Software Vulnerability Mining and Analysis Based on Deep Learning

In recent years,the rapid development of computer software has led to numerous security problems,particularly software vulnerabilities.These flaws can cause significant harm to users’privacy and property.Current security defect detection technology relies on manual or professional reasoning,leading to missed detection and high false detection rates.Artificial intelligence technology has led to the development of neural network models based on machine learning or deep learning to intelligently mine holes,reducing missed alarms and false alarms.So,this project aims to study Java source code defect detection methods for defects like null pointer reference exception,XSS(Transform),and Structured Query Language(SQL)injection.Also,the project uses open-source Javalang to translate the Java source code,conducts a deep search on the AST to obtain the empty syntax feature library,and converts the Java source code into a dependency graph.The feature vector is then used as the learning target for the neural network.Four types of Convolutional Neural Networks(CNN),Long Short-Term Memory(LSTM),Bi-directional Long Short-Term Memory(BiLSTM),and Attention Mechanism+Bidirectional LSTM,are used to investigate various code defects,including blank pointer reference exception,XSS,and SQL injection defects.Experimental results show that the attention mechanism in two-dimensional BLSTM is the most effective for object recognition,verifying the correctness of the method.

Forecasting the Academic Performance by Leveraging Educational Data Mining

The study aims to recognize how efficiently Educational DataMining(EDM)integrates into Artificial Intelligence(AI)to develop skills for predicting students’performance.The study used a survey questionnaire and collected data from 300 undergraduate students of Al Neelain University.The first step’s initial population placements were created using Particle Swarm Optimization(PSO).Then,using adaptive feature space search,Educational Grey Wolf Optimization(EGWO)was employed to choose the optimal attribute combination.The second stage uses the SVMclassifier to forecast classification accuracy.Different classifiers were utilized to evaluate the performance of students.According to the results,it was revealed that AI could forecast the final grades of students with an accuracy rate of 97%on the test dataset.Furthermore,the present study showed that successful students could be selected by the Decision Tree model with an efficiency rate of 87.50%and could be categorized as having equal information ratio gain after the semester.While the random forest provided an accuracy of 28%.These findings indicate the higher accuracy rate in the results when these models were implemented on the data set which provides significantly accurate results as compared to a linear regression model with accuracy(12%).The study concluded that the methodology used in this study can prove to be helpful for students and teachers in upgrading academic performance,reducing chances of failure,and taking appropriate steps at the right time to raise the standards of education.The study also motivates academics to assess and discover EDM at several other universities.

Deep seabed mining:Frontiers in engineering geology and environment

Ocean mining activities have been ongoing for nearly 70 years,making great contributions to industrialization.Given the increasing demand for energy,along with the restructuring of the energy supply catalyzed by efforts to achieve a low-carbon economy,deep seabed mining will play an important role in addressing energy-and resource-related problems in the future.However,deep seabed mining remains in the exploratory stage,with many challenges presented by the high-pressure,low-temperature,and complex geologic and hydrodynamic environments in deep-sea mining areas,which are inaccessible to human activities.Thus,considerable efforts are required to ensure sustainable,economic,reliable,and safe deep seabed mining.This study reviews the latest advances in marine engineering geology and the environment related to deep-sea min-ing activities,presents a bibliometric analysis of the development of ocean mineral resources since the 1950s,summarizes the development,theory,and issues related to techniques for the three stages of ocean mining(i.e.,exploration,extraction,and closure),and discusses the engineering geology environment,geological disasters,in-situ monitoring techniques,envi-ronmental protection requirements,and environmental effects in detail.Finally,this paper gives some key conclusions and future perspectives to provide insights for subsequent studies and commercial mining operations.

Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach

Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social media.Recently,massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult.Since OM find useful in business sectors to improve the quality of the product as well as services,machine learning(ML)and deep learning(DL)models can be considered into account.Besides,the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process.Therefore,in this paper,a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory(AFSO-BLSTM)model has been developed for OM process.The major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual data.In addition,the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction process.Besides,BLSTM model is employed for the effectual detection and classification of opinions.Finally,the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model,shows the novelty of the work.A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.

Human Verification over Activity Analysis via Deep Data Mining

Human verification and activity analysis(HVAA)are primarily employed to observe,track,and monitor human motion patterns using redgreen-blue(RGB)images and videos.Interpreting human interaction using RGB images is one of the most complex machine learning tasks in recent times.Numerous models rely on various parameters,such as the detection rate,position,and direction of human body components in RGB images.This paper presents robust human activity analysis for event recognition via the extraction of contextual intelligence-based features.To use human interaction image sequences as input data,we first perform a few denoising steps.Then,human-to-human analyses are employed to deliver more precise results.This phase follows feature engineering techniques,including diverse feature selection.Next,we used the graph mining method for feature optimization and AdaBoost for classification.We tested our proposed HVAA model on two benchmark datasets.The testing of the proposed HVAA system exhibited a mean accuracy of 92.15%for the Sport Videos in theWild(SVW)dataset.The second benchmark dataset,UT-interaction,had a mean accuracy of 92.83%.Therefore,these results demonstrated a better recognition rate and outperformed other novel techniques in body part tracking and event detection.The proposed HVAA system can be utilized in numerous real-world applications including,healthcare,surveillance,task monitoring,atomic actions,gesture and posture analysis.

Study of a low-disturbance pressure-preserving corer and its coring performance in deep coal mining conditions

With the increasing depth of coal mining,the requirements for coring devices that maintain pressure are increasing.To adapt to the special environment in deep coal seams and improve the accuracy of testing gas content,a low-disturbance pressure-preserving corer was developed.The measurement of gas content using this corer was analyzed.The coring test platform was used to complete a coring function test.A pressurized core with a diameter of 50 mm was obtained.The pressure was 0.15 MPa,which was equal to the pressure of the liquid column of the cored layer,indicating that the corer can be successfully used in a mud environment.Next,a pressure test of the corer was conducted.The results showed that under conditions of low pressure(8 MPa)and high pressure(25 MPa),the internal pressure of the corer remained stable for more than 1 h,indicating that the corer has good ability to maintain pressure.Therefore,the corer can be applied at deep coal mine sites.The results of this research can be used to promote the safe exploitation of deep coal mines and the exploitation of methane resources in coalbeds.

Data Mining with Comprehensive Oppositional Based Learning for Rainfall Prediction

Data mining process involves a number of steps fromdata collection to visualization to identify useful data from massive data set.the same time,the recent advances of machine learning(ML)and deep learning(DL)models can be utilized for effectual rainfall prediction.With this motivation,this article develops a novel comprehensive oppositionalmoth flame optimization with deep learning for rainfall prediction(COMFO-DLRP)Technique.The proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental changes.Primarily,data pre-processing and correlation matrix(CM)based feature selection processes are carried out.In addition,deep belief network(DBN)model is applied for the effective prediction of rainfall data.Moreover,COMFO algorithm was derived by integrating the concepts of comprehensive oppositional based learning(COBL)with traditional MFO algorithm.Finally,the COMFO algorithm is employed for the optimal hyperparameter selection of the DBN model.For demonstrating the improved outcomes of the COMFO-DLRP approach,a sequence of simulations were carried out and the outcomes are assessed under distinct measures.The simulation outcome highlighted the enhanced outcomes of the COMFO-DLRP method on the other techniques.

Statistical Data Mining with Slime Mould Optimization for Intelligent Rainfall Classification

Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so on.Statistical data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the data.It varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other resolves.Thus,this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)model.In the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation complexity.For rainfall prediction.Convolution neural network with long short-term memory(CNN-LSTM)technique is exploited.At last,this study involves the pelican optimization algorithm(POA)as a hyperparameter optimizer.The experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive class.The comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.

深部采矿岩石力学进展

随着煤炭开采日益向深部发展,深部采矿引发的围岩大变形破坏和强冲击动力灾害日益严峻。在深部高地应力、高地温、高渗透压、强采动、强流变及多场耦合的复杂地质力学环境下,深部采场的应力场特征、煤岩体破碎性质、岩层移动及能量的积聚释放规律等均发生了显著变化。针对深部采矿中的岩石力学问题,论述了笔者及团队在深部采煤方法、深部巷道破坏机理与围岩控制、深井热害与地热利用三大方向取得的进展,主要包括:(1)提出了平衡开采理论和实现平衡开采的110/N00工法,进行了千米深井现场工程应用;(2)构建了深部“非均压建井”模式,研发了实现深井稳定提升的SAP系统,形成了可大幅简化井巷工程量和提高矿井采出率的建井方法;(3)研发了多套适用于研究深部岩体在水、高温、高压、结构效应及多场耦合作用下发生宏观破坏的实验系统和可进行微观层面演算的超算系统,揭示了深部软岩大变形破坏机理及多尺度力学特性;(4)研制了深部岩体冲击型和应变型岩爆实验系统,阐述了深部岩体冲击能量沿开挖临空面瞬间释放的非线性动力学行为;(5)提出了深部巷道开挖补偿支护理论,进一步发展了具有高恒阻、高延伸率、强吸能和耐冲击超常力学特性的NPR支护材料和技术;(6)研发了模拟深部高温、高湿和高压环境下的岩体热力学实验系统,提出了热害治理和热能资源化利用方法,建立了深部热害治理与热能综合利用系统(HEMS)。相关研究成果已在深部开采领域得以应用,可为深部采矿面临的复杂岩石力学问题提供借鉴。

深部矿山不同埋藏深度岩石拉压力学特性试验研究

岩石的物理力学特性随埋藏深度增大会发生变化。本文以云南某矿山900~1200 m深度范围内4个不同埋藏深度的矿体上下盘围岩(灰质白云岩)为研究对象,开展了不同含水条件下(自然状态和饱水状态)的单轴压缩和巴西劈裂试验,探明深部岩石力学基本物理力学特性随埋深的变化规律。研究结果表明:在研究的深度范围内,岩石基本物理成分组成及结构相似,随着岩石埋藏深度的增加,岩石试样的密度、纵波波速、单轴抗压强度、抗拉强度和弹性模量等均略微增大,而泊松比呈先增大后减小的趋势。含水条件对岩石的力学特性影响显著,不同埋藏深度的岩石试样进行饱水处理后其抗压强度、抗拉强度和弹性模量均明显减小,泊松比显著增大,其中岩石单轴抗压强度对水的敏感性高于其他参数对水的敏感性,各参数对水的敏感性排序为:抗压强度>弹性模量>泊松比>抗拉强度。本研究很好地揭示了深部矿山不同埋藏深度围岩的拉压力学特性变化规律。

煤电化基地大宗固废“三化”协同利用基础与技术

我国14个大型煤炭基地及89个大宗固体废弃物综合利用示范基地建设,标志着矿区大宗固废利用已被纳入全国战略发展布局。长期以来煤炭资源高强度的开发与利用,造成矿区浅埋煤层资源临近枯竭,煤电化基地大规模固废堆积及地表沉陷,已成为制约矿区绿色低碳、高质量发展的难题。大宗煤基固废协同利用与绿色充填是解放“三下一上”压煤,延长矿井服务年限,实现固废无害化、资源化、规模化“三化”利用的有效途径。基于产煤大省山西省、“华东能源粮仓”安徽两淮基地及宁东能源化工基地的煤基固废种类和产量,详细阐述了以煤矸石、粉煤灰、炉底渣、气化渣和脱硫石膏等为主要材料的煤基固废通过重金属吸附解吸和络合钝化技术实现无害化处置,列举煤基固废分类应用于低热值煤基固废发电、制备建筑材料如水泥、砖瓦等资源化利用途径,对比分析煤基固废采煤沉陷区复垦回填及井下充填规模化利用途径,突出煤基固废井下充填的优越性。基于煤电化基地深部煤炭资源,提出绿色充填开采理论与关键技术,包括深部煤矸石源头减量与采选充协同技术、充填材料高效制备与深部井下输送技术及煤基固废充填材料深部多场耦合机理,探究多源煤基固废从源头、过程到终端的深部充填开采全过程的技术原理与方法,以解决矿区深部井下充填的技术难题。根据宁东基地任家庄煤矿、山西省霍尔辛赫煤矿及淮北矿区地质条件和充填目的,分别提出超前钻孔注充低位充填方案、关键层非典型特征条件下多离层梯级注浆方案和煤基固废协同利用关键技术,综合矿山固废处置与利用、深部煤炭资源开发利用、地表沉陷控制、生态环境保护等优势,形成煤电化基地大宗固废协同利用与绿色开采模式,为煤炭开采高质量化和环境低损伤化提供参考。

深井松软围岩煤巷采动增跨效应及防控技术

针对深井松软煤巷围岩变形严重、巷道支护困难等问题,以城郊煤矿LW21106工作面沿空巷道为工程背景,建立了采动巷道增跨模型,揭示了采动增跨效应演化机理。通过构建巷道顶板横纵弯曲梁模型,指出顶板横向受力、巷道等效跨度、煤岩强度是巷道围岩损伤破坏的主控因素,提出了采动增跨效应防控对策并进行工业性试验。研究结果表明:受采动影响,巷道经历“初始围岩稳定—围岩裂隙发育扩展—围岩剪切破坏加剧—等效跨度增加”过程;巷道顶板最大正应力与应力集中系数、顶板等效跨度、巷道断面尺寸及埋深成正相关关系;巷道顶板在高应力环境下易发生拉剪破坏,增加顶板锚索数量以及锚索预紧力有利于增强顶板初期完整性。基于巷道变形破坏主控因素,提出“围岩加固–卸压–强化支护”协同防控策略;针对现场条件,采用煤柱侧向切顶+注浆加固并对破碎区域补充锚索强化支护的防控技术。现场监测结果表明,煤柱帮最大移近量为18.89cm,顶板下沉量为25.86cm,两帮移近量为29.65 cm,有效控制了煤巷围岩变形,为深井松软围岩巷道变形控制提供了参考。

河南省嵩县九仗沟-东湾金矿区深部地球物理特征与找矿预测

深部金属矿探测是目前资源勘查的重要课题和方向,地球物理方法探测深度大、分辨率高,是深部金属矿探测最有效的手段之一。河南省嵩县九仗沟—东湾矿区处于熊耳山—外方山矿集区内的蛮峪—店房金矿带之北段,目前九仗沟—东湾矿区已发现的金矿床主要为500 m以浅深度,深部(500~2000 m)找矿勘查工作基本为空白。为了查明九仗沟—东湾矿区深、边部成矿潜力,实现接替资源找矿突破,在九仗沟—东湾矿区主矿段南北两端延伸方向上,布设EH-4双源大地电磁测深和大功率激电测深剖面。以九仗沟—东湾金矿床为背景,在分析地质背景、岩石物理性质基础上,综合区域重磁资料、物探剖面反演结果,分析各物探方法异常特征,厘清了研究区内与金矿有关的F1构造破碎蚀变带深部空间分布特征等信息,揭示了研究区内深部F1构造带附近的中低电阻、高极化区为找矿有利部位,根据此特征在500~2000 m深度范围内确定了4个深部预测找矿靶区,为下一步找矿勘查提供了相关依据。研究方法和成果为区域上开展同类型金矿床的深部找矿工作提供了思路和方向,具有重要的指导和实践意义。

中远海区域水雷威胁分析

根据布雷作战方在远离大陆港岸依托的较远大陆架和一岛链海区及一岛链以外中远海区域的布雷作战形式,针对海区环境特点对中远海主要水雷威胁区的水雷类型和障碍样式进行分析,可为海上编队中远海对水雷防御需求提供依据,对中远海反水雷能力建设具有重要意义。

福州市深远海养殖发展现状与建议

推进福州市深远海养殖发展,是缓解近海养殖环境压力、优化海水养殖空间布局、保障水产品有效供给、促进福州市海水养殖业可持续发展的重要举措。本文介绍了福州市重力式深水抗风浪网箱发展情况、深远海大型设施化养殖平台发展现状及深远海养殖平台产权登记情况,指出福州市深远海养殖发展的困境,并对福州市深远海养殖的发展提出建议,以期为福州市深远海养殖发展提供参考。

地下矿山非爆机械化智能采掘

非爆机械化采掘是替代传统钻爆法进行深部硬岩开挖的一种新方法,其智能化升级包括智能感知、智能决策和智能控制。在综合分析岩石采掘工程涉及的两大问题(破碎和稳定)和三大要素(环境条件、岩体特性、采掘参数)的基础上,建立包括岩体及环境特性原位监测、硬岩可切割性改善、采掘参数智能控制和采掘表现性能评价在内的非爆机械化智能采掘工艺,以实现岩石采掘装备的机械化、自动化、无人化,采掘工艺的连续化、精细化、协同化,以及采掘管理的信息化、数字化、智能化。此外,建立非爆机械化智能采掘PDCA循环管理模式,通过深部高应力诱导利用和能量调控、硬岩诱变改性降危增割和多源载荷联合破岩3个子循环的协同,实现地下矿山安全高效采掘。

面向技术识别的专利实体抽取--以类脑智能领域为例

[研究目的]专利实体抽取是基于专利文本的技术识别的基础。目前专利实体抽取任务面临自动化程度和准确率较低等问题,该研究从两方面对此进行改进:一是建立特定领域的高质量专利语料库,二是将先进的算法模型运用到专利实体抽取中。[研究方法]定义了包含13种实体类型的细粒度信息体系,并据此对921篇类脑智能专利的标题和摘要进行人工标注,此后运用Bert-BiLSTM-CRF模型,融合深度学习和机器学习对类脑智能专利实体进行识别。[研究结论]模型在总体上获得0.8的准确率、召回率和F1值,不同类型实体的识别效果具有差异。为了验证模型的性能,设计了几个对比实验。结果显示,微调数据和增加训练规模可以提高模型性能,本模型性能优于同时期一些经典模型。

深部煤层开采导水裂隙发育规律研究

导水裂隙发育规律是深部煤层安全开采的一个重要参数,以孔庄煤矿7303工作面为依托,采用相似模拟实验、钻孔注水漏失量探测和钻孔窥视相结合的方法,对工作面导水裂隙发育特征进行研究。结果表明:相似模拟实验表明工作面平均周期来压步距19.5 m,垮落带呈梯形,随着工作面推进,离层裂隙被压实,导水裂隙发育由低速向高速转变,导水裂隙最大发育高度69.8 m,裂采比21.8。通过现场实测得到导水裂隙最大发育高度65.27 m,裂采比18.13,与浅部煤层工作面裂隙发育特征进行对比分析,裂采比与煤层埋深呈正相关。该研究成果对孔庄煤矿防治水害具有重要的指导意义。

深部煤炭流态化开采装备自主行走机构多缸推进同步控制

我国浅部煤炭正逐渐开采殆尽,向地球深部开发资源已成为必然趋势和国家需求。针对2000 m以深的深部煤炭开采难题,设计了一种适用于深部煤炭流态化开采装备的自主行走机构,并重点对自主行走机构中液压推进系统的多缸同步控制难题进行了研究。首先,基于流态化开采工艺原理及装备组成,设计了一种增阻迈步式自主行走机构,可实现采掘、转化和输出等多舱体的分段式自主行走;其次,针对液压推进系统的多缸同步控制要求,分析对比了主从控制、相邻交叉耦合控制、偏差耦合控制、均值耦合控制4种控制策略以及比例、积分、微分控制(PID)算法、自抗扰控制器(ADRC)的优缺点,分别开展了在均匀负载、突变负载、时变负载3种工况下的控制性能仿真试验;再次,采用雷达图测评法对不同控制策略下的同步控制性能进行综合评价,最终选定基于ADRC的均值耦合控制方法为最佳同步控制策略;最后,研制自主行走机构试验台并开展了多液压缸同步控制试验,试验结果表明:当采用基于ADRC的均值耦合同步控制策略时,4个液压缸在不同工况下的最大同步误差均能保持在±5 mm以内,且具有优异的鲁棒性,可满足流态化开采装备自主行走机构的多缸同步推进要求。

深海多金属结核集矿装置水力输送流场分析与试验

对深海多金属结核集矿装置水力输送流场进行了理论计算,得到了多金属结核粒径与输送通道最小输送速度的关系;仿真分析了喷嘴射流速度分别为15、20、25 m/s时输送通道内水流流态分布,给出了流道下表面30 mm处的流速。在实验室进行了输送喷嘴不同射流速度的采集试验,实验结果与理论计算及仿真分析结果相符。