Research on the Training Mode of College Students’Computational Thinking Ability Based on Issues-driven and Course Objectives

Computer science concept and ability is crucial to modern information technology.As a basic computer science education,computational thinking has become one of the first courses for college students and the foundation for training for non-computer professionals.Computational thinking is a way of thinking that students in all majors should master.In the process of teaching,students learn to use computational thinking to solve professional and technical problems and make good use of computer’s powerful tools.It is one of the major themes of recent teaching research.We set course objectives from issues-driven and correspond each class to the module objectives,so that the focus of course content is clear.The learning clues for the students are clear,which is conducive to the cultivation and improvement of computational thinking.

A Smart Room to Promote Autonomy of Disabled People due to Stroke

A cerebral vascular accident,known as common language stroke,is one of the main causes of mortality and remains the primary cause of acquired disabilities in adults.Those disabled people spend most of their time at home in their living rooms.In most cases,appliances of a living room(TV,light,cooler/heater,window blinds,etc.)are generally controlled by direct manipulation of a set of remote controls.Handling many remote controls can be disturbing and inappropriate for these people.In addition,in many cases these people could be alone at home and must open the door for visitors after their identification by either moving to the door or using an intercom system which requires in both cases a physical activity.Furthermore,these people need a continuous health monitoring especially blood pressure to avoid a recurrent stroke.Smart spaces and assisted technologies would be beneficial to assist person with disabilities to live indepen-dently,enhance their quality of life and empower their autonomy.A complete sys-tem which improves and facilitates the daily life and covers all aspects such as appliances automation,appropriate interaction mode and health monitoring of these people is still lacking.The aim of this work is to create a safe and high-quality living environment for persons with disabilities to enable them to live more independently by automating the operation of a living room appliances according to the current context without the need to use remote control devices,the use of a suitable interaction modality with appliances that require direct interaction and a remote health monitoring system which can alert relatives and caregivers in case of an emergency.

Research on Distributed System Course Under the Background of New Engineering

Distributed System Course is a professional computer course of Harbin Institute of Technology.With the guidance of the education strategy under the background of New Engineering,we adhere to the concept of cultivating diverse and innovative outstanding engineering and technology talents.This course conducts research on the teaching content,teaching mode,and evaluation system.It combines the traditional teaching mode with the online teaching mode,as well as problem-driven theories with practice,and adopts a diversified evaluation system.The research of the course has fully mobilized students’learning enthusiasm,improved teaching quality,and achieved significant teaching results.

Security Test Case Prioritization through Ant Colony Optimization Algorithm

Security testing is a critical concern for organizations worldwide due to the potential financial setbacks and damage to reputation caused by insecure software systems.One of the challenges in software security testing is test case prioritization,which aims to reduce redundancy in fault occurrences when executing test suites.By effectively applying test case prioritization,both the time and cost required for developing secure software can be reduced.This paper proposes a test case prioritization technique based on the Ant Colony Optimization(ACO)algorithm,a metaheuristic approach.The performance of the ACO-based technique is evaluated using the Average Percentage of Fault Detection(APFD)metric,comparing it with traditional techniques.It has been applied to a Mobile Payment Wallet application to validate the proposed approach.The results demonstrate that the proposed technique outperforms the traditional techniques in terms of the APFD metric.The ACO-based technique achieves an APFD of approximately 76%,two percent higher than the second-best optimal ordering technique.These findings suggest that metaheuristic-based prioritization techniques can effectively identify the best test cases,saving time and improving software security overall.

A Trailblazing Framework of Security Assessment for Traffic Data Management

Connected and autonomous vehicles are seeing their dawn at this moment.They provide numerous benefits to vehicle owners,manufacturers,vehicle service providers,insurance companies,etc.These vehicles generate a large amount of data,which makes privacy and security a major challenge to their success.The complicated machine-led mechanics of connected and autonomous vehicles increase the risks of privacy invasion and cyber security violations for their users by making them more susceptible to data exploitation and vulnerable to cyber-attacks than any of their predecessors.This could have a negative impact on how well-liked CAVs are with the general public,give them a poor name at this early stage of their development,put obstacles in the way of their adoption and expanded use,and complicate the economic models for their future operations.On the other hand,congestion is still a bottleneck for traffic management and planning.This research paper presents a blockchain-based framework that protects the privacy of vehicle owners and provides data security by storing vehicular data on the blockchain,which will be used further for congestion detection and mitigation.Numerous devices placed along the road are used to communicate with passing cars and collect their data.The collected data will be compiled periodically to find the average travel time of vehicles and traffic density on a particular road segment.Furthermore,this data will be stored in the memory pool,where other devices will also store their data.After a predetermined amount of time,the memory pool will be mined,and data will be uploaded to the blockchain in the form of blocks that will be used to store traffic statistics.The information is then used in two different ways.First,the blockchain’s final block will provide real-time traffic data,triggering an intelligent traffic signal system to reduce congestion.Secondly,the data stored on the blockchain will provide historical,statistical data that can facilitate the analysis of traffic conditions according to past behavior.

Dynamic Integrity Measurement Model Based on vTPM

With the development of cloud computing, virtualization technology has been widely used in our life. Meanwhile, it became one of the key targets for some attackers. The integrity measurement in virtual machine has become an urgent problem. Some of the existing virtualization platform integrity measurement mechanism introduces the trusted computing technology, according to a trusted chain that the Trusted Platform Module(TPM) established for trusted root to measure the integrity of process in static. But this single chain static measurement cannot ensure the dynamic credible in platform running. To solve the problem that the virtual trusted platform can not guarantee the dynamic credibility, this paper put forward Dynamic Integrity Measurement Model(DIMM) based on virtual Trusted Platform Module(v TPM) which had been implemented with typical virtual machine monitor Xen as an example. DIMM combined with virtual machine introspection and event capture technology to ensure the security of the entire user domain. Based on the framework, this paper put forward Self-modify dynamic measurement strategy which can effectively reduce the measurement frequency and improve the measurement performance. Finally, it is proved that the validity and feasibility of the proposed model with comparison experiments.

Effective multicasting algorithm for dynamic membership with delay constraint

This paper proposes an effective heuristic algorithm for dynamic multicast routing with delay-constrained DDMR. The tree constructed by DDMR has the following characteristics: (1) multicast tree changes with the dynamic memberships; (2) the cost of the tree is as small as possible at each node addition/removal event; (3) all of the path delay meet a fixed delay constraint; (4) minimal perturbation to an existing tree. The proposed algorithm is based on “damage” and “usefulness” concepts proposed in previous work, and has a new parameter bf (Balancing Factor) for judging whether or not to rearrange a tree region when mem- bership changes. Mutation operation in Genetic Algorithm (GA) is also employed to find an attached node for a new adding node. Simulation showed that our algorithm performs well and is better than static heuristic algorithms, in term of cost especially.

AN EFFICIENT SIGNATURE SCHEME FROM CATALANO’S TRAPDOOR

Digital signature is one of the most important cryptographic primitives. We proposed a new digital signature scheme based on Catalano’s trapdoor. Since Catalano’s trapdoor is more efficient than existing trapdoors in number theory, our scheme need not modular exponentiation but several modular multiplications in the signing algorithm. We also proved our scheme is provably secure against adap-tively chosen message attack by using the Forking lemma.

Influence of smoking on postpartum depression in Japan

Aim: To investigate the influence of smoking on postpartum depression. Methods: One thousand fifty-one women, in a rural city in Aomori Prefecture, Japan, prospectively fulfilled the selection criteria and completed self-reporting questionnaires on postnatal depression at 5-6 days, 1 month, 4 months, 7 months and 12 months after childbirth, using the Edinburgh Postnatal Depression Scale (EPDS) and a life and social events scales including smoking habits. Results: Seven hundred seventy-seven women were non-smokers. Among two hundred seventy-four women who were smokers before becoming pregnant (26% of pregnant women), 241 women quit smoking during pregnancy and 33 women continued smoking. Smoking habits were significantly associated with sociopsychological states and we found that EPDS scores of smokers were significantly higher than EPDS of non-smokers. The EPDS scores of both non-smokers and smokers were higher at 5-6 days, but were stable from 1 month to 12 months, after childbirth. Fifty-one women who quit smoking after childbirth resumed smoking during he 1-12 month periods after childbirth. The EPDS scores of 51 women who resumed smoking were significantly reduced after they resumed smoking. Conclusion: The EPDS scores of smokers were higher than those of non-smokers and a smoking habit may help to alleviate postpartum depression.

A KAD Network Evolution Model Based on Node Behavior

An evolution model of KAD Dynamic Model Network(KDMN) is proposed to study the reason of hot node and simulate the process of network evolution based on node behavior from a holistic perspective.First,some symbols and meanings are introduced to describe nodes relationship and network states at a time step.Second,some evolution rules for network are formulated when node behaviors of join,exit,routing table update,data retrieval and content index distribution happen with different contextual scene in KAD network.In addition,a lightweight simulator is designed to implement the KDMN model.Moreover,an example of how to use the simulator to simulate the network changes in order to observe the result is described in detail.Finally,the KDMN is applied to analyze the reason for the formation of hot nodes in the BT and eMule network in the experiment.The different evolution principles of local priority,global priority and hybrid random are adopted based on the provision of network protocol of BT and eMule.The result of this experiment demonstrates that there are some hot nodes exist in the KAD network.However,the principle of hybrid random can effectively alleviate the phenomenon that a node is widely linked with others compared with global and local priority.

A novel algorithm for Ethernet-passive-optical-network dynamic bandwidth allocation based on threshold

The Ethernet passive optical network(EPON) is the next generation of broad-band network technique.A crucial issue in EPONs is the sharing of uplink bandwidth among optical network units(ONUs).This article provides a novel dynamic bandwidth allocation algorithm,i.e.threshold dynamic bandwidth allocation(TDBA),which is based on adaptive threshold,to increase resource utilization.The algorithm uses ONU data-transmitting rate to adjust optical line terminal(OLT) receiving data threshold from an ONU.Simulation results show that this algorithm can decrease average packet delay and increase network throughput in a 10G EPON system.

A PEER-TO-PEER INCENTIVE SCHEME FOR OVERCOMING FREE RIDING

Free riding has a great influence on the expandability,robustness and availability of Peer-to-Peer(P2P) network.Controlling free riding has become a hot research issue both in academic and industrial communities.An incentive scheme is proposed to overcoming free riding in P2P network in this paper.According to the behavior and function of nodes,the P2P network is abstracted to be a Distributed and Monitoring-based Hierarchical Structure Mechanism(DMHSM) model.A utility function based on several influencing factors is defined to determine the contribution of peers to the whole system.This paper also introduces reputation and permit mechanism into the scheme to guarantee the Quality of Service(QoS) and to reward or punish peers in the network.Finally,the simulation results verify the effectiveness and feasibility of this model.

FARMER:A novel approach to file access correlation mining and evaluation reference model

File semantic has proven effective in optimizing large scale distributed file system.As a consequence of the elaborate and rich I/O interfaces between upper layer applications and file systems,file system can provide useful and insightful information about semantic.Hence,file semantic mining has become an increasingly important practice in both engineering and research community.Unfortunately,it is a challenge to exploit file semantic knowledge because a variety of factors coulda ffect this information exploration process.Even worse,the challenges are exacerbated due to the intricate interdependency between these factors,and make it difficult to fully exploit the potentially important correlation among various semantic knowledges.This article proposes a file access correlation miming and evaluation reference（FARMER） model,where file is treated as a multivariate vector space,and each item within the vector corresponds a separate factor of the given file.The selection of factor depends on the application,examples of factors are file path,creator and executing program.If one particular factor occurs in both files,its value is non-zero.It is clear that the extent of inter-file relationships can be measured based on the likeness of their factor values in the semantic vectors.Benefit from this model,FARMER represents files as structured vectors of identifiers,and basic vector operations can be leveraged to quantify file correlation between two file vectors.FARMER model leverages linear regression model to estimate the strength of the relationship between file correlation and a set of influencing factors so that the ＂bad knowledge＂ can be filtered out.To demonstrate the ability of new FARMER model,FARMER is incorporated into a real large-scale object-based storage system as a case study to dynamically infer file correlations.In addition FARMER-enabled optimize service for metadata prefetching algorithm and object data layout algorithm is implemented.Experimental results show that is FARMER-enabled prefetching algorithm is shown to reduce the metadata operations latency by approximately 30%-40% when compared to a state-of-the-art metadata prefetching algorithm and a commonly used replacement policy.

An Improved Apriori Algorithm

In this paper,We study the Apriori and FP-growth algorithm in mining association rules and give a method for computing all the frequent item-sets in a database.Its basic idea is giving a concept based on the boolean vector business product,which be computed between all the businesses,then we can get all the two frequent item-sets(minsup=2).We basis their inclusive relation to construct a set-tree of item-sets in database transaction,and then traverse path in it and get all the frequent item-sets.Therefore,we can get minimal frequent item sets between transactions and items in the database without scanning the database and iteratively computing in Apriori algorithm.

QL-CBR Hybrid Approach for Adapting Context-Aware Services

A context-aware service in a smart environment aims to supply services according to user situational information,which changes dynamically.Most existing context-aware systems provide context-aware services based on supervised algorithms.Reinforcement algorithms are another type of machine-learning algorithm that have been shown to be useful in dynamic environments through trialand-error interactions.They also have the ability to build excellent self-adaptive systems.In this study,we aim to incorporate reinforcement algorithms(Q-learning)into a context-aware system to provide relevant services based on a user’s dynamic context.To accelerate the convergence of reinforcement learning(RL)algorithms and provide the correct services in real situations,we propose a combination of the Q-learning and case-based reasoning(CBR)algorithms.We then analyze how the incorporation of CBR enables Q-learning to become more effi-cient and adapt to changing environments by continuously producing suitable services.Simulation results demonstrate the effectiveness of the proposed approach compared to the traditional CBR approach.

Improving the Light Extraction Efficiency of GaN-Based Light-Emitting Diode

The light extraction efficiency caused by total internal reflection is low. Based on the analysis of the existing technology, a new design scheme is proposed in this paper to improve the light extraction efficiency. The air gap photonic crystal is embedded on the GaN-based patterned sapphire substrate, which can reduce line misalignment and improve light extraction efficiency. The internal structure of the GaN-based LED epitaxial layer is composed of an electron emission layer, a quantum well in the light-emitting recombination region, and an electron blocking layer. Experimental results show that this method significantly improves the extraction efficiency of LED light.

Application of Sensitivity Encoding Reconstruction for MRI with BOLD Signal

The work of this paper analyzes the performance of Sensitivity Encoding (SENSE) through actual data sets and determines the problem of computational efficiency. It corrects the error of the detection signal through the calibration function of the percentage signal change, and uses the three-dimensional sensor image reconstruction technology to calibrate the sensitivity of the blood to the magnetic change, enhances the sensitivity of the magnetic susceptibility gradient, and reduces the scanning time of the MRI experiment. The actual data set handles the image resolution. The performance and experimental results of SENSE are analyzed through actual data sets.

Sensitivity Encoding Reconstruction for MRI with Gridding Algorithm

The Sensitivity Encoding (SENSE) parallel reconstruction scheme for magnetic resonance imaging (MRI) is studied and implemented with gridding algorithm in this paper. In this paper, the sensitivity map profile, field map information and the spiral k-space data collected from an array of receiver coils are used to reconstruct un-aliased images from under-sampled data. The gridding algorithm is implemented with SENSE due to its ability in evaluating forward and adjoins operators with non-Cartesian sampled data. This paper also analyzes the performance of SENSE with real data set and identifies the computational issues that need to be improved for further research.

Diets of obese and non-obese older subjects

Aim: To compare diets between obese and non-obese in healthy older subjects. Methods: Forty-five obese and eighty-seven non-obese older subjects were recruited and their habitual factors that may contribute to obesity were assessed. Intakes of food by food-group in the obesity and non-obesity groups were checked using a visual type presentation of model nutriational balance chart (MNBC). Results: Average intake ratio of food relative to ideal food intake was significantly higher in the obesity group than the non-obesity group. The relationship of obesity and exercise or habitual activities was not significant. Conclusion: Food intake is a primary factor of obesity but regular exercise or habitual activities is not a key factor for obesity in older subjects. Since exercise habit is difficult to achieve in older subjects, particularly those who are obese, food control using the present visualtype MNBC would be one strategy forthe management of obesity.