The excellent text-to-image synthesis capability of diffusion models has driven progress in synthesizing coherent visual stories. The current state-of-the-art method combines the features of historical captions, historical frames, and the current captions as conditions for generating the current frame. However, this method treats each historical frame and caption as the same contribution. It connects them in order with equal weights, ignoring that not all historical conditions are associated with the generation of the current frame. To address this issue, we propose Causal-Story. This model incorporates a local causal attention mechanism that considers the causal relationship between previous captions, frames, and current captions. By assigning weights based on this relationship, Causal-Story generates the current frame, thereby improving the global consistency of story generation. We evaluated our model on the PororoSV and FlintstonesSV datasets and obtained state-of-the-art FID scores, and the generated frames also demonstrate better storytelling in visuals.

Multibaseline interferometric synthetic aperture radar (InSAR), multibaseline polarimetric InSAR (PolInSAR), and SAR tomography (TomoSAR) are the advanced techniques for forest height inversion. However, accurate large-scale inversion using these techniques still faces two key problems: 1) for InSAR and PolInSAR, the existing inversion models fail to accurately describe the vertical structure, reducing the inversion performance; 2) for TomoSAR, its baseline configuration requirement is too high to reconstruct the vertical structure of the large-scale forested area. To solve these two problems, this paper proposes a high inversion accuracy forest vertical structure heterogeneity (FVSH) coherent scattering model and a small-scale tomography-aided multibaseline (Pol)InSAR (STMI) forest height inversion framework, which provide a synergic observation and inversion scheme of these multibaseline SAR techniques using machine learning approaches. The different-frequency InSAR and PolInSAR data acquired above the different types of forested areas are selected for validation. The experimental results show the effectiveness of the proposed framework.

This article studies the data-driven design of parity space based replay attack detection method for cyber-physical systems. By utilizing the input and output data, the proposed data-driven detection can uncover stealthy replay attacks. First, the replay attack detector is formulated by following the parity space method. Subsequently, data-driven parity space identifi-cation, residual quantification, and parity matrix optimization are presented to support data-driven residual generation and evaluation for the detector. To enhance detection performance, an integration of attack defense model information and process data is presented, and the data-driven detector design is proposed. Finally, simulations are conducted to testify the performance of our schemes.

Spectrum auction is one of the most effective ways to achieve dynamic spectrum allocation in cognitive radio networks , and it provides one effective way to manage the spectrum demands of IoT devices with limited resources. Most spectrum auctions focus on protecting bidder privacy and achieving excellent social efficiency, but few tackles the verification of auction results that are controlled by the auctioneer. In this paper, we propose a cloud-based framework for verifiable privacy-preserving spectrum auctions. Our framework adopts a modified AFGH re-encryption algorithm that achieves both bid privacy protection and auction results verification at the same time. The cloud server helps to compute auction results based on homomorphic encryption , and an auctioneer decrypts the encrypted data from the server to obtain auction results. Meanwhile, the property of re-encryption makes it possible for any bidder to verify the auction results without compromising other bidders’ privacy.

Estimating vegetation height from polarimetric interferometric synthetic aperture radar (PolInSAR) data using the random volume over ground (RVoG) model has long been used. Most of these methods propose models and apply them to real airborne data to demonstrate their potential. The single-baseline PolInSAR forest height estimation based on the RVoG model lacks sufficient observation information. For this reason, multibaseline data are introduced to address this. This paper fits the relationship of model parameters in multibaseline observation scenarios, and focuses the forest height inversion on the calculation of pure volume decorrelation. Subsequently, a multibaseline forest height joint inversion method based on the least squares principle is adopted. Finally, we use airborne PolInSAR data from the Lope and Mondah sites collected by UAVSAR and F-SAR systems during AfriSAR 2016 to verify the proposed method. The experimental results show that the accuracy of the proposed method (Lope: root mean square error (RMSE) = 5.8 m, Mondah: RMSE = 5.12 m) is 38.1% and 34.53% higher than the coherence separation product (Lope: RMSE = 9.37 m, Mondah: RMSE = 7.82 m).

Multibaseline polarimetric synthetic aperture radar interferometry (PolInSAR) is one of the advanced technologies of forest height inversion, as it provides rich observation information. In this letter, we propose a new iterative reweighted method for multibaseline PolInSAR joint inversion of forest height. First, we establish a better stochastic model and weight function to obtain more accurate parameter estimation considering the relationship between vertical wavenumber and forest height. Second, according to the proposed inversion criterion, the baseline observations with unsuitable interferometric geometry are regarded as gross errors and eliminated through reweighted iteration. Finally, we select airborne P-band synthetic aperture radar (SAR) data collected by the F-SAR system during the AfriSAR 2016 campaign for experimental validation. The experimental results show that using initial iteration values obtained by three optimal baseline selection methods, the accuracy of the proposed method achieves 4.47, 4.46, and 4.24 m, which is about 34.74%, 32.32%, and 33.23% higher than those of the existing multibaseline joint inversion method [root mean square error (RMSE) = 6.85, 6.59, and 6.35 m], respectively.

Crop rotation is subsidized by the government because of its many advantages. Monitoring whether crop rotation is beneficial for agricultural management, and can also provide a reference for government subsidy policies for crop rotation. In this paper, we propose an unsupervised object-oriented crop rotation detection method using time-series polarimetric SAR (PolSAR) data. On the one hand, we construct the change detection matrix based on the likelihood ratio test (LRT) distance to perform temporal filtering. Then, the pixel-level temporal change image is generated using Shannon entropy and maximum between-class variance (OTSU). On the other hand, we perform temporal segmentation on time-series PolSAR images to obtain superpixel results. Finally, the object-level crop rotation results are obtained with the probabilistic label relaxation (PLR) model. 42 Sentinel-1 dual-polarization SAR datasets during 2018 and 2019 are selected for detecting crop rotation changes on farms within Jinchang, China. Experimental results show that the crop rotation detection accuracy andKappacoefficients of this method can reach 96.21% and 0.8989, respectively.

With the dramatically increasing deployment of IoT devices, storing and protecting the large volume of IoT data has become a significant issue. Traditional cloud-based IoT structures impose extremely high computation and storage demands on the cloud servers. Meanwhile, the strong dependencies on the centralized servers bring significant trust issues. To mitigate these problems, we propose a distributed data storage scheme employing blockchain and cetrificateless cryptography. Our scheme eliminates the traditional centralized servers by leveraging the blockchain miners who perform “transaction” verifications and records audit with the help of certificateless cryptography. We present a clear definition of the transactions in a non-cryptocurrency system and illustrate how the transactions are processed. To the best of our knowledge, this is the first work designing a secure and accountable IoT storage system using blockchain. Additionally, we extend our scheme to enable data trading and elaborate how data trading can be efficiently and effectively achieved.

This paper analyzes the secrecy transmission capacity (STC) over wireless ad hoc networks, where the legitimate nodes and eavesdroppers are distributed as Poisson point process. We calculate the connection outage probability (COP) and the bounds of the secrecy outage probability (SOP) based on the tools from stochastic geometry and establish a theoretical model for STC. Different from previous work considering the lower bound of STC, we characterize the relationship between target outage constraints and the densities of legitimate transmitters and eavesdroppers, and propose a necessary condition to achieve an upper bound of the positive STC. That is, the length of legitimate transmitter-receiver pair has an upper bound.

Today, there is widespread use of mobile applications that take advantage of a user's location. Popular usages of location information include geotagging on social media websites, driver assistance and navigation, and querying nearby locations of interest. However, the average user may not realize the high energy costs of using location services (namely the GPS) or may not make smart decisions regarding when to enable or disable location services-for example, when indoors. As a result, a mechanism that can make these decisions on the user's behalf can significantly improve a smartphone's battery life. In this paper, we present an energy consumption analysis of the localization methods available on modern Android smartphones and propose the addition of an indoor localization mechanism that can be triggered depending on whether a user is detected to be indoors or outdoors. Based on our energy analysis and implementation of our proposed system, we provide experimental results-monitoring battery life over time-and show that an indoor localization method triggered by indoor or outdoor context can improve smartphone battery life and, potentially, location accuracy.

The Internet of Things (IoT) is changing human lives by connecting everyday objects together. For example, in a grocery store, all items can be connected with each other, forming a smart shopping system. In such an IoT system, an inexpensive radio frequency identification (RFID) tag can be attached to each product which, when placed into a smart shopping cart, can be automatically read by a cart equipped with an RFID reader. As a result, billing can be conducted from the shopping cart itself, preventing customers from waiting in a long queue at checkout. Additionally, smart shelving can be added into this system, equipped with RFID readers, and can monitor stock, perhaps also updating a central server. Another benefit of this kind of system is that inventory management becomes much easier, as all items can be automatically read by an RFID reader instead of manually scanned by a laborer. To validate the feasibility of such a system, in this paper we identify the design requirements of a smart shopping system, build a prototype system to test functionality, and design a secure communication protocol to make the system practical. To the best of our knowledge, this is the first time a smart shopping system is proposed with security under consideration.

The development of the Internet of Things has made extraordinary progress in recent years in both academic and industrial fields. There are quite a few smart home systems (SHSs) that have been developed by major companies to achieve home automation. However, the nature of smart homes inevitably raises security and privacy concerns. In this paper, we propose an improved energy-efficient, secure, and privacy-preserving communication protocol for the SHSs. In our proposed scheme, data transmissions within the SHS are secured by a symmetric encryption scheme with secret keys being generated by chaotic systems. Meanwhile, we incorporate message authentication codes to our scheme to guarantee data integrity and authenticity. We also provide detailed security analysis and performance evaluation in comparison with our previous work in terms of computational complexity, memory cost, and communication overhead.

The use of mobile phones while driving has become a major source of distraction to drivers, leading to a large number of car accidents. In this paper, we study the problem of automatically detecting driver phone calls by monitoring smartphone activities and utilizing the vehicle on-board unit. The challenges to overcome include: i) passenger phone calls should be allowed while the calls of the driver should be blocked; ii) the detection mechanism should be phone position-independent and phone owner-independent as the driver may put the smartphone at any position in the front row and make calls via an earphone, or the driver may borrow a passenger's phone to make a call; iii) the in-vehicle environment is noisy resulted from the operating engine, the music the driver and passenger may listen to, and the conversation between passengers and/or the driver; and iv) the computational cost at the smartphone should be light as realtime phone call detection is expected to effectively block an ongoing call to and from the driver. To overcome these challenges and achieve our objective of detecting driver phone calls, we take advantage of the uniqueness of individual's voice features. Through a short period of learning stage, our proposed system can recognize the driver's voice from the collected audio data. Combined with the smartphone's call state, our scheme can determine whether the driver is participating in the current phone call or not. Our strategy takes into account the complicated in-vehicle environment, and the proposed algorithm does not rely on the location of the phone within the vehicle nor the ownership of the smartphone, as the most existing driver phone call detection mechanisms do. We develop a client-server based system with the smartphones being the clients and the vehicle on-board unit being the server. To validate our mechanism, we perform extensive real-world experiments under different scenarios. The results demonstrate a high probability of detecting driver phone calls with a small false alarm rate.