Reviewerly/exHarmony huggingface dataset:
Few-Shot Unsupervised Domain Adaptation via Meta Learning

Unsupervised domain adaptation (UDA) has raised a lot of interests in recent years. However, current UDA methods are still not capable enough in dealing with two issues: 1) the scarcity of labeled data in source domain and 2) the need of a general model that can quickly adapt to solve new UDA tasks. To address this situation, we investigate available but rarely-studied setting called few-shot unsupervised domain adaptation (FS-UDA), in which the data of source domain is few-shot per category and the data of target domain remains unlabeled. To realize effective adaptation for FS-UDA tasks in the same source and target domains, we propose a novel meta learning method namely meta-FUDA, which leverages meta learning to perform task-level transfer and domain-level transfer jointly. Extensive experiments demonstrate the promising performance of our method on multiple benchmark data sets.
Accurate Nuclei Segmentation in Breast Cancer Tumour Biopsies

Breast cancer identification is a arduous process and diagnosing it using Haematoxylin and Eosin (H&E) stained pathology images is a significant challenge, with pathologists struggling to segment cancer nuclei accurately. This study will evaluate the efficacy of different methods utilising deep learning techniques for breast cancer nuclei segmentation, with a particular emphasis on U-Net architectures. The proposed methodology is divided into four stages: image enhancement, individual nuclei segmentation, feature extraction, and whole image binary segmentation. This work then conducts a rigorous comparison of different segmentation techniques.
Multi-Granularity Semantic Clues Extraction for Video Question Answering

Conventional methods for video question answering focus on encoding video and questions in sequence, and carefully de-signing multi-modal interactions to fuse multi-modal infor-mation. Although achieving promising results, these meth-ods mainly focus on the sequential structure of video and fail to explore the underlying hierarchical semantic structure of the video. In this work, we argue that video content is se-quential in time space is organized in a hierarchical structure in semantic space (e.g., object-action-scene). Corresponding to the complex video structure, the questions also involve multi-granularity queries in the video. To cope with queries of different granularities in videos, we propose an Object-to-Scene relational reasoning (O2SR) framework, which en-codes videos in multi-granularities in the semantic space under the guidance of questions. By modeling the local object relations and the global scene dependencies while encapsu-lating corresponding questions semantic into visual elements, the O2SR achieves better generalization on different types of questions. Experimental evaluations show our method achieves state-of-the-art performance on four datasets. Code will be released at: https://github.com/zophe98/O2SR.
TAFM: A Recommendation Algorithm Based on Text-Attention Factorization Mechanism

The click-through rate (CTR) prediction task is used to estimate the probabilities of users clicking on recommended items, which are extremely important in recommender systems. Recently, the deep factorization machine (DeepFM) algorithm was proposed. The DeepFM algorithm incorporates a factorization machine (FM) to learn not only low-order features but also the interactions of higher-order features. However, DeepFM lacks user diversity representations and does not consider the text. In view of this, we propose a text-attention FM (TAFM) based on the DeepFM algorithm. First, the attention mechanism in the TAFM algorithm is used to address the diverse representations of users and goods and to mine the features that are most interesting to users. Second, the TAFM model can fully learn text features through its text component, text attention component, and N-gram text feature extraction component, which can fully explore potential user preferences and the diversity among user interests. In addition, the convolutional autoencoder in the TAFM can learn some higher-level features, and the higher-order feature mining process is more comprehensive. On the public dataset, the better performing models in the existing models are deep cross network (DCN), DeepFM, and product-based neural network (PNN), respectively, and the AUC score metrics of these models hover between 0.698 and 0.699. The AUC score of our design model is 0.730, which is at least 3% higher than that of the existing models. The accuracy metric of our model is at least 0.1 percentage points higher than that of existing models.
Improving Automated Essay Scoring by Prompt Prediction and Matching

Automated essay scoring aims to evaluate the quality of an essay automatically. It is one of the main educational application in the field of natural language processing. Recently, Pre-training techniques have been used to improve performance on downstream tasks, and many studies have attempted to use pre-training and then fine-tuning mechanisms in an essay scoring system. However, obtaining better features such as prompts by the pre-trained encoder is critical but not fully studied. In this paper, we create a prompt feature fusion method that is better suited for fine-tuning. Besides, we use multi-task learning by designing two auxiliary tasks, prompt prediction and prompt matching, to obtain better features. The experimental results show that both auxiliary tasks can improve model performance, and the combination of the two auxiliary tasks with the NEZHA pre-trained encoder produces the best results, with Quadratic Weighted Kappa improving 2.5% and Pearson's Correlation Coefficient improving 2% on average across all results on the HSK dataset.
An Optimization Method for Personnel Statistics Based on YOLOv4 + DPAC

Compared to traditional detection methods, image-based flow statistics that determine the number of people in a space are contactless, non-perceptual, and high-speed statistical methods that have broad application prospects and potential economic value in business, education, transportation, and other fields. In this paper, we propose that the distributed probability-adjusted confidence (DPAC) function can optimize the reliability of model prediction according to the actual situation. That is, the reliability can be adjusted using the distribution characteristics of the target in the field of view, and a target can be determined with a confidence level that is greater than 0.5 and more accurately. DPAC can assign different target occurrence probability weights to different regions according to target distribution. Adding the DPAC function to a YOLOv4 network model on the basis of having the target confidence of the YOLOv4 network can reduce or improve confidence according to the target distribution and can then output the final confidence level. Using YOLOv4 + DPAC on the brainwash dataset can improve precision by 0.05% compared to the YOLOv4 model when the target confidence threshold is equal to 0.5; it can improve the recall of the model by 0.12% and the AP of the model by 0.12%. This paper also proposes that the distribution in the DPAC function be obtained based on unsupervised learning and verifies its effectiveness.
DP-RBAdaBound: A differentially private randomized block-coordinate adaptive gradient algorithm for training deep neural networks

In order to rapidly train deep learning models, many adaptive gradient methods have been proposed in recent years, such as Adam and AMSGrad. However, the computation of the full gradient vectors in the above methods becomes expensive prohibitively at each iteration for handling high dimensional data. Moreover, the private information may be leaked in the process of training. For these reasons, we propose a differentially private randomized block-coordinate adaptive gradient algorithm, called DP-RBAdaBound, for training deep learning model. To reduce the computation of the full gradient vectors, we randomly choose a block-coordinate to update the model parameter at each iteration. Meanwhile, we add the Laplace noise to the block-coordinate of the gradient vector per iteration for preserving the privacy of users. Furthermore, we rigorously show that the proposed algorithm can preserve ϵ-differential privacy, where ϵ>0 denotes the privacy level. Moreover, we also rigorously prove that the square-root regret bound is also achieved in convex settings, i.e., O(T), where T is a time horizon. Besides, we offer a tradeoff between regret bound and privacy, i.e., the regret bound has order of O(1/ϵ4) by fixing other parameters when ϵ-differential privacy is achieved. Finally, we confirm the computational benefit by training DenseNet-121 and ResNet-34 models on CIFAR-10 dataset, respectively. Meanwhile, the effectiveness of DP-RBAdaBound is also validated through training the DenseNet-121 model on CIFAR-100 dataset and LSTM model on Penn TreeBank dataset, respectively.
Grass band detection in soccer images for improved image registration

The registration of images of soccer matches is a key stage in many computer vision applications. Until now, this task has been typically carried out from key points obtained from the white line marks drawn on the field of play, but in many cases this does not yield enough keypoints for a robust registration. This article proposes a strategy to detect the borders between the grass bands of the field of play and therefore makes it possible to locate many more key points that will allow to carry out a subsequent registration of the images. First, a preprocessing is applied to obtain a grayscale image in which the grass bands are easily distinguishable, and also to obtain a binary mask of the entire field of play that determines the area of interest. Then, a local analysis is carried out to detect most of the borders between grass bands. Finally, a global analysis based on the intersections between lines is applied to group the detected borders and rule out false detections. The strategy has been evaluated on two databases composed of hundreds of annotated images from matches in several stadiums with different characteristics and light conditions. The results obtained have shown that most of the lines delimiting the grass bands are found successfully, while the number of false detections is very small.
Brain Tumor Segmentation Based on Bendlet Transform and Improved Chan-Vese Model

Automated segmentation of brain tumors is a difficult procedure due to the variability and blurred boundary of the lesions. In this study, we propose an automated model based on Bendlet transform and improved Chan-Vese (CV) model for brain tumor segmentation. Since the Bendlet system is based on the principle of sparse approximation, Bendlet transform is applied to describe the images and map images to the feature space and, thereby, first obtain the feature set. This can help in effectively exploring the mapping relationship between brain lesions and normal tissues, and achieving multi-scale and multi-directional registration. Secondly, the SSIM region detection method is proposed to preliminarily locate the tumor region from three aspects of brightness, structure, and contrast. Finally, the CV model is solved by the Hermite-Shannon-Cosine wavelet homotopy method, and the boundary of the tumor region is more accurately delineated by the wavelet transform coefficient. We randomly selected some cross-sectional images to verify the effectiveness of the proposed algorithm and compared with CV, Ostu, K-FCM, and region growing segmentation methods. The experimental results showed that the proposed algorithm had higher segmentation accuracy and better stability.
Network representation learning based on social similarities

Analysis of large-scale networks generally requires mapping high-dimensional network data to a low-dimensional space. We thus need to represent the node and connections accurate and effectively, and representation learning could be a promising method. In this paper, we investigate a novel social similarity-based method for learning network representations. We first introduce neighborhood structural features for representing node identities based on higher-order structural parameters. Then the node representations are learned by a random walk approach that based on the structural features. Our proposed truss2vec is able to maintain both structural similarity of nodes and domain similarity. Extensive experiments have shown that our model outperforms the state-of-the-art solutions.
Evaluating COVID-19 Exposure Notification Effectiveness With SimAEN: A Simulation Tool Designed for Public Health Decision Making

Exposure notification (EN) supplements traditional contact tracing by using proximity sensors in smartphones to record close contact between persons. This ledger is used to alert persons of potential SARS-CoV-2 exposure, so they can quarantine until their infection status is determined. We describe a model that estimates the impact of EN implementation on reducing the spread of SARS-CoV-2 and on the workload of public health officials, in combination with other key public health interventions such as traditional contact tracing, face mask wearing, and testing.We created an agent-based model, Simulated Automated Exposure Notification (SimAEN), to explore the effectiveness of EN to slow the spread of SARS-CoV-2. We varied selected simulation variables, such as population adoption of EN and EN detector sensitivity configurations, to illustrate the potential effects of EN. We executed 20 simulations with SimAEN for each scenario and derived results for each simulation.When more sensitive versus more specific EN configurations were compared, the effective reproductive number, RE, was minimally affected (a decrease <0.03). For scenarios with increasing levels of EN adoption, an increasing number of additional infected persons were identified through EN, and total infection counts in the simulated population decreased; RE values for this scenario decreased with increasing EN adoption (a decrease of 0.1 to 0.2 depending on the scenario).Estimates from SimAEN can help public health officials determine which levels of EN adoption in combination with other public health interventions can maximize prevention of COVID-19 while minimizing unnecessary quarantine in their jurisdiction.
On the Design and Implementation of the External Data Integrity Tracking and Verification System for Stream Computing System in IoT

Data integrity is a prerequisite for ensuring data availability of IoT data and has received extensive attention in the field of IoT big data security. Stream computing systems are widely used in the field of IoT for real-time data acquisition and computing. However, the real-time, volatility, suddenness, and disorder of stream data make data integrity verification difficult. According to the survey, there is no mature and universal solution. To solve this issue, we constructed a data integrity verification algorithm scheme of the stream computing system (S-DIV) by utilizing homomorphic message authentication code and pseudo-random function security assumption. Furthermore, based on S-DIV, an external data integrity tracking and verification system is constructed to track and analyze the message data stream in real time. By verifying the data integrity of message during the whole life cycle, the problem of data corruption or data loss can be found in time, and error alarm and message recovery can be actively implemented. Then, we conduct the formal security analysis under the standard model and, finally, implement the S-DIV scheme in simulation environment. Experimental results show that the scheme can guarantee data integrity in an acceptable time without affecting the efficiency of the original system.
A Novel Steganography Method for Character-Level Text Image Based on Adversarial Attacks

The Internet has become the main channel of information communication, which contains a large amount of secret information. Although network communication provides a convenient channel for human communication, there is also a risk of information leakage. Traditional image steganography algorithms use manually crafted steganographic algorithms or custom models for steganography, while our approach uses ordinary OCR models for information embedding and extraction. Even if our OCR models for steganography are intercepted, it is difficult to find their relevance to steganography. We propose a novel steganography method for character-level text images based on adversarial attacks. We exploit the complexity and uniqueness of neural network boundaries and use neural networks as a tool for information embedding and extraction. We use an adversarial attack to embed the steganographic information into the character region of the image. To avoid detection by other OCR models, we optimize the generation of the adversarial samples and use a verification model to filter the generated steganographic images, which, in turn, ensures that the embedded information can only be recognized by our local model. The decoupling experiments show that the strategies we adopt to weaken the transferability can reduce the possibility of other OCR models recognizing the embedded information while ensuring the success rate of information embedding. Meanwhile, the perturbations we add to embed the information are acceptable. Finally, we explored the impact of different parameters on the algorithm with the potential of our steganography algorithm through parameter selection experiments. We also verify the effectiveness of our validation model to select the best steganographic images. The experiments show that our algorithm can achieve a 100% information embedding rate and more than 95% steganography success rate under the set condition of 3 samples per group. In addition, our embedded information can be hardly detected by other OCR models.
Rethinking Gradient Weight’s Influence over Saliency Map Estimation

Class activation map (CAM) helps to formulate saliency maps that aid in interpreting the deep neural network's prediction. Gradient-based methods are generally faster than other branches of vision interpretability and independent of human guidance. The performance of CAM-like studies depends on the governing model's layer response and the influences of the gradients. Typical gradient-oriented CAM studies rely on weighted aggregation for saliency map estimation by projecting the gradient maps into single-weight values, which may lead to an over-generalized saliency map. To address this issue, we use a global guidance map to rectify the weighted aggregation operation during saliency estimation, where resultant interpretations are comparatively cleaner and instance-specific. We obtain the global guidance map by performing elementwise multiplication between the feature maps and their corresponding gradient maps. To validate our study, we compare the proposed study with nine different saliency visualizers. In addition, we use seven commonly used evaluation metrics for quantitative comparison. The proposed scheme achieves significant improvement over the test images from the ImageNet, MS-COCO 14, and PASCAL VOC 2012 datasets.
Blockchain-Based Land Management for Sustainable Development

In recent years, many papers have been published on the topics of the blockchain (BC) and blockchain technology (BCT). Some papers put BCT in the context of land registries (LRs), land cadastres (LCs), land registration, land administration (LA) and land management (LM) and its implementation benefits. Some eight years later, from its beginnings in 2014, the question of the future of the proposed concept and whether it has one, has been raised. The Scopus database was analysed using bibliometric analysis methodology and Rstudio software with the Bibliometrix R-package and the Shiny package environment. Based on this research, significant interest and growth in the topic was found in both technical and land-governance directions. Different approaches to the topic have been established in the global north and global south. From today’s perspective, the future of BCT in both worlds is guaranteed.
Pure state tomography with adaptive Pauli measurements

Quantum state tomography provides a key tool for validating and fully exploiting quantum resources. However, current protocols of pure-state informationally-complete (PS-IC) measurement settings generally involve various multi-qubit gates or complex quantum algorithms, which are not practical for large systems. In this study, we present an adaptive approach to &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$N$\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="JUSTC-2022-0037_M1.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="JUSTC-2022-0037_M1.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt;-qubit pure-state tomography with Pauli measurements. First, projective measurements on each qubit in the &lt;i&gt;Z&lt;/i&gt;-direction were implemented to determine the amplitude of each base of the target state. Then, a set of Pauli measurement settings was recursively deduced by the &lt;i&gt;Z&lt;/i&gt;-measurement results, which can be used to determine the phase of each base. The number of required measurement settings is &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$O(N)$\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="JUSTC-2022-0037_M2.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="JUSTC-2022-0037_M2.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt; for certain quantum states, including cluster and &lt;i&gt;W&lt;/i&gt; states. Finally, we numerically verified the feasibility of our strategy by reconstructing a 1-D chain state using a neural network algorithm.
Fusion of DWT as a Novel Approach for Efficient, Secured and Reversible Video Steganography

Abstract This research is inspired by the constraints of the present video steganography methods and to enhance their performance. It contributes its objective via employing the transforming steps to involve the processing of both cover videos and clandestine information for improving their robustness and security. Consuming a part of every frame as areas of concentration for the hiding procedure, this leads to improve the video imperceptibility resulted from steganography. In view of that, selecting the coefficients for embedding information in the video. Subsequently it adds obstacle in front of intruders to identify the place of the hidden information within frames since they are just covered within such coefficients, this dynamically changes depending on both the video frame and clandestine information, it is essential to save the embedding robustness and security. Converting the cover into a frequency domain like DWT will enhance the steganographic robustness counter to threats.
Color Image Encryption Based on New Integral Transform SEE

Abstract Due to the worldwide expansion of the internet, the consumption and transmission of multimedia materials have risen significantly. This growth in demand necessitated a corresponding increase in data security measures. This work investigates encryption technology and proposes a novel color picture encryption and decryption approach based on the “SEE transform” developed by Sadiq, Emad, and Eman. We used the SEE transform to encrypt the original image, and the corresponding inverse SEE transform for decryption. The SEE transform has been performed to enhance the security of the encryption method. After using the SEE transform, we show that the suggested method considerably improves security measures and immunity to attacks, as demonstrated by comprehensive experiments and statistical analysis.