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As information and communication technologies create an ever-increasing complexity in interconnected systems and devices, cybersecurity and privacy issues are constantly at the fore, highlighting the need to strengthen the protection and resilience of these systems against the ever-evolving threats of modern cyberspace. This particular work, taking into account that preschool children now have significant needs to ensure their digital identity and, in general, their protection from their contacts with the internet, aspires to provide an understandable and practical guide to strengthen the security of information systems and information from both public and private school agencies. Specifically, a preschool cybersecurity management system based on intelligent agents is proposed. Using sophisticated, intelligent techniques, it aims to improve the ability of preschools to resist modern threats adequately, respond to cyber-attack incidents with the least possible impact, and protect their critical systems, services offered, and the personal data they hold and process. The system intends to link and control distributed systems that currently exist, as well as to solve issues that are beyond the knowledge and skills of a single agent. This novel research idea has never been offered in the relevant literature, and we think it has the potential to advance the state of the art in cybersecurity significantly.

As of late 2019, the COVID19 pandemic has been causing huge concern around the world. Such a pandemic posed serious threats to public safety, the well-being of healthcare workers, and the overall health of the population. If automation can be implemented in healthcare systems, patients could be better cared for and health industries could be less burdened. To combat such challenges, e-health requires apps and intelligent systems. Using WBAN sensors and networks, a doctor or medical professional can advise patients on the best course of action. Patients’ fitness could be assessed using WBAN sensors without interfering with their daily activities. When designing a monitoring system, system performance reliability for competent healthcare is critical. Existing research has failed to create a large device capable of handling a large network or to improve WBAN topologies for fast transmitting and receiving patient data. As a result, in this research, we create a multisensor WBAN (MSWBAN) intelligent system for transmitting and receiving critical patient data. To gather information from all cluster nodes and send it to multisensor WBAN, a novel additive distance-threshold routing protocol (ADTRP) is proposed. In small networks where data are managed by the transmitting node and the best data route is determined, this protocol has less redundancy. An edge-cutting-based routing optimization (ES-EC-R ES-EC-RO) is used to find the best route. The Trouped blowfish MD5 (TB-MD5) algorithm is used to encrypt and decrypt data, and the encrypted data are stored in a cloud database for security. The performance metrics of our proposed model are compared to current techniques for the best results. End-to-end latency is 63 ms, packet delivery is 95%, security is 95.7%, and throughput is 9120 bps, according to the results. The purpose of this article is to encourage engineers and front-line workers to develop digital health systems for tracking and controlling virus outbreaks.

In modern medicine, smart wireless connected devices are gaining an increasingly important role in aiding doctors' job of monitoring patients. More and more complex systems, with a high density of sensors capable of monitoring many biological signals, are arising. Merging the data offers a great opportunity for increasing the reliability of diagnosis. However, a huge problem is constituted by synchronization. Multi-board wireless-connected monitoring systems are a typical example of distributed systems and synchronization has always been a challenging issue. In this paper, we present a distributed full synchronized system for monitoring patients' health capable of heartbeat rate, oxygen saturation, gait and posture analysis, and muscle activity measurements. The time synchronization is guaranteed thanks to the Fractional Low-power Synchronization Algorithm (FLSA).

Speech is one form of biometric that combines both physiological and behavioral features. It is beneficial for remote-access transactions over telecommunication networks. Presently, this task is the most challenging one for researchers. People’s mental status in the form of emotions is quite complex, and its complexity depends upon internal behavior. Emotion and facial behavior are essential characteristics through which human internal thought can be predicted. Speech is one of the mechanisms through which human’s various internal reflections can be expected and extracted by focusing on the vocal track, the flow of voice, voice frequency, etc. Human voice specimens of different ages can be emotions that can be predicted through a deep learning approach using feature removal behavior prediction that will help build a step intelligent healthcare system strong and provide data to various doctors of medical institutes and hospitals to understand the physiological behavior of humans. Healthcare is a clinical area with data concentrated where many details are accessed, generated, and circulated periodically. Healthcare systems with many existing approaches like tracing and tracking continuously disclose the system’s constraints in controlling patient data privacy and security. In the healthcare system, majority of the work involves swapping or using decisively confidential and personal data. A key issue is the modeling of approaches that guarantee the value of health-related data while protecting privacy and observing high behavioral standards. This will encourage large-scale perception, especially as healthcare information collection is expected to continue far off this current ongoing pandemic. So, the research section is looking for a privacy-preserving, secure, and sustainable system by using a technology called Blockchain. Data related to healthcare and distribution among institutions is a very challenging task. Storage of facts in the centralized form is a targeted choice for cyber hackers and initiates an accordant sight of patients’ facts which will cause a problem in sharing information over a network. So, this research paper’s approach based on Blockchain for sharing sufferer data in a secured manner is presented. Finally, the proposed model for extracting optimum value in error rate and accuracy was analyzed using different feature removal approaches to determine which feature removal performs better with different voice specimen variations. The proposed method increases the rate of correct evidence collection and minimizes the loss and authentication issues and using feature extraction based on text validation increases the sustainability of the healthcare system.

This study studies the problem of efficient multimedia data acquisition and decreasing whole energy expenditure of wireless multimedia sensor networks and proposes a three-step multimedia data acquisition and wireless energy supplement strategy. Firstly, for network partition, this study proposes a network partition scheme based on vicinity likeness and distance of sensor nodes (VLD), which divides the whole sensor network into multiple regions. The physical links inside the region are dense and concentrated, while the link connections between regions are sparse. Disconnecting the connections between regions hardly affects the data transmission of sensor nodes. Secondly, this study proposes an efficient data acquisition and processing scheme for wireless multimedia sensor network ASS. Compared with other anchor selection schemes, this scheme has obvious performance advantages. Then, the problem of minimizing network energy expenditure is defined, and the optimal sensor node data perception rate and network link transmission rate of the optimization function are obtained by dual decomposition and sub-gradient method. Finally, in the case of a given network energy threshold, the performance of the overall strategy in this study is verified by comparing the amount of data collected by the base station.

Topological information is provided, and research on the design of routing protocols for UAV self-assembling networks is conducted, in order to enable fleet communication transfer between UAVs and UAVs and enhance their communication transmission rate in the self-assembling network. A new routing protocol is proposed through greedy forwarding and peripheral forwarding of UAV self-assembling network communication data, UAV self-assembling network planarization processing, dynamic adjustment of routing mode based on topological information, and routing protocol decision content generation. The proposed network is described using stochastic geometry theory, with the UAV and building locations modeled as two independently distributed Poisson point processes and the building shape modeled as a rectangular body with height obeying the Rayleigh distribution. An estimated equation for typical user coverage is produced using this model. The simulation results show that the approximate expression matches with the simulation results with reduced computational complexity, which verifies the validity of the approximate analysis. By comparing it with the clustering-based routing protocol, it is concluded that the new routing protocol conditions for UAV self-assembly network can realize the communication transmission between UAVs and drones and further promote their communication transmission rate.

Faced with the AI (artificial intelligence) era, it is both theoretically and practically important to examine the challenges and opportunities that vocational college education in China faces, as well as to actively explore how vocational college education can overcome the challenges and achieve a realistic path. This paper proposes and implements a wireless network-based vocational college talent management system. The main personnel management system primarily completes business operations related to daily personnel file management, while the data mining subsystem mines talent data using the DT (decision tree) classification algorithm to aid talent selection. At the same time, a new topology optimization algorithm based on the principle of minimum rigidity graph is proposed for talent management system wireless network optimization. The maximum-minimum balance criterion and the user-by-user optimization mechanism are designed to obtain the optimal relay selection and subchannel allocation strategy, ensuring the system’s reliability and fairness. The optimized algorithm has a user security satisfaction of 0.93 in the range of 250 m, which is higher than other algorithms. It demonstrates that this algorithm’s communication link is short, and it has good network connectivity and structural stability.

Because the distributed storage system is based on network technology, it can store data in multiple independent low-cost physical storage devices, and it is also suitable for large-capacity storage, so it has become more and more popular. Today, common applications of distributed storage systems include cloud storage services, data center storage services, and P2P storage services. Typical ones are GFS, HDFS, OceanStore, and Dynamo. Due to regional and economic differences, the development level of global e-commerce (b2c) is very inconsistent. b2c contains the following key tags: buying and selling, which is the core of the website platform. E-commerce provides business users with transparent information and high-quality cheap products. Logistics is the basic guarantee for customers to execute transactions, and it is also a strict indicator of the website platform. There will be many visits during the operation of the e-commerce system, and the number of users in the early stage will increase exponentially. A safe and efficient e-commerce system can provide users with one-stop transaction support and convenient transaction processes. The personalized recommendation system has formulated some rules for certain fields, based on these rules, and defined certain types of knowledge for certain items to meet the needs of certain users and use the defined reasoning rules to generate recommendation results.

With the rapid development of the economy and society, the low efficiency and high loss of the traditional power grid can no longer meet the growing social demand, and the power grid market is facing a reform. Smart grid, as a next-generation power system, it can effectively improve the performance of traditional power grids. The ubiquitous power Internet of Things (UPIOT) replaces the traditional grids with efficient, safe, reliable, and flexible new grids, improves the utilization efficiency of the grid, reduces the loss of the power grid in the transmission process, and can meet the needs of different types of markets and users. As an advanced information acquisition and processing technology, wireless sensor networks have been widely used in medical, industrial, agricultural, commercial, and public management fields. It is an important means to promote future economic development and build a harmonious society. In the power system, wireless sensor network technology can be widely used in many fields such as line fault location, real-time monitoring, smart meter reading, and relay protection. In this paper, the basic concepts and overall architecture of ubiquitous power Internet of Things are summarized. Then, we summarize the research status of the wireless sensor network in smart grid, including power equipment, line monitoring, smart grid wireless automatic meter reading, distribution network relay protection, power assets life-cycle management, power grid fault location, and power grid fault diagnosis. In view of the technical characteristics of wireless sensor networks, combined with the production links of power systems, the application framework of wireless sensor network technology in the power systems is constructed. The application of wireless sensor networks is prospected from the aspects of network development of relay protection, application research of smart substation, application research of power grid catastrophe, security protection of power system, and deep-seated ubiquitous power Internet of Things.

When Donald A.B. Lindberg M.D. was sworn in as Director of the National Library of Medicine (NLM) in 1984, MEDLINE, NLM’s online database of citations and abstracts to biomedical journal articles, was searched primarily by librarians trained to use its command language interface. There were fees for searching, primarily to recover the cost of using commercial value-added telecommunications networks. Thirteen years later, in 1997, MEDLINE became free to anyone with an Internet connection and a Web browser. This chapter provides an insider’s view of how Dr. Lindberg’s vision and leadership - combined with new technology, astute handling of policy issues, and key help from political supporters and influential advocates - enabled a tremendous expansion in access to biomedical and health information for scientists, health professionals, patients, and the public.

Contemporary art is gradually diversifying, and the aesthetics of artists have changed and progressed together with the times. Some brush painters, while inheriting the essence of tradition, continue to summarize and search for a painting language in line with the atmosphere of the times, gradually shifting their perspective to a separate, tiny “corner” of life, recording the painter’s own subtle emotional fluctuations through such “tiny” scenes. The approach models services through a context-aware service model and establishes flexible driving relationships between IoT data and services through context and scenario concepts. Moreover, it reduces the amount of business data to be processed by the system through contextual events. The process of the approach for service development is described, and relevant context-aware service development tools are introduced. Finally, the feasibility of the approach proposed in this article is illustrated by a service development example. An event-triggered sensor node clustering algorithm based on beamforming data transmission technology is proposed, which can improve network energy efficiency and clustering success rate. Meanwhile, a clustering algorithm based on the energy consumption rate of sensor nodes is designed to optimize the task allocation and scheduling of mobile node access, reduce the node charging delay, and enable the sensor nodes in the network to get energy replenishment in a timely and reasonable manner according to their own energy consumption rate demand.

With the improvement of the ability to acquire natural information, wireless sensor networks also need to transmit corresponding information in terms of collecting information. Wireless sensor nodes have great application prospects as a key component of wireless sensors. Therefore, different wireless sensors play an important decisive role in the operation of wireless network applications. With the continuous development of wireless sensor networks, existing wireless sensor network nodes exhibit limitations and shortcomings such as inflexible structure, low variability, and low versatility. Specifically, the learning and neural networks obtained by different artificial intelligence expert systems in computing technology are different. On the one hand, it can meet the needs of users for information systems to a certain extent, and on the other hand, it can also help accelerate the development of computer science. At present, the new generation of information technology industry is listed in the seven emerging strategic industries of the country. The new cloud computing technology has gradually expanded to important corporate governance capabilities in terms of information technology. The intelligent application of cloud computing technology replaces traditional enterprise management technology. Efficiency management and risk management can improve the quality and business capabilities of the entire enterprise, improve system applications according to the actual situation of the enterprise, improve system applications, and implement health and the sustainable development of the enterprise, thereby promoting the sustainable development of the computer technology industry.

For the sensing layer of the Internet of Things, the mobile wireless sensor network has problems such as limited energy of the sensor nodes, unbalanced energy consumption, unreliability, and long transmission delay in the data collection process. It is proved by mathematical derivation and theory that this is a typical multiobjective optimization problem. In this paper, the optimization goal is to minimize the energy consumption and improve the reliability under time-delay constraints and propose a path optimization mechanism to optimize the mobile Sink of mobile wireless sensor networks based on the improved dragonfly optimization algorithm. The algorithm takes full advantage of the abundant storage space, sufficient energy, and strong computing power of the mobile Sink to ensure network connectivity and improve network communication efficiency. Through simulation comparison and analysis, compared with random movement method, artificial bee colony algorithm, and basic dragonfly optimization algorithm, the energy consumption of the network is reduced, the lifespan of the network is increased, and the connectivity and transmission delay of the network are improved. The proposed algorithm balances the energy consumption of the sensors nodes to meet the network service quality and improve the reliability of the network.

From 1992 to 1995 Donald A.B. Lindberg M.D. served concurrently as the founding director of the National Coordination Office (NCO) for High Performance Computing and Communications (HPCC) and NLM director. The NCO and its successors coordinate the Presidential-level multi-agency HPCC research and development (R&D) program called for in the High-Performance Computing Act of 1991. All large Federal science and technology R&D and applications agencies, including those involved in medical research and health care, participate in the now-30-year-old program. Lindberg’s HPCC efforts built on his pioneering work in developing and applying advances in computing and networking to meet the needs of the medical research and health care communities. As part of NLM’s participation in HPCC, Lindberg promoted R&D and demonstrations in telemedicine, including testbeds, medical data privacy, medical decision-making, and health education. That telemedicine technologies were ready to meet demand during the COVID-19 pandemic is testament to Lindberg’s visionary leadership.

In the medical environment, teamwork among medical care personnel is closely related to patient safety and care quality. This research has developed an Intranet-based call-for-help system to establish an emergency support system. Few studies have explored the effects of the timely call-for-help system on teamwork. This study explored the effects of the timely call-for-help system intervention.This study was designed by 2 groups of pretest and posttest. This research was conducted at a medical center in Taiwan. The subjects of the study were nurses in inpatient wards. The newly established call-for-help system was used in the 4 experimental wards, enabling nurses to immediately seek help from each other when faced with challenges and difficulties. This study was blind with the outcome evaluator and data analyst blinded to the group of participants.A total of 165 nurses were included in the study, 84 in the intervention group and 81 in the control group. After the intervention of the call-for-help system, the experimental group had significantly better teamwork, work efficiency, job satisfaction, and lower job stress than the control group ( Plt; 0.01). The generalized estimating equation showed that the progress scores of the experimental group on the 4 scales were significantly higher than those of the control group ( Plt; 0.01).The implementation of timely call-for-help system can enable nursing staff to get immediate support, strengthen teamwork, create a positive nursing practice environment, and improve patient safety and quality of care. These improvements in teamwork and support are very important and worthy of promotion.

Wireless sensor network is an ad hoc network with sensing capability. Usually, a large number of sensor nodes are randomly deployed in an unreachable environment or complex area for data collection and transmission, which can realize the perception and monitoring of the target area or specific objects and transmit the obtained data to the remote end of the system. Human health activity recognition algorithm is a hot topic in the field of computer. Based on the small sample problem and the linear indivisibility of real samples encountered in metric learning, this paper proposes a human activity recognition algorithm for wireless sensor networks. Human activity recognition algorithm for wireless sensor networks uses human activity recognition algorithm to solve the singularity of intraclass divergence matrix, so as to reduce the impact of small sample problem. The algorithm maps two different feature spaces to the high-dimensional linearly separable kernel space through the corresponding kernel function, calculates the distance between samples in the two projected feature subspaces to obtain two distance measurement functions, and finally linearly combines them with weights to obtain the final distance measurement function.

Data redundancy or fusion is one of the common issues associated with the resource-constrained networks such as Wireless Sensor Networks (WSNs) and Internet of Things (IoTs). To resolve this issue, numerous data aggregation or fusion schemes have been presented in the literature. Generally, it is used to decrease the size of the collected data and, thus, improve the performance of the underlined IoTs in terms of congestion control, data accuracy, and lifetime. However, these approaches do not consider neighborhood information of the devices (cluster head in this case) in the data refinement phase. In this paper, a smart and intelligent neighborhood-enabled data aggregation scheme is presented where every device (cluster head) is bounded to refine the collected data before sending it to the concerned server module. For this purpose, the proposed data aggregation scheme is divided into two phases: (i) identification of neighboring nodes, which is based on the MAC address and location, and (ii) data aggregation using k-mean clustering algorithm and Support Vector Machine (SVM). Furthermore, every CH is smart enough to compare data sets of neighboring nodes only; that is, data of nonneighbor is not compared at all. These algorithms were implemented in Network Simulator 2 (NS-2) and were evaluated in terms of various performance metrics, such as the ratio of data redundancy, lifetime, and energy efficiency. Simulation results have verified that the proposed scheme performance is better than the existing approaches.

With the development of communication technology, train control operation system develops gradually, which significantly improves the reliability and efficiency of train operation. The current mobile Internet has gradually highlighted the many limitations of the mobile Internet in the high-speed mobile environment, which seriously deteriorate the service quality and user experience, and cause a waste of resources. In order to meet the real-time requirements of network communication resource scheduling in the mobile environment, aiming at the multidimensional dynamic adaptation framework constructed in a mobile environment, a service and network adaptation mechanism based on link failure state prediction is proposed in the paper. First, cross-layer theoretical analysis and actual data analysis are combined to construct a wireless link failure probability model. Then, reliable transmission requirements and transmission overhead are applied to optimize goals. Finally, simulation experiments are carried out according to the railway network data to evaluate the E-GCF adaptation algorithm. The experiment results show that compared with the current mainstream algorithms, the prediction accuracy of this adaptation algorithm is improved by 25%. The execution time of the algorithm is reduced by 9.6 seconds and the successful submission rate is as high as 99.99%. The advantages of the algorithm are significantly superior other algorithms. It proves that the research method of this paper can effectively improve the satisfaction rate and utility value of reliable transmission, as well as enhance the throughput performance. It solves the adaptation problems of frequent switching and low utilization of heterogeneous networks in a mobile environment, which contributes to the high-quality communication service of mobile network.

In recent years, with the development of information technology, the Internet has become an essential tool for human daily life. However, as the popularity and scale of the Internet continue to expand, malware has also emerged as an increasingly widespread trend, and its development has brought many negative impacts to the society. As the number of types of malware is getting enormous, the attacks are constantly updated, and at the same time, the spread is very fast, causing more and more damage to the network, the requirements and standards for malware detection are constantly rising. How to effectively detect malware is a research trend; in order to tackle the new needs and problems arising from the development of malware, this paper proposes to guide machine learning algorithms to implement malware detection in a distributed environment: firstly, each detection node in the distributed network performs anomaly detection on the captured software information and data, then performs feature analysis to discover unknown malware and obtain its samples, updates the new malware features to all feature detection nodes in the whole distributed network, and trains the random forest-based machine learning algorithm for malware classification and detection, thus completing the global response processing capability for malware. By building a distributed system framework, the global capture capability of malware detection is enhanced to robustly respond to the increasing and rapid spread of malware, and machine learning algorithms are integrated into it to achieve effective detection of malware. Extended experiments on the Ember 2017 and Ember 2018 databases show that our proposed approach achieves advanced performance and effectively addresses the problem of malware detection.

The biosensors on a human body form a wireless body area network (WBAN) that can examine various physiological parameters, such as body temperature, electrooculography, electromyography, electroencephalography, and electrocardiography. Deep learning can use health information from the embedded sensors on the human body that can help monitoring diseases and medical disorders, including breathing issues and fever. In the context of communication, the links between the sensors are influenced by fading due to diffraction, reflection, shadowing by the body, clothes, body movement, and the surrounding environment. Hence, the channel between sensors and the central unit (CU), which collects data from sensors, is practically imperfect. Therefore, in this article, we propose a deep learning-based COVID-19 detection scheme using a WBAN setup in the presence of an imperfect channel between the sensors and the CU. Moreover, we also analyze the impact of correlation on WBAN by considering the imperfect channel. Our proposed algorithm shows promising results for real-time monitoring of COVID-19 patients.

We perform experiments exploring the use of white rabbit precision time protocol (WR-PTP) for time and frequency dissemination over long-distance optical fiber links. We use unidirectional links, to ensure compatibility with active telecommunication networks, and White Rabbit equipment with modifications for improved performance. Using fiber spools, we realize a 500 km, four-span cascaded white rabbit link. We show short term fractional frequency stability of 2×10

International audience; Longitudinal observational cohort studies are being conducted worldwide to understand cognition, biomarkers, and the health of the aging population better. Cross-cohort comparisons and networks of registries in Alzheimer's disease (AD) foster scientific exchange, generate insights, and contribute to the evolving clinical science in AD. A scientific working group was convened with invited investigators from established cohort studies in AD, in order to form a research collaboration network as a resource to address important research questions. The Connecting Cohorts to Diminish Alzheimer's Disease (CONCORD-AD) collaboration network was created to bring together global resources and expertise, to generate insights and improve understanding of the natural history of AD, to inform design of clinical trials in all disease stages, and to plan for optimal patient access to disease-modifying therapies once they become available. The network brings together expertise and data insights from 7 cohorts across Australia, Europe, and North America. Notably, the network includes populations recruited through memory clinics as well as population-based cohorts, representing observations from individuals across the AD spectrum. This report aims to introduce the CONCORD-AD network, providing an overview of the cohorts involved, reporting the common assessments used, and describing the key characteristics of the cohort populations. Cohort study designs and baseline population characteristics are compared, and available cognitive, functional, and neuropsychiatric symptom data, as well as the frequency of biomarker assessments, are summarized. Finally, the challenges and opportunities of cross-cohort studies in AD are discussed.

Controller area network (CAN) are widely used in smart vehicles to realize information interactions between electronic control units and other devices in vehicles. Owing to an increase in external communication interfaces, the cybersecurity of in-vehicle CAN bus networks is threatened. In-vehicle CAN intrusion detection systems with high detection rates and low false-negative rates have become important security protection measures for automotive networks. The boundary of the current machine learning-based in-vehicle CAN bus intrusion detection algorithm to determine the anomalous behavior triggered by CAN messages is unclear, and a validity check is required after the intrusion detection algorithm is designed. To solve the low coverage rate problem in the process of validating intrusion detection algorithms, an in-vehicle CAN fuzz-testing message generation model, the field-associative mutation generation adversarial network (FAMGAN), is proposed. To improve the defects of high randomness in generating messages in traditional fuzz-testing algorithms, FAMGAN adopts field division based on a conditional random field and the field association method based on the Apriori algorithm. Experiments were conducted on a real car using a code-built intrusion detection algorithm. The results demonstrate that FAMGAN can efficiently generate anomalous CAN messages and evaluate the performance of an in-vehicle CAN intrusion detection algorithm.

The current wave of technologies with increased awareness among customers and retaining customers has a vital role in the growth of the company. A good indicator of service satisfaction of customers and service quality is customer churn. In order to enable the organizations to understand customers for churning, intelligible and accurate models are needed. There have been several techniques of data mining that were applied for the prediction of churn. The extensive research in Artificial Intelligence has made it feasible to study and learn the aspects accounting for such customer churn. The work presents effective solutions to all these challenging problems in Customer Churn Prediction (CCP). The study uses datasets in the telecommunication industry, the Artificial Neural Networks (ANN), and the Random Forests (RF) to determine the factors that influence consumer churn. A hybrid ANN-based work is proposed for predicting CCP. The results of the experiment proved that the proposed method achieves better levels of performance. The classification accuracy of ANN-4 hidden layers improves its result compared to RF and ANN-2 hidden layers. The maximum accuracy attained by ANN-2 hidden layers is 88.14% and by ANN-4 hidden layers is 90.34%.

With the development of online retailing on third-party e-commerce platforms such as Taobao, the churn of individual shops is becoming increasingly severe. Platform operators are eager to learn the causes of this eroding trust. Based on Hofstede’s cultural dimensions theory, this paper aims to investigate how masculinity, uncertainty avoidance and long-term orientation impact platform trust and the interaction between these three cultural variables. Based on data collected from sellers on the largest Chinese C2C platform, we found that uncertainty avoidance had a negative effect on platform trust, while masculinity and long-term orientation exerted a positive impact on platform trust. A significant association among the cultural factors was also found.

Owing to the recent fuel in investigation of lip as a biometrics trait both in physiological and behavioral aspect, and in healthcare application, it is imperative to comprehensively study and document the literature. Hence, in this work we enlist the works related to the lip as a biometirc trait and other applications-based on its behavior and physiology. Consequently, in this survey we first discuss the lip anatomy that permits to identify the physiological and behavioral properties. Followed by the list of challenges to characterize lip behavior and physiology. Next, we proceed to provide an insight of several inspiring ideas proposed in the literature to establish lip behavior and physiology for identity and other applications such as health monitoring and forensic. Whilst, it can be concluded at the end of the survey that their are several open research area and unsolved issues, which we discuss in details at the end of this survey to attract the attention of researchers.

In collective foraging, interactions between conspecifics can be exploited to increase foraging efficiencies. Many collective systems exhibit short interaction ranges, making information about patches rich in resources only locally available. In environments wherein these patches are difficult to locate, collective systems might exhibit altruistic traits that increase average resource intake compared to non-interacting systems. In this work, we show that resource ephemerality and availability highly influence the benefits of altruistic behavior. We study an agent-based model wherein foragers can recruit others to feed on patches, instead of exploiting these individually. We show that the net gain by recruiting conspecifics can be estimated, effectively reducing the decision on patch detection to one based on a threshold. Patches with qualities above this threshold are expected to increase foraging efficiencies and should therefore induce recruiting of others. By letting foragers assume Levy searches, we show that recruitment strategies with contrasting diffusion characteristics optimize conspecific encounter rates. Our results further indicate that active recruitment is only beneficial when patches are scarce and persistent. Most interestingly, the effect of choosing suboptimal threshold values is small over a wide range of resource ephemeralities. This suggests that the decision of whether to recruit others is more impactful than fine-tuning the recruitment decision. Finally, we show that the advantages of active recruitment depend greatly on both forager density and their interaction radius, as we observe passive strategies to be more efficient, but only when forager densities or interaction ranges are large.

This paper introduces a novel and simple approach of high-level scene classification. Knowing that objects are the essence of any given scene, the proposed method uses them to construct a well-structured background knowledge, which is composed of ranking functions and a statistical collection, in order to support the scene classification process. Since not all objects are relevant, only the most salient ones are identified and used in computing the appropriate scene category. To prove the efficiency of the proposed method, experiments are conducted on state of the art datasets: MIT Indoor, SUN900, SUN2012, SUN397 and LabelMe+.Comparisons with other methods were also introduced. The obtained results are reported and discussed.

The privacy and utility are treated as a major factor in influencing the role of data privacy preservation in cloud environments. There exists a trade-off between these two factors, where one factor should compromise its functionality over the other. It is hence necessary to maintain both utility and privacy for a data offloaded or accessed across cloud computing environment. In this paper, we develop a utility privacy model that established utility using Deep Adaptive Clustering (DAC) and privacy using Elliptic Curve Digital Signature Algorithm (ECDSA). The utility is performed using clustering of the input datasets using DAC and the privacy is maintained using ECDSA. The simulation is conducted on specific datasets to test the efficacy of the model and the results shows improved accuracy on clustering, and efficient privacy metrics than existing methods.

High utility itemset (HUI) mining is a powerful data mining technique to discover profitable patterns. The utility of an item is computed by using two measures named quantity and per-unit profit. All existing HUI mining algorithms consider a single value of external utility (per unit profit) for the entire database. However, the per-unit profit of items might fluctuate over time in many applications. This research introduces three novel strategies to comprise the external utilities of items as input for the HUI mining algorithm. Traditional HUI mining algorithms have been developed for the standalone system and do not fit for big data processing due to the limited computing resources (CPU, memory). Big data are efficiently processed on distributed frameworks like Apache Hadoop, Spark, etc. This paper introduces a distributed HUI mining algorithm named Spark-based Top-k high utility itemset (k-SHUI) miner. We also propose a fair load distribution strategy to divide the search space equally among the cluster nodes. The k-SHUI produces top-k HUIs without the requirement of the minimum utility threshold. We conducted extensive experiments on six real-life datasets to compare the proposed algorithm's performance with the existing algorithm. The experimental results demonstrate that the proposed algorithm outperforms the existing algorithms.

Transformation in higher education is one of the main objectives to meet the global challenges and deliver higher sustainable and knowledge-based development levels. It is pivotal to inspire the quality of teaching and innovative pedagogy. Advancement in technology has led to growth of many effectual methods for state-of-the-art pedagogies in the field of education. Engineering education is built on a innovative standard in which engineering is to be practiced with technical expertise as well as social responsibility. The teaching community has assimilated a lot of teaching strategies for changing teacher-centric education to student-centric education. This paper targets to present the facets of the teaching–learning process, teaching pedagogies, and curricular approaches of engineering pedagogy and study the teaching strategies followed by engineering teaching fraternity. In order to study the different teaching strategies followed by engineering teachingfraternity, an online survey on teaching strategies was conducted for the engineering teaching fraternity all over India. This online survey shows that there is a shift in the teaching process from teacher-centric education to student-centric education. Nearly 85–90% of the teaching fraternity follow student-centric teaching approach by using many strategies and tools, which consecutively results in Outcome-Based Education.

Aggregation statistics in mobile sensing are very useful for many applications. Therefore, a large number of privacy-preserving data aggregation protocols have been proposed, most of which however, only consider privacy-preserving sum aggregation. Non-additive aggregation statistics are also very important for their widely applications, such as max/min value and variance. The max/min aggregation can be regarded as a kind of comparison based aggregation because to obtain the max/min value, comparison among sensed data is required. Few protocols takes comparison based aggregation into consideration for the reason that it is difficult to implement privacy-preserving comparison. In this paper, we present two methods, i.e., a distributed one and a centralized one, for comparing all the sensed data in a privacy-preserving manner. And based on the two comparison methods, we propose two privacy-preserving comparison based data aggregation protocols, respectively, which are further applied to achieve max/min aggregation and top X aggregation.

Real-time and safety-critical systems are an integration of multiple functionalities onto a single computing platform. Some of the functionalities are safety-critical and subject to certification while the rest of the functionalities are nonsafety-critical and do not need the certification. Various researches have been done for the scheduling theory of mixed-criticality systems. But the time-triggered scheduling of mixed-criticality systems is very popular and used in industry. Since the schedule is prepared offline in a time-triggered mixed-criticality system, we need to prepare the schedule in such a way that the schedule must tolerate fault online. Hence the problem of fault-tolerance in the time-triggered system is important. This work proposes a new and novel time-triggered fault-tolerant algorithm for mixed-criticality systems. Then we show that the proposed algorithm is correct and tolerate at most one fault over the hyperperiod. Finally, we compare the proposed algorithm with the existing time-triggered scheduling algorithms for mixed-criticality systems.

As a kind of frequent bad weather, Agglomerate fog is a serious danger to people's safe driving, especially on the highway. Therefore, the research on the detection of fog is of great practical significance to ensure the safety of pedestrians. This paper proposes a shallow convolutional neural network for agglomerate fog detection in images, including the framework of the network and the detailed design of each component. Firstly, the image is divided into several sub-images; and then a shallow convolutional neural network is constructed and employed to identify the existence of fog for each of the sub-area images; lastly, the decision results of each sub-area images were integrated to determine whether the whole image contained agglomerate fog. A large quantity of simulation data and real data were used to test the performance of the proposed method, the experimental results show that the presented method can achieve more than 90% detection accuracy, which demonstrated that the advantage of the proposed method comparing with several existed methods.

Supporting transfers of science big data over Wide Area Networks (WANs) with Data Transfer Nodes (DTNs) requires optimizing multiple parameters within the underlying infrastructure. New solutions for such data movement require new paradigms and technologies, such as NVMe over Fabrics, which provides high-performance data movement with direct remote NVMe device access over traditional fabrics. However, recent NVMe over Fabrics studies have been limited to local storage fabrics. To support increasing demands for the large volume of science data movement during Supercomputing (SC) conferences, we proposed a SCinet DTN-as-a-Service framework orchestrating the desired optimization to meet users, applications, and providers’ requirements. Furthermore, we extend the SCinet DTN-as-a-Service framework to incorporate new techniques, solve optimization issues in data-intensive science and evaluate NVMe over Fabrics with multiple WAN testbeds to examine its performance and discover new opportunities for optimization.

In cloud, sensitive data is stored in an encrypted form, and searchable encryption is a technique which allows user to search over encrypted data by keyword and it leaks no useful information. However, the secure searchable encryption scheme which can prevent inside keyword guessing attacks (IKGA) and satisfy multi-trapdoor privacy has not been proposed. We first review the security of Wu et al.’s scheme and demonstrate that their scheme is insecure under IKGA and multi-keyword guessing attacks in this paper. To solve these issues, we propose an improved certificateless public key searchable encryption scheme. Furthermore, we give the proof of the security of our scheme under the computational bilinear Diffie-Hellman assumption. It shows that our scheme can resist IKGA successfully, and provides multi-ciphertext indistinguishability and multi-trapdoor privacy. The better performance is shown in our experiment.

In this article, we compare the impact of state-of-the-art light field compression methods. It addresses quality of (a) refocused images and (b) point clouds reconstructed from 4D light field data. The methods include recent video compression formats, specifically H.265, AV1, XVC, and H.266/VVC (finalized in 2020). In addition, we have extended a standard image compression method into four dimensions and compared it with the video compression formats. It turned out that the new VVC format demonstrated superior performance, closely followed by the underrated XVC. Apart from the comparison, we show that the four-dimensional light field data can be compressed with a higher ratio than independent still images while maintaining the same visual quality of a perceived picture.

In this paper, allocation memory algorithms (i.e. First Fit, Next Fit and Best Fit) are redesigned to offload the tasks of end devices in multi-device multi-task D2D communication. The proposed algorithms offload each task to single device, which enhance the performance by 4x at their maximum performance. To enhance the performance, the next fit algorithm is redesigned to offload the task to multiple devices, which enhances the performance to 88x at its maximum performance. The proposed algorithms are evaluated for different cell scenarios (i.e. femto, pico, micro and macro cells). Simulation results demonstrates that utilizing computation offloading minimizes the latency.

In today’s world most of the people are becoming more and more dependent on visual media information particularly digital images and videos. However, the advancement in technology has lead to increase in number of editing tools that make the image and video tampering easier and faster. The content of the digital video can be manipulated or altered effectively with the help of such editing tools without leaving any noticeable signs. Numerous attempts have been made over the previous decade to recognize the altered videos and localization of the altered frames with high exactness dependent on some extraordinarily structured system. This paper gives a detailed review of existing methodologies for recognizing tampered videos, localization of the altered frames and reconstruction of the tampered video.

Some of the vital problems around the conventional CMOS technology are leakage-power consumption, physical-scalability limits, and short-channel effects. These deficiencies have led to many studies about nano-scale designs. Quantum dot cellular automata (QCA) is a potential answer in nanotechnology. Scholars have considered the four-dot squared cell as the main factor in the QCA. Also, a full-adder is a fundamental unit in every digital system. However, the importance of cell and area consumption limitation in circuit designing has been completely ignored in most of the related studies. Therefore, in this paper, we have offered a one-bit multi-layer full-adder cell. The practical accuracy of the proposed circuits has been assessed using QCADesigner. According to the obtained results and the design, the presented design has efficient cell usage against all the prior designs regarding cell counts and area occupation, leading to around 7% improvement in cell number than the common full-adder design. The simulation outcomes have also shown that the introduced design has excellent efficiency regarding cell and area aspects.

Privacy image classification can help people detect privacy images when people share images. In this paper, we propose a novel method using multi-level and multi-scale features for privacy image classification. We first use CNN (Convolutional Neural Network) to extract multi-levels features. Then, max-pooling layers are employed to obtain multi-scale features at each level. Finally, we propose two feature aggregation models, called Privacy-MSML and Privacy-MLMS to fuse those features for image privacy classification. In Privacy-MSML, multi-scale features of the same level are first integrated and then the integrated features are fused. In Privacy-MLMS, multi-level features of the same scale are first integrated and then the integrated features are fused. Our experiments on a real-world dataset demonstrate the proposed method can achieve better performance compared with the state-of-the-art solutions.

Fog computing is considered a formidable next-generation complement to cloud computing. Nowadays, in light of the dramatic rise in the number of IoT devices, several problems have been raised in cloud architectures. By introducing fog computing as a mediate layer between the user devices and the cloud, one can extend cloud computing's processing and storage capability. Offloading can be utilized as a mechanism that transfers computations, data, and energy consumption from the resource-limited user devices to resource-rich fog/cloud layers to achieve an optimal experience in the quality of applications and improve the system performance. This paper provides a systematic and comprehensive study to evaluate fog offloading mechanisms' current and recent works. Each selected paper's pros and cons are explored and analyzed to state and address the present potentialities and issues of offloading mechanisms in a fog environment efficiently. We classify offloading mechanisms in a fog system into four groups, including computation-based, energy-based, storage-based, and hybrid approaches. Furthermore, this paper explores offloading metrics, applied algorithms, and evaluation methods related to the chosen offloading mechanisms in fog systems. Additionally, the open challenges and future trends derived from the reviewed studies are discussed.

In precision marketing for online communities, the existing text-based methods of user positioning cannot position new users rapidly, and they have low positioning efficiency when there is a large number of users. This research proposes a systematic method for the positioning of online community users. In this method, text mining and clustering algorithms are combined to cluster users, and then the user clusters are effectively matched with users' basic attributes through a multinomial logistic regression model. By this means, efficient positioning under the circumstances of a rapid increase in new users and a large number of users can be achieved. Calculation results from a real world example show that this method can effectively solve the problems found in traditional user positioning methods and provides a productive new approach to community user positioning. The study also offers suggestions for user classification management from the perspective of precision marketing.

The development of web cameras and smart phones is mature, and more and more facial recognition-related applications are implemented on embedded systems. The demand for real-time face recognition on embedded systems is also increasing. In order to improve the accuracy of face recognition, most of the modern face recognition systems consist of multiple deep neural network models for recognition. However, in an embedded system, integrating these complex neural network models and execute simultaneously is not easy to achieve the goal of real-time recognition of human faces and their identities. In view of this, this study proposes a new frame analysis mechanism, continuous frames skipping mechanism (CFSM), which can analyze the frame in real time to determine whether it is necessary to perform face recognition on the current frame. Through the analysis of CFSM, the frames that do not need to be re-recognized for face are omitted. In this way, the workload of the face recognition system will be greatly reduced to achieve the goal of real-time face recognition in the embedded system. The experimental results show that the proposed CFSM mechanism can greatly increase the speed of face recognition in the video on the embedded system, achieving the goal of real-time face recognition.

Accessible websites are a necessity for everyone, especially for people with disabilities. However, this is an objective that has not been achieved yet and requires initiatives that continue guiding the way towards an increasingly inclusive web and lowering accessibility barriers. One of the first steps in this regard is to identify the accessibility level of a particular website, and for this purpose, a set of automatic tools and manual instruments have been developed. These tools allow calculating an accessibility level from the expert’s perspective and from the website’s source code using as a reference the accessibility guidelines defined by the W3C. The authors state that evaluating accessibility with just one of these methods is not enough, so therefore its integration is required. This article presents a tool called Accessibility Evaluation Tool, which allows and facilitates the integration of the accessibility evaluation delivered by an automatic validator with the manual evaluation of elements by experts.

ASCON is one of the elegant designs of authenticated encryption with associated data (AEAD) that was selected as the first choice for lightweight applications in the CAESAR competition, which also has been submitted to NIST lightweight cryptography standardization. ASCON has been in the literature for a while; however, there has been no successful AEAD which is secure and at the same time lighter than ASCON. In this article, we have overcome the challenge of constructing a permutation that is lighter than the ASCON permutation while ensuring a similar performance, and based on which we achieve a more lightweight AEAD which we call Sycon. Extensive security analysis of Sycon confirms that it provides the same level of security as that of ASCON. Our hardware implementation result shows that the Sycon permutation has 5.35% reduced area, compared to the ASCON permutation. This leads to a remarkable area reduction for Sycon AEAD which is about 14.95% as compared to ASCON AEAD. We regard Sycon as a new milestone as it is the lightest among all the AEADs belonging to the ASCON family.

A standout amongst the most dangers to the cyber security is known as Botnet since it offers a conveyed stage for many undesirable activities. From the network traffic flow, the identification of Botnet is a fundamental test. Artificial Neural Network–Particle Swarm Optimization (ANN–PSO) based botnet discovery is proposed in this paper. In this paper, ISCX dataset is utilized for botnet location. The features are classified as botnet flow and normal flow by giving the features separated from the dataset as a contribution to the grouping. For grouping, we have displayed ANN–PSO which lessens the false classification ratio and time multifaceted nature to 3.3% and 14 s. We contrast our proposed work with other existing work and demonstrate that our work is superior to anything that of alternate works in the simulation results.

There have been considerable advancements in multimedia technologies over the past 5 years. It has been observed that state-of-the-art multimedia systems face three broad categories of challenges: (1) Dependency on continuous network connections, (2) Data-sharing applications & collaboration, and (3) Security issues. Among these, security vulnerability poses a major threat to modern multimedia systems. Therefore, it is imperative to carefully investigate the security issues that can endanger wireless and mobile communications. At present, multimedia security research mainly focuses on wireless traffic monitoring, wireless system attacks, and wireless and mobile security. In this paper, we have used the network attack-type, “Reconnaissance”, which contains two types of malicious activities: (1) OS scanning, and (2) Fuzzing. The goal of this paper is to quantify multimedia security risks due to Fuzzing by using various types of machine learning models. The highest accuracy i.e., 96.8%, is obtained using the XGBoost classifier, which is good compared to the existing models present in the literature. This is the first paper, to the best of our knowledge, that uses machine learning methods to differentiate between benign and malignant Fuzzing attacks.

In ciphertext-policy attribute-based encryption (CP-ABE), once malicious users successfully decrypt the encrypted data, they can obtain the real original personal privacy data, leading to serious privacy leakages problems. Thus, if the user does not access the original private data but the perturbed data while guaranteeing statistical characteristics, the privacy protection capabilities of CP-ABE will be greatly improved. Motivated by this, an integration of basic CP-ABE and local differential privacy (LDP) or achieving multiple privacy levels data sharing (MPLDS) is constructed to provide double privacy protection for data owners, which is with a relatively lower complexity and higher data utility. To prevent different trusted users from colluding and gaining more privacy beyond their trust levels, a randomized perturbation strategy is elaborately designed for resisting collusion attacks (RCA) while ensuring the fact that the output of RCA perturbation strategy is the same as that of the original perturbation, which has been proved from the theoretical level. Finally, the proposed MPLDS scheme is simulated and verified on both synthetic and real data sets, which indicates that the proposed MPLDS scheme outperforms the existing MPPDS scheme while greatly reducing the complexity.