Real-Time Neural Video Recovery and Enhancement on Mobile Devices

As mobile devices become increasingly popular for video streaming, it's crucial to optimize the streaming experience for these devices. Although deep learning-based video enhancement techniques are gaining attention, most of them cannot support real-time enhancement on mobile devices. Additionally, many of these techniques are focused solely on super-resolution and cannot handle partial or complete loss or corruption of video frames, which is common on the Internet and wireless networks. To overcome these challenges, we present a novel approach in this paper. Our approach consists of (i) a novel video frame recovery scheme, (ii) a new super-resolution algorithm, and (iii) a receiver enhancement-aware video bit rate adaptation algorithm. We have implemented our approach on an iPhone 12, and it can support 30 frames per second (FPS). We have evaluated our approach in various networks such as WiFi, 3G, 4G, and 5G networks. Our evaluation shows that our approach enables real-time enhancement and results in a significant increase in video QoE (Quality of Experience) of 24\% - 82\% in our video streaming system

Transferable Attack for Semantic Segmentation

We analysis performance of semantic segmentation models wrt. adversarial attacks, and observe that the adversarial examples generated from a source model fail to attack the target models. i.e The conventional attack methods, such as PGD and FGSM, do not transfer well to target models, making it necessary to study the transferable attacks, especially transferable attacks for semantic segmentation. We find two main factors to achieve transferable attack. Firstly, the attack should come with effective data augmentation and translation-invariant features to deal with unseen models. Secondly, stabilized optimization strategies are needed to find the optimal attack direction. Based on the above observations, we propose an ensemble attack for semantic segmentation to achieve more effective attacks with higher transferability. The source code and experimental results are publicly available via our project page: https://github.com/anucvers/TASS.Comment: Source code is available at: https://github.com/anucvers/TAS

Neural Video Recovery for Cloud Gaming

Cloud gaming is a multi-billion dollar industry. A client in cloud gaming sends its movement to the game server on the Internet, which renders and transmits the resulting video back. In order to provide a good gaming experience, a latency below 80 ms is required. This means that video rendering, encoding, transmission, decoding, and display have to finish within that time frame, which is especially challenging to achieve due to server overload, network congestion, and losses. In this paper, we propose a new method for recovering lost or corrupted video frames in cloud gaming. Unlike traditional video frame recovery, our approach uses game states to significantly enhance recovery accuracy and utilizes partially decoded frames to recover lost portions. We develop a holistic system that consists of (i) efficiently extracting game states, (ii) modifying H.264 video decoder to generate a mask to indicate which portions of video frames need recovery, and (iii) designing a novel neural network to recover either complete or partial video frames. Our approach is extensively evaluated using iPhone 12 and laptop implementations, and we demonstrate the utility of game states in the game video recovery and the effectiveness of our overall design

Optimized Live 4K Video Multicast

4K videos are becoming increasingly popular. However, despite advances in wireless technology, streaming 4K videos over mmWave to multiple users is facing significant challenges arising from directional communication, unpredictable channel fluctuation and high bandwidth requirements. This paper develops a novel 4K layered video multicast system. We (i) develop a video quality model for layered video coding, (ii) optimize resource allocation, scheduling, and beamforming based on the channel conditions of different users, and (iii) put forward a streaming strategy that uses fountain code to avoid redundancy across multicast groups and a Leaky-Bucket-based congestion control. We realize an end-to-end system on commodity-off-the-shelf (COTS) WiGig devices. We demonstrate the effectiveness of our system with extensive testbed experiments and emulation

HLungDB: an integrated database of human lung cancer research

The human lung cancer database (HLungDB) is a database with the integration of the lung cancer-related genes, proteins and miRNAs together with the corresponding clinical information. The main purpose of this platform is to establish a network of lung cancer-related molecules and to facilitate the mechanistic study of lung carcinogenesis. The entries describing the relationships between molecules and human lung cancer in the current release were extracted manually from literatures. Currently, we have collected 2585 genes and 212 miRNA with the experimental evidences involved in the different stages of lung carcinogenesis through text mining. Furthermore, we have incorporated the results from analysis of transcription factor-binding motifs, the promoters and the SNP sites for each gene. Since epigenetic alterations also play an important role in lung carcinogenesis, genes with epigenetic regulation were also included. We hope HLungDB will enrich our knowledge about lung cancer biology and eventually lead to the development of novel therapeutic strategies. HLungDB can be freely accessed at http://www.megabionet.org/bio/hlung

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Blockchain Applications in Forestry: A Systematic Literature Review

Blockchain applications have received a lot of attention in recent years. They provide enormous benefits and advantages to many different sectors. To date, there have not been any systematic studies comprehensively reviewing current blockchain-based applications in the forestry sector. This paper examines published work on blockchain-based applications in the forestry sector. A systematic review was conducted to identify, analyze, and discuss current literature on current blockchain applications deployed (and/or proposed) in the forestry sector, grouping results into three domains of forest management, traceability of forest-based products, and forest fire detection based on content analysis. The analyses highlight reported benefits, opportunities, and challenges of blockchain applications in the forestry sector. The study results show that blockchain has great potential in sustainable forestry, minimizing illegal logging, conserving biodiversity, and many other areas in forestry. It also shows that blockchain in forestry is still immature and complex, since it requires specialists to adopt. This paper contributes towards filling the existing research gap through this systematic review on blockchain applications in forestry. This review offers insights into a deep understanding of blockchain applications for managers, practitioners, and consultants interested in forestry. The paper identifies existing research gaps on related topics of blockchain applications in forestry and makes recommendations on potential future directions for research into blockchain in forestry

A Systematic Review on Technologies and Industry 4.0 in the Forest Supply Chain: A Framework Identifying Challenges and Opportunities

Background: Forestry products and forestry organizations play an essential role in our lives and significantly contribute to the global economy. They are also being impacted by the rapid development of advanced technologies and Industry 4.0. More specifically, several technologies associated with Industry 4.0 have been identified for their potential to optimize traditional forest supply chains. However, to date, there has been limited research that has systematically investigated these technologies and the scientific evidence on their impact on forest supply chains. This research systematically reviews the state-of-the-art technologies applied in the forest supply chain and reports on the current (and/or potential) impacts of technologies on the transformation of the forest supply chain towards ‘Forest Industry 4.0′. Methods: The systematic literature review methodology identified 45 peer-reviewed studies for inclusion that are analyzed, interpreted and discussed in this paper. Results: This study developed a framework on the forest supply chain in Industry 4.0. This framework has three components related to forest supply chains: current supportive technologies, improvements and characteristics of the forest supply chain in Industry 4.0, and the strategic outcomes in economic, environmental and social dimensions. The reported impacts of technologies in different phases of the forest supply chain are interpreted and discussed. Conclusion: The study results confirm that most technologies in Industry 4.0 have real or perceived positive impacts on the forest supply chain and reported obstacles and challenges are identified. The results of this study also contribute insights on the wide range of options in terms of technologies available to decision-makers to optimize the forest supply chain towards ‘Forest Industry 4.0′

A Systematic Review on Technologies and Industry 4.0 in the Forest Supply Chain: A Framework Identifying Challenges and Opportunities

Background: Forestry products and forestry organizations play an essential role in our lives and significantly contribute to the global economy. They are also being impacted by the rapid development of advanced technologies and Industry 4.0. More specifically, several technologies associated with Industry 4.0 have been identified for their potential to optimize traditional forest supply chains. However, to date, there has been limited research that has systematically investigated these technologies and the scientific evidence on their impact on forest supply chains. This research systematically reviews the state-of-the-art technologies applied in the forest supply chain and reports on the current (and/or potential) impacts of technologies on the transformation of the forest supply chain towards ‘Forest Industry 4.0′. Methods: The systematic literature review methodology identified 45 peer-reviewed studies for inclusion that are analyzed, interpreted and discussed in this paper. Results: This study developed a framework on the forest supply chain in Industry 4.0. This framework has three components related to forest supply chains: current supportive technologies, improvements and characteristics of the forest supply chain in Industry 4.0, and the strategic outcomes in economic, environmental and social dimensions. The reported impacts of technologies in different phases of the forest supply chain are interpreted and discussed. Conclusion: The study results confirm that most technologies in Industry 4.0 have real or perceived positive impacts on the forest supply chain and reported obstacles and challenges are identified. The results of this study also contribute insights on the wide range of options in terms of technologies available to decision-makers to optimize the forest supply chain towards ‘Forest Industry 4.0′

Understanding structure-based social network de-anonymization techniques via empirical analysis

Abstract The rapid development of wellness smart devices and apps, such as Fitbit Coach and FitnessGenes, has triggered a wave of interaction on social networks. People communicate with and follow each other based on their wellness activities. Though such IoT devices and data provide a good motivation, they also expose users to threats due to the privacy leakage of social networks. Anonymization techniques are widely adopted to protect users’ privacy during social data publishing and sharing. However, de-anonymization techniques are actively studied to identify weaknesses in current social network data-publishing mechanisms. In this paper, we conduct a comprehensive analysis on the typical structure-based social network de-anonymization algorithms. We aim to understand the de-anonymization approaches and disclose the impacts on their application performance caused by different factors, e.g., topology properties and anonymization methods adopted to sanitize original data. We design the analysis framework and define three experiment environments to evaluate a few factors’ impacts on the target algorithms. Based on our analysis architecture, we simulate three typical de-anonymization algorithms and evaluate their performance under different pre-configured environments