Utilizing Machine Learning with Unique Pentaplet Data Structure to Enhance Data Integrity

Data protection in databases is critical for any organization,as unauthorized access or manipulation can have severe negative consequences.Intrusion detection systems are essential for keeping databases secure.Advancements in technology will lead to significant changes in the medical field,improving healthcare services through real-time information sharing.However,reliability and consistency still need to be solved.Safeguards against cyber-attacks are necessary due to the risk of unauthorized access to sensitive information and potential data corruption.Dis-ruptions to data items can propagate throughout the database,making it crucial to reverse fraudulent transactions without delay,especially in the healthcare industry,where real-time data access is vital.This research presents a role-based access control architecture for an anomaly detection technique.Additionally,the Structured Query Language(SQL)queries are stored in a new data structure called Pentaplet.These pentaplets allow us to maintain the correlation between SQL statements within the same transaction by employing the transaction-log entry information,thereby increasing detection accuracy,particularly for individuals within the company exhibiting unusual behavior.To identify anomalous queries,this system employs a supervised machine learning technique called Support Vector Machine(SVM).According to experimental findings,the proposed model performed well in terms of detection accuracy,achieving 99.92%through SVM with One Hot Encoding and Principal Component Analysis(PCA).

The Practice and Method of Integrating Fine Traditional Culture into Data Structure Course Teaching

To capitalize on the primary role of major course teaching and to facilitate students’understanding of abstract concepts in the data structure course,it is essential to increase their interest in learning and develop case studies that highlight fine traditional culture.By incorporating these culture-rich case studies into classroom instruction,we employ a project-driven teaching approach.This not only allows students to master professional knowledge,but also enhances their abilities to solve specific engineering problems,ultimately fostering cultural confidence.Over the past few years,during which educational reforms have been conducted for trial runs,the feasibility and effectiveness of these reform schemes have been demonstrated.

Multiresolution analysis over triangle meshes: method and data structure

A robust and efficient algorithm is presented to build multiresolution models (MRMs) of arbitrary meshes without requirement of subdivision connectivity. To overcome the sampling difficulty of arbitrary meshes, edge contraction and vertex expansion are used as downsampling and upsampling methods. Our MRMs of a mesh are composed of a base mesh and a series of edge split operations, which are organized as a directed graph. Each split operation encodes two parts of information. One is the modification to the mesh, and the other is the dependency relation among splits. Such organization ensures the efficiency and robustness of our MRM algorithm. Examples demonstrate the functionality of our method.

The Analysis of Seismic Data Structure and Oil and Gas Prediction

In this paper, a new concept called numerical structure of seismic data is introduced and the difference between numerical structure and numerical value of seismic data is explained. Our study shows that the numerical seismic structure is closely related to oil and gas-bearing reservoir, so it is very useful for a geologist or a geophysicist to precisely interpret the oil-bearing layers from the seismic data. This technology can be applied to any exploration or production stage. The new method has been tested on a series of exploratory or development wells and proved to be reliable in China. Hydrocarbon-detection with this new method for 39 exploration wells on 25 structures indi- cates a success ratio of over 80 percent. The new method of hydrocarbon prediction can be applied for: (1) depositional environment of reservoirs with marine fades, delta, or non-marine fades (including fluvial facies, lacustrine fades); (2) sedimentary rocks of reservoirs that are non-marine clastic rocks and carbonate rock; and (3) burial depths range from 300 m to 7000 m, and the minimum thickness of these reservoirs is over 8 m (main frequency is about 50 Hz).

Predicting the distribution of reservoirs by applying the method of seismic data structure characteristics： Example from the eighth zone in Tahe Oilfield

Seismic data structure characteristics means the waveform character arranged in the time sequence at discrete data points in each 2-D or 3-D seismic trace. Hydrocarbon prediction using seismic data structure characteristics is a new reservoir prediction technique. When the main pay interval is in carbonate fracture and fissure-cavern type reservoirs with very strong inhomogeneity, there are some difficulties with hydrocarbon prediction. Because of the special geological conditions of the eighth zone in the Tahe oil field, we apply seismic data structure characteristics to hydrocarbon prediction for the Ordovician reservoir in this zone. We divide the area oil zone into favorable and unfavorable blocks. Eighteen well locations were proposed in the favorable oil block, drilled, and recovered higher output of oil and gas.

The Practice-oriented Platform of“Data Structure and Algorithm”Curriculum

"Data Structure and Algorithm",which is an important major subject in computer science,has a lot of problems in teaching activity.This paper introduces and analyzes the situation and problems in this course study.A "programming factory" method is then brought out which is indeed a practice-oriented platform of the teachingstudy process.Good results are obtained by this creative method.

GRAPHICS DATA STRUCTURES IN MICROCOMPUTER AIDED DESIGN FOR OPEN PIT PLANNING

This paper discribes the importance and necessity of the study on the data structures for displaying the mining field using the interactive technology in open pit design and planning,based upon the grid block model. The commonly used data structures--rectangular array structure and quadtree structure ,are analyzed. Two compressed data structures--compressed circular link array structure and compressed doubly-linked circular array structure,are proposed,which are much more suitable for displaying the regularly gridded block model. When the two compressed data structures are adopted,the storage space can be tremendously saved and the algorithms are simple,while the requirements of the accuracy and the manipulating speed will be both satisfied for the interactive open pit short range plan formulation.

An Analysis on the Teaching Reform of Data Structure Course in China

For the course of“Data Structures”,this paper introduces the importance and existing problems of the data structure course.Through the literature and the current teaching in major universities,the existing teaching methods and their disadvantages are analyzed.The authors put forward teaching reform suggestions and designed an online course platform.

Open Volumetric Mesh-An Efficient Data Structure for Tetrahedral and Hexahedral Meshes

This work introduces a scalable and efficient topological structure for tetrahedral and hexahedral meshes. The design of the data structure aims at maximal flexibility and high performance. It provides a high scalability by using hierarchical representa-tions of topological elements. The proposed data structure is array-based, and it is a compact representation of the half-edge data structure for volume elements and half-face data structure for volumetric meshes. This guarantees constant access time to the neighbors of the topological elements. In addition, an open-source implementation named Open Volumetric Mesh （OVM） of the pro-posed data structure is written in C＋＋ using generic programming concepts.

Feedback control can make data structure layout randomization more cost-effective under zero-day attacks

In the wake of the research community gaining deep understanding about control-hijacking attacks,data-oriented attacks have emerged.Among data-oriented attacks,data structure manipulation attack(DSMA)is a major category.Pioneering research was conducted and shows that DSMA is able to circumvent the most effective defenses against control-hijacking attacks-DEP,ASLR and CFI.Up to this day,only two defense techniques have demonstrated their effectiveness:Data Flow Integrity(DFI)and Data Structure Layout Randomization(DSLR).However,DFI has high performance overhead,and dynamic DSLR has two main limitations.L-1:Randomizing a large set of data structures will significantly affect the performance.L-2:To be practical,only a fixed sub-set of data structures are randomized.In the case that the data structures targeted by an attack are not covered,dynamic DSLR is essentially noneffective.To address these two limitations,we propose a novel technique,feedback-control-based adaptive DSLR and build a system named SALADSPlus.SALADSPlus seeks to optimize the trade-off between security and cost through feedback control.Using a novel feedback-control-based adaptive algorithm extended from the Upper Confidence Bound(UCB)algorithm,the defender(controller)uses the feedbacks(cost-effectiveness)from previous randomization cycles to adaptively choose the set of data structures to randomize(the next action).Different from dynamic DSLR,the set of randomized data structures are adaptively changed based on the feedbacks.To obtain the feedbacks,SALADSPlus inserts canary in each data structure at the time of compilation.We have implemented SALADSPlus based on gcc-4.5.0.Experimental results show that the runtime overheads are 1.8%,3.7%,and 5.3% when the randomization cycles are selected as 10s,5s,and 1s respectively.

Data Structures in Multi-Objective Evolutionary Algorithms

Data structures used for an algorithm can have a great impact on its performance, particularly for the solution of large and complex problems, such as multi-objective optimization problems （MOPs）. Multi-objective evolutionary algorithms （MOEAs） are considered an attractive approach for solving MOPs~ since they are able to explore several parts of the Pareto front simultaneously. The data structures for storing and updating populations and non-dominated solutions （archives） may affect the efficiency of the search process. This article describes data structures used in MOEAs for realizing populations and archives in a comparative way, emphasizing their computational requirements and general applicability reported in the original work.

Analysis of Spatial Data Structures for Proximity Detection

Construction is a dangerous business. According to statistics, in every of the past thirteen years more than 1000 workers died in the USA construction industry. In order to minimize the overall number of these incidents, the research presented in this paper investigates to monitor and analyze the trajectories of construction resources first in a simulated environment and later on the actual job site. Due to the complex nature of the construction environment, three dimensional (3D) positioning data of workers is hardly collected. Although technology is available that allows tracking construction assets in real-time, indoors and outdoors, in 3D, at the same time, the continuously changing spatial and temporal arrangement of job sites requires any successfully working data processing system to work in real-time. This research paper focuses is safety on spatial data structures that offer the capability of realigning itself and reporting the distance of the closest neighbor in real-time. This paper presents results to simulations that allow the processing of real-time location data for collision detection and proximity analysis. The presented data structures and perform-ance results to the developed algorithms demonstrate that real-time tracking and proximity detection of resources is feasible.

A Form Evaluation System and Its Data Structure for Brush-Written Chinese Characters

A form evaluation system for brush-written Chinese characters is developed.Calligraphic knowledge used in the system is represented in the form of ruleswith the help of a data structure proposed in this paper. Reflecting the spe-cific hierarchical relations among radicals and strokes of Chinese characters,the proposed data structure is based upon a character model that can generatebrush-written Chinese characters on a computer. Through evaluation experi-ments using the developed system, it is shown that representation of calligraphicknowledge and form evaluation of Chinese characters can be smoothly realizedif the data structure is utilized.

A star-based data structure to store efficiently 3D topography in a database

For storing and modeling three-dimensional(3D)topographic objects(e.g.buildings,roads,dykes,and the terrain),tetrahedralizations have been proposed as an alternative to boundary representations.While in theory they have several advantages,current implementations are either not space efficient or do not store topological relationships(which makes spatial analysis and updating slow,or require the use of an expensive 3D spatial index).We discuss in this paper an alternative data structure for storing tetrahedralizations in a database management system(DBMS).It is based on the idea of storing only the vertices and stars of edges;triangles and tetrahedra are represented implicitly.It has been used previously in main memory,but not in a DBMS.We describe how to modify it to obtain an efficient implementation in a DBMS,and we describe how it can be used for modeling 3D topography.As we demonstrate with different real-world examples,the structure is compacter than known alternatives,it permits us to store attributes for any primitives,and has the added benefit of being topological,which permits us to query it efficiently.The structure can be easily implemented in most DBMS(we describe our implementation in PostgreSQL),and we present some of the engineering choices we made for the implementation.

Buildings and terrain unified-multidimensional dual data structure for GIS

3D city models are widely used in many disciplines and applications,such as urban planning,disaster management,and environmental simulation.Usually,the terrain and embedded objects like buildings are taken into consideration.A consistent model integrating these elements is vital for GIS analysis,especially if the geometry is accompanied by the topological relations between neighboring objects.Such a model allows for more efficient and errorless analysis.The memory consumption is another crucial aspect when the wide area of a city is considered-light models are highly desirable.Three methods of the terrain representation using the geometrical-topological data structure-the dual half-edge-are proposed in this article.The integration of buildings and other structures like bridges with the terrain is also presented.

GPU parallel computing： Programming language,debugging tools and data structures

With many cores driven by high memory bandwidth, today＇s graphics processing unit （GPU） has involved into an absolute computing workhorse. More and more scientists, researchers and software developers are using GPUs to accelerate their algorithms and ap- plications. Developing complex programs and software on the GPU, however, is still far from easy with ex- isting tools provided by hardware vendors. This article introduces our recent research efforts to make GPU soft- ware development much easier. Specifically, we designed BSGP, a high-level programming language for general- purpose computation on the GPU. A BSGP program looks much the same as a sequential C program, and is thus easy to read, write and maintain. Its performance on the GPU is guaranteed by a well-designed compiler that converts the program to native GPU code. We also developed an effective debugging system for BSGP pro- grams based on the GPU interrupt, a unique feature of BSGP that allows calling CPU functions from inside GPU code. Moreover, using BSGP, we developed GPU algorithms for constructing several widely-used spatial hierarchies for high-performance graphics applications.

INVERSE REGRESSION METHOD IN DATA STRUCTURE ANALYSIS

In order to explore the nonlinear structure hidden in high-dimensional data, some dimen-sion reduction techniques have been developed, such as the Projection Pursuit technique (PP).However, PP will involve enormous computational load. To overcome this, an inverse regressionmethod is proposed. In this paper, we discuss and develop this method. To seek the interestingprojective direction, the minimization of the residual sum of squares is used as a criterion, andspline functions are applied to approximate the general nonlinear transform function. The algo-rithm is simple, and saves the computational load. Under certain proper conditions, consistencyof the estimators of the interesting direction is shown.

Teaching Reform and Research of Data Structure Course Based on BOPPPS Model and Rain Classroom

Data structure is the core course for computer science majors.How to improve their‘computational thinking’ability is crucial and challenging in this course.To optimize the teaching effect,a classroom teaching model is proposed.This model combines Yu Classroom online teaching tools,Chinese university MOOC teaching resources and BOPPPS model.The empirical experiment was implemented and the results showthat the comprehensive application of this teaching model helps to cultivate students’high-level cognitive ability of‘analysis,evaluation and creation’.It is proved the validity of this teaching model improve students’.

A Secure Microgrid Data Storage Strategy with Directed Acyclic Graph Consensus Mechanism

The wide application of intelligent terminals in microgrids has fueled the surge of data amount in recent years.In real-world scenarios,microgrids must store large amounts of data efficiently while also being able to withstand malicious cyberattacks.To meet the high hardware resource requirements,address the vulnerability to network attacks and poor reliability in the tradi-tional centralized data storage schemes,this paper proposes a secure storage management method for microgrid data that considers node trust and directed acyclic graph(DAG)consensus mechanism.Firstly,the microgrid data storage model is designed based on the edge computing technology.The blockchain,deployed on the edge computing server and combined with cloud storage,ensures reliable data storage in the microgrid.Secondly,a blockchain consen-sus algorithm based on directed acyclic graph data structure is then proposed to effectively improve the data storage timeliness and avoid disadvantages in traditional blockchain topology such as long chain construction time and low consensus efficiency.Finally,considering the tolerance differences among the candidate chain-building nodes to network attacks,a hash value update mechanism of blockchain header with node trust identification to ensure data storage security is proposed.Experimental results from the microgrid data storage platform show that the proposed method can achieve a private key update time of less than 5 milliseconds.When the number of blockchain nodes is less than 25,the blockchain construction takes no more than 80 mins,and the data throughput is close to 300 kbps.Compared with the traditional chain-topology-based consensus methods that do not consider node trust,the proposed method has higher efficiency in data storage and better resistance to network attacks.

Choosing meaningful structure data for improving web search

In order to improve the quality of web search,a new query expansion method by choosing meaningful structure data from a domain database is proposed.It categories attributes into three different classes,named as concept attribute,context attribute and meaningless attribute,according to their semantic features which are document frequency features and distinguishing capability features.It also defines the semantic relevance between two attributes when they have correlations in the database.Then it proposes trie-bitmap structure and pair pointer tables to implement efficient algorithms for discovering attribute semantic feature and detecting their semantic relevances.By using semantic attributes and their semantic relevances,expansion words can be generated and embedded into a vector space model with interpolation parameters.The experiments use an IMDB movie database and real texts collections to evaluate the proposed method by comparing its performance with a classical vector space model.The results show that the proposed method can improve text search efficiently and also improve both semantic features and semantic relevances with good separation capabilities.