IoT Task Offloading in Edge Computing Using Non-Cooperative Game Theory for Healthcare Systems

In this paper,we present a comprehensive system model for Industrial Internet of Things(IIoT)networks empowered by Non-Orthogonal Multiple Access(NOMA)and Mobile Edge Computing(MEC)technologies.The network comprises essential components such as base stations,edge servers,and numerous IIoT devices characterized by limited energy and computing capacities.The central challenge addressed is the optimization of resource allocation and task distribution while adhering to stringent queueing delay constraints and minimizing overall energy consumption.The system operates in discrete time slots and employs a quasi-static approach,with a specific focus on the complexities of task partitioning and the management of constrained resources within the IIoT context.This study makes valuable contributions to the field by enhancing the understanding of resourceefficient management and task allocation,particularly relevant in real-time industrial applications.Experimental results indicate that our proposed algorithmsignificantly outperforms existing approaches,reducing queue backlog by 45.32% and 17.25% compared to SMRA and ACRA while achieving a 27.31% and 74.12% improvement in Qn O.Moreover,the algorithmeffectively balances complexity and network performance,as demonstratedwhen reducing the number of devices in each group(Ng)from 200 to 50,resulting in a 97.21% reduction in complexity with only a 7.35% increase in energy consumption.This research offers a practical solution for optimizing IIoT networks in real-time industrial settings.

一般间隙与One-Off条件的序列模式匹配

带有间隙约束的模式匹配问题是序列模式挖掘的关键问题之一.目前,大多数的研究都为非负间隙,对字符串中每个字符的出现顺序有着严格的要求.为了增加匹配的灵活性,并且考虑到在序列模式挖掘中采用one-off条件更加合理,研究一般间隙与one-off条件下的模式匹配问题.该问题为NP-Hard问题.为了有效地求解该问题,提出了MSAING(maximum sequential pattern matching with one-off and general gaps condition)算法:首先,利用Reverse策略使模式与序列达到最佳的匹配状态;然后,使用线性表的结构使匹配过程中消耗的时间和空间大幅度地降低,同时,利用回溯机制提高匹配的成功率;最后,根据inside_Checking机制判断模式串是否会产生内部重复现象,以进一步提高算法的执行效率.理论证明了MSAING算法的完备性,实验结果验证了MSAING算法匹配结果的准确性以及在时间和空间方面的高效性.

The Skin Deformation of a 3D Virtual Human

This paper presents a skin deformation algorithm for creating 3D characters or virtual human models. The algorithm can be applied to rigid deformation, joint dependent localized deformation, skeleton driven deformation, cross contour deformation, and free-form deformation （FFD）. These deformations are computed and demonstrated with examples and the algorithm is applied to overcome the difficulties in mechanically simulating the motion of the human body by club-shape models. The techniques described in this article enables the reconstruction of dynamic human models that can be used in defining and representing the geometrical and kinematical characteristics of human motion.

Web Security and Log Management: An Application Centric Perspective

The World Wide Web has been an environment with many security threats and lots of reported cases of security breaches. Various tools and techniques have been applied in trying to curb this problem, however new attacks continue to plague the Internet. We discuss risks that affect web applications and explain how network-centric and host-centric techniques, as much as they are crucial in an enterprise, lack necessary depth to comprehensively analyze overall application security. The nature of web applications to span a number of servers introduces a new dimension of security requirement that calls for a holistic approach to protect the information asset regardless of its physical or logical separation of modules and tiers. We therefore classify security mechanisms as either infrastructure-centric or application-centric based on what asset is being secured. We then describe requirements for such application-centric security mechanisms.

MapReduce与Spark用于大数据分析之比较

评述了MapReduce与Spark两种大数据计算算法和架构,从背景、原理以及应用场景进行分析和比较,并对两种算法各自优点以及相应的限制做出了总结.当处理非迭代问题时,MapReduce凭借其自身的任务调度策略和shuffle机制,在中间数据传输数量以及文件数目方面的性能要优于Spark;而在处理迭代问题和一些低延迟问题时,Spark可以根据数据之间的依赖关系对任务进行更合理的划分,相较于MapReduce,有效地减少了中间数据传输数量与同步次数,提高了系统的运行效率.

一般间隙序列模式挖掘的关键词抽取

本文提出了有监督的关键词抽取算法——KEING(Keyphrase Extraction using sequentIal patterns with oNe-off and General gaps condition)算法.首先,将每篇文档作为一个序列库,利用SPING(Sequential Patterns mIning with oNe-off and General gaps condition)算法获取词语之间的关系及其多种变化形式,并利用统计模式特征的方式描述候选关键词;然后,通过朴素贝叶斯分类算法对大量带标记的训练数据进行训练,构造分类器;最后利用分类器从测试文档中识别出关键词.通过实验验证了SPING算法的完备性以及KEING算法的有效性.

Research on Modelling Digital Paper-cut Preservation

This paper studies the model of the digital preservation of paper-cut after analyzing the state-of-the-art of the preservation of intangible cultural heritage at home and abroad, focusing on paper-cutting in Hebei, China in a comprehensive approach of sociology, anthropology, folk art, folklore, communication, computer, and information science. Models, methods, and solutions for the preservation and retention of national and folk cultural heritage are proposed. A virtual multimedia interactive system framework of the digital preservation of national and folk cultural heritage is constructed. The standards of paper-cut digitization are studied. The main content of the paper involves： regulated data collection and recording of the scattered Chinese folk paper-cut works in Hebei; digitization, optimization, compression, classification, icon and pattern extraction, vectorization, and work analysis of the first-hand material. This paper also investigates the demonstration, dissemination, database construction, and retrieval of the classified material, icons, and patterns; the demonstration of the reconstruction and application of icon and pattern database; the design and development of an immersive virtual gaming platform of the multi-media scenes and production process of folk paper-cut.

Sentiment Classification Based on Piecewise Pooling Convolutional Neural Network

Recently,the effectiveness of neural networks,especially convolutional neural networks,has been validated in the field of natural language processing,in which,sentiment classification for online reviews is an important and challenging task.Existing convolutional neural networks extract important features of sentences without local features or the feature sequence.Thus,these models do not perform well,especially for transition sentences.To this end,we propose a Piecewise Pooling Convolutional Neural Network(PPCNN)for sentiment classification.Firstly,with a sentence presented by word vectors,convolution operation is introduced to obtain the convolution feature map vectors.Secondly,these vectors are segmented according to the positions of transition words in sentences.Thirdly,the most significant feature of each local segment is extracted using max pooling mechanism,and then the different aspects of features can be extracted.Specifically,the relative sequence of these features is preserved.Finally,after processed by the dropout algorithm,the softmax classifier is trained for sentiment classification.Experimental results show that the proposed method PPCNN is effective and superior to other baseline methods,especially for datasets with transition sentences.

An Optimized Data Fusion Paradigm for WSN Based on Neural Networks

Wireless sensor networks(WSNs)have gotten a lot of attention as useful tools for gathering data.The energy problem has been a fundamental constraint and challenge faced by many WSN applications due to the size and cost constraints of the sensor nodes.This paper proposed a data fusion model based on the back propagation neural network(BPNN)model to address the problem of a large number of invalid or redundant data.Using three layeredbased BPNNs and a TEEN threshold,the proposed model describes the cluster structure and filters out unnecessary details.During the information transmission process,the neural network’s output function is used to deal with a large amount of sensing data,where the feature value of sensing data is extracted and transmitted to the sink node.In terms of life cycle,data traffic,and network use,simulation results show that the proposed data fusion model outperforms the traditional TEEN protocol.As a result,the proposed scheme increases the life cycle of the network thereby lowering energy usage and traffic.

Empirical Study on B/C Apparel Consumption Behavior Based on Data Mining Technology

In order to accurately identify the characters associated with consumption behavior of apparel online shopping, a typical B/ C clothing enterprise in China was chosen. The target experimental database containing 2000 data records was obtained based on web service logs of sample enterprise. By means of clustering algorithm of Clementine Data Mining Software, K-means model was set up and 8 clusters of consumer were concluded. Meanwhile, the implicit information existed in consumer＇s characters and preferences for clothing was found. At last, 31 valuable association rules among casual wear, formal wear, and tie-in products were explored by using web analysis and Aprior algorithm. This finding will help to better understand the nature of online apparel consumption behavior and make a good progress in personalization and intelligent recommendation strategies.

Directional Modulation Based on a Quantum Genetic Algorithm for a Multiple-Reflection Model

Directional modulation is one of the hot topics in data security researches.To fulfill the requirements of communication security in wireless environment with multiple paths,this study takes into account the factors of reflections and antenna radiation pattern for directional modulation.Unlike other previous works,a novel multiple-reflection model,which is more realistic and complex than simplified two-ray reflection models,is proposed based on two reflectors.Another focus is a quantum genetic algorithm applied to optimize antenna excitation in a phased directional modulation antenna array.The quantum approach has strengths in convergence speed and the globe searching ability for the complicated model with the large-size antenna array and multiple paths.From this,a phased directional modulation transmission system can be optimized as regards communication safety and improve performance based on the constraint of the pattern of the antenna array.Our work can spur applications of the quantum evolutionary algorithm in directional modulation technology,which is also studied.

An Improved Dictionary Cracking Scheme Based on Multiple GPUs for Wi-Fi Network

The Internet has penetrated all aspects of human society and has promoted social progress.Cyber-crimes in many forms are commonplace and are dangerous to society and national security.Cybersecurity has become a major concern for citizens and governments.The Internet functions and software applications play a vital role in cybersecurity research and practice.Most of the cyber-attacks are based on exploits in system or application software.It is of utmost urgency to investigate software security problems.The demand for Wi-Fi applications is proliferating but the security problem is growing,requiring an optimal solution from researchers.To overcome the shortcomings of the wired equivalent privacy(WEP)algorithm,the existing literature proposed security schemes forWi-Fi protected access(WPA)/WPA2.However,in practical applications,the WPA/WPA2 scheme still has some weaknesses that attackers exploit.To destroy a WPA/WPA2 security,it is necessary to get a PSK pre-shared key in pre-shared key mode,or an MSK master session key in the authentication mode.Brute-force cracking attacks can get a phase-shift keying(PSK)or a minimum shift keying(MSK).In real-world applications,many wireless local area networks(LANs)use the pre-shared key mode.Therefore,brute-force cracking of WPA/WPA2-PSK is important in that context.This article proposes a new mechanism to crack theWi-Fi password using a graphical processing unit(GPU)and enhances the efficiency through parallel computing of multiple GPU chips.Experimental results show that the proposed algorithm is effective and provides a procedure to enhance the security of Wi-Fi networks.

Deployment Optimization Strategy for a Two-Tier Wireless Visual Sensor Network

Wireless visual sensor network (VSN) can be said to be a special class of wireless sensor network (WSN) with smart-cameras. Due to its visual sensing capability, it has become an effective tool for applications such as large area surveillance, environmental monitoring and objects tracking. Different from a conventional WSN, VSN typically includes relatively expensive camera sensors, enhanced flash memory and a powerful CPU. While energy consumption is dominated primarily by data transmission and reception, VSN consumes extra power onimage sensing, processing and storing operations. The well-known energy-hole problem of WSNs has a drastic impact on the lifetime of VSN, because of the additional energy consumption of a VSN. Most prior research on VSN energy issues are primarily focusedon a single device or a given specific scenario. In this paper, we propose a novel optimal two-tier deployment strategy for a large scale VSN. Our two-tier VSN architecture includes tier-1 sensing network with visual sensor nodes (VNs) and tier-2 network having only relay nodes (RNs). While sensing network mainly performs image data collection, relay network only for wards image data packets to the central sink node. We use uniform random distribution of VNs to minimize the cost of VSN and RNs are deployed following two dimensional Gaussian distribution so as to avoid energy-hole problem. Algorithms are also introduced that optimizes deployment parameters and are shown to enhance the lifetime of the VSN in a cost effective manner.

INFORMATION SHARING AND STRATEGIC SIGNALING IN SUPPLY CHAINS

Information sharing in procurement occurs in rich and varied industry contexts in which managerial decisions are made and organizational strategy is formulated. We explore how information sharing ought to work in procurement contexts that involve investments in inter-organizational information systems （IOS） and collaborative planning, forecasting and replenishment （CPFR） practices. How and under what circumstances does a firm that plays the role of a supply chain buyer decide to share information on key variables, such as point-of-sale consumer demand data with its supplier, up the supply chain？ This is a key issue that crosses the boundary between supply chain management and information systems （IS） management. The answers that we provide are based on our use of a game-theoretic signaling model of buyer and supplier strategy in the presence of uncertainties about final consumer demand. We also explore the connection between operational costs that are associated with the firm＇s information sharing and information withholding strategies. Our results provide normative guidance to supply chain buyers about how to interpret different demand uncertainty scenarios to improve their decisions and generate high value. From the IS management perspective, we show the impacts on the firm of different information sharing approaches that are made possible by present day technologies.

Technical and Vocational Education Training in Brunei Darussalam——Pathway to Higher Educations

In the 21stcentury educational landscape,higher educational institutions(HEI)play a crucial role in the shaping of social and economic strategies of a developing country.HEI’s main role is to develop and train the workforce for the middle to higher level employment.Technical and vocational education(TVE)on the other hand,plays the role in filling the major work force at the lower or blue collar level and at the same time,providing pathways and opportunities for them to pursue further educational needs.In Brunei Darussalam,HEI works closely with TVE to ensure there is a continuation pathway for TVE to HEI although there are fundamental differences in the philosophy,pedagogy and assessment measures.In Universiti Teknologi Brunei(UTB),the design of the curriculum has always been outcome based;heavily related to skills and practices of the job market,similar to TVE’s teaching and learning philosophy.In this study,we are looking at the brief development of TVE and HE across the world and Brunei,the qualification framework of Brunei and curriculum design of UTB.The study will highlight how UTB provides a smooth transition for TVE feeders to the university to support the value of practical learning in workplace settings,combined with conceptual learning in an academic setting.The primary role of UTB is to assist graduates in the transition to professional practice.