Real-time collaborative programming is an emerging technology that supports a team of programmers to concurrently view and edit source code documents, with the benefits of enhancing team productivity and reducing project cost. Shared terminal is one crucial component of real-time collaborative programming environments, which facilitates interactive and instant peer support in debugging scenarios. In this study, we propose a novel approach named FlexSecure to address two major challenges in existing shared terminals. FlexSecure supports unconstrained and flexible shared terminal sessions that allow any collaborator to initiate, and meanwhile, preserves the local security of the initiator by incorporating fine-grained permission control to prevent risky command execution. Prototype implementation has validated the feasibility of FlexSecure, and user evaluation has demonstrated its effectiveness and satisfactory performance.

With the advancement of autonomous driving technology, trajectory prediction has become a critical task for ensuring traffic safety and intelligent decision-making. Existing motion forecasting models suffer from HD (High-Definition) map dependency, leading to high costs and poor adaptability. Furthermore, their accuracy sharply declines when maps are unavailable, motivating research into map-free alternatives. However, map-free models typically exhibit lower accuracy. To address this issue, we propose a universal enhancement framework that employs trajectory-map contrastive learning, utilizing a trajectory-to-map encoder to extract implicit map representations from raw trajectories, thereby improving performance. Extensive experiments on the Argoverse dataset demonstrate that, after incorporating our trajectory-to-map encoder into map-free models, the average minADE and minFDE are improved by 2.7% and 3.5%, respectively. These results underscore our method’s robustness and generalizability in enhancing map-free models, confirming the efficacy of implicit map representation learning and offering a promising solution for HD-map-free autonomous driving in dynamic open-road environments.

Learned Image Compression (LIC) has experienced rapid growth with the emergence of diverse frameworks. However, the variability in model design and training datasets poses a challenge for the universal application of a single coding model. To address this problem, this paper introduces a pioneering multi-model image coding framework that integrates various image codecs to overcome these limitations. By dynamically allocating codecs to different image regions, our framework optimizes reconstruction quality within the constraints of limited bitrate and decoding time, offering a high-performance, ubiquitous solution for the rate-distortion-complexity trade-off. Our framework features a detailed codec assignment algorithm based on the Simulated Annealing (SA) method, selected for its proven efficacy in managing the discrete and intricate nature of codec assignment optimization. We have implemented a coarse-to-fine strategy, which significantly enhances efficiency. Notably, our framework maintains compatibility with all standard image codecs without necessitating structural modifications. Empirical results indicate that our framework establishes a new standard in LIC, advancing the Pareto frontier for performance-complexity trade-offs. It achieves a significant 70% reduction in decoding time compared to current state-of-the-art methods, without compromising reconstruction quality. Furthermore, under comparable conditions, our approach not only outperforms but significantly eclipses existing Rate-Distortion-Complexity (RDC) optimized codecs, with decoding speeds up to 30 times faster.

Ensuring the resilience of deep learning algorithms against adversarial attacks during edge-cloud data transmission between edge and cloud systems is crucial. Although significant strides have been made in enhancing the accuracy of hazardous driving scenario detection in autonomous vehicles, bolstering the robustness against adversarial attacks during data transfer and model recognition remains an urgent challenge. In this paper, we propose a novel approach to enhance adversarial robustness and maintain high accuracy in benign data. Using a lightweight CNN model, we detect hazardous driving scenarios, while a generative adversarial network generates decision boundary samples from original scenario images. These samples are incorporated into adversarial training, preventing overfitting on adversarial examples. Experimental results show our approach achieves robust accuracy (AUC) of 0.83 and 0.66 under FGSM and PGD attacks, surpassing vanilla adversarial training and TRADES methods with improving the standard accuracy (AUC) on benign samples by 0.22 and 0.12 relatively. Our proposed method advances beyond current adversarial training techniques to significantly enhance the model’s resilience to adversarial attacks during edge-cloud data transmission phases and minimize the loss of standard accuracy simultaneously. This advancement is crucial in enhancing the capability to correctly detect a wider range of hazardous driving scenarios, thereby providing significant support for the secure deployment of autonomous vehicles in edge-cloud collaborative environments.

Real-time collaborative programming supports a team of programmers to concurrently edit a shared set of source code at the same time. To support semantic conflict prevention in real-time collaboration, prior work had proposed a dependency-based automatic locking (DAL) approach, which grants locks on selected source code regions based on a set of prefixed rules. To further improve the flexibility of the DAL scheme by utilizing programmers’ knowledge on semantic conflict risks and collaboration requirements, we propose a novel Request-Invitation-Approval (RIA) scheme, which allows any programmer to manually request the editing permission on a locked code region, or invite another programmer to share locks on a region. To support the proposed scheme, we have further proposed two modes for the permission transfer process, and contributed detailed techniques on four request patterns. Prototype system implementation has validated the feasibility of the approach and techniques, and user evaluations have demonstrated the satisfactory usability of the system.

Rate control (RC) is crucial in achieving stable and smooth bitrate variation in video compression and transmission. Existing RC methods for neural video compression (NVC) have made strong assumptions on solving bit allocation parameters using a pre-defined model, leading to high bit-rate errors (BRE). In response, the study introduces a simple yet effective one-pass RC strategy tailored for NVC frameworks in a plug-in fashion. This strategy consists of two key components: the NVC rate-adaptive model and the associated RC approach. The former model constructs the basis of the latter approach. The proposed RC approach employs a progressive online updating technique for parameter estimation to achieve a lower BRE and maintain the original quality structure of frameworks. Experimental results demonstrate that our approach achieves an impressive RC performance on standard test sequences, outperforming the conventional optimal R-λ RC model with lower BRE and better rate-distortion (R-D) performances. Furthermore, we extend our method to three representative NVC frameworks, consistently showcasing its effectiveness in achieving lower BRE with only moderated R-D performance degradation.

Real-time collaborative programming is a novel approach that enables programmers to simultaneously edit shared source code at the same time, which has been applied in a variety of software development scenarios. To achieve semantic conflict prevention in real-time collaboration, a dependency-based automatic locking (DAL) approach was proposed in prior work, which prevents programmers' concurrent editing on selected source code regions. However, DAL's reliance on source code analysis techniques may lead to failures when there exists syntax errors in the source code. To overcome such limitation, we propose an error-tolerant code segmentation (ECS) approach, as well as supporting algorithms, to improve semantic conflict prevention. Technically, the ECS approach continuously identifies source code regions and maintains their range information to ensure stable source code segmentation and code region tracking during the collaboration process, without the need to deal with complex syntax issues. The proposed approach and algorithms have been implemented in a prototype, and experimental evaluations have demonstrated their effectiveness and efficiency.

Real-time collaborative programming environments support a group of programmers, who are geographically distributed, to concurrently view and edit a shared set of source code in a real-time fashion. However, this emerging technology has not been widely applied yet, and one critical challenge is the semantic conflict during real-time collaboration. Existing conflict prevention approaches are limited in supporting individual source code files only, with pre-programmed rules that might be inflexible. To address these challenges, we propose a novel semantic conflict prevention approach based on source code annotations, which allow programmers to manually, conveniently and flexibly apply fine-grained conflict prevention rules during real-time collaboration. The proposed approach and techniques have been successfully implemented in a prototype system. User studies and performance evaluations have demonstrated the technical feasibility of the proposed approach and techniques, as well as the prototype's satisfactory efficiency and usability.

In cloud computing environments, virtual machines (VMs) running on cloud servers are vulnerable to shared cache attacks, such as Spectre and Foreshadow. By exploiting memory sharing among VMs, these attacks can compromise cryptographic keys in software modules. Program obfuscation serves as a promising countermeasure against key compromises by transforming a program into an unintelligent form while preserving its functionality. Unfortunately, for certain cryptographic algorithms such as the digital signature schemes, it is extremely difficult to construct provably secure obfuscators using traditional obfuscation approaches. To address such a challenge, this study proposes a novel approach to construct obfuscators for cryptographic algorithms named space-hard obfuscation, which can mitigate the threats from adversaries with the capability of acquiring a limited size of memory in shared cache attacks. Considering the extensive use of the Elliptic Curve Digital Signature Algorithm (ECDSA) in cloud-based Blockchain-as-a-Service (BaaS) and its potential vulnerability to shared cache attacks, we construct an exemplary scheme with provable security using space-hard obfuscation for ECDSA. Experimental results have demonstrated the scheme's high efficiency on cloud servers, as well as its successful integration with Hyperledger Fabric and Ethereum, two widely used blockchain systems.

Real-time collaborative programming supports a team of programmers to concurrently view and edit the same set of source code at the same time, which is beneficial in meeting particular collaboration needs. However, during the collaboration process, programmers are not able to compile and debug the source code with syntactic errors as it is being continuously edited by other collaborators. To address this challenge, we propose a novel approach named Reversion of Error-free Code with Workspace Isolation (RECON) and contribute supporting techniques with prototype implementation. In this approach, the system continuously monitors the files being collaboratively edited, detects source code without syntactic error, and maintains additional Error-free source code copies. Whenever a programmer attempts to compile and debug the code, the system creates a temporary isolated workspace and replaces the source code files with the latest Error-free copies. The proposed approach and solution have been implemented in a prototype system named CoIDEA, which has indicated the feasibility of the scheme and techniques.

Real-time collaborative programming enables a group of programmers to edit shared source code at the same time, which significantly complements the traditional non-real-time collaborative programming supported by version control systems. However, one critical issue with this emerging technique is the lack of integration with non-real-time collaboration. Specifically, contributions from multiple programmers in a real-time collaboration session cannot be distinguished and accurately recorded in the version control system. In this study, we propose a scheme that integrates real-time and non-real-time collaborative programming with a novel workflow, and contribute enabling techniques to realize such integration. As a proof-of-concept, we have successfully implemented two prototype systems named CoEclipse and CoIDEA, which allow programmers to closely collaborate in a real-time fashion while preserving the work's compatibility with traditional non-real-time collaboration. User evaluation and performance experiments have confirmed the feasibility of the approach and techniques, demonstrated the good system performance, and presented the satisfactory usability of the prototypes.

Real-time collaborative programming has attracted increasing attention and interest in recent years. To resolve the semantic conflict problem in real-time collaborative programming, a Dependency-based Automatic Locking (DAL) scheme was proposed in prior work. The DAL scheme prevents other collaborators from editing semantically related regions by automatically detecting depended regions and locking them. However, the DAL scheme lacks flexibility and is not well suited to the needs of programmers in a real-world development scenario. When the programmer switches to a new region, all previous locks are automatically released. For this reason, we propose the Multiple Locking Group (MLG) scheme, where each programmer can hold multiple locking groups and switch freely between multiple working regions. Accordingly, three release modes for releasing locking groups are proposed. Each programmer can customize the release modes in a fine-grained manner. In supporting the scheme, we have devised techniques and solutions, implemented a prototype system and conducted a preliminary user evaluation to validate the feasibility, effectiveness and usability of the MLG scheme.

Real-time collaborative programming environments support a team of programmers to edit shared source code at the same time, where each local editing operation is captured and immediately transmitted to remote sites in a fine-grained manner. However, under real-world network conditions, collaborators are usually plagued by data congestion and transmitted errors. In this study, we define and analyze the content relationships among operations, and propose a Context-based Operation Merging Algorithm (COMA). Technically, the COMA examines the content relationships among a series of editing operations and merges content-related operations. Powered by the COMA, real-time collaborative programming environments can significantly compress editing operations to cope with complex network situations (such as network fluctuation and interruption) and improve the user experience of real-time collaboration. The proposed COMA has been implemented in a real-time collaborative programming environment prototype, namely CoEclipse. Preliminary user evaluations, correctness analysis and performance evaluations have demonstrated the effectiveness, correctness, and efficiency of the algorithm.

In this work, we propose a no-reference video quality assessment method, aiming to achieve high-generalization capability in cross-content, -resolution and -frame rate quality prediction. In particular, we evaluate the quality of a video by learning effective feature representations in spatial-temporal domain. In the spatial domain, to tackle the resolution and content variations, we impose the Gaussian distribution constraints on the quality features. The unified distribution can significantly reduce the domain gap between different video samples, resulting in more generalized quality feature representation. Along the temporal dimension, inspired by the mechanism of visual perception, we propose a pyramid temporal aggregation module by involving the short-term and long-term memory to aggregate the frame-level quality. Experiments show that our method outperforms the state-of-the-art methods on cross-dataset settings, and achieves comparable performance on intra-dataset configurations, demonstrating the high-generalization capability of the proposed method. The codes are released athttps://github.com/Baoliang93/GSTVQA

Recent years have witnessed a surge of professional user-generated content (PUGC) based video services, coinciding with the accelerated proliferation of video acquisition devices such as mobile phones, wearable cameras, and unmanned aerial vehicles. Different from traditional UGC videos by impromptu shooting, PUGC videos produced by professional users tend to be carefully designed and edited, receiving high popularity with a relatively satisfactory playing count. In this paper, we systematically conduct the comprehensive study on the perceptual quality of PUGC videos and introduce a database consisting of 10,000 PUGC videos with subjective ratings. In particular, during the subjective testing, we collect the human opinions based upon not only the MOS, but also the attributes that could potentially influence the visual quality including face, noise, blur, brightness, and color. We make the attempt to analyze the large-scale PUGC database with a series of video quality assessment (VQA) algorithms and a dedicated baseline model based on pretrained deep neural network is further presented. The cross-dataset experiments reveal a large domain gap between the PUGC and the traditional user-generated videos, which are critical in learning based VQA. These results shed light on developing next-generation PUGC quality assessment algorithms with desired properties including promising generalization capability, high accuracy, and effectiveness in perceptual optimization. The dataset and the codes are released at https://github.com/wlkdb/pugcq_create.

Positive label is often used as the supervisory information in the learning scenario, which refers to the category that a sample is assigned to. However, another side information lying in the labels, which describes the categories that a sample is exclusive of, have been largely ignored. In this paper, we propose a nonnegative matrix factorization (NMF) based classification method leveraging both positive and negative label information, which is termed as positive and negative label-driven NMF (PNLD-NMF). The proposed scheme concurrently accomplishes data representation and classification in a joint manner. Owing to the complementary characteristics between positive and negative labels, we further design a new regularization framework to take advantage of these two label types. Extensive experiments on six image classification benchmark datasets show that the proposed scheme is able to consistently deliver better classification accuracy.

In this paper, we quest the capability of transferring the quality of natural scene images to the images that are not acquired by optical cameras (e.g., screen content images, SCIs), rooted in the widely accepted view that the human visual system has adapted and evolved through the perception of natural environment. Here, we develop the first unsupervised domain adaptation based no reference quality assessment method for SCIs, leveraging rich subjective ratings of the natural images (NIs). In general, it is a non-trivial task to directly transfer the quality prediction model from NIs to a new type of content (i.e., SCIs) that holds dramatically different statistical characteristics. Inspired by the transferability of pair-wise relationship, the proposed quality measure operates based on the philosophy of improving the transferability and discriminability simultaneously. In particular, we introduce three types of losses which complementarily and explicitly regularize the feature space of ranking in a progressive manner. Regarding feature discriminatory capability enhancement, we propose a center based loss to rectify the classifier and improve its prediction capability not only for source domain (NI) but also the target domain (SCI). For feature discrepancy minimization, the maximum mean discrepancy (MMD) is imposed on the extracted ranking features of NIs and SCIs. Furthermore, to further enhance the feature diversity, we introduce the correlation penalization between different feature dimensions, leading to the features with lower rank and higher diversity. Experiments show that our method can achieve higher performance on different source-target settings based on a light-weight convolution neural network. The proposed method also sheds light on learning quality assessment measures for unseen application-specific content without the cumbersome and costing subjective evaluations.

In this article, with the rapid development of industrial Internet of Things, a large number of embedded devices, such as sensors and tag readers, have been widely deployed for gathering and sending data. These devices are commonly unreliable and vulnerable to many threats, because they are located in unattended areas which are vulnerable to device capture attacks. Such environments can be regarded as white-box attack contexts, in which the adversary has total visibility and full control of the implementations. White-box cryptography (WBC) aims to protect implementations of symmetric encryption algorithms in white-box attack contexts. Unfortunately, existing WBC schemes are vulnerable to various attacks, and most of them are insufficiently secure in strict white-box attack contexts. Based on the investigation of existing designs and the corresponding cryptanalysis, we propose a novel design approach for securing WBC schemes, which is named state-dependent selectable random substitutions (SDSRS). It uses SDSRSs to defeat various related white-box cryptanalytic approaches. With special considerations for IIoT systems, such as high performance for supporting real-time applications and small block size for fitting industrial protocols, a concrete WBC scheme designed with the proposed approach has been provided. Our theoretical analysis shows that the proposed scheme is secure. Additionally, experimental results indicate that the scheme performs well in practice, and it is significantly efficient in time and energy consumptions compared with existing secure white-box cryptographic schemes.

Practically, it is more feasible to collect compact visual features rather than the video streams from hundreds of thousands of cameras into the cloud for big data analysis and retrieval. Then the problem becomes which kinds of features should be extracted, compressed and transmitted so as to meet the requirements of various visual tasks. Recently, many studies have indicated that the activations from the convolutional layers in convolutional neural networks (CNNs) can be treated as local deep features describing particular details inside an image region, which are then aggregated (e.g., using Fisher Vectors) as a powerful global descriptor. Combination of local and global features can satisfy those various needs effectively. It has also been validated that, if only local deep features are coded and transmitted to the cloud while the global features are recovered using the decoded local features, the aggregated global features should be lossy and consequently would degrade the overall performance. Therefore, this paper proposes a joint coding framework for local and global deep features (DFJC) extracted from videos. In this framework, we introduce a coding scheme for real-valued local and global deep features with intra-frame lossy coding and inter-frame reference coding. The theoretical analysis is performed to understand how the number of inliers varies with the number of local features. Moreover, the inter-feature correlations are exploited in our framework. That is, local feature coding can be accelerated by making use of the frame types determined with global features, while the lossy global features aggregated with the decoded local features can be used as a reference for global feature coding. Extensive experimental results under three metrics show that our DFJC framework can significantly reduce the bitrate of local and global deep features from videos while maintaining the retrieval performance.

The commit messages in source code repositories are valuable but not easy to be generated manually in time for tracking issues, reporting bugs, and understanding codes. Recently published works indicated that the deep neural machine translation approaches have drawn considerable attentions on automatic generation of commit messages. However, they could not deal with out-of-vocabulary (OOV) words, which are essential context-specific identifiers such as class names and method names in code diffs. In this paper, we propose PtrGNCMsg, a novel approach which is based on an improved sequence-to-sequence model with the pointer-generator network to translate code diffs into commit messages. By searching the smallest identifier set with the highest probability, PtrGNCMsg outperforms recent approaches based on neural machine translation, and first enables the prediction of OOV words. The experimental results based on the corpus of diffs and manual commit messages from the top 2,000 Java projects in GitHub show that PtrGNCMsg outperforms the state-of-the-art approach with improved BLEU by 1.02, ROUGE-1 by 4.00 and ROUGE-L by 3.78, respectively.

Multistep flow prediction is an essential task for the car-sharing systems.An accurate flow prediction model can help system operators to pre-allocate the cars to meet the demand of users.However, this task is challenging due to the complex spatial and temporal relations among stations.Existing works only considered temporal relations (e.g., using LSTM) or spatial relations (e.g., using CNN) independently.In this paper, we propose an attention multi-graph convolutional sequenceto-sequence model (AMGC-Seq2Seq), which is a novel deep learning model for multistep flow prediction.The proposed model uses the encoder-decoder architecture, wherein the encoder part, spatial and temporal relations are encoded simultaneously.Then the encoded information is passed to the decoder to generate multistep outputs.In this work, specific multiple graphs are constructed to reflect spatial relations from different aspects, and we model them by using the proposed multi-graph convolution.Attention mechanism is also used to capture the important relations from previous information.Experiments on a large-scale real-world car-sharing dataset demonstrate the effectiveness of our approach over state-of-the-art methods.

Multistep flow prediction is an essential task for the car-sharing systems. An accurate flow prediction model can help system operators to pre-allocate the cars to meet the demand of users. However, this task is challenging due to the complex spatial and temporal relations among stations. Existing works only considered temporal relations (e.g. using LSTM) or spatial relations (e.g. using CNN) independently. In this paper, we propose an attention to multi-graph convolutional sequence-to-sequence model (AMGC-Seq2Seq), which is a novel deep learning model for multistep flow prediction. The proposed model uses the encoder–decoder architecture, wherein the encoder part, spatial and temporal relations are encoded simultaneously. Then the encoded information is passed to the decoder to generate multistep outputs. In this work, specific multiple graphs are constructed to reflect spatial relations from different aspects, and we model them by using the proposed multi-graph convolution. Attention mechanism is also used to capture the important relations from previous information. Experiments on a large-scale real-world car-sharing dataset demonstrate the effectiveness of our approach over state-of-the-art methods.

Distributed embedded devices are widely used in sensor networks and the Internet of Things for gathering and sending data. Many of them are deployed in an unattended manner (e.g., sensor nodes and tag readers), while others may be easily lost (e.g., smart wristbands and watches). These distributed embedded devices could be potentially captured and accessed in an unauthorized manner due to their physical natures. From a security perspective, they are typically working in the white-box attack context, where adversaries have total visibility on the implementations of built-in cryptosystems and full control over their execution processes. It is undoubtedly a significant challenge to deal with white-box attacks on these devices. Existing encryption algorithms for white-box attack contexts require large memory footprint and thus are not suitable for resource- constrained embedded devices. To address this challenge, we propose a novel light-weight encryption scheme for protecting data confidentiality. The encryption is conducted with specialized secret components, and the encryption algorithm requires a small volume of static data for storing critical information. In addition, this scheme uniquely supports efficient key-updating at very small cost. The security and the cost of the proposed scheme have been theoretically analyzed with positive results, and the extensive experimental evaluations indicate that the new scheme satisfies the requirements of distributed embedded devices in terms of limited memory usage and low computational cost.

Encoding multiple videos in parallel and transmitting them as one joint stream over a limited bandwidth have become a popular strategy for broadcasting, which brings an opportunity to allocate different bitrate for each sequence to meet different demands. In this paper, considering visual experience for human beings, we propose a joint rate allocation scheme aims to reach an equal visual quality among all sequences by minimizing the distortion variance of all the sequences (denoted as minVAR problems). Existing methods assigned bits directly in proportion to their complexity measures and we named them as complexity based allocation scheme (CAS) methods. CAS methods rely on the accuracy of the complexity measures which can hardly be improved under limited computing resources. Also complexities may not be directly related to the distortions. To address these problems, we present a novel joint rate-distortion (R-D) based allocation scheme (RDAS) in this paper. Our proposed scheme can fit for different R-D models and in our method we model the R-D relationship with a hyperbolic function (RDAS-H). We also derive a closed-form solution of RDAS-H by a proposed joint R-D relationship. We integrated the RDAS-H method in high efficiency video coding reference software HM16.0. Experimental results demonstrate that our RDAS-H saves 75.29% variance on average over the related CAS-based method, where we apply both low delay and random access configurations with four different overall bandwidths for all classes recommended by the Joint Collaborative Team on Video Coding. Besides, RDAS-H also saves 36.62% variance on average over our previous method. The proposed RDAS-H method improves the performance significantly while requiring negligible computational cost.

Mobile agents are useful in collaborative business systems due to their mobility and autonomy, which can roam over the Internet to purchase goods and services on behalf of their owners. However, given attacks from a malicious host, it is a challenge to securely sign a contract on behalf of the owner (the original signer). In this paper, we propose an intrusion-resilient undetachable digital signature (IR-UDS) approach to mitigate the security risk of signing key leakage on the signer's host, base device, and potentially malicious remote hosts, as well as the risk of misusing the signing algorithm on remote hosts. An attacker will be unable to forge the past and future signatures as long as the base device is secure, even if the current signing key of the original signer has been gained. When the base device is compromised, although the future signatures could be forged, all past signatures remain secure. Furthermore, the encrypted signing function has been combined with the original signer's requirement to prevent the misuse of signing algorithm and the exposure of original signing key on malicious hosts. Security analysis has indicated that our scheme can defeat a variety of attacks, and experimental evaluations have demonstrated the good performance of the scheme.

Real-time collaborative programming allows programmers to concurrently edit shared source code over communication networks. To support semantic conflict prevention, prior work has proposed a bask dependency-based automatic locking (DAL) approach to automatically grant locks on source code regions with dependency relationships, under the assumptions that there exists no locking-scope overlapping among concurrent editing operations, and the source code structure remains static during the collaboration process. To address major restrictions of the basic DAL scheme, this paper presents a shard-locking approach and techniques to fully support unconstrained real-time collaborative programming with semantic conflict prevention. The approach allows multiple programmers to concurrently edit source code regions with overlapping locking scopes in the presence of concurrent editing operations and dynamic source code structures. The techniques and solutions have been implemented in a research prototype for evaluations.

Rate-Distortion Optimized Quantization (RDOQ) brings significant improvement of coding performance in High Efficiency Video Coding (HEVC). However, it results in high computational complexity to determine the best quantization levels for each transform coefficient when applying rate and distortion optimization operation. In this paper, we firstly proposed an efficient way to skip the all zero coding units. Then we further propose a fast RDOQ scheme by skipping the optimization step of All Quantized Zero Blocks except DC Coefficient (AQZB-DC). Moreover, a rate difference model is established to select optimal quantization level for AQZB-DC blocks. Experiments are carried out on reference software HM 16.0 and the results show that the proposed method achieves 42.00% and 39.63% quantization time saving under Random Access (RA) and Low Delay (LD) configuration on average, while the BD-Rate loss is only 0.03% and 0.06%, respectively.

Recently, low‐rank representation (LRR)‐based techniques have manifested remarkable results for data representation. To exploit the latent manifold structure of data, the graph regulariser is incorporated into the model of LRR. However, it is critical to construct an appropriate graph model and set the corresponding parameters. In addition, this procedure is usually time‐consuming and proved to be overfitting when using cross validation or discrete grid search. Two novel LRR‐based methods, called multiple graph regularised LRR and multiple hypergraph regularised LLR, are proposed to represent the high‐dimensional data. To guarantee the smoothness along the estimated manifold, the multiple graph regulariser and the multiple hypergraph regulariser are incorporated into the traditional LRR method, respectively, which results in a unified framework. Moreover, the augmented Lagrange multiplier is adopted to solve the proposed models. Extensive experiments on real image datasets show the effectiveness of the proposed methods.

Signcryption is a cryptographic technique for simultaneously performing both digital signature and data encryption. It is effective for protecting the confidentiality and unforgeability of communications in Internet of Things (IoT) systems, especially when a number of generated ciphertexts can be aggregated into a compact form. However, device capture attacks are commonly threatening the implementations of signcryption on unattended devices by enabling an attacker to extract the cryptographic key from a captured device. Motivated by this issue, we propose a novel and specialized obfuscatable aggregatable signcryption scheme (OASC) together with an obfuscator for the signcryption algorithm, which has been designed by taking into account that the computational and communication costs should be sufficiently small (light-weighted) to fit applications in resource-constrained embedded devices. The proposed obfuscator can protect signcryption programs from key-extraction attacks by transforming the programs into unintelligible obfuscated programs. To the best of our knowledge, this is the first OASC in the community. The scheme's security features with respect to obfuscation, confidentiality, and unforgeability have been theoretically proved. Moreover, in comparison with other (nonobfuscatable) aggregatable signcryption schemes, the scheme's computational efficiency is positioned at a medium level while the communication cost is also relatively small, with extra unique security features benefiting from obfuscation. Experiments on different devices indicated that the proposed scheme performs reasonably well as expected. The scheme is widely applicable for various scenarios of IoT, where information is sent from unattended leaf nodes to a sink point.

With the explosion in the use of cameras in mobile phones or video surveillance systems, it is impossible to transmit a large amount of videos captured from a wide area into a cloud for big data analysis and retrieval. Instead, a feasible solution is to extract and compress features from videos and then transmit the compact features to the cloud. Meanwhile, many recent studies also indicate that the features extracted from the deep convolutional neural networks will lead to high performance for various analysis and recognition tasks. However, how to compress video deep features meanwhile maintaining the analysis or retrieval performance still remains open. To address this problem, we propose a high-efficiency deep feature coding (DFC) framework in this paper. In the DFC framework, we define three types of features in a group-of-features (GOFs) according to their coding modes (i.e., I-feature, P-feature, and S-feature). We then design two prediction structures for these features in a GOF, including a sequential prediction structure and an adaptive prediction structure. Similar to video coding, it is important for P-feature residual coding optimization to make a tradeoff between feature bitrate and analysis/retrieval performance when encoding residuals. To do so, we propose a rate-performance-loss optimization model. To evaluate various feature coding methods for large-scale video retrieval, we construct a video feature coding data set, called VFC-1M, which consists of uncompressed videos from different scenarios captured from real-world surveillance cameras, with totally 1M visual objects. Extensive experiments show that the proposed DFC can significantly reduce the bitrate of deep features in the videos while maintaining the retrieval accuracy.

In this paper we propose an efficient hybrid zero block early detection method for high efficiency video coding (HEVC). Our method detects both genuine zero blocks (GZBs) and pseudo zero blocks (PZBs). For GZB detection, we use a two sum of absolute difference bounds and a one sum of absolute transformed difference threshold to decrease the GZB detection complexity. A fast rate-distortion estimation algorithm for HEVC is proposed to improve the PZB detection rate. Experimental results on the HM platform show that the proposed method saves about 50% of the rate-distortion optimization (RTO) time, with negligible Bjøntegaard delta bit rate loss. Our method is faster than other state-of-the-art ZB detection methods for HEVC by 10%-30%.

Operational Transformation (OT) is commonly used for conflict resolution in real-time collaborative applications, but none of existing OT techniques is able to solve a special type of conflict - orthogonal conflict, which may occur when concurrent operations are inserting/deleting an arbitrary number of objects in different dimensions of a two-dimensional (2D) workspace, such as spreadsheet documents. This paper is the first to identify and solve the orthogonal conflict problem by extending OT with a new capability of resolving 2D conflicts. Extending OT from one- to two-dimensional conflict resolution is fundamental to the theory and application of OT, and technically challenging as well because 2D orthogonal conflict is different from but intimately related to the one-dimensional positional shifting conflict and necessitates new and integral solutions for multi-dimensional conflicts. In this paper, we present formal definitions of orthogonal conflict, pseudo-code description, design rationale analysis, and correctness verification and complexity analysis of the 2DOT solution.

Real-time collaborative programming environments support a team of programmers to edit the same shared source code document concurrently over communication networks. This paper presents the design and implementation of a novel real-time collaborative programming system named CoEclipse, which transparently converts the single-user Eclipse IDE into a multi-user real-time collaborative programming tool, incorporates and integrates syntactic and semantic consistency maintenance techniques derived from our prior work, and provides advanced awareness features for supporting semantic conflict prevention. The novelties of CoEclipse include its full compatibility with existing single-user programming environments in terms of user interfaces and working processes, seamless integration of syntactic and semantic consistency maintenance features, and providing complementary approaches of awareness and locking in collectively supporting semantic conflict prevention. Furthermore, the CoEclipse approach in supporting advanced real-time collaboration is generic, which can be applied to other application domains for achieving similar design objectives and rationales.

Real-time collaborative programming and non-real-time collaborative programming are two classes of methods and techniques for supporting programmers to jointly conduct complex programming work in software development. They are complementary to each other, and both are useful and effective under different programming circumstances. However, most existing programming tools and environments have been designed for supporting only one of them, and little has been done to provide integrated support for both. In this paper, we contribute a novel Any-Time Collaborative Programming Environment (ATCoPE) to seamlessly integrate conventional non-real-time collaborative programming tools and environments with emerging real-time collaborative programming techniques and support collaborating programmers to work in and flexibly switch among different collaboration modes according to their needs. We present the general design objectives for ATCoPE, the system architecture, functional design and specifications, rationales beyond design decisions, and major technical issues and solutions in detail, as well as a proof-of-concept implementation of the ATCoEclipse prototype system.

A new solution to stereoscopic panorama roaming is proposed in this paper, which involves stereoscopic panorama acquisition, synchronous panorama roaming technique and some key issues of implementations. It synthesizes traditional panorama technique and stereoscopic displaying technique successfully, and enhances the immersion sense of panorama roaming significantly. The developed prototype and related experiments have sufficiently shown the feasibility and effectiveness of the newly presented approach.