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The malicious mining pool can sacrifice part of its revenue to employ the computing power of blockchain network. The employed computing power carries out the pool mining attacks on the attacked mining pool. To realize the win-win game between the malicious mining pool and the employee, the paper proposes an Employment Attack Pricing Algorithm (EAPA) of mining pools in blockchain based on game theory. In the EAPA, the paper uses mathematical formulas to express the revenue of malicious mining pools under the employment attack, the revenue increment of malicious mining pools, and the revenue of the employee. It establishes a game model between the malicious mining pool and the employee under the employment attack. Then, the paper proposes an optimal computing power price selection strategy of employment attack based on model derivation. In the strategy, the malicious mining pool analyzes the conditions for the employment attack, and uses the derivative method to find the optimal utilization value of computing power, employees analyze the conditions for accepting employment, and use the derivative method to find the optimal reward value of computing power. Finally, the strategy finds the optimal employment computing power price to realize Nash equilibrium between the malicious mining pool and the employee under the current computing power allocation. The simulation results show that the EAPA could find the employment computing power price that realizes the win-win game between the malicious mining pool and the employee. The EAPA also maximizes the unit computing power revenue of employment and the unit computing power revenue of honest mining in malicious mining pool at the same time. The EAPA outperforms the state-of-the-art methods such as SPSUCP, DPSACP, and FPSUCP.

Abstract This paper introduces the efficient medical-images-aimed segment anything model (EMedSAM), addressing the high computational demands and limited adaptability of using SAM for medical image segmentation tasks. We present a novel, compact image encoder, DD-TinyViT, designed to enhance segmentation efficiency through an innovative parameter tuning method called med-adapter. The lightweight DD-TinyViT encoder is derived from the well-known ViT-H using a decoupled distillation approach.The segmentation and recognition capabilities of EMedSAM for specific structures are improved by med-adapter, which dynamically adjusts the model parameters specifically for medical imaging. We conducted extensive testing on EMedSAM using the public FLARE 2022 dataset and datasets from the First Hospital of Zhejiang University School of Medicine. The results demonstrate that our model outperforms existing state-of-the-art models in both multi-organ and lung segmentation tasks.

Abstract As one of the significant challenges to human health, cancer has long been a focal point in medical treatment. With ongoing advancements in the field of medicine, numerous methodologies for cancer therapy have emerged, among which oncolytic virus therapy has gained considerable attention. However, oncolytic viruses still exhibit limitations. Combining them with various therapies can further enhance the efficacy of cancer treatment, offering renewed hope for patients. In recent research, scientists have recognized the promising prospect of amalgamating oncolytic virus therapy with diverse treatments, potentially surmounting the restrictions of singular approaches. The central concept of this combined therapy revolves around leveraging oncolytic virus to incite localized tumor inflammation, augmenting the immune response for immunotherapeutic efficacy. Through this approach, the patient's immune system can better recognize and eliminate cancer cells, simultaneously reducing tumor evasion mechanisms against the immune system. This review delves deeply into the latest research progress concerning the integration of oncolytic virus with diverse treatments and its role in various types of cancer therapy. We aim to analyze the mechanisms, advantages, potential challenges, and future research directions of this combination therapy. By extensively exploring this field, we aim to instill renewed hope in the fight against cancer.

In a class of large-scale distributed optimization, the calculation of RELM based on the Moore–Penrose inverse matrix is prohibitively expensive, which hinders the formulation of a computationally efficient optimization model. Attempting to improve the model’s convergence performance, this paper proposes a low computing cost Alternating Direction Method of Multipliers (ADMM), where the original update in ADMM is solved inexactly with approximate curvature information. Based on quasi-Newton techniques, the ADMM approach allows us to solve convex optimization with reasonable accuracy and computational effort. By introducing this algorithm into the RELM model, the model fitting problem can be decomposed into a set of subproblems that can be executed in parallel to achieve efficient classification performance. To avoid the storage of expensive Hessian for large problems, BFGS with limited memory is proposed with computational efficiency. And the optimal parameter values of the step-size search method are obtained through Wolfe line search strategy. To demonstrate the superiority of our methods, numerical experiments are conducted on eight real-world datasets. Results on problems arising in machine learning suggest that the proposed method is competitive with other similar methods, both in terms of better computational efficiency as well as accuracy.

One of the significant features of extreme learning machines (ELMs) is their fast convergence. However, in the big data environment, the ELM based on the Moore–Penrose matrix inverse still suffers from excessive calculation loads. Leveraging the decomposability of the alternating direction method of multipliers (ADMM), a convex model-fitting problem can be split into a set of sub-problems which can be executed in parallel. Using a maximally splitting technique and a relaxation technique, the sub-problems can be split into multiple univariate sub-problems. On this basis, we propose an adaptive parameter selection method that automatically tunes the key algorithm parameters during training. To confirm the effectiveness of this algorithm, experiments are conducted on eight classification datasets. We have verified the effectiveness of this algorithm in terms of the number of iterations, computation time, and acceleration ratios. The results show that the method proposed by this paper can greatly improve the speed of data processing while increasing the parallelism.

Abstract In order to improve the revenue of attacking mining pools and miners under block withholding attack, we propose the miner revenue optimization algorithm (MROA) based on Pareto artificial bee colony in blockchain network. MROA establishes the revenue optimization model of each attacking mining pool and revenue optimization model of entire attacking mining pools under block withholding attack with the mathematical formulas such as attacking mining pool selection, effective computing power, mining cost and revenue. Then, MROA solves the model by using the modified artificial bee colony algorithm based on the Pareto method. Namely, the employed bee operations include evaluation value calculation, selection probability calculation, crossover operation, mutation operation and Pareto dominance method, and can update each food source. The onlooker bee operations include confirmation probability calculation, crowding degree calculation, neighborhood crossover operation, neighborhood mutation operation and Pareto dominance method, and can find the optimal food source in multidimensional space with smaller distribution density. The scout bee operations delete the local optimal food source that cannot produce new food sources to ensure the diversity of solutions. The simulation results show that no matter how the number of attacking mining pools and the number of miners change, MROA can find a reasonable miner work plan for each attacking mining pool, which increases minimum revenue, average revenue and the evaluation value of optimal solution, and reduces the spacing value and variance of revenue solution set. MROA outperforms the state of the arts such as ABC, NSGA2 and MOPSO.

Considering the movement of a sink node in this study to solve the data transmission problem of sensor nodes in a 3D network environment, we propose a Data Collection Algorithm of a 3D wireless sensor network (DCA_3D) that weighs node coverage rate and lifetime. DCA_3D establishes a data collection optimization model that weighs node coverage rate and lifetime with the constraints of the sink node's moving path selection, data flow, energy consumption, and link transmission. Subsequently, DCA_3D calculates the fitness value of the sink node's moving path by solving the data collection optimization model with the known sink node's moving path. Then it uses a modified artificial bee colony algorithm to solve the moving path selection problem of the sink node, and finally obtains the optimal scheme. In the scheme, sink node can find the optimal moving path, whereas the sensor node can find the optimal data communication path. The simulation results show that regardless of the moving path length of the sink node, the maximum data collection hops of the sink node and the number of static sensor nodes change, DCA_3D can find the optimal moving path of the sink node. It can improve the coverage rate of the sensor nodes, the network lifetime and average data transmission amount, and reduce the average energy consumption variance and the average packet loss rate. DCA_3D outperforms the state-of-arts such as RAND, GREED, EDG_3D, and ANT.

Currently, a significant focus has been established on the privacy protection of multi-dimensional data publishing in various application scenarios, such as scientific research and policy-making. The K-anonymity mechanism based on clustering is the main method of shared-data desensitization, but it will cause problems of inconsistent clustering results and low clustering accuracy. It also cannot defend against several common attacks, such as skewness and similarity attacks at the same time. To defend against these attacks, we propose a K-anonymity privacy protection algorithm for multi-dimensional data against skewness and similarity attacks (KAPP) combined with t-closeness. Firstly, we propose a multi-dimensional sensitive data clustering algorithm based on improved African vultures optimization. More specifically, we improve the initialization, fitness calculation, and solution update strategy of the clustering center. The improved African vultures optimization can provide the optimal solution with various dimensions and achieve highly accurate clustering of the multi-dimensional dataset based on multiple sensitive attributes. It ensures that multi-dimensional data of different clusters are different in sensitive data. After the dataset anonymization, similar sensitive data of the same equivalence class will become less, and it eventually does not satisfy the premise of being theft by skewness and similarity attacks. We also propose an equivalence class partition method based on the sensitive data distribution difference value measurement and t-closeness. Namely, we calculate the sensitive data distribution’s difference value of each equivalence class and then combine the equivalence classes with larger difference values. Each equivalence class satisfies t-closeness. This method can ensure that multi-dimensional data of the same equivalence class are different in multiple sensitive attributes, and thus can effectively defend against skewness and similarity attacks. Moreover, we generalize sensitive attributes with significant weight and all quasi-identifier attributes to achieve anonymous protection of the dataset. The experimental results show that KAPP improves clustering accuracy, diversity, and anonymity compared to other similar methods under skewness and similarity attacks.

Time series have broad usage in the wireless Internet of Things. This article proposes a nonlinear time series prediction algorithm based on the Small-World Scale-Free Network after the AIC-Optimized Subtractive Clustering Algorithm (AIC-DSCA-SSNET, AD-SSNET) to predict the nonlinear and unstable time series, which improves the prediction accuracy. The AD-SSNET is introduced as a reservoir based on the echo state network to improve the predictive capability of nonlinear time series, and combined with artificial intelligence method to construct the prediction model training samples. First, the optimal clustering scheme of randomly distributed neurons in the network is adaptively obtained by the AIC-DSCA, then the AD-SSNET is constructed according to the intra-cluster priority connection algorithm. Finally, the reservoir synaptic matrix is calculated according to the synaptic information. Experimental results show that the proposed nonlinear time series prediction algorithm extends the feasible range of spectral radii of the reservoir, improves the prediction accuracy of nonlinear time series, and has great significance to time series analysis in the era of wireless Internet of Things.

Recommender systems help users filter items they may be interested in from massive multimedia content to alleviate information overload. Collaborative filtering-based models perform recommendation relying on users’ historical interactions, which meets great difficulty in modeling users’ interests with extremely sparse interactions. Fortunately, the rich semantics hidden in items may be promising in helping to describing users’ interests. In this work, we explore the semantic correlations between items on modeling users’ interests and propose knowledge-aware multispace embedding learning (KMEL) for personalized recommendation. KMEL attempts to model users’ interests across semantic structures to leverage valuable knowledge. High-order semantic collaborative signals are extracted in multiple independent semantic spaces and aggregated to describe users’ interests in each specific semantic. The semantic embeddings are adaptively integrated with a target-aware attention mechanism to learn cross-space multisemantic embeddings for users and items, which are fed to the subsequent pairwise interaction layer for personalized recommendation. Experiments on real-world datasets demonstrate the effectiveness of the proposed KMEL model.

Large and dense mobile ad hoc networks often meet scalability problems, the hierarchical structures are needed to achieve performance of network such as cluster control structure. Clustering in mobile ad hoc networks is an organization method dividing the nodes in groups, which are managed by the nodes called cluster-heads. As far as we know, the difficulty of clustering algorithm lies in determining the number and positions of cluster-heads. In this article, the subtractive clustering algorithm based on the Akaike information criterion is proposed. First, Akaike information criterion is introduced to formulate the optimal number of the cluster-heads. Then, subtractive clustering algorithm is used in mobile ad hoc networks to get several feasible clustering schemes. Finally, the candidate schemes are evaluated by the index of minimum of the largest within-cluster distance variance to determine the optimal scheme. The results of simulation show that the performance of the proposed algorithm is superior to widely referenced clustering approach in terms of average cluster-head lifetime.

To improve the regional coverage rate and network lifetime of heterogeneous wireless sensor networks, a sensor node scheduling algorithm for heterogeneous wireless sensor networks is proposed. In sensor node scheduling algorithm, heterogeneous perception radius of sensor node is considered. Incomplete coverage constraint and arc coverage interval are analyzed. Regional coverage increment optimization model, arc coverage increment optimization model, and residual energy optimization model are proposed. Multi-objective scheduling model is established using weight factors and integrated function. Furthermore, the heuristic method is proposed to solve the multi-objective optimization model, and scheduling scheme of heterogeneous sensor nodes is obtained. When the network is in operation for a period of time, some sensor nodes are invalid and relevant regions are uncovered. The repair method is proposed to wake up sleep sensor nodes and repair the coverage blind area. The simulation results show that if keeping the same regional coverage rate, sensor node scheduling algorithm improves network lifetime, increases number of living sensor nodes, and keeps average node energy consumption at a low level. Under certain conditions, sensor node scheduling algorithm outperforms DGREEDY, two-tiered scheduling, and minimum connected cover.

There are real-time transmission problem of multimedia data and network lifetime optimization problem in wireless multimedia sensor networks. In order to solve the problems, multi-path routing algorithm based on minimum cost maximum flow（MRAMCMF）was proposed. MRAMCMF analyzed energy consumption of data transmission, node remaining energy and minimum data transmission delay, proposed a new weighting function, and used the minimum cost maximum flow algorithm to construct the multi-path routing scheme. All multimedia sensor nodes transmited data to sink node along the paths. Simulation results show that MRAMCMF can prolong network lifetime, reduce and balance node energy consumption, and reduce data transmission delay. Under certain conditions, MRAMCMF outperforms Ratio_w and TPGF.

When the position of sink node is fixed,the sensor nodes which are distributed around the sink node easily become hub nodes,forward a lot of data and fail prematurely.In order to solve this problem,the lifetime optimization algorithm for mobile wireless sensor network（LOAMWSN）was proposed.LOAMWSN algorithm considers the mobility of sink node,uses subtractive cluster algorithm to determine mobile anchor points of sink node,uses the nearest neighbor interpolation method to find the approximate solution of shortest route which traverse all mobile anchor points,uses distributed asynchronous Bellman-Ford algorithm to construct the shortest path tree in the k-hop range of sink node.Finally,sensor nodes transmit data to sink node along the shortest path tree.Simulation results show that in wireless sensor network of node uniform distribution and non-uniform distribution,LOAMWSN algorithm can prolong the network lifetime,balance the node energy consumption,and remain the average node energy consumption at a low level.Under certain conditions,it outperforms Ratio_w and TPGF algorithms.

To save network node energy consumption and reduce data transmission delay in streetlight monitoring system, a chain routing algorithm for wireless sensor network（CRASMS）was proposed. The algorithm divided the monitoring region into several clustering regions according the information of nodes and monitoring region, successively and circularly selected a node to the cluster head node in each clustering region, established the star network in each cluster by the communication of cluster head node and sensor node. Finally, cluster head node received the data of sensor nodes, used the data fusion algorithm to reduce data redundancy, transmitted the fused data to sink node with multi-hop routing among cluster head nodes and transmitted the client's commands to the controlled nodes. The simulation results show that CRASMS algorithm keeps the advantage of PEGASIS algorithm in terms of energy consumption and the advantage of LEACH algorithm in terms of data transmission delay, overcomes the lack of PEGASIS algorithm in terms of data transmission delay and the lack of LEACH algorithm in terms of node energy consumption. It keeps the average node energy consumption and the average data transmission delay at a low level. Under certain conditions, CRASMS algorithm outperforms LEACH and PEGASIS algorithms.

Mobile sensor networks (MSN) in the city environment have dynamic topology and large amounts of data. In order to overcome the problem of data uploading and exchange, data uploading and exchange algorithm (DUEA) is proposed. In DUEA, the three-tier network architecture composed of vehicle nodes, relay nodes and sink nodes is considered. The link estimator, routing engine, and forwarding engine are modified based on CTP (collection tree protocol). In the link estimator, beacon packet is simplified. In the routing engine, the numbers of elements in the send queue of neighbor nodes, and link quality are weighed. The new calculation formulae of ETX value and selection method of father node are proposed. In the forwarding engine, different snooping methods are used for two types of data named general data and warning data. Simulation results show that compared with CTP, DUEA increases data throughput and transmission rate and reduces energy consumption of packet transmission when data is uploaded. DUEA also reduces average transmission delay when data is exchanged. Physical test results show that DUEA achieves desired effect in the testbed. DUEA is effective and feasible and outperforms CTP in the city traffic environment.

In order to overcome the energy hole problem in some wireless sensor networks (WSNs), lifetime optimization algorithm with mobile sink nodes for wireless sensor networks (LOA_MSN) is proposed. In LOA_MSN, hybrid positioning algorithm of satellite positioning and RSSI positioning is proposed to save energy. Based on location information, movement path constraints, flow constraint, energy consumption constraint, link transmission constraint, and other constraints are analyzed. Network optimization model is established and decomposed into movement path selection model and lifetime optimization model with known grid movement paths. Finally, the two models are solved by distributed method. Sink nodes gather data of sensor nodes along the calculated paths. Sensor nodes select father nodes and transmit data to corresponding sink node according to local information. Simulation results show that LOA_MSN makes full use of node energy to prolong network lifetime. LOA_MSN with multiple sink nodes also reduces node energy consumption and data gathering latency. Under certain conditions, it outperforms MCP, subgradient algorithm, EASR, and GRAND.

Long-running applications are often subject to failures. Once failures occur, it will lead to unacceptable system overheads. The checkpoint technology is used to reduce the losses in the event of a failure. For the two-level checkpoint recovery scheme used in the long-running tasks, it is unavoidable for the system to periodically transfer huge memory context to a remote stable storage. Therefore, the overheads of setting checkpoints and the re-computing time become a critical issue which directly impacts the system total overheads. Motivated by these concerns, this paper presents a new model by introducing i-checkpoints into the existing two-level checkpoint recovery scheme to deal with the more probable failures with the smaller cost and the faster speed. The proposed scheme is independent of the specific failure distribution type and can be applied to different failure distribution types. We respectively make analyses between the two-level incremental and two-level checkpoint recovery schemes with the Weibull distribution and exponential distribution, both of which fit with the actual failure distribution best. The comparison results show that the total overheads of setting checkpoints, the total re-computing time and the system total overheads in the two-level incremental checkpoint recovery scheme are all significantly smaller than those in the two-level checkpoint recovery scheme. At last, limitations of our study are discussed, and at the same time, open questions and possible future work are given.