This study uses the approach-avoidance theory to investigate the impact of the atmospherics of a social commerce page, which comprises page aesthetics and page interaction. The study looks at how a... Show More

This study uses the approach-avoidance theory to investigate the impact of the atmospherics of a social commerce page, which comprises page aesthetics and page interaction. The study looks at how a digital influencer's perceived influence affects a customer's purchase intention. The research also examines whether perceived risk influences customers' purchase intent. Four hundred twenty-eight customers who had recently engaged with a social commerce page were empirically surveyed using structural equation modeling (SEM). The research shows that page atmospherics and digital influencers do influence a customer's purchase intention through emotions and cognition in social commerce. Emotions and purchase intentions, as well as cognition and purchase intentions, are moderated by perceived risk. The findings have implications for marketers who want to develop customer engagement strategies based on social commerce platforms.

Aiming at the speed of frequent itemset mining, a new frequent itemset mining algorithm based on a linear table is proposed. The linear table can store more shared information and reduce the number... Show More

Aiming at the speed of frequent itemset mining, a new frequent itemset mining algorithm based on a linear table is proposed. The linear table can store more shared information and reduce the number of scans to the original dataset. Furthermore, operations such as pruning and grouping are also used to optimize the algorithm. For different datasets, the algorithm shows different mining speeds. (1) In sparse datasets, the algorithm achieves an average 45% improvement in mining speed over the bit combination algorithm, and a 2-3 times improvement for the classic FP-growth algorithm. (2) In dense datasets, the average improvement over the classic FP-growth algorithm is 50-70%. For the bit combination algorithm, there are dozens of times of improvement. In fact, the algorithm that integrates bit combinations with bitwise AND operation can effectively avoid recursive operations and it is beneficial to the parallelization. Further analysis shows that the linear table is easy to split to facilitate the data batch mining processing.

Large-scale manufacturing enterprises have complex business processes in their production workshops, and the edge-edge collaborative business model cannot adapt to the traditional computation... Show More

Large-scale manufacturing enterprises have complex business processes in their production workshops, and the edge-edge collaborative business model cannot adapt to the traditional computation offloading methods, which leads to the problem of load imbalance. For this problem, a computation offloading algorithm based on edge-edge collaboration mode for large-scale factory access is proposed, called the edge and edge collaborative computation offloading (EECCO) algorithm. First, the method partitions the directed acyclic graphs (DAGs) on edge server and terminal industrial equipment, then updates the tasks using a synchronization policy based on set theory to improve the accuracy effectively, and finally achieves load balancing through processor allocation. The experimental results show that the method shortens the processing time by improving computational resource utilization and employs a heterogeneous distributed system to achieve high computing performance when processing large-scale task sets.

In 2013, a lightweight block cipher SIMON is proposed by NSA. This paper tries to investigate this design criterion in terms of resisting against impossible differential cryptanalysis. On one hand... Show More

In 2013, a lightweight block cipher SIMON is proposed by NSA. This paper tries to investigate this design criterion in terms of resisting against impossible differential cryptanalysis. On one hand, starting from all the possible rotation constants, this paper sieves those “bad parameters” step by step, for each step, the regular patterns for those “bad parameters” are deduced. Accordingly, basic rules for selecting rotation constants on SIMON-type ciphers to construct shorter longest impossible differentials are proposed. On the other hand, the authors categorize the optimal parameters proposed in CRYPTO 2015, according to these results, some “good parameters” in terms of differential cryptanalysis may be rather “bad parameters” while considering impossible differential cryptanalysis. Finally, a concrete attack on 26-round SIMON(13,0,10) is proposed, which is a suggested SIMON variant in CRYPTO 2015 against differential cryptanalysis and linear cryptanalysis. The result in this paper indicates that it is very important to choose appropriate rotation constants when designing a new block cipher.

Supervised Meta-event extraction suffers from two limitations: (1) The extracted meta-events only contain local semantic information and do not present the core content of the text; (2) model... Show More

Supervised Meta-event extraction suffers from two limitations: (1) The extracted meta-events only contain local semantic information and do not present the core content of the text; (2) model performance is easily degraded because of labeled samples with insufficient number and poor quality. To overcome these limitations, this study presents an approach called frame-incorporated semi-supervised topic event extraction (FISTEE), which aims to extract topic events containing global semantic information. Inspired by the frame-based knowledge representation, a topic event frame is developed to integrate multiple meta-events into a topic event. Combined with the tri-training algorithm, a strategy for selecting unlabeled samples is designed to expand the training sets, and labeling models based on conditional random field (CRF) are constructed to label meta-events. The experimental results show that the event extraction performance of FISTEE is better than supervised learning-based approaches. Furthermore, the extracted topic events can present the core content of the text.

LBS-RT (location-based service in a real-time manner) has become popular because it can provide quick and timely services. Range query is often used in LBS-RT, which finds objects in a specified... Show More

LBS-RT (location-based service in a real-time manner) has become popular because it can provide quick and timely services. Range query is often used in LBS-RT, which finds objects in a specified area, and spatial indices are often used to speed up range query. However, in LBS-RT, there are some difficulties. Spatial index was originally designed to index static dataset, but the dataset is dynamic in LBS-RT, which needs lots of insert and delete operations. To meet the gap, this paper proposes a new distributed spatial index called GQ-QBS. It's a two-layer master-slave mode that consists of a global index and multiple local indices. The global index (GQ-tree) is responsible for the dynamic load balancing and auto-scaling, while the local index (QBS-tree) is for quickly updating and querying. Experiments show the index has a significant advantage in LBS-RT.

Job automation is a critical decision that has brought about profound changes in the workplace. However, the question of what drives job automation remains unclear. This study conducts an... Show More

Job automation is a critical decision that has brought about profound changes in the workplace. However, the question of what drives job automation remains unclear. This study conducts an interdisciplinary review of five theoretical frameworks on job automation, paying particular attention to the role played by artificial intelligence and machine learning. It highlights the concepts and mechanisms underlying each of the frameworks, compares and contrasts their similarities and differences, and highlights challenges and suggests opportunities of job automation. It also proposes an integrated framework on job automation by addressing the research gaps in extant frameworks and thereby contributes to the research and practice on this important topic.

National standards for natural resources (NSNR) plays an important role in promoting efficient use of China's natural resources, which sets standards for many domains such as marine and land... Show More

National standards for natural resources (NSNR) plays an important role in promoting efficient use of China's natural resources, which sets standards for many domains such as marine and land resources. Its revision is difficult since standards in different domains may overlap or conflict. To facilitate the revision of NSNR, this paper extracts structural knowledge from the NSNR files to assist its revision. NSNR files are in multi-domain texts, where the traditional knowledge extraction methods could fall short in recalling multi-domain entities. To address this issue, this paper proposes a knowledge extraction method for multi-domain texts, including sub-domain relation discovery (SRD) and domain semantic features fusion (DSFF) module. SRD splits NSNR into sub-domains to facilitate the relation discovery. DSFF integrates relation features in the conditional random field (CRF) model to improve the capability of multi-domain entity recognition. Experimental results demonstrate that the proposed method could effectively extract structural knowledge from NSNR.

Outlier detection is currently applied in many fields, where existing research focuses on improving imbalanced data or enhancing classification accuracy. In the financial area, financial fraud... Show More

Outlier detection is currently applied in many fields, where existing research focuses on improving imbalanced data or enhancing classification accuracy. In the financial area, financial fraud detection puts higher demands on real-time and interpretability. This paper attempts to develop a credit risk model for financial fraud detection based on an extreme gradient boosting tree (XGBoost). SMOTE is adopted to deal with imbalanced data. AUC is the assessment indicator, and the running time is taken as the reference to compare with other frequently used classification algorithms. The results indicate that the method proposed by this paper performs better than others. At the same time, XGBoost can obtain a ranking of important features that impact the classification results when performing classification tasks, making the evaluation results of the model interpretable. The above shows that the model proposed in the paper is more practical in solving credit risk assessment problems. It has faster response times, reduced costs, and better interpretability.

Numerous web services with the same function but different service qualities are constantly emerging on the network. Optimizing web service composition based on multiple candidate services sets an... Show More

Numerous web services with the same function but different service qualities are constantly emerging on the network. Optimizing web service composition based on multiple candidate services sets an urgent problem in the service composition neighborhood. This paper modifies the traditional Firefly algorithm and adds exchange and mutation mechanisms to optimize the Web service composition efficiently in multiple candidate service sets. Meanwhile, it discretizes the continuous space of its solution set and better adapts to the service composition optimization problem. Experimental results show that compared with the GA, IA, SA, ACO, FACO, and EFACO algorithms, this algorithm has better optimization performance, faster speed, and higher energy efficiency for solving service composition optimization problems in the case of large-scale data. The higher the combined complexity of the solution, the stronger the performance compared to other algorithms. It can better deal with the increasingly complex situation of Web service composition problems.

Climate-related catastrophes leave people in dire need of aid. A major obstacle in providing help to people is the lack of trust in the aid process. Charity organizations want to ensure that funds... Show More

Climate-related catastrophes leave people in dire need of aid. A major obstacle in providing help to people is the lack of trust in the aid process. Charity organizations want to ensure that funds and materials reach the intended destinations. Blockchain technology injects trust into business transactions through impeccable record keeping and can alleviate the trust problems in aid delivery. Another major problem in disaster recovery is broken infrastructure (e.g., broken bridges and unavailable roads). Unmanned aerial vehicles (UAV), generally referred to as drones, can address this access problem. In this paper, the authors design a system that uses drone technology for delivery of aid and blockchain technology for the assurance of such delivery. This system records and shares data on the interaction of various participants involved in a disaster aid delivery scenario. The simulation studies validate the applicability of this proposed system showing high throughput and satisfactory performance are attainable with integration of blockchain in large-scale aid delivery.

Meta-heuristic algorithms have been widely used in deep learning. A hybrid algorithm EO-GWO is proposed to train the parameters of long short-term memory (LSTM), which greatly balances the abilities... Show More

Meta-heuristic algorithms have been widely used in deep learning. A hybrid algorithm EO-GWO is proposed to train the parameters of long short-term memory (LSTM), which greatly balances the abilities of exploration and exploitation. It utilizes the grey wolf optimizer (GWO) to further search the optimal solutions acquired by equilibrium optimizer (EO) and does not add extra evaluation of objective function. The short-term prediction of traffic flow has the characteristics of high non-linearity and uncertainty and has a strong correlation with time. This paper adopts the structure of LSTM and EO-GWO to implement the prediction, and the hyper parameters of the LSTM are optimized by EO-GWO to transcend the problems of backpropagation. Experiments show that the algorithm has achieved wonderful results in the accuracy and computation time of the three prediction models in the highway intersection.

The advent of the era of big data not only enables us to have more information that we can use, but also creates conditions for us to create and disseminate information in a timely manner. This... Show More

The advent of the era of big data not only enables us to have more information that we can use, but also creates conditions for us to create and disseminate information in a timely manner. This article systematically sorts out and analyzes the overall development and investment of new energy in China, as well as the current new energy incentive policies implemented, and points out the problems in the development of new energy. On this basis, the technical risks, policy risks, and market risks faced by this new energy investment are analyzed, and a risk evaluation model based on DHGF and entropy technology is established. It can help investors identify potential investment opportunities. Investors can use the option of investment projects granted by real options to reduce the impact of uncertainty, thereby increasing the value of the company, and making more scientific and reasonable investment decisions. The experimental results of this article show that since 2009, stock market financing has become a financing channel favored by developers.

Automation of financial data collection, generation, accumulation, and interpretation for decision making may reduce volatility in the stock market and increase liquidity occasionally. Thus, future... Show More

Automation of financial data collection, generation, accumulation, and interpretation for decision making may reduce volatility in the stock market and increase liquidity occasionally. Thus, future markets' prediction factoring in the sentiment of investors and algorithmic traders is an exciting area for research with deep learning techniques emerging to understand the market and its future direction. The paper develops two FINBERT deep neural network models pre-trained on the financial phrase dataset, the first one to extract sentiment from the NSE market news. The second model is adopted to predict the stock market movement of NSE with the above sentiment, historical stock prices, return on investment, and risk as predictors. The accuracy is compared with RNN and LSTM and baseline machine learning classifiers like naïve bayes and support vector machine (SVM). The accuracy of the FINBERT model is found to out-perform the deep learning algorithms and above baseline machine learning classifiers thus justifying the importance of the FINBERT model in stock market prediction.

This article introduces a text detection model for historical documents images. The handwritten characters in historical documents are always difficult to detect because they contain fuzzy or... Show More

This article introduces a text detection model for historical documents images. The handwritten characters in historical documents are always difficult to detect because they contain fuzzy or missing ink, or weathering features and stains; these features will seriously affect the detection accuracy. In order to reduce the influence mentioned above, an effective ATD model is proposed to detect the textbox of characters in historical documents image, and ATD model includes a CNN-based text-box generation network and an NMS-based MSER text-box generation model. As a post-processing method, a text merging algorithm is proposed to achieve higher detection accuracy. The test results on historical document datasets such as Yi, English, Latin, and Italian datasets show that the method in this paper has good accuracy, and it has taken a solid step for the detection of historical documents.

The study of graph kernels has been an important area of graph analysis, which is widely used to solve the similarity problems between graphs. Most of the existing graph kernels consider either... Show More

The study of graph kernels has been an important area of graph analysis, which is widely used to solve the similarity problems between graphs. Most of the existing graph kernels consider either local or global properties of the graph, and there are few studies on multiscale graph kernels. In this article, the authors propose a framework for graph kernels based on truss decomposition, which allows multiple graph kernels and even any graph comparison algorithms to compare graphs at different scales. The authors utilize this framework to derive variants of five graph kernels and compare them with the corresponding basic graph kernels on graph classification tasks. Experiments on a large number of benchmark datasets demonstrate the effectiveness and efficiency of the proposed framework.

In order to provide more efficient and reliable power services than the traditional grid, it is necessary for the smart grid to accurately predict the electric load. Recently, recurrent neural... Show More

In order to provide more efficient and reliable power services than the traditional grid, it is necessary for the smart grid to accurately predict the electric load. Recently, recurrent neural networks (RNNs) have attracted increasing attention in this task because it can discover the temporal correlation between current load data and those long-ago through the self-connection of the hidden layer. Unfortunately, the traditional RNN is prone to the vanishing or exploding gradient problem with the increase of memory depth, which leads to the degradation of predictive accuracy. Many RNN architectures address this problem at the expense of complex internal structures and increased network parameters. Motivated by this, this article proposes two adaptive modularized RNNs to tackle the challenge, which can not only solve the gradient problem effectively with a simple architecture, but also achieve better performance with fewer parameters than other popular RNNs.

With the massive growth of edge devices, how to provide users with video recommendation services in a mobile edge environment has become a research hotspot. Most traditional video recommendation... Show More

With the massive growth of edge devices, how to provide users with video recommendation services in a mobile edge environment has become a research hotspot. Most traditional video recommendation methods regard the relationship between user and neighbor to be linear and ignore higher-order connectivity among users, which results in poor recommendation performance. Besides, these methods use a single feature to represent user preferences, which cannot effectively alleviate the data sparsity problem. To improve the performance of video recommendation, this article proposes a multi-feature video recommendation method based on hypergraph convolution (MVRHC). Hypergraph convolution is adopted to compute user neighborhood-level features for modeling high-order correlations among users. Final features are obtained by fusing multi-party features through attention mechanism. And video recommendation is then implemented based on the obtained features. Experimental results on two real-world datasets demonstrate that MVRHC has better performance compared with baseline methods.

Digital transformation of enterprises is driving the need for a digital identity to recognize people for delivering services. The implementation of digital identity is complex, requiring several... Show More

Digital transformation of enterprises is driving the need for a digital identity to recognize people for delivering services. The implementation of digital identity is complex, requiring several technological solutions and much coordination. Capturing and processing data is challenging because biometric issues may arise due to imaging errors. This article addresses this issue and proposes a computer vision-based framework for contactless recognition process using a focus group discussion approach for inputs from experts. The proposed framework enhances image capturing process, extraction of high-quality features from captured images, image processing, contactless face detection, and authentication. The study also derives lessons for other biometric-based identity projects based on image analysis. The proposed framework can be used as a reference for understanding multidimensional perspectives, scalability, and adoption of technological solutions in other similar projects in developing countries in future.

Today, in the era of big data, an increasingly serious problem is the security of digital media in the healthcare domain. Encryption is a popular technique to resolve the security concern of medical... Show More

Today, in the era of big data, an increasingly serious problem is the security of digital media in the healthcare domain. Encryption is a popular technique to resolve the security concern of medical data. In the paper, the authors propose a key-based encryption algorithm – namely, SeMIE, designed by RDWT and RSVD for healthcare applications – which can guarantee the security of the medical images. Initially, the image normalisation procedure along with RDWT-RSVD is followed to generate hash value. Here, image normalisation is used to ensure the high resistance against the geometric modifications. Then, a key expansion process is utilised with the hash value for generating the secure keys. Finally, the encryption process uses Feistel structure along with constant substitution-permutation functions to provide sufficient confusion and diffusion of cipher data. Experimental evaluation indicates that the SeMIE algorithm is secure against several attacks. From the simulation findings, it is inferred that the algorithm exhibits improved security compared to existing methods.

Currently, malware attacks pose a high risk to compromise the security of Android-IoT apps. These threats have the potential to steal critical information, causing economic, social, and financial... Show More

Currently, malware attacks pose a high risk to compromise the security of Android-IoT apps. These threats have the potential to steal critical information, causing economic, social, and financial harm. Because of their constant availability on the network, Android apps are easily attacked by URL-based traffic. In this paper, an Android malware classification and detection approach using deep and broad URL feature mining is proposed. This study entails the development of a novel traffic data preprocessing and transformation method that can detect malicious apps using network traffic analysis. The encrypted URL-based traffic is mined to decrypt the transmitted data. To extract the sequenced features, the N-gram analysis method is used, and afterward, the singular value decomposition (SVD) method is utilized to reduce the features while preserving the actual semantics. The latent features are extracted using the latent semantic analysis tool. Finally, CNN-LSTM, a multi-view deep learning approach, is designed for effective malware classification and detection.

Currently, the internet of everything (IoE) enabled smart surveillance systems are widely used in various fields to prevent various forms of abnormal behaviors. The authors assess the vulnerability... Show More

Currently, the internet of everything (IoE) enabled smart surveillance systems are widely used in various fields to prevent various forms of abnormal behaviors. The authors assess the vulnerability of surveillance systems based on human gait and suggest a defense strategy to secure them. Human gait recognition is a promising biometric technology, but one significantly hindered because of universal adversarial perturbation (UAP) that may trigger system failure. More specifically, in this research study, the authors emphasize on sample convolutional neural network (CNN) model design for gait recognition and assess its susceptibility to UAPs. The authors compute the perturbation as non-targeted UAPs, which trigger a model failure and lead to an inaccurate label to the input sample of a given subject. The findings show that a smart surveillance system based on human gait analysis is susceptible to UAPs, even if the norm of the generated noise is substantially less than the average norm of the images. Later, in the next stage, the authors illustrate a defense mechanism to design a secure surveillance system based on human gait.

Internet of everything (IoE) has the power of reforming the healthcare sector - various medical devices, hardware, and software applications that are interconnected, tendering a massive volume of... Show More

Internet of everything (IoE) has the power of reforming the healthcare sector - various medical devices, hardware, and software applications that are interconnected, tendering a massive volume of data. The huge interconnected medical-based network is prone to significant malicious attacks that can modify the medical data being communicated and transferred. IoE permits dynamic two-way communication and empowers the network with intellect, sophisticated data handling, caching, and allocation mechanisms. In this paper, an improvement in the conventional variable-sized detector generation for healthcare - IVD-IMT algorithm under Artificial Immune System (AIS) based Intrusion Detection System (IDS) capable of handling enormous data generated by the IoE medical network is proposed. Algorithm efficiency is dependent on two performance metrics - detection rate and false alarm rate. The input parameters were tuned using synthetic datasets and then tested over the NSL-KDD dataset. The research lays emphasis on lowering the false alarm rate without compromising on the detection rate.

The primary concern of every individual and organization is the security of sensitive information generated via authorized activities; nonetheless, illicit data drawing and extraction by attackers... Show More

The primary concern of every individual and organization is the security of sensitive information generated via authorized activities; nonetheless, illicit data drawing and extraction by attackers is prevalent, which may be mitigated by covert approaches. Although cypher techniques give excellent protection, they raise suspicion in the eyes of adversaries, resulting in both passive and active assaults on the information sent. Steganography, on the other hand, helps to reduce third-party suspicion. This method conceals sensitive information on cover data and transports it to the targets without skepticism. However, the issue depends entirely on the effectiveness of the embedding method; it must also satisfy other data concealing features such as embedding capacity. As payload grows, so does skepticism. This article handled this issue to lessen suspicion while maintaining embedding capacity. The article proposes a format-based text concealing algorithm, a traditional way for dealing with embedding capacity and invisibility. The authors compared our results to those of other similar current methods. They discovered that theirs are pretty decent—the present study offered both standard public communication security and medical data protection.

Internet of everything (IoET) is one of the key integrators in Industry 4.0, which contributes to large-scale deployment of low-power and lossy (LLN) networks to connecting people, processes, data... Show More

Internet of everything (IoET) is one of the key integrators in Industry 4.0, which contributes to large-scale deployment of low-power and lossy (LLN) networks to connecting people, processes, data, and things. The RPL is one of the unique standardized routing protocols that enable efficient use of smart devices energy, compute resources to address the properties and constraints of LLN networks. The authors investigate the RPL-AODV routing protocol's performance in combining the advantages of both RPL and AODV routing protocol, which works together in a low power resource-constrained network. The main challenging issue is collaborating the AODV and RPL routing protocol in the LLN network. This paper also models the collaborative attacks such as wormhole, blackhole attack for AODV, and rank and sinkhole attacks to exploit the vulnerability of RPL protocol. Finally, the cooperative IDS combining specification-based and signature-based IDS is proposed to detect the collaborative attacks against the RPL-AODV routing protocol that effectively monitors and provides security to the LLN networks.

While mobile application (app) software is becoming increasingly important in people's daily lives, researchers have the limitation of understanding the details of user operations inside the app.... Show More

While mobile application (app) software is becoming increasingly important in people's daily lives, researchers have the limitation of understanding the details of user operations inside the app. With the update of the Android system and application user interface, relying on manually defined user operation event templates or modifying the app source code can no longer meet the needs of fine-grained user operation analysis in multiparallel applications. In this article, a novel method is proposed for effectively analyzing user operations in parallel apps based on the temporal context of user operation sequences. The authors provide a general framework in the Android system to parse out fine-grained user operations. In addition, the authors build a deep learning model with LSTM-TextCNN to complete user operations in parallel app from global temporal context and app temporal context. The authors collected 240k operations of 12 users over a month. Comparative experiments with a baseline show that the proposed method can efficiently and accurately analyze parallel app user operations.

With the development of China's economy, the urban floating population is also increasing, resulting in a sharp increase in the amount of urban waste. How to recycle and dispose of municipal waste... Show More

With the development of China's economy, the urban floating population is also increasing, resulting in a sharp increase in the amount of urban waste. How to recycle and dispose of municipal waste more efficiently has become the top concern of municipalities and other relevant departments. In this article, the above problem is transformed into the municipal waste collection vehicle routing problem (MWCVRP) to solve the problem with the minimum total waste transportation cost. Because the carrying capacity of different models is different, this article introduces a cost calculation criterion that combines the total mileage of different models of transport vehicles and the number of station services. A multi-model garbage truck path optimization model is established, and then a heuristic-based task dynamic assignment algorithm is designed to solve the problem. The Solomon dataset is used to verify the feasibility and effectiveness of the model and algorithm through experiments.

As the demand for high quality and high resolution in real-time rendering grows, superresolution is on its way to becoming a necessary component in modern real-time rendering applications (e.g.... Show More

As the demand for high quality and high resolution in real-time rendering grows, superresolution is on its way to becoming a necessary component in modern real-time rendering applications (e.g., video games). The superresolution technique allows graphic applications to save computational costs by rendering at a lower resolution and reconstructing a high-resolution result. Nvidia introduced DLSS to the market as the first superresolution application in 2020, and NSRR was published on Siggraph the same year. Each of these approaches has shown powerful capabilities and is well suited to the needs of the industrial sector. In this paper, the authors propose the optimization potential of superresolution algorithms by introducing feature enhancement and feature caching modules and attempt to improve the current algorithms.

The Internet of Things provides convenience to health systems, especially for remote monitoring of patient physical indicators. While providing convenience, there may be more security... Show More

The Internet of Things provides convenience to health systems, especially for remote monitoring of patient physical indicators. While providing convenience, there may be more security vulnerabilities in protecting patient and doctor information and storing health data effectively. As an important research branch in the field of the Internet of Things, the Internet of Medical Things is important for the overall improvement of public health in terms of how to safely conduct technology development and application research and to effectively implement healthcare needs. Blockchain technology is decentralized and untrusted as well as prevents tampering with data and reduces the cost of trust. Its good performance has a strong developmental nature in the healthcare field. This paper analyses how to solve security problems through access control under the Internet of Medical Things, and optimizes three access control methods. The Internet of Medical Things accesses control approach that introduces blockchain technology enhances computational and storage capabilities and is a good solution to the problem of third-party trustworthiness. Even in the face of the rapid growth of end devices, blockchain technology can solve some of the problems arising from access control of massive devices through three directions: hierarchical management, compressed storage and performance optimization. Finally, it provides directions for future research on the security aspects of blockchain technology under the Internet of Medical Things.

The existing one-factor cancellable biometrics algorithms generally require random sequences to reorder the biometrics, which reduces the discrimination of the transformed biometrics. Some... Show More

The existing one-factor cancellable biometrics algorithms generally require random sequences to reorder the biometrics, which reduces the discrimination of the transformed biometrics. Some algorithms hide and transmit the random sequence by XORing the random sequence with original biometrics, which may cause the leakage of the original biometrics. Therefore, this paper proposes a one-factor cancellable fingerprint template protection based on index self-encoding. First, the integer sequence generated by the hash function is used as the index. The random sequence is automatically encoded directly by the index value, and the generated binary sequence retains the original biological characteristics to the greatest extent. Second, self-encoding binary sequence and random binary sequence are XORed to obtain the encoded key without directly storing binary factor sequences. Experiments are implemented on the fingerprint database of FVC2002 and FVC2004, the results show that the recognition rate is enhanced; meanwhile, it fits the design criteria of cancellable biometrics.

Traditional methods of extracting finger vein texture changes and orientation features are susceptible to illumination, translation, noise, and rotation, and the process has difficulty directly... Show More

Traditional methods of extracting finger vein texture changes and orientation features are susceptible to illumination, translation, noise, and rotation, and the process has difficulty directly extracting structural features through the original image. In this paper, the histogram of competitive Gabor directional binary statistics (HCGDBS) is proposed to extract discriminative structural features. First, the index of the largest filter value is obtained based on the multidirectional Gabor filter as the dominant direction, thereby obtaining the rotation-invariance feature. Second, according to the filter response size of each pixel in different directions, the order difference relationship between the adjacent three directions is compared, and a highly discriminative competitive Gabor direction binary pattern (CGDBP) is constructed. Finally, the CGDBP features are extracted in blocks, and the HCGDBS is constructed to overcome image translation. Experimental results show that it improves the recognition performance and overcomes illumination, translation, noise, and rotation.

Shuffled frog leaping algorithm is a biological swarm intelligent optimization algorithm and improved into capacity-limited vehicle routing problem. However, the optimization performance is limited... Show More

Shuffled frog leaping algorithm is a biological swarm intelligent optimization algorithm and improved into capacity-limited vehicle routing problem. However, the optimization performance is limited with improvement strategies in major of the improvement algorithm. A novel framework of algorithm is proposed to solve capacity-limited vehicle routing problem, including three modules such as origin oriented shuffled frog leaping algorithm strategy, origin oriented shuffled frog leaping vehicle routing multiobjective optimization algorithm strategy, and output module. The frog individuals gather near the origin with the maximum probability and in the area circle, with the frog leaping radius or frog-oriented radius, as the neighborhood. The negative value of the maximum entropy and the shortest total path length of the vehicle are selected as the fitness. The performance test shows that it overcomes the defect of slow convergence compared with other five algorithms. It performs well to solve vehicle routing problems.

The critical challenge of video object counting is to avoid counting the same object multiple times in different frames. By comparing the appearance and motion feature information of the detection... Show More

The critical challenge of video object counting is to avoid counting the same object multiple times in different frames. By comparing the appearance and motion feature information of the detection results, the authors use the multi-object tracking method to assign an independent ID number to each object. From the time the ID tag is obtained until the end of the video, each object is counted only once. However, even minor amounts of image noise can cause irreversible changes in feature information, resulting in severe tracking drifts. This paper introduces the concept of scene awareness and addresses unreasonable ID assignment caused by unreliable feature matching in the context of region division. Through the macro analysis of the scene, the authors define the region (called the transition region) where the number of objects can increase or decrease and require that all ID assignments for new objects and ID deletions for existing objects take place only in the transition region. Because the actual number of objects in the non-transition region is constant, they rematch unmatched objects with existing IDs in the region (called ID relocation) because changes in object ID are caused by feature matching failure. In this paper, the authors create algorithms for dynamically generating transition regions, detecting object increases and decreases, and relocating object IDs. Experimental results show that the method effectively improves the accuracy of video object counting.

The images of motion states are time-varying, and when actually detecting their internal motion targets, the formed detection frames overlap, resulting in small confidence values for the detection... Show More

The images of motion states are time-varying, and when actually detecting their internal motion targets, the formed detection frames overlap, resulting in small confidence values for the detection frames and low accuracy of the detection results. To address this problem, the authors propose a target detection for motion image using the improved YOLO algorithm. First, the YOLO algorithm is improved using deformable convolution; the edge weights of the front and back views within the image are collated, and the motion image is segmented using the improved YOLO algorithm. Second, the structure formed by the initial convolution is used as the initial detection frame structure, the parallel cross-ratio value is set, the overlap generated by the detection frame is controlled, the parameters of the detection frame compression processing are output, the threshold trigger value relationship is constructed, and finally, the detection of the motion image target is realized. The results show that the target false detection rate of the proposed method is only about 15%. The detection a priori frame height value is 80 pixels, and the average detection time consumed is 6.8ms, which proves that the proposed algorithm can be widely used in motion image target detection to improve the detection level.

How to extract a collection of trajectories for different vessels from the raw AIS data to discover vessel meeting knowledge is a heavily studied focus. Here, the AIS database is created based on... Show More

How to extract a collection of trajectories for different vessels from the raw AIS data to discover vessel meeting knowledge is a heavily studied focus. Here, the AIS database is created based on the raw AIS data after parsing, noise reduction and dynamic Ramer-Douglas-Peucker compression. Potential encountering trajectory pairs will be recorded based on the candidate meeting vessel searching algorithm. To ensure consistent features extracted from the trajectories in the same time period, time alignment is also adopted. With statistical analysis of vessel trajectories, sailing segment labels will be added to the input feature. All motion features and sailing segment labels are combined as input to one trajectory similarity matching method based on convolutional neural network to recognize crossing, overtaking or head-on situations for each potential encountering vessel pair, which may lead to collision if false actions are adopted. Experiments on AIS data show that our method is effective in classifying vessel encounter situations to provide decision support for collision avoidance.

As the need for handling data from various sources becomes crucial for making optimal decisions, managing multi-model data has become a key area of research. Currently, it is challenging to strike a... Show More

As the need for handling data from various sources becomes crucial for making optimal decisions, managing multi-model data has become a key area of research. Currently, it is challenging to strike a balance between two methods: polyglot persistence and multi-model databases. Moreover, existing studies suggest that current benchmarks are not completely suitable for comparing these two methods, whether in terms of test datasets, workloads, or metrics. To address this issue, the authors introduce MDBench, an end-to-end benchmark tool. Based on the multi-model dataset and proposed workloads, the experiments reveal that ArangoDB is superior at insertion operations of graph data, while the polyglot persistence instance is better at handling the deletion operations of document data. When it comes to multi-thread and associated queries to multiple tables, the polyglot persistence outperforms ArangoDB in both execution time and resource usage. However, ArangoDB has the edge over MongoDB and Neo4j regarding reliability and availability.

Many real-world applications require prediction that takes the most advantage of data. Classic data mining mechanisms tend to feed a prediction model pivotal data to achieve a promising result... Show More

Many real-world applications require prediction that takes the most advantage of data. Classic data mining mechanisms tend to feed a prediction model pivotal data to achieve a promising result, which needs to be adjusted in different application scenarios. Recent studies have shown the potential of I Ching mechanism to improve prediction capacity. However, the I Ching prediction mechanism has several issues, including underutilized I Ching knowledge and incomplete data conversion. To address these issues, the authors designed a model to leverage I Ching knowledge and transform traditional I Ching prediction processing into data mining. The authors' investigation revealed promising results in the stock market compared to popular machine learning and deep learning algorithms such as support vector machine (SVM), extreme gradient boosting (XGBoost), and long short-term memory (LSTM).

The world is facing one of the greatest public health threats in modern history. Various techniques based on contact tracing have been developed to support non-pharmaceutical interventions. The... Show More

The world is facing one of the greatest public health threats in modern history. Various techniques based on contact tracing have been developed to support non-pharmaceutical interventions. The growing evidence shows that app-based contact tracing can reduce the spread of COVID-19 if a certain proportion of the population uses the apps. However, the risk of privacy breaches that comes with such apps has long been a public concern which may hinder the uptake of the apps. In this paper, the authors attempt to find a solution to complete the spatiotemporal intersection computation without exposing the infected patient location and the user location to one another. The authors implement the solution in the WeChat applet to aid the local health center. This study conducts experiments for six scenarios to justify the applicability of the applet. Experiment results indicate that the applet is a promising non-pharmaceutical tool for curbing the spread of COVID-19.

In recent years, the problem of traffic congestion has become a hot topic. Accurate traffic flow prediction methods have received extensive attention from many researchers all over the world.... Show More

In recent years, the problem of traffic congestion has become a hot topic. Accurate traffic flow prediction methods have received extensive attention from many researchers all over the world. Although many methods proposed at present have achieved good results in the field of traffic flow prediction, most of them only consider the static characteristic of traffic data, but do not consider the dynamic characteristic of traffic data. The factors that affect traffic flow prediction are changeable, and they will change over time. In response to this dynamic characteristic, the authors propose a model fusion mechanism based on transformer (TransFusion). The authors adopt two basic forecasting models (TCN and LSTM) as the underlying architectures. In view of the performance of different models on the traffic data at different times, the authors design a model fusion mechanism to assign dynamic weights to basic models at different times. Experiments on three datasets have proved that TransFusion has a significant improvement compared with basic models.