A One-dimensional HEVC video steganalysis method using the Optimality of Predicted Motion Vectors

Among steganalysis techniques, detection against motion vector (MV) domain-based video steganography in High Efficiency Video Coding (HEVC) standard remains a hot and challenging issue. For the purpose of improving the detection performance, this paper proposes a steganalysis feature based on the optimality of predicted MVs with a dimension of one. Firstly, we point out that the motion vector prediction (MVP) of the prediction unit (PU) encoded using the Advanced Motion Vector Prediction (AMVP) technique satisfies the local optimality in the cover video. Secondly, we analyze that in HEVC video, message embedding either using MVP index or motion vector differences (MVD) may destroy the above optimality of MVP. And then, we define the optimal rate of MVP in HEVC video as a steganalysis feature. Finally, we conduct steganalysis detection experiments on two general datasets for three popular steganography methods and compare the performance with four state-of-the-art steganalysis methods. The experimental results show that the proposed optimal rate of MVP for all cover videos is 100\%, while the optimal rate of MVP for all stego videos is less than 100\%. Therefore, the proposed steganography scheme can accurately distinguish between cover videos and stego videos, and it is efficiently applied to practical scenarios with no model training and low computational complexity.Comment: Submitted to TCSV

Leveraging Multimodal Fusion for Enhanced Diagnosis of Multiple Retinal Diseases in Ultra-wide OCTA

Ultra-wide optical coherence tomography angiography (UW-OCTA) is an emerging imaging technique that offers significant advantages over traditional OCTA by providing an exceptionally wide scanning range of up to 24 x 20 $mm^{2}$, covering both the anterior and posterior regions of the retina. However, the currently accessible UW-OCTA datasets suffer from limited comprehensive hierarchical information and corresponding disease annotations. To address this limitation, we have curated the pioneering M3OCTA dataset, which is the first multimodal (i.e., multilayer), multi-disease, and widest field-of-view UW-OCTA dataset. Furthermore, the effective utilization of multi-layer ultra-wide ocular vasculature information from UW-OCTA remains underdeveloped. To tackle this challenge, we propose the first cross-modal fusion framework that leverages multi-modal information for diagnosing multiple diseases. Through extensive experiments conducted on our openly available M3OCTA dataset, we demonstrate the effectiveness and superior performance of our method, both in fixed and varying modalities settings. The construction of the M3OCTA dataset, the first multimodal OCTA dataset encompassing multiple diseases, aims to advance research in the ophthalmic image analysis community

Two-phase Framework for Automatic Kidney and Kidney Tumor Segmentation

Precise segmentation of kidney and kidney tumor is essential for computer aided diagnosis. Considering the diverse shape, low contrast with surrounding tissues and small tumor volumes, it’s still challenging to segment kidney and kidney tumor accurately. To solve this problem, we proposed a two-phase framework for automatic segmentation of kidney and kidney tumor. In the first phase, the approximate localization of kidney and kidney tumor is predicted by a coarse segmentation network to overcome GPU memory limitation. Taking the coarse prediction from first phase as input, the region of kidney and tumor is cropped and trained in an enhanced two-stage network to achieve a fine-grained segmentation result in the second phase. Besides, our network prediction flows into multiple post-processing steps to remove false positive such as cyst by taking medical prior knowledge into consideration. Data argumentation through registration and ensemble models are used to further enhance performance

A Unified Dual-view Model for Review Summarization and Sentiment Classification with Inconsistency Loss

Acquiring accurate summarization and sentiment from user reviews is an essential component of modern e-commerce platforms. Review summarization aims at generating a concise summary that describes the key opinions and sentiment of a review, while sentiment classification aims to predict a sentiment label indicating the sentiment attitude of a review. To effectively leverage the shared sentiment information in both review summarization and sentiment classification tasks, we propose a novel dual-view model that jointly improves the performance of these two tasks. In our model, an encoder first learns a context representation for the review, then a summary decoder generates a review summary word by word. After that, a source-view sentiment classifier uses the encoded context representation to predict a sentiment label for the review, while a summary-view sentiment classifier uses the decoder hidden states to predict a sentiment label for the generated summary. During training, we introduce an inconsistency loss to penalize the disagreement between these two classifiers. It helps the decoder to generate a summary to have a consistent sentiment tendency with the review and also helps the two sentiment classifiers learn from each other. Experiment results on four real-world datasets from different domains demonstrate the effectiveness of our model.Comment: Accepted by SIGIR 2020. Updated the results of balanced accuracy scores in Table 3 since we found a bug in our source code. Nevertheless, our model still achieves higher balanced accuracy scores than the baselines after we fixed this bu

Wastewater to clinical case (WC) ratio of COVID-19 identifies insufficient clinical testing, onset of new variants of concern and population immunity in urban communities

Clinical testing has been the cornerstone of public health monitoring and infection control efforts in communities throughout the COVID-19 pandemic. With the extant and anticipated reduction of clinical testing as the disease moves into an endemic state, SARS-CoV-2 wastewater surveillance (WWS) is likely to have greater value as an important diagnostic tool to inform public health. As the widespread adoption of WWS is relatively new at the scale employed for COVID-19, interpretation of data, including the relationship to clinical cases, has yet to be standardized. An in-depth analysis of the metrics derived from WWS is required for public health units/agencies to interpret and utilize WWS-acquired data effectively and efficiently. In this study, the SARS-CoV-2 wastewater signal to clinical cases (WC) ratio was investigated across seven different cities in Canada over periods ranging from 8 to 21 months. Significant increases in the WC ratio occurred when clinical testing eligibility was modified to appointment-only testing, identifying a period of insufficient clinical testing in these communities. The WC ratio decreased significantly during the emergence of the Alpha variant of concern (VOC) in a relatively non-immunized community’s wastewater (40-60% allelic proportion), while a more muted decrease in the WC ratio signaled the emergence of the Delta VOC in a relatively well-immunized community’s wastewater (40-60% allelic proportion). Finally, a rapid and significant decrease in the WC ratio signaled the emergence of the Omicron VOC, likely because of the variant’s greater effectiveness at evading immunity, leading to a significant number of new reported clinical cases, even when vaccine-induced community immunity was high. The WC ratio, used as an additional monitoring metric, complements clinical case counts and wastewater signals as individual metrics in its ability to identify important epidemiological occurrences, adding value to WWS as a diagnostic technology during the COVID-19 pandemic and likely for future pandemics.Ontario's Ministry of Environment, Conservation and Parks||Alberta Healt

Application of system concept in vibration and noise reduction

Although certain vibration and noise control technologies are maturing, such as vibration absorption, vibration isolation, sound absorption and sound insulation, and new methods for specific frequency bands or special environments have been proposed unceasingly, there is still no guarantee that practical effective vibration and noise reduction can be obtained. An important constraint for vibration and noise reduction is the lack of a system concept, and the integrity and relevance of such practical systems as ship structure have not obtained enough attention. We have tried to use the system engineering theory in guiding vibration and noise reduction, and have already achieved certain effects. Based on the system concept, the noise control of a petroleum pipeline production workshop has been completed satisfactorily, and the abnormal noise source identification of an airplane has been accomplished successfully. We want to share our experience and suggestions to promote the popularization of the system engineering theory in vibration and noise control

New Construction of PVPKE Scheme and Its Application in Information Systems and Mobile Communication

In SCN12, Nieto et al. discussed an interesting property of public key encryption with chosen ciphertext security, that is, ciphertexts with public verifiability. Independently, we introduced a new cryptographic primitive, CCA-secure publicly verifiable public key encryption without pairings in the standard model (PVPKE), and discussed its application in proxy reencryption (PRE) and threshold public key encryption (TPKE). In Crypto’09, Hofheiz and Kiltz introduced the group of signed quadratic residues and discussed its application; the most interesting feature of this group is its “gap” property, while the computational problem is as hard as factoring, and the corresponding decisional problem is easy. In this paper, we give new constructions of PVPKE scheme based on signed quadratic residues and analyze their security. We also discuss PVPKE’s important application in modern information systems, such as achieving ciphertext checkable in the cloud setting for the mobile laptop, reducing workload by the gateway between the open internet and the trusted private network, and dropping invalid ciphertext by the routers for helping the network to preserve its communication bandwidth