In the field of the Internet of Medical Things (IoMT), efficiently deploying deep learning models for breast cancer pathological diagnosis is both important and challenging. Traditional cloud computing stores data on a central server, which poses a risk of data leakage. Although conventional federated learning can protect data privacy, it still faces limitations in computing resources. In addition, most existing pathological image segmentation methods use single-resolution features, making it difficult to jointly represent both global context and local lesion details. To address these challenges, we introduce a Breast Cancer Pathological Diagnosis Cloud-Edge-Collaborative Platform based on split federated learning. We developed a lightweight edge device that is built upon the Jetson Nano deep learning computing platform. The model uses heatmap-guided region search to efficiently discover and examine ROIs over histopathological images, which simulates the expert observation process by pathologists. The platform enables collaborative learning of global and local information without leaking out sensitive patients’ data. Our method ensures data security and privacy while significantly improving model accuracy. Experimental results show that our model achieves Dice scores of 81.31% on the public Breast Cancer Semantic Segmentation (BCSS) dataset and 87.03% on clinical data collected from the Chinese People’s Liberation Army (PLA) General Hospital, improving the Dice score from 79.36% to 81.31% on BCSS, and from 86.18% to 87.03% on our clinical dataset. These results outperform existing models, advancing a meaningful step towards the distributed pathological diagnosis of breast cancer.

Achieving real-time, remote control and precise localization of early gastric cancer (EGC) lesions in endoscopic capsules is a significant obstacle in biomedical imaging development. This work outlines an innovative integrated system that uses an effective combination of Zynq UltraScale+ and Gizwits IoT to overcome this challenge. Our work employs a fully convolutional neural network underpinning grid-search clustering-driven multi-modals gating information local patch learning (GS-MGIF-LPLs). This system, designed as an adaptive location computing acceleration platform (ACAP), elegantly marries a double threshold fast search strategy with patch-based FCNN, fueling efficient training, testing, and performance metrics with an accuracy of 99.53%, precision coefficient of 86.06%, and an IoU of 84.26%. Upon benchmarking against four EGC types, our GS-MGIF-LPLs model demonstrates exceptional superiority against five established methods, providing a significant stride in computational efficiency and diagnostic advancements for gastrointestinal diseases.

Abstract Recent advanced methods address the cross‐domain person re‐identification (Re‐ID) problem primarily by the pseudo label estimation. However, person images with similar backgrounds are usually incorrectly clustered due to the interference of background noise. Furthermore, it is the lack of filtering of incorrect pseudo labels that results in the poor quality of the final generated pseudo labels. Here, a progressive learning approach based on background suppression and identity consistency for cross‐domain person Re‐ID (BSIC‐reID) is proposed. In the background suppression module, background mask attention and reverse attention are combined to effectively extract pedestrian features and suppress background noise, highlighting the foreground person information for person Re‐ID. In addition, the BSIC‐reID model is used to extract multi‐scale person features and generate different perspective pseudo labels for the target domain images. The incorrect pseudo labels are filtered by comparing the potential similarity of multi‐scale person features so that the higher quality pseudo labels for the target domain images could be generated. This method is performed on the Market‐1501, DukeMTMC‐reID, and MSMT17, evaluated by the Cumulated Matching Characteristics (CMC) and mean Average Precision (mAP). The experimental results demonstrate that this method also achieves state‐of‐the‐art performance.

In this paper, a unified virtual cloud storage method for federated data based on the middle layer is proposed, which aims at solving the problems of the heterogeneous data sources of remote sensing data in the shared platform among the federated remote sensing data management application under loose coupling mode. A federated remote sensing image data management model is developed based on NASA's Unified Metadata Model (UMM). This platform implements services such as unified access to multi-source heterogeneous image data which effectively solves the problem of heterogeneous data sources in the loosely coupled federated system, and provides better access methods for upper-level applications.

With the maturity of cloud computing technology in terms of reliability and efficiency, a large number of services have migrated to the cloud platform. To convenient access to the services and protect the privacy of communication in the public network, three-factor Mutual Authentication and Key Agreement (MAKA) protocols for multi-server architectures gain wide attention. However, most of the existing three-factor MAKA protocols don't provide a formal security proof resulting in various attacks on the related protocols, or they have high computation and communication costs. And most of the three-factor MAKA protocols haven't a dynamic revocation mechanism, which leads to malicious users can not be promptly revoked. To address these drawbacks, we propose a provable dynamic revocable three-factor MAKA protocol that achieves the user dynamic management using Schnorr signatures and provides a formal security proof in the random oracle. Security analysis shows that our protocol can meet various demands in the multi-server environments. Performance analysis demonstrates that the proposed scheme is well suited for computing resource constrained smart devices. The full version of the simulation implementation proves the feasibility of the protocol.

Process mining can be used to analyze business processes based on logs of their execution. These execution logs are often obtained by querying a database and storing the results in a file. The mining itself is then done on the file, such that the data processing power of the database cannot be used after the log is extracted. Enabling process mining directly on a database therefore provides additional flexibility and efficiency. To help facilitate this, this paper formally defines a database operator that extracts the ‘directly follows’ relation—one of the relations that is at the heart of process mining—from an operational database. It defines the operator using the well-known relational algebra and formally proves equivalence properties of the operator that are useful for query optimization. Subsequently, it presents time-complexity properties of the operator. Finally, it presents an implementation of the operator as part of the H2 DBMS and demonstrates that this implementation extracts the ‘directly follows’ relation from a database with an arbitrary database structure within a fraction of a second; several orders of magnitude faster than is currently possible.

The 5G technology will promote the development of vehicle ad hoc networks (VANET). However, almost all the existing authentication protocols rely on a completely trusted authority (TA), which undoubtedly raises a heavy burden on the TA. On the other hand, these protocols can rarely resist some special attacks, such as registration authority leaks registration information attack and ephemeral secret leakage attack. To address these problems, based on the self‐certified public keys and Schnorr signatures, the authors propose a hierarchical revocable authentication protocol in VANET. The proposed protocol is provably secure in the random oracle model under the Diffie‐Hellman assumption. Performance analysis illustrates that the protocol greatly saves computation resources and satisfies the well‐known security requirements. Therefore, the proposed protocol is suitable for the VANET environment.

This paper presents a novel method to infer regular expressions from positive examples. The method consists of a candidate’s construction phase and an optimization phase. We first propose multiscaling sample augmentation to capture the cycle patterns from single examples during the candidate’s construction phase. We then use common substrings to build regular expressions that capture patterns across multiple examples, and we show this algorithm is more general than those based on common prefixes or suffixes. Furthermore, we propose a pruning mechanism to improve the efficiency of useful common substring mining, which is an important part of common substring-based expression building algorithm. Finally, in the optimization phase, we model the problem of choosing a set of regular expressions with the lowest cost as an integer linear program, which can be solved to obtain the optimal solution. The experimental results on synthetic and real-life samples demonstrate the effectiveness of our approach in inferring concise and semantically meaningful regular expressions for string datasets.

The Internet of Things (IoT) makes our life more intelligent. Its combination with the cloud server can solve big data processing problems to meet users' needs and bring us great convenience. However, there are two challenges we need to face: data sharing and key leakage. To solve the above challenges, attribute-based encryption (ABE) is used to achieve data sharing combined with searchable encryption (SE). Most of the existing attribute-based searchable encryption (SE) schemes are inefficient and not suitable for IoT devices because of the large amount of attributes and keys. The key-leakage problem is serious in practice which very little literature focused on it. In order to address both problems, in this article, we propose a key aggregation searchable encryption (KASE) scheme based on the blockchain with auxiliary input (AI), which is capable of achieving secure data sharing on the encrypted data. Our scheme is presented through a novel chosen plaintext attack (CPA) secure scheme. We prove our scheme is chosen ciphertext attack (CCA) secure against key leakage under the decisional Diffie-Hellman assumption and Goldreich-Leivin theorem. Moreover, we adopt the proposed scheme to establish a data-sharing system based on blockchain, which improves search efficiency and connects the global ecology. In addition, extensive performance evaluations are conducted, and the results indicate our scheme is really efficient in cloud-computing-enhanced IoT.

The dipole orientation of fluorophores could be resolved by fluorescence polarization microscopy (FPM), which in turn reveals structural specificity for the labeled organelles. Conventional FPM can detect only the averaged fluorescence anisotropy collected from dipoles within the diffraction-limited volume. Super-resolution dipole orientation mapping (SDOM) method, which applies sparse deconvolution and least square estimation to fluorescence polarization modulation data, achieves the dipole orientation measurement within a sub-diffraction focal area. However, during SDOM analysis, some pixels with fluorescence signal are not resolved with orientation for relatively small adjusted R2. Here we report group-sparsity-based SDOM (GS-SDOM), which utilizes the relevance of modulation sequences to effectively improve the SDOM reconstruction model. More credible resolved dipole orientations with higher adjusted R2can be mapped and false positive estimation for local dipole orientation is vitally corrected. In addition to achieving the same spatial super-resolution as SDOM does, GS-SDOM accesses more morphological information with more credible orientations and more accurate local dipole distribution estimation. During the GS-SDOM analysis of actin filaments in mammalian kidney cells, the dipole orientation of fluorescence is detected always parallel to the direction of the actin filaments. Also with dipole orientation information extracted by GS-SDOM, the reconstructed visual circle from intensity dimension is discerned as jointed by double close filaments and 3-dimensional co-localization is accomplished in the intersection of actin filaments.

Mobile payment system in a disaster area have the potential to provide electronic transactions for people purchasing recovery goods like foodstuffs, clothes, and medicine. Conversely, to enable transactions in a disaster area, current payment systems need communication infrastructures (such as wired networks and cellular networks) which may be ruined during such disasters as large-scale earthquakes and flooding and thus cannot be depended on in a disaster area. In this paper, we introduce a new mobile payment system utilizing infrastructureless mobile adhoc networks to enable transactions that permit users to shop in disaster areas. Specifically, we introduce an endorsement-based mechanism to provide payment guarantees for a customer-to-merchant transaction and a multilevel endorsement (MLE) mechanism with a lightweight scheme based on Bloom filter and Merkle tree to reduce communication overheads. Our mobile payment system achieves secure transaction by adopting various schemes such as location-based mutual monitoring scheme and blind signature, while our newly introduced event chain mechanism prevents double spending attacks. As validated by simulations, the proposed mobile payment system is useful in a disaster area, achieving high transaction completion ratio, 65%-90% for all scenario tested, and is storage-efficient for mobile devices with an overall average of 7 MB merchant message size.

ZGA(zygotic genome activation) is not only important for early embryonic development, but also sheds light on understanding totipotency acquisition of mammalian cells. Recent studies has revealed that DUX severs as an important pioneering factor for mouse ZGA initiation, and MERVL(mouse ERV-like element) plays an important role to activate the downstream genes of DUX. However, how MERVLs functions during ZGA remains mysterious. Here we investigated two important aspects of MERVLs regulation, the characteristics of MERVLs regulation, and why DUX4 is needed to activate downstream genes via MERVLs. Then we proposed a ZGA-initiating model supported by Zscan4 case study, to understand and appreciate the significance of MERVLs in ZGA process.

Byzantine attacks are constant threats in many applications such as disaster management, and battlefield applications. In this paper, we introduce a monitoring scheme in the link state routing protocol to secure the packets' route against Byzantine attacks. The goal of our proposed scheme is to guarantee communication among connected benign nodes in the network. Specifically, each node monitors the action of neighboring nodes and compares the optimal packet route against the packet route history. Also, our scheme uses a statistical method to know if a node is dropping packets intentionally by analyzing the packet dropping behavior of each node. The proposed scheme provides protection against colluding attacks and other Byzantine attacks.

WiFi offloading, where mobile device users (e.g., smart phone users) transmit packets through WiFi networks rather than cellular networks, is a promising solution to alleviating the heavy traffic burden of cellular networks due to data explosion. However, since WiFi networks are intermittently available, a mobile device user in WiFi offloading usually needs to wait for WiFi connection and thus experiences longer delay of packet transmission. To motivate users to participate in WiFi offloading, cellular network operators give incentives (rewards like coupons, e-coins) to users who wait for WiFi connection and transmit packets through WiFi networks. In this paper, we aim at maximizing users' rewards while meeting constraints on queue stability and energy consumption. However, we face scheduling challenges from random packet arrivals, intermittent WiFi connection and time varying wireless link states. To address these challenges, we first formulate the problem as a stochastic optimization problem. We then propose an optimal scheduling policy, named Optimal scheduling Policy under Energy Constraint (OPEC), which makes online decisions as to when to delay packet transmission to wait for WiFi connection and which wireless link (WiFi link or cellular link) to transmit packets on. OPEC automatically adapts to random packet arrivals and time varying wireless link states, not requiring a priori knowledge of packet arrival and wireless link probabilities. As verified by simulations, OPEC scheduling policy can achieve the maximum rewards while keeping queue stable and meeting energy consumption constraint.

Identity-based signature has become an important technique for lightweight authentication as soon as it was proposed in 1984. Thereafter, identity-based signature schemes based on the integer factorization problem and discrete logarithm problem were proposed one after another. Nevertheless, the rapid development of quantum computers makes them insecure. Recently, many efforts have been made to construct identity-based signatures over lattice assumptions against attacks in the quantum era. However, their efficiency is not very satisfactory. In this study, an efficient identity-based signature scheme is presented over the number theory research unit (NTRU) lattice assumption. The new scheme is more efficient than other lattice- and identity-based signature schemes. The new scheme proves to be unforgeable against the adaptively chosen message attack in the random oracle model under the hardness of the γ-shortest vector problem on the NTRU lattice.

Abstract The first multi‐use unidirectional proxy re‐signature scheme was proposed by Benoît and Damien in 2008, which allowed translating in one direction and can be re‐translated several times. However, constructing multi‐use unidirectional proxy re‐signature scheme where the size of signature and the verification cost did not increase linearly with the number of translations was left as the open problem. This paper presents an efficient multi‐use unidirectional proxy re‐signature scheme and a multi‐use identity‐based unidirectional proxy re‐signature. They are proven secure in the standard model under the small integer solution assumption. And they solve the open problem left in 2008. Moreover, as proxy re‐signature scheme based on the number theory assumptions is no longer secure in quantum era, they are still secure. Copyright © 2017 John Wiley & Sons, Ltd.

A payment system in a disaster area is essential for people to buy necessities such as groceries, clothing, and medical supplies. However, existing payment systems require the needed communication infrastructures (like wired networks and cellular networks) to enable transactions, so that these systems cannot be relied on in disaster areas, where these communication infrastructures may be destroyed. In this paper, we propose a mobile payment system, adopting infrastructure less mobile adhoc networks (MANETs), which allow users to shop in disaster areas while providing secure transactions. Specifically, we propose an endorsement-based scheme to guarantee each transaction and a scheme to provide monitoring based on location information, and thus achieve transaction validity and reliability. Our mobile payment system can also prevent collusion between two parties and reset and recover attacks by any user. Security is ensured by using location-based mutual monitoring by nearby users, avoiding thereby double spending in the system.

In smart grid, a huge amount of privacy data need to be transmitted securely and efficiently. Compressed sensing can be used to improve the transmission efficiency by exploiting the data sparsity, while this sparsity is usually destroyed by the encryption process, making compressed sensing inapplicable. A possible workaround is to perform compressed sensing first and then encrypt the compressed data. However, this two-step workaround lowers the efficiency. In this paper, we propose a novel data transmission scheme EncryCS. Compressed sensing in EncryCS provides security and simultaneously enhances the transmission efficiency in one step. The prerequisite of compressed sensing is to construct a measurement matrix satisfying the restricted isometry property that ensures the perfect recovery of the signal. To make the matrix secret and feasible, we generate it with a pseudorandom sequence generator. By using this matrix, EncryCS transforms a large amount of plaintext data into a small amount of ciphertext data. EncryCS is proven to possess a high security. Extensive simulations are performed based on the real-world data obtained from the PowerNet project at Stanford University. The results demonstrate that EncryCS can compress data by order of magnitude, and the transmitted data can be almost perfectly recovered.

Due to their simplicity and efficiency, the two-hop relay algorithm and its variants serve as a class of attractive routing schemes for mobile ad hoc networks (MANETs). With the available two-hop relay schemes, a node, whenever getting an opportunity for transmission, randomly probes only once a neighbor node for the possible transmission. It is notable that such single probing strategy, although simple, may result in a significant waste of the precious transmission opportunities in highly dynamic MANETs. To alleviate such limitation for a more efficient utilization of limited wireless bandwidth, this paper explores a more general probing-based two-hop relay algorithm with limited packet redundancy. In such an algorithm with probing round limit τ and packet redundancy limit f, each transmitter node is allowed to conduct up to τ rounds of probing for identifying a possible receiver and each packet can be delivered to at most f distinct relays. A general theoretical framework is further developed to help us understand that under different setting of τ and f, how we can benefit from multiple probings in terms of the per node throughput capacity.

Due to their simplicity and efficiency, the two-hop relay algorithm and its variants serve as a class of attractive routing schemes for mobile ad hoc networks (MANETs). With the available two-hop relay schemes, a node, whenever getting an opportunity for transmission, randomly probes only once a neighbor node for the possible transmission. It is notable that such single probing strategy, although simple, may result in a significant waste of the precious transmission opportunities in highly dynamic MANETs. To alleviate such limitation for a more efficient utilization of limited wireless bandwidth, this paper proposes a more general probing-based two-hop relay algorithm with limited packet redundancy. In such an algorithm with probing round limit τ and packet redundancy limit f, each transmitter is allowed to conduct up to τ rounds of probing for identifying a possible receiver and each packet can be delivered to at most f distinct relays. A general theoretical framework is further developed to help us understand that under different setting of τ and f, how we can benefit from multiple probings in terms of the per node throughput capacity and the expected end-to-end packet delay.