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Human activity recognition(HAR) is an important research focus in ubiquitous computing. It has been widely applied in various domains, such as smart homes, healthcare assistance, and sports training. Accurate human activity recognition directly impacts the performance of downstream tasks. Existing methods for human activity recognition primarily rely on extracting temporal or spatial features from the data. These features are used for the task of human activity recognition. The existing methods mainly use CNN or RNN models to model the Euclidean correlations among spatially adjacent sensors or channels. However, non-Euclidean pairwise correlations among all sensors or channels are even critical for accurate classification, which has been ignored by the existing methods. In this paper, we incorporate fine-grained spatial structural data into the model to overcome these limitations. A novel deep learning model for human activity recognition is proposed, which is called the fine-grained data-oriented Spatial-Temporal Graph Transformer network (STGT). The introduction of the STGT model can eliminate the limitations of existing spatial feature extraction methods by leveraging a novel data organization approach proposed in this study. This model enhances the spatial features within the time feature extraction module for effective spatio-temporal feature extraction. We conducted experiments on four large-scale real-world HAR datasets to evaluate its performance. The experimental results demonstrate the superiority of our method over state-of-the-art approaches.

Oriented remote sensing object detection (ORSOD) has gained increasing significance in both military and civilian applications due to the necessity of accurately identifying objects with varying shapes and orientations in remote sensing data. Traditional ORSOD methods often employ fixed label assignment strategies to differentiate between positive and negative samples. However, most of them frequently overlook the impact of object shape on sample quality, leading to an imbalanced distribution of positive samples and exacerbating the inconsistency between classification and regression tasks, thereby limiting detection performance. To address these challenges, we propose a novel shape-dependent assignment (SDA) method that dynamically differentiates positive and negative samples based on object shape. It introduces a new metric for evaluating sample box quality by considering angular differences relative to ground truth (GT) boxes and adjusts the sample scoring threshold according to the aspect ratio of each GT box. In addition, we present a DIoU-adaptive weighting (DAW) module that enhances the interaction between classification and regression tasks by leveraging the distance-IoU metric. This approach not only balances the quantity of samples but also improves their quality, enabling more effective training schemes for samples of varying qualities. We validate our proposed methods through extensive experiments on three challenging ORSOD datasets: DOTA-1.0, HRSC2016, and UCAS-AOD. The results demonstrate that our approach achieves significant improvements, especially for objects with large aspect ratios.

A review of coreflood experiments for chemically enhanced oil recovery (EOR) is presented in this paper, particularly surfactant-polymer (SP) and alkali-surfactant-polymer (ASP) processes. The objective of this review is to gain a general outlook and insight from coreflood experiments injecting SP or ASP slug as tertiary recovery. The discussion is separated into sections based on relevant core and fluid properties as well as surfactant selection and SP/ASP slug design and their impact on incremental recovery. Most studies in this review have been published within the last twenty years but few older coreflood works have been included for benchmarking. Parameters in each reviewed study have been summarized in tables to help readers gain detailed observation. Lessons learned from these past experiments should help other chemical EOR practitioners or students of the field in benchmarking or improving the outcomes of their future SP/ASP experiments.

With advancements in computing technology and the rapid progress of data science, machine learning has been widely applied in various fields, showing great potential, especially in digital healthcare. In recent years, conversational diagnostic systems have been used to predict diseases through symptom checking. Early systems predicted the likelihood of a single disease by minimizing the number of questions asked. However, doctors typically perform differential diagnoses in real medical practice, considering multiple possible diseases to address diagnostic uncertainty. This requires systems to ask more critical questions to improve diagnostic accuracy. Nevertheless, such systems in acute medical situations need to process information quickly and accurately, but the complexity of differential diagnosis increases the system’s computational cost. To improve the efficiency and accuracy of telemedicine diagnostic systems, this study developed an optimized algorithm for the Top-K algorithm. This algorithm dynamically adjusts the number of the most likely diseases and symptoms by real-time monitoring of case progress, optimizing the diagnostic process, enhancing accuracy (99.81%), and increasing the exclusion rate of severe pathologies. Additionally, the Top-K algorithm optimizes the diagnostic model through a policy network loss function, effectively reducing the number of symptoms and diseases processed and improving the system’s response speed by 1.3–1.9 times compared to the state-of-the-art differential diagnosis systems.

Abstract Underwater images intuitively reflect the underwater environment information. However, underwater images have defects such as colour distortion and low contrast, which seriously affect the processing of complex underwater visual tasks. Here, a novel mixed model called mixed underwater image generative adversarial network (MUGAN) is presented, consisting of a generator and corresponding discriminator. The generator is built following the U‐shaped architecture, where a mixed block of convolution and self‐attention is developed. It effectively exploits the complementarity between the two paradigms. In addition, a dual discriminator is employed to induce the generator to produce realistic images at both the global semantic and local detail levels, which is only discriminates based on the patch‐level information. Meanwhile, a multi‐term loss function is formulated to supervise adversarial training by evaluating the perceptual quality of an image based on its global content, local texture and illumination smoothness. To validate the proposed approach, extensive experiments are conducted on the public underwater datasets. MUGAN achieves promising performance in terms of colour, contrast and naturalness, showing a significant improvement over other competitive models in visual quality and quantitative metrics.

Spray cooling has been widely used to dissipate heat in high heat flux situations such as electronic chips. In this study, a spray cooling experimental system was built to study the spray cooling characteristics and energy consumption in the two-phase region. The experimental results show that the critical heat flux can reach 300.2 W/cm2 at a height of 28 mm; reducing the spray height can increase the critical heat flux, but the system energy consumption also increases. The obtained results have certain guiding significance for the optimization of spray cooling applications.

Solar thermoelectric generators (TEGs) need concentrators to increase the irradiation intensity, thus achieving increased generation performance. However, the irradiation intensity on the surface of the TEG is not uniform, which in turn affects the thermoelectric performance of solar thermoelectric generators. In this study, a numerical model of the TEG is constructed to study the effect of non-uniform heat flux on the performance. The results show that at a total input heat of 150 W, the temperature of the center of the module gradually increases with the increase of the unevenness of the heat flux at the hot end of the TEG. The maximum temperature difference is up to 46.2 K, while the output power is reduced by 3.8%. The effect of non-uniformity of heat flux should be considered in concentrated solar thermoelectric generation systems.

Building-integrated photovoltaic-thermal (BIPVT) systems can provide both electrical and thermal energy for building, realizing efficient use of solar power and decreasing the energy consumption of the building. In this study, water-cooled walls were installed on both sides of the air flow channel to improve the heat exchange between the photovoltaic (PV) panels and the air by cooling the air, thus realizing an improvement in the thermal/electrical performance of air-type BIPVT. An experimental system of coupled BIPVTs was conducted to study the influence of cooling water temperature on performance of the system. The results show that a 10 K reduction in cooling water temperature increases the heat efficiency by 26% and the electrical efficiency by approximately 2%. The results can be used for the optimized operation of BIPVT system.

We present 850 μ m polarization observations of the IC 348 star-forming region in the Perseus molecular cloud as part of the B-fields In STar-forming Region Observation survey. We study the magnetic properties of two cores (HH 211 MMS and IC 348 MMS) and a filamentary structure of IC 348. We find that the overall field tends to be more perpendicular than parallel to the filamentary structure of the region. The polarization fraction decreases with intensity, and we estimate the trend by power law and the mean of the Rice distribution fittings. The power indices for the cores are much smaller than 1, indicative of possible grain growth to micron size in the cores. We also measure the magnetic field strengths of the two cores and the filamentary area separately by applying the Davis–Chandrasekhar–Fermi method and its alternative version for compressed medium. The estimated mass-to-flux ratios are 0.45–2.20 and 0.63–2.76 for HH 211 MMS and IC 348 MMS, respectively, while the ratios for the filament are 0.33–1.50. This result may suggest that the transition from subcritical to supercritical conditions occurs at the core scale (∼0.05 pc) in the region. In addition, we study the energy balance of the cores and find that the relative strength of turbulence to the magnetic field tends to be stronger for IC 348 MMS than for HH 211 MMS. The result could potentially explain the different configurations inside the two cores: a single protostellar system in HH 211 MMS and multiple protostars in IC 348 MMS.

We report 850 μ m continuum polarization observations toward the filamentary high-mass star-forming region NGC 2264, taken as part of the B -fields In STar forming Regions Observations large program on the James Clerk Maxwell Telescope. These data reveal a well-structured nonuniform magnetic field in the NGC 2264C and 2264D regions with a prevailing orientation around 30° from north to east. Field strength estimates and a virial analysis of the major clumps indicate that NGC 2264C is globally dominated by gravity, while in 2264D, magnetic, gravitational, and kinetic energies are roughly balanced. We present an analysis scheme that utilizes the locally resolved magnetic field structures, together with the locally measured gravitational vector field and the extracted filamentary network. From this, we infer statistical trends showing that this network consists of two main groups of filaments oriented approximately perpendicular to one another. Additionally, gravity shows one dominating converging direction that is roughly perpendicular to one of the filament orientations, which is suggestive of mass accretion along this direction. Beyond these statistical trends, we identify two types of filaments. The type I filament is perpendicular to the magnetic field with local gravity transitioning from parallel to perpendicular to the magnetic field from the outside to the filament ridge. The type II filament is parallel to the magnetic field and local gravity. We interpret these two types of filaments as originating from the competition between radial collapsing, driven by filament self-gravity, and longitudinal collapsing, driven by the region's global gravity.

Process-based reactive transport modeling (RTM) integrates thermodynamic and kinetically controlled fluid-rock interactions with fluid flow through porous media in the subsurface and surface environment. RTM is usually conducted through numerical programs based on the first principle of physical processes. However, the calculation for complex chemical reactions in most available programs is an iterative process, where each iteration is in general computationally intensive. A workflow of neural network-based surrogate model as a proxy for process-based reactive transport simulation is established in this study. The workflow includes (1) base case RTM design, (2) development of training experiments, (3) surrogate model construction based on machine learning, (4) surrogate model validation, and (5) prediction with the calibrated model. The training experiments for surrogate modeling are generated and run prior to the predictions using RTM. The results show that the predictions from the surrogate model agree well with those from processes-based RTM but with a significantly reduced computational time. The well-trained surrogate model is especially useful when a large number of realizations are required, such as the sensitivity analysis or model calibration, which can significantly reduce the computational time compared to that required by RTM. The benefits are (1) it automatizes the experimental design during the sensitivity analysis to get sufficient numbers and coverage of the training cases; (2) it parallelizes the calculations of RTM training cases during the sensitivity analysis to reduce the simulation time; (3) it uses the neural network algorithm to rank the sensitivity of the parameters and to search the optimal solution for model calibration.

Underwater target detection is an indispensable part of marine environmental engineering and a fast and accurate method of detecting underwater targets is essential. Although many target detection algorithms have achieved great accuracy in daily scenes, there are issues of low-quality images due to the complex underwater environment, which makes applying these deep learning algorithms directly to process underwater target detection tasks difficult. In this paper, we presented an algorithm for underwater target detection based on improved You Only Look Once (YOLO) v4 in response to the underwater environment. First, we developed a new convolution module and network structure. Second, a new intersection over union loss was defined to substitute the original loss function. Finally, we integrated some other useful strategies to achieve more improvement, such as adding one more prediction head to detect targets of varying sizes, integrating the channel attention into the network, utilizing K-means++ to cluster anchor box, and utilizing different activation functions. The experimental results indicate that, in comparison with YOLOv4, our proposed algorithm improved the average accuracy of the underwater dataset detection by 10.9%, achieving 91.1%, with a detection speed of 58.1 frames per second. Therefore, compared to other mainstream target detection algorithms, it is superior and feasible for applications in intricate underwater environments.

In emergency rescue missions, rescue teams can use UAVs and efficient path planning strategies to provide flexible rescue services for trapped people, which can improve rescue efficiency and reduce personnel risks. However, since the task environment of UAVs is usually complex, uncertain, and communication-limited, traditional path planning methods may not be able to meet practical needs. In this paper, we introduce a whale optimization algorithm into a deep Q-network and propose a path planning algorithm based on a whale-inspired deep Q-network, which enables UAVs to search for targets faster and safer in uncertain and complex environments. In particular, we first transform the UAV path planning problem into a Markov decision process. Then, we design a comprehensive reward function considering the three factors of path length, obstacle avoidance, and energy consumption. Next, we use the main framework of the deep Q-network to approximate the Q-value function by training a deep neural network. During the training phase, the whale optimization algorithm is introduced for path exploration to generate a richer action decision experience. Finally, experiments show that the proposed algorithm can enable the UAV to autonomously plan a collision-free feasible path in an uncertain environment. And compared with classic reinforcement learning algorithms, the proposed algorithm has a better performance in learning efficiency, path planning success rate, and path length.

Deep neural networks have made great achievements in remote sensing image analyses; however, previous studies have shown that deep neural networks exhibit incredible vulnerability to adversarial examples, which raises concerns about regional safety and production safety. In this paper, we propose an adversarial denoising method based on latent representation guidance for remote sensing image scene classification. In the training phase, we train a variational autoencoder to reconstruct the data using only the clean dataset. At test time, we first calculate the normalized mutual information between the reconstructed image using the variational autoencoder and the reference image as denoised by a discrete cosine transform. The reconstructed image is selectively utilized according to the result of the image quality assessment. Then, the latent representation of the current image is iteratively updated according to the reconstruction loss so as to gradually eliminate the influence of adversarial noise. Because the training of the denoiser only involves clean data, the proposed method is more robust against unknown adversarial noise. Experimental results on the scene classification dataset show the effectiveness of the proposed method. Furthermore, the method achieves better robust accuracy compared with state-of-the-art adversarial defense methods in image classification tasks.

Abstract Background Penile squamous cell carcinoma (PSCC) represents an important public health problem for developing countries. The major prognostic factors in PSCC are pathological subtype, perineural invasion, lymphovascular invasion, depth of invasion and grade, which are hard to obtain precisely before the operation. Besides, micro-metastases will be detected in about 30% of intermediate-risk patients with clinically non-palpable inguinal lymph nodes after inguinal lymph node dissection (ILND). It means approximately 70% of patients are unable to benefit from ILND who might suffered from the complications of surgery. We hope some biomarkers could be found which are able to predict the outcome before surgery and reflect the inguinal lymph nodes metastasis. Methods A total of 349 consecutive patients of penile cancer in Yunnan cancer hospital in China between October 2002 and December2017. Two hundred twenty-five was succeed to follow-up. The association between NLR, LMR, PLR, LDH and Overall survival (OS), progression free survival (PFS), inguinal lymph node (N stage) was analyzed with K-M analysis, univariable, multivariable logistic regression and Kendall’s tau-b correlation coefficient. Results Multivariable analysis reveal that only PLR was significant independent factor which is associated with inferior OS and PFS; Age and LDH was associated with inferior OS; Lymph node and metastatic status remained significant for OS and PFS as NCCN and EAU Guidelines indicated; the tumor type, initial treatment and NLR LMR were not significant in predicting both OS and PFS. NLR, LMR and PLR were corresponded to N stage, while LDH was not associated with the N stage based on logistic regression model analysis. NLR, LMR and PLR were found weakly related to N stage through an application of Kendall’s tau-b correlation coefficient. Conclusions PLR was significant independent factors for OS and PFS, Age and LDH was significant independent factors for OS. NLR, LMR, PLR was corresponded to N stage.

Magmatic and tectonic activity at mid-oceanic ridges can give detailed insights into high-temperature hydrothermal circulation of fluids. The authors here present geochemical and geophysical datasets that suggest a hydrothermal system penetrating the upper lithospheric mantle at an ultra-slow spreading mid-oceanic ridge.

A new neural network prediction model is proposed for predicting ship motion attitude with high accuracy. This prediction model is based on an adaptive dynamic particle swarm optimization algorithm (ADPSO) and bidirectional long short-term memory (BiLSTM) neural network, which is to optimize the hyperparameters of BiLSTM neural network by the proposed ADPSO algorithm. The ADPSO algorithm introduces dynamic search space strategy into the classical particle swarm optimization algorithm and adjusts the learning factor adaptively to balance the global and local search ability, so as to improve the optimization performance and improve its optimization effect in BiLSTM parameter optimization process. The results show that the model can obtain higher prediction accuracy and faster convergence speed, and has better prediction performance in the prediction of ship motion attitude.

The training of deep neural networks relies on massive high-quality labeled data which is expensive in practice. To tackle this problem, domain adaptation is proposed to transfer knowledge from label-rich source domain to unlabeled target domain to learn a classifier that can well classify target data. However, people don't consider privacy issues in domain adaptation. In this paper, we introduce a novel method that builds an effective model without sharing sensitive data between source and target domain. Target domain party can benefit from label-rich source domain without revealing its privacy data. We transfer the traditional domain adaptation into a federated setting, where a global server contains a shared global model. Additionally, homomorphic encryption (HE) algorithm is used to guarantee the computing security. Experiments show that our method performs effectively without reducing the accuracy. Our method can achieve secure knowledge transfer and privacy-preserving domain adaptation.

We present observations of polarized dust emission at 850 μ m from the L43 molecular cloud, which sits in the Ophiuchus cloud complex. The data were taken using SCUBA-2/POL-2 on the James Clerk Maxwell Telescope as a part of the BISTRO large program. L43 is a dense ( ${N}_{{{\rm{H}}}_{2}}\sim {10}^{22}$ –10 ^23 cm ^−2 ) complex molecular cloud with a submillimeter-bright starless core and two protostellar sources. There appears to be an evolutionary gradient along the isolated filament that L43 is embedded within, with the most evolved source closest to the Sco OB2 association. One of the protostars drives a CO outflow that has created a cavity to the southeast. We see a magnetic field that appears to be aligned with the cavity walls of the outflow, suggesting interaction with the outflow. We also find a magnetic field strength of up to ∼160 ± 30 μ G in the main starless core and up to ∼90 ± 40 μ G in the more diffuse, extended region. These field strengths give magnetically super- and subcritical values, respectively, and both are found to be roughly trans-Alfvénic. We also present a new method of data reduction for these denser but fainter objects like starless cores.

We study the H ii regions associated with the NGC 6334 molecular cloud observed in the submillimeter and taken as part of the B -fields In STar-forming Region Observations Survey. In particular, we investigate the polarization patterns and magnetic field morphologies associated with these H ii regions. Through polarization pattern and pressure calculation analyses, several of these bubbles indicate that the gas and magnetic field lines have been pushed away from the bubble, toward an almost tangential (to the bubble) magnetic field morphology. In the densest part of NGC 6334, where the magnetic field morphology is similar to an hourglass, the polarization observations do not exhibit observable impact from H ii regions. We detect two nested radial polarization patterns in a bubble to the south of NGC 6334 that correspond to the previously observed bipolar structure in this bubble. Finally, using the results of this study, we present steps (incorporating computer vision; circular Hough transform) that can be used in future studies to identify bubbles that have physically impacted magnetic field lines.

We present BISTRO Survey 850 μ m dust emission polarization observations of the L1495A-B10 region of the Taurus molecular cloud, taken at the James Clerk Maxwell Telescope (JCMT). We observe a roughly triangular network of dense filaments. We detect nine of the dense starless cores embedded within these filaments in polarization, finding that the plane-of-sky orientation of the core-scale magnetic field lies roughly perpendicular to the filaments in almost all cases. We also find that the large-scale magnetic field orientation measured by Planck is not correlated with any of the core or filament structures, except in the case of the lowest-density core. We propose a scenario for early prestellar evolution that is both an extension to, and consistent with, previous models, introducing an additional evolutionary transitional stage between field-dominated and matter-dominated evolution, observed here for the first time. In this scenario, the cloud collapses first to a sheet-like structure. Uniquely, we appear to be seeing this sheet almost face on. The sheet fragments into filaments, which in turn form cores. However, the material must reach a certain critical density before the evolution changes from being field dominated to being matter dominated. We measure the sheet surface density and the magnetic field strength at that transition for the first time and show consistency with an analytical prediction that had previously gone untested for over 50 yr.

Automatic waveform classification and arrival picking methods are widely studied to reduce or replace the manual works. Machine learning based methods, especially neural networks, and clustering based methods have shown great potentials in previous studies. However, most of the existing methods are sensitive to noise. The convolution neural networks (CNNs), developed from the traditional neural networks, have been successfully applied in many different fields, but are rarely studied in seismic waveform classification. In this paper, we propose a novel antinoise CNN architecture for waveform classification and also propose to combine k‐means clustering (KC) with CNN classification to pick arrivals (CNN‐KC). Seismic data are sampled to 1‐D vectors using a specific time window. Using the trained CNN classifier, these 1‐D vectors are classified into two categories: waveform and nonwaveform. With the constraint of the first waveform, CNN‐KC can pick the arrival more accurately. We also apply the proposed methods to the synthetic microseismic data with different noise levels and the actual field microseismic data to test their robustness. CNNs perform much better than the traditional multilayer perceptron on the waveform classification of the noisy microseismic data. Based on the analysis of the CNN internal architecture, we also conclude that the main reason that CNN is insensitive to noise is the convolution and pooling layers which behave like smooth operation in some ways. The final results show that the CNN and CNN‐KC are effective and robust methods for waveform classification and arrival picking.

In order to cope with the problems of resist the attack of user tracking with attributes and resist the disguised attack in road networks,based on the conception of attribute generalization and the methods of homomorphic encryption,a privacy protection method to complete the calculation of attribute generalization in private state and achieve attribute generalization was proposed.The proposed method utilized the homomorphic encryption to achieve secret bidding selection with an agent,and then utilize secure multi-party computation to calculate the similar attributes and achieve the attribute generalization in the end.With the help of fully attribute generalization,this method can solve the problem of users can be tracked by the potential attributes,and at the same time the privacy calculation also does not reveal any information to participants,so this method can prevent the disguised attacker that obtains user’s information in mix-zone.At last,in order to demonstrate the superiority of the proposed method in both of academically and practicality,the security analysis and experimental verification are given,and the procedure of formulation verification and the result of experiment further substantiate the superiorities of the proposed method.

Abstract To give full play to the high efficiency and parallelism of multi-processor systems, the fireworks algorithm (FWA) is improved, and a multi-processor task scheduling algorithm based on improved FWA, named IMFWA, is proposed. IMFWA maps continuous space to discrete space by designing the fireworks location coding method, improves the Gaussian mutation process, and sets adaptive dimensions to accelerate the convergence speed of the algorithm. At the same time, in order to reduce the time complexity of the algorithm and shorten the time finding the optimal task scheduling sequence, the fitness-based tournament selection strategy is used instead of the rule based on Euclidean distance. Finally, IMFWA is compared with the basic fireworks algorithm and the genetic algorithms on the Matlab platform for performance analysis. The results show that the IMFWA has advantages in the convergence speed, and the negative impact of the number of tasks is also lower than the fireworks algorithm and genetic algorithm.

As the location trajectory contains more spatial-temporal information about the user, it will be even dangerous for jeopardizing the privacy of the user. In order to cope with the correlation, an algorithm that utilizes the query division had been proposed. In this algorithm, random blocks of query context was used, so as the adversary was obfuscated and difficult to correlate the real result. However, this algorithm fails to dispose the size of each query block, as once same size blocks were obtained by the adversary continuously, so the adversary can regard them as blocks from the same query context, and then obtains the query context to correlate the discrete locations. In view of above conditions, in this paper we propose a fine granularity block division algorithm based on the conception of granularity measurement as well as granularity layer division, so with the help of collaborative users the location privacy of the user will be protected. In this algorithm, the query context will be divided into fine granularity size of information blocks that difficult to be distinguished with others, and then these blocks will be exchanged with other collaborative users to eliminate the difference in block size. In addition, as each block is divided into fine granularity size, the adversary will be difficult to correlate the discrete locations into location trajectory, so the location privacy will be protected. At last, through security analysis and experimental verification, this granularity indistinguishable algorithm is analyzed and verified at both theoretical and practical levels, which further demonstrate the superiority of the proposed algorithm compared with other similar algorithms.

Explicit structural inference is one key point to improve the accuracy of scene parsing. Meanwhile, adversarial training method is able to reinforce spatial contiguity in output segmentations. To take both advantages of the structural learning and adversarial training simultaneously, we propose a novel deep learning network architecture called Structural Inference Embedded Adversarial Networks (SIEANs) for pixel-wise scene labeling. The generator of our SIEANs, a novel designed scene parsing network, makes full use of convolutional neural networks and long short-term memory networks to learn the global contextual information of objects in four different directions from RGB-(D) images, which is able to describe the (three-dimensional) spatial distributions of objects in a more comprehensive and accurate way. To further improve the performance, we explore the adversarial training method to optimize the generator along with a discriminator, which can not only detect and correct higher-order inconsistencies between the predicted segmentations and corresponding ground truths, but also exploit full advantages of the generator by fine-tuning its parameters so as to obtain higher consistencies. The experimental results demonstrate that our proposed SIEANs is able to achieve a better performance on PASCAL VOC 2012, SIFT FLOW, PASCAL Person-Part, Cityscapes, Stanford Background, NYUDv2, and SUN-RGBD datasets compared to the most of state-of-the-art methods.

Location sharing service has become an indispensable part in mobile social networks. However, location sharing may introduce a new class of privacy threats ranging from localizing an individual to profiling and identifying him based on the places he shared. Although users may avoid releasing geocontent in sensitive locations, it does not necessarily prevent the adversary from inferring users’ privacy through space-temporal correlations and historical information. In this paper, we design a Prophet framework, which provides an effective security scheme for users sharing their location information. First, we define fingerprint identification based on Markov chain and state classification to describe the users’ behavior patterns. Then, we propose a novel location anonymization mechanism, which adopts a ε-indistinguishability strategy to protect user’s sensitive location information published. Finally, experimental results are given to illustrate good performance and effectiveness of the proposed scheme.

Because of the mobility, computing power and changeable topology of dynamic networks, it is difficult for random linear network coding (RLNC) in static networks to satisfy the requirements of dynamic networks. To alleviate this problem, a minimal increase network coding (MINC) algorithm is proposed. By identifying the nonzero elements of an encoding vector, it selects blocks to be encoded on the basis of relationship between the nonzero elements that the controls changes in the degrees of the blocks; then, the encoding time is shortened in a dynamic network. The results of simulations show that, compared with existing encoding algorithms, the MINC algorithm provides reduced computational complexity of encoding and an increased probability of delivery.