in identifying vehicle targets in wide-area SAR images. Our method effectively addresses the challenges of detecting vehicle targets in wide-area SAR images, and has the potential to significantly enhance real-time reconnaissance tasks and promote the widespread application of remote sensing technology in various fields.
Configurable Memory-Based NTT Architecture for Homomorphic Encryption

Fully Homomorphic Encryption (FHE) is currently seen to be a promising solution for privacy-preserving applications. However, FHE suffers from a computational bottleneck due to the need to perform large polynomial calculations. Number Theoretic Transform (NTT) as a fundamental component of FHE is widely used to reduce latency and computation complexity while performing polynomial multiplication. Designing FHE can be challenging because it requires different settings depending on the application, such as the polynomial degree and coefficient modulus size. The hardware design has to stick to specific parameter values, which can make things harder to be implemented. In this brief, we present a configurable hardware accelerator for NTT that supports a wide range of parameter settings without any recompilation. This module is highly parallelized and designed for high throughput on the butterfly unit array. We provide a conflict-free access memory between the NTT Core and memory to increase performance. For the evaluation, our NTT and INTT modules show significant performance improvements compared to the CPU implementation using Microsoft’s SEAL 4.0 library by factors of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8.88\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$11.2\times $ </tex-math></inline-formula> , respectively. In comparison with the state-of-the-art hardware implementation, our module showed better performance with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.46\times $ </tex-math></inline-formula> improvement of throughput/slice metric as a fair comparison while consuming fewer hardware resources.
A Survey on Auto-Parallelism of Large-Scale Deep Learning Training

Deep learning (DL) has gained great success in recent years, leading to state-of-the-art performance in research community and industrial fields like computer vision and natural language processing. One of the reasons for this success is the huge amount parameters adopted in DL models. However, it is impractical to train a moderately large model with a large number of parameters on a typical single device. Thus, It is necessary to train DL models in clusters with distributed training algorithms. However, traditional distributed training algorithms are usually sub-optimal and highly customized, which owns the drawbacks to train large-scale DL models in varying computing clusters. To handle the above problem, researchers propose auto-parallelism, which is promising to train large-scale DL models efficiently and practically in various computing clusters. In this survey, we perform a broad and thorough investigation on challenges, basis, and strategy searching methods of auto-parallelism in DL training. First, we abstract basic parallelism schemes with their communication cost and memory consumption in DL training. Further, we analyze and compare a series of current auto-parallelism works and investigate strategies and searching methods which are commonly used in practice. At last, we discuss several trends in auto-parallelism which are promising in further research.
Relational Temporal Graph Reasoning for Dual-task Dialogue Language Understanding

Dual-task dialog language understanding aims to tackle two correlative dialog language understanding tasks simultaneously via leveraging their inherent correlations. In this paper, we put forward a new framework, whose core is relational temporal graph reasoning. We propose a speaker-aware temporal graph (SATG) and a dual-task relational temporal graph (DRTG) to facilitate relational temporal modeling in dialog understanding and dual-task reasoning. Besides, different from previous works that only achieve implicit semantics-level interactions, we propose to model the explicit dependencies via integrating prediction-level interactions. To implement our framework, we first propose a novel model Dual-tAsk temporal Relational rEcurrent Reasoning network (DARER), which first generates the context-, speaker- and temporal-sensitive utterance representations through relational temporal modeling of SATG, then conducts recurrent dual-task relational temporal graph reasoning on DRTG, in which process the estimated label distributions act as key clues in prediction-level interactions. And the relational temporal modeling in DARER is achieved by relational graph convolutional networks (RGCNs). Then we further propose Relational Temporal Transformer (ReTeFormer), which achieves fine-grained relational temporal modeling via Relation- and Structure-aware Disentangled Multi-head Attention. Accordingly, we propose DARER with ReTeFormer (DARER 2), which adopts two variants of ReTeFormer to achieve the relational temporal modeling of SATG and DTRG, respectively. The extensive experiments on different scenarios verify that our models outperform state-of-the-art models by a large margin. Remarkably, on the dialog sentiment classification task in the Mastodon dataset, DARER and DARER 2 gain relative improvements of about 28% and 34% over the previous best model in terms of F1.
A new lightweight data security system for data security in the cloud computing

In recent decades, data has proved indispensable to all facets of human existence. The development of several applications has resulted in the exponential expansion of data. This information can be encrypted and stored in secure areas. Cloud computing is the technology that can be used to store these massive data sets. The article suggests a Cloud-based Data Security System (C-DSS) that employs a five-tiered trust model for cloud-edge data-sharing architectures. The data owner can select an appropriate trust level and Cyber Threat Information (CTI) sanitization procedure before releasing CTI for analytic strategy. In addition, this cleansing method is conducted either by an end device or by the cloud service supplier, based on the organization's degree of confidence in latter. Research presents the trust architecture, cloud architecture, and installation methodology, all of which are intended to meet widest variety of needs for exchanging secret CTI information. The testing findings high degree of data security and an evident improvement in terms of cypher processing time and security services when compared to the encryption systems that are most often employed in cloud technology. In conclusion, research briefly outlines development and evaluation performed so far by pilot applications confirming the architecture.
Quantum computing based state domain equations and feedback control

Quantum computing is gaining popularity in a variety of engineering fields. Especially in the case of automated process control. In light of ever-increasing technological sophistication and complexity, as well as demands on computing resources, the need for quantum versions of basic mathematical modelling and control theory tools is imminent. This paper presents a quantum computing formulation of state domain equations, a new quantum computing-based solver of differential equations based on a single qubit state, and a validation on a DC motor drive illustrative example using Matlab-based quantum algorithm. The quantum formulation of state equations is carried out with the help of a qubit state expressed as a time varying quantum spin based on the Bloch representation. A new method for solving state domain equations is presented, as well as the concept of quantum state feedback control and the ability to maintain a qubit state in a desired state. Two state domain control strategies developed with quantum computing tools are presented and compared to the exact classical solution, highlighting the methods’ effectiveness.
Establishing Genealogies of Born Digital Content: The Suitability of Revision Identifier (RSID) Numbers in MS Word for Forensic Enquiry

Born-digital content is rapidly becoming the norm for literary works, professional reports, academic journal articles, and formal corporate correspondence. From the perspective of digital forensics, there is a need to understand the origin of a document and its entire creation process, from outlining and drafting to editing the final version of the text. Revision save identifier (RSID) numbers embedded in MS Word documents have been used to examine the nature and extent of individual edits within a document. These RSIDs remain logged in the metadata even if the text with which they were associated has been removed. As copies of such files retain the original’s RSIDs, this metadata can be used to determine the order in which documents were cloned from each other. As a proof-of-concept, this paper examined over 400 template files generated by a single publisher for manuscript submissions to its journals. The study can show that it is possible to establish genealogies and thus relative chronologies of born digital content by first identifying those documents that share a document (root) RSID and then seriating those RSIDs that are shared between two or more documents.
Modular Reinforcement Learning for Autonomous UAV Flight Control

Recently, research on unmanned aerial vehicles (UAVs) has increased significantly. UAVs do not require pilots for operation, and UAVs must possess autonomous flight capabilities to ensure that they can be controlled without a human pilot on the ground. Previous studies have mainly focused on rule-based methods, which require specialized personnel to create rules. Reinforcement learning has been applied to research on UAV autonomous flight; however, it does not include six-degree-of-freedom (6-DOF) environments and lacks realistic application, resulting in difficulties in performing complex tasks. This study proposes a method of efficient learning by connecting two different maneuvering methods using modular learning for autonomous UAV flights. The proposed method divides complex tasks into simpler tasks, learns them individually, and then connects them in order to achieve faster learning by transferring information from one module to another. Additionally, the curriculum learning concept was applied, and the difficulty level of individual tasks was gradually increased, which strengthened the learning stability. In conclusion, modular learning and curriculum learning methods were used to demonstrate that UAVs can effectively perform complex tasks in a realistic, 6-DOF environment.
Long-term liver lesion tracking in contrast-enhanced ultrasound videos via a siamese network with temporal motion attention

Propose: Contrast-enhanced ultrasound has shown great promises for diagnosis and monitoring in a wide range of clinical conditions. Meanwhile, to obtain accurate and effective location of lesion in contrast-enhanced ultrasound videos is the basis for subsequent diagnosis and qualitative treatment, which is a challenging task nowadays. Methods: We propose to upgrade a siamese architecture-based neural network for robust and accurate landmark tracking in contrast-enhanced ultrasound videos. Due to few researches on it, the general inherent assumptions of the constant position model and the missing motion model remain unaddressed limitations. In our proposed model, we overcome these limitations by introducing two modules into the original architecture. We use a temporal motion attention based on Lucas Kanade optic flow and Karman filter to model the regular movement and better instruct location prediction. Moreover, we design a pipeline of template update to ensure timely adaptation to feature changes. Results: Eventually, the whole framework was performed on our collected datasets. It has achieved the average mean IoU values of 86.43% on 33 labeled videos with a total of 37,549 frames. In terms of tracking stability, our model has smaller TE of 19.2 pixels and RMSE of 27.6 with the FPS of 8.36 ± 3.23 compared to other classical tracking models. Conclusion: We designed and implemented a pipeline for tracking focal areas in contrast-enhanced ultrasound videos, which takes the siamese network as the backbone and uses optical flow and Kalman filter algorithm to provide position prior information. It turns out that these two additional modules are helpful for the analysis of CEUS videos. We hope that our work can provide an idea for the analysis of CEUS videos.
Spatial Distribution of Soil Heavy Metal Concentrations in Road-Neighboring Areas Using UAV-Based Hyperspectral Remote Sensing and GIS Technology

Monitoring and restoring soil quality in areas neighboring roads affected by traffic activities require a thorough investigation of heavy metal concentrations. This study examines the spatial heterogeneity of copper (Cu) and chromium (Cr) concentrations in a 0.113 km² area adjacent to Jin-Long Avenue in Wuhan, China, using Unmanned Aerial Vehicle (UAV)-based hyperspectral remote sensing technology. Through this UAV-based remote sensing technology, we innovatively achieve a small-scale and fine-grained analysis of soil heavy metal pollution related with traffic activities, which represents a major contribution of this research study. In our approach, we generated 4375 spectral variates by transforming the original spectrum. To enhance result accuracy, we applied the Boruta algorithm and correlation analysis to select optimal spectral variates. We developed the retrieval model using the Gradient Boosting Decision Tree (GBDT) regression method, selected from a set of four regression methods using the LOOCV method. The resulting model yielded R-square values of 0.325 and 0.351 for Cu and Cr, respectively, providing valuable insights into the heavy metal concentrations. Based on the retrieved heavy metal concentrations from bare soil pixels (17,420 points), we analyzed the relationship between heavy metal concentrations and the perpendicular distance from the road. Additionally, we employed the universal kriging interpolation method to map heavy metal concentrations across the entire area. Our findings reveal that the concentration of heavy metals in this area exceeds background values and decreases as the distance from the road increases. This research significantly contributes to the understanding of spatial distribution characteristics and pollution caused by heavy metal concentrations resulting from traffic activities.
Self-Supervised Monocular Depth Estimation Using Global and Local Mixed Multi-Scale Feature Enhancement Network for Low-Altitude UAV Remote Sensing

Estimating depth from a single low-altitude aerial image captured by an Unmanned Aerial System (UAS) has become a recent research focus. This method has a wide range of applications in 3D modeling, digital terrain models, and target detection. Traditional 3D reconstruction requires multiple images, while UAV depth estimation can complete the task with just one image, thus having higher efficiency and lower cost. This study aims to use deep learning to estimate depth from a single UAS low-altitude remote sensing image. We propose a novel global and local mixed multi-scale feature enhancement network for monocular depth estimation in low-altitude remote sensing scenes, which exchanges information between feature maps of different scales during the forward process through convolutional operations while maintaining the maximum scale feature map. At the same time, we propose a Global Scene Attention (GSA) module in the decoder part of the depth network, which can better focus on object edges, distinguish foreground and background in the UAV field of view, and ultimately demonstrate excellent performance. Finally, we design several loss functions for the low-altitude remote sensing field to constrain the network to reach its optimal state. We conducted extensive experiments on public dataset UAVid 2020, and the results show that our method outperforms state-of-the-art methods.
Experimental and Theoretical Study for the Popular Shilling Attacks Detection Methods in Collaborative Recommender System

The stability and reliability of filtration and recommender systems are crucial for continuous operation. The presence of fake profiles, known as "shilling attacks," can undermine the reliability of these systems. Therefore, it is important to detect and classify these attacks. Numerous techniques for detecting shilling attacks have been proposed, including supervised, semi-supervised, unsupervised, Deep Learning, and hyper deep learning methods. These techniques utilize well-known shilling attack models to target collaborative recommender systems. While previous research has focused on evaluating shilling attack strategies from a global perspective, considering factors such as attack size and attacker’s knowledge, there is a lack of comparative studies on the various existing and commonly used attack detection methods. This paper aims to fill this gap by providing a comprehensive survey of shilling attack models, detection attributes, and detection algorithms. Furthermore, we explore the traits of injected profiles that are exploited by detection algorithms, which has not been thoroughly investigated in prior works. We also conduct experimental studies on popular attack detection methods. Our experimental results reveal that hybrid deep learning algorithms exhibit the highest performance in shilling detection, followed by supervised learning algorithms and semi-supervised learning algorithms. In contrast, the unsupervised technique performs poorly. The deep learning-based Shilling Attack Detection demonstrates accuracy and quality in accurately identifying a variety of mixed attacks. This study provides valuable insights into shilling attack models, detection attributes, and detection algorithms. Our findings highlight the superior performance of hybrid deep learning algorithms in shilling detection, as well as the limitations of unsupervised techniques. Deep learning-based Shilling Attack Detection showcases its effectiveness and accuracy in identifying various types of attacks.
DL4ALL: Multi-Task Cross-Dataset Transfer Learning for Acute Lymphoblastic Leukemia Detection

Methods for the detection of Acute Lymphoblastic (or Lymphocytic) Leukemia (ALL) are increasingly considering Deep Learning (DL) due to its high accuracy in several elds, including medical imaging.In most cases, such methods use transfer learning techniques to compensate for the limited availability of labeled data.However, current methods for ALL detection use traditional transfer learning, which requires the models to be fully trained on the source domain, then netuned on the target domain, with the drawback of possibly overtting the source domain and reducing the generalization capability on the target domain.To overcome this drawback and increase the classication accuracy that can be obtained using transfer learning, in this paper we propose our method named Deep Learning for Acute Lymphoblastic Leukemia (DL4ALL), a novel multi-task learning DL model for ALL detection, trained using a cross-dataset transfer learning approach.The method adapts an existing model into a multi-task classication problem, then trains it using transfer learning procedures that consider both source and target databases at the same time, interleaving batches from the two domains even when they are signicantly dierent.The proposed DL4ALL represents the rst work in the literature using a multi-task cross-dataset transfer learning procedure for ALL detection.Results on a publicly-available ALL database conrm the validity of our approach, which achieves a higher accuracy in detecting ALL with respect to existing methods, even when not using manual labels for the source domain.