Reviewerly/exHarmony huggingface dataset:
Dataset mismatched steganalysis using subdomain adaptation with guiding feature

The generalization problem in deep learning has always been an important problem to be solved. In the field of steganalysis, generalization is also an important factor that makes steganalysis models difficult to deploy in real-world scenarios. For a group of suspicious images that never appeared in the training set, the pre-trained deep learning-based steganalysis models tend to suffer from distinct performance degradation. To address this limitation, in this paper, a feature-guided subdomain adaptation steganalysis framework is proposed to improve the performance of the pre-trained models when detecting new data. Initially, the source domain and target domain will be divided into subdomains according to class, and the distributions of the relevant subdomains are aligned by subdomain adaptation. Afterward, the guiding feature is generated to make the division of subdomains more stable and precise. When it is used to detect three spatial steganographic algorithms with a wide variety of datasets and payloads, the experimental results show that the proposed steganalysis framework can significantly improve the average accuracy of SRNet model by 5.4% at 0.4bpp, 8.5% at 0.2bpp, and 8.0% at 0.1bpp in the case of dataset mismatch.
Semi-automatic Labeling and Training Strategy for Deep Learning-based Facial Wrinkle Detection

Facial wrinkle is very important in measuring aging. Image processing-based methods have been proposed for wrinkle detection, but their performance was not enough because wrinkles have a wide variety of thickness, shape, orientation, and vague boundaries. Recently, deep learning-based methods have been widely applied in the field of image recognition with a lot of labeled image dataset. To extend this technology to facial wrinkle detection, labeling work for wrinkles to generate ground truth is very important. However, it is difficult to label wrinkles accurately because of the wide variety. In this paper, we propose a semiautomatic labeling strategy incorporating a texture map and a deep learning model. Specifically, the proposed method extracted the texture map from an original image and removed non-wrinkle textures on the map by multiplying with a roughly labeled wrinkle mask. Then, the map is converted into ground truth by thresholding. Using the ground truth, a deep learning model was trained with the original image and the texture map. The trained model was evaluated with facial images obtained from real skin diagnosis devices, and the results showed superior performance to those of existing image processing-based methods.
Computer simulation of processes that influence adolescent learning motivation

In order for the learning process to always retain personal value for the learner, it is necessary that his or her motivation be maintained through an awareness of his or her purpose and goals. This article presents a local model (at the individual object level) of enhancing external motivation, which give to determine students’ efforts to get rewards. The concept of this model based on describing the behavior of agents (in our case students). The characteristics of the phenomenon in the motivation of learning at different stages of adolescent development are analyzed. The problem of computer modeling of educational processes with the help of agent modeling on the example of studying student motivation is considered. Internal and external factors that may strengthen or weaken the adolescent’s motivation to study have been studied. The expediency of using information technologies of agent modeling to study the dynamics of strengthening or weakening student motivation is substantiated. Using the AnyLogic Cloud computing environment the change of dynamics of strengthening of motivation of teenagers on an example of model of strengthening of external motivation is defined.
Multi-granularity heterogeneous graph attention networks for extractive document summarization

Extractive document summarization is a fundamental task in natural language processing (NLP). Recently, several Graph Neural Networks (GNNs) are proposed for this task. However, most existing GNN-based models can neither effectively encode semantic nodes of multiple granularity level apart from sentences nor substantially capture different cross-sentence meta-paths. To address these issues, we propose MHgatSum, a novel Multi-granularity Heterogeneous Graph ATtention networks for extractive document SUMmarization. Specifically, we first build a multi-granularity heterogeneous graph (HetG) for each document, which is better to represent the semantic meaning of the document. The HetG contains not only sentence nodes but also multiple other granularity effective semantic units with different semantic levels, including keyphrases and topics. These additional nodes act as the intermediary between sentences to build the meta-paths involved in sentence node (i.e., Sentence-Keyphrase-Sentence and Sentence-Topic-Sentence). Then, we propose a heterogeneous graph attention networks to embed the constructed HetG for extractive summarization, which enjoys multi-granularity semantic representations. The model is based on a hierarchical attention mechanism, including node-level and semantic-level attentions. The node-level attention can learn the importance between a node and its meta-path based neighbors, while the semantic-level attention is able to learn the importance of different meta-paths. Moreover, to better integrate sentence global knowledge, we further incorporate sentence node global importance in local node-level attention. We conduct empirical experiments on two benchmark datasets, which demonstrates the superiority of MHgatSum over previous SOTA models on the task of extractive summarization.
LNFCOS: Efficient Object Detection through Deep Learning Based on LNblock

In recent deep-learning-based real-time object detection methods, the trade-off between accuracy and computational cost is an important consideration. Therefore, based on the fully convolutional one-stage detector (FCOS), which is a one-stage object detection method, we propose a light next FCOS (LNFCOS) that achieves an optimal trade-off between computational cost and accuracy. In LNFCOS, the loss of low- and high-level information is minimized by combining the features of different scales through the proposed feature fusion module. Moreover, the light next block (LNblock) is proposed for efficient feature extraction. LNblock performs feature extraction with a low computational cost compared with standard convolutions, through sequential operation on a small amount of spatial and channel information. To define the optimal parameters of LNFCOS suggested through experiments and for a fair comparison, experiments and evaluations were conducted on the publicly available benchmark datasets MSCOCO and PASCAL VOC. Additionally, the average precision (AP) was used as an evaluation index for quantitative evaluation. LNFCOS achieved an optimal trade-off between computational cost and accuracy by achieving a detection accuracy of 79.3 AP and 37.2 AP on the MS COCO and PASCAL VOC datasets, respectively, with 36% lower computational cost than the FCOS.
An Overview of Recent Work in Multimedia Forensics

In this paper, we review recent work in media forensics for digital images, video, audio, and documents.
Fake Face Images Detection and Identification of Celebrities Based on Semantic Segmentation

Convolutional Neural Networks (CNN) based detectors perform well in face manipulation detection, but are still limited by redundant information. Some methods focus on blending boundary to localize manipulation regions, discarding a part of useless information like background of image. But these methods still contain deceptive information such as facial regions without texture, which occupies resources and affects detection accuracy. Besides, these methods left out some features useful for identification. Therefore, this paper proposes a module by conducting semantic masks to guide detectors focus on face. The semantic segmentation masks focus on the facial features such as hair, eyes and other important areas, which can offer effective face identification high level semantic features. Our method uses masks as an attention-based data augmentation module and is simple for many DeepFake detection models to integrate. Experiments on multiple detectors with and without our module show our module's effectiveness. Without modifying their structural design, our approach enables CNN-based detectors to perform better. Especially, our method is well-suited for protecting the person of interest against face forgery.
Multimodal detection of hateful memes by applying a vision-language pre-training model

Detrimental to individuals and society, online hateful messages have recently become a major social issue. Among them, one new type of hateful message, "hateful meme", has emerged and brought difficulties in traditional deep learning-based detection. Because hateful memes were formatted with both text captions and images to express users' intents, they cannot be accurately identified by singularly analyzing embedded text captions or images. In order to effectively detect a hateful meme, the algorithm must possess strong vision and language fusion capability. In this study, we move closer to this goal by feeding a triplet by stacking the visual features, object tags, and text features of memes generated by the object detection model named Visual features in Vision-Language (VinVl) and the optical character recognition (OCR) technology into a Transformer-based Vision-Language Pre-Training Model (VL-PTM) OSCAR+ to perform the cross-modal learning of memes. After fine-tuning and connecting to a random forest (RF) classifier, our model (OSCAR+RF) achieved an average accuracy and AUROC of 0.684 and 0.768, respectively, on the hateful meme detection task in a public test set, which was higher than the other eleven (11) published baselines. In conclusion, this study has demonstrated that VL-PTMs with the addition of anchor points can improve the performance of deep learning-based detection of hateful memes by involving a more substantial alignment between the text caption and visual information.
An Approach to Federated Learning of Explainable Fuzzy Regression Models

Federated Learning (FL) has been proposed as a privacy preserving paradigm for collaboratively training AI models: in an FL scenario data owners learn a shared model by aggregating locally-computed partial models, with no need to share their raw data with other parties. Although FL is today extensively studied, a few works have discussed federated approaches to generate explainable AI (XAI) models. In this context, we propose an FL approach to learn Takagi-Sugeno-Kang Fuzzy Rule-based Systems (TSK-FRBSs), which can be considered as XAI models in regression problems. In particular, a number of independent data owner nodes participate in the learning process, where each of them generates its own local TSK-FRBS by exploiting an ad-hoc defined procedure. Then, these models are forwarded to a server that is responsible for aggregating them and generating a global TSK-FRBS, which is sent back to the nodes. An appropriate aggregation strategy is proposed to preserve the explainability of the global TSK-FRBS. A thorough experimental analysis highlights that the proposed approach brings benefits, in terms of accuracy, to data owners participating in the federation preserving the privacy of the data. Indeed, the accuracy achieved by the global TSK-FRBS is higher than the ones of the TSK-FRBSs learned by exploiting only local training data.
Post-Quantum Blockchain-Based Data Sharing for IoT Service Providers

Quantum technologies have made significant advances and are likely to lead to important security challenges and threats to networks in the near future. On the other hand, sharing the huge amount of data from the Internet of Things (IoT) in the context of data as a service could provide new revenue streams for infrastructure providers and service providers. However, post-quantum computing exposes the entire data sharing ecosystem to a new set of security risks. In this article, we propose a novel blockchain-based system for data sharing in the post-quantum era. The proposed system facilitates data sharing among multiple organizations while meeting compliance and regulatory requirements via private blockchain. We implemented the proposed architecture and information flow using three blockchain networks (namely Hyperledger Fabric, Ethereum, and Quorum) and selected NTRU as our quantum resistant security algorithm (QRSA) to compare the parallelization performance of Toom-Cook's and Karatsuba's computation methods. Experimental results show that parallel computation has a positive impact when the security level of QRSAs is lowered, and the transaction time savings is almost 50 percent in favor of Quorum. Finally, we outline the main challenges and potential solutions.
A 0.95 mJ/frame DNN Training Processor for Robust Object Detection with Real-World Environmental Adaptation

A DNN training processor with a maximum of 332 TOPS/W is proposed for efficient and robust object detection. The proposed processor is able to support both quantization and pruning-based personalization to make a user-optimized lightweight network. In addition to personalization, it supports real-time adaptation to compensate for accuracy degradation caused by environmental changes or unpredictable situations. It maintains conventional input slice skipping architecture and stochastic rounding-based computing for the efficient acceleration of the DNN training. It further improves efficiency by removing pseudo-RNGs during the stochastic rounding and adding blocks to pruning-aware training. Moreover, it suggests an LT-flag-based reconfigurable accumulation network and enables multi-learning-task-allocation for low-latency DNN training with the backward unlocking solution. Fabricated in 28-nm technology, the proposed processor demonstrates 46.6 FPS object detection with 0.95 mJ/frame energy consumption which is the state-of-the-art performance compared with the previous processors.
Personalized Recommendation in the Adaptive Learning System: The Role of Adaptive Testing Technology

Personalized recommendation plays an important role on content selection during the adaptive learning process. It is always a challenge on how to recommend effective items to improve learning performance. The aim of this study was to examine the feasibility of applying adaptive testing technology for personalized recommendation. We proposed the adaptation of applicable adaptive testing technology as a solution based on the widely accepted teaching philosophy that providing learners with challenging content can stimulate their potential and improve their performance. Specifically, we adapted item selection algorithms to make the difficulty of recommended items above the learner’s current knowledge level on the basis of the uniform scale of learner’s ability and item difficulty in the adaptive testing area. Participants were recruited from two classes in a junior middle school, one served as the experimental group by applying the adaptive testing recommendation, and the other served as the control group by applying the random recommendation. The results showed that the experimental group students achieved significantly higher scores and demonstrated higher learning abilities than the control group students. Therefore, the adapted testing technology that is being used for personalized recommendation is effective in improving learning performance.
Pixel-wise structured light calibration method with a color calibration target

We propose to use a calibration target with a narrow spectral color range for the background (e.g., from blue) and broader spectral color range for the feature points (e.g., blue + red circles), and fringe patterns matching the background color for accurate phase extraction. Since the captured fringe patterns are not affected by the high contrast of the calibration target, phase information can be accurately extracted without edging artifacts. Those feature points can be clearly “seen” by the camera if the ambient light matches the feature color or without the background color. We extract each calibration pose for three-dimensional coordinate determination for each pixel, and then establish pixel-wise relationship between each coordinate and phase. Comparing with our previously published method, this method significantly fundamentally simplifies and improves the algorithm by eliminating the computational framework estimate smooth phase near high-contrast feature edges. Experimental results demonstrated the success of our proposed calibration method.
CARNet: An effective method for SAR image interference suppression

Synthetic aperture radar (SAR) routinely confronts the interference of radiofrequency devices in normal missions, causing ineffective imaging and seriously affecting Earth observation capability. In general, it is a great challenge to ensure interference suppression performance and image quality. To address this problem, we present an efficient method for SAR image interference suppression based on the Combined-Attention Restoration Network (CARNet). SAR image model is established, including target image, interference image, and background noise image. Specifically, we first propose a new feature extraction scheme to capture image model information over space and channels for enriching the context. Then encoder–decoder is employed to suppress interference and produce different-dimensional feature maps for target information exchange. Moreover, the image attention mechanism is introduced to calibrate the target features under the guidance of original images for essential information propagation. Besides, several attentional connections exist to prevent further loss of target details. The effectiveness of the proposed method is validated on simulated data and measured Sentinel-1 images. Compared with conventional and state-of-the-art algorithms, the results indicate that CARNet achieves better interference suppression performance and can generate high-resolution images closer to the ground truth.
BlumNet: Graph Component Detection for Object Skeleton Extraction

In this paper, we present a simple yet efficient framework, BlumNet, for extracting object skeletons in natural images and binary shapes. With the need for highly reliable skeletons in various multimedia applications, the proposed BlumNet is distinguished in three aspects: (1) The inception of graph decomposition and reconstruction strategies further simplifies the skeleton extraction task into a graph component detection problem, which significantly improves the accuracy and robustness of extracted skeletons. (2) The intuitive representation of each skeleton branch with multiple structured and overlapping line segments can effectively prevent the skeleton branch vanishing problem. (3) In comparison to traditional skeleton heatmaps, our approach directly outputs skeleton graphs, which is more feasible for real-world applications. Through comprehensive experiments, we demonstrate the advantages of BlumNet: significantly higher accuracy than the state-of-the-art AdaLSN (0.826 vs. 0.786) on the SK1491 dataset, a marked improvement in robustness on mixed object deformations, and also a state-of-the-art performance on binary shape datasets (e.g. 0.893 on the MPEG7 dataset).
Analysis on methods to effectively improve transfer learning performance

Transfer learning has become a prevailing machine learning technique thanks to its superiority in learning knowledge from limited training data for prediction. In the existing works, collection and collaboration are two major approaches to realize the improvement of transfer learning performance. Even though the effectiveness of these approaches has been validated in extensive experiments, there lacks the support of theoretical analysis. Consequently, how to enhance transfer learning effectively is an open problem. In light of this, in this paper, we thoroughly and deeply study the methods of improving transfer learning performance in order to provide the guidelines for applying transfer learning in real applications. Through our proof process, critical conclusions are drawn to help learn the motivation of implementing collection and collaboration, the performance gap between collection and collaboration, and the impacts of data sharing strategies on transfer learning in collaboration. These conclusions can further build a theoretical foundation for future research on transfer learning.
Impact of OSNMA Configurations, Operations and User’s Strategies on Receiver Performances

This paper assesses the impact OSNMA data retrieval strategies have on a receiver’s authentication performance. In particular, it quantifies the impact of the OSNMA configurations and operations on the receiver capability to retrieve the different elements of the OSNMA protocol. The results show that the retrieval of the OSNMA data from the SIS should be carefully considered when implementing the authentication function. Several data retrieval strategies and optimisations are presented that can bring considerable improvements. This analysis is relevant as different approaches may be selected to implement OSNMA in a receiver as a function of the receiver’s operating environment, computational capabilities and needs.
Object Discovery via Contrastive Learning for Weakly Supervised Object Detection

Weakly Supervised Object Detection (WSOD) is a task that detects objects in an image using a model trained only on image-level annotations. Current state-of-the-art models benefit from self-supervised instance-level supervision, but since weak supervision does not include count or location information, the most common “argmax” labeling method often ignores many instances of objects. To alleviate this issue, we propose a novel multiple instance labeling method called object discovery. We further introduce a new contrastive loss under weak supervision where no instance-level information is available for sampling, called weakly supervised contrastive loss (WSCL). WSCL aims to construct a credible similarity threshold for object discovery by leveraging consistent features for embedding vectors in the same class. As a result, we achieve new state-of-the-art results on MS-COCO 2014 and 2017 as well as PASCAL VOC 2012, and competitive results on PASCAL VOC 2007. The code is available at https://github.com/jinhseo/OD-WSCL .