Three-dimensional automatic artificial intelligence driven augmented-reality selective biopsy during nerve-sparing robot-assisted radical prostatectomy:A feasibility and accuracy study

Objective:To evaluate the accuracy of our new three-dimensional(3D)automatic augmented reality(AAR)system guided by artificial intelligence in the identification of tumour’s location at the level of the preserved neurovascular bundle(NVB)at the end of the extirpative phase of nerve-sparing robot-assisted radical prostatectomy.Methods:In this prospective study,we enrolled patients with prostate cancer(clinical stages cT1ce3,cN0,and cM0)with a positive index lesion at target biopsy,suspicious for capsular contact or extracapsular extension at preoperative multiparametric magnetic resonance imaging.Patients underwent robot-assisted radical prostatectomy at San Luigi Gonzaga Hospital(Orbassano,Turin,Italy),from December 2020 to December 2021.At the end of extirpative phase,thanks to our new AAR artificial intelligence driven system,the virtual prostate 3D model allowed to identify the tumour’s location at the level of the preserved NVB and to perform a selective excisional biopsy,sparing the remaining portion of the bundle.Perioperative and postoperative data were evaluated,especially focusing on the positive surgical margin(PSM)rates,potency,continence recovery,and biochemical recurrence.Results:Thirty-four patients were enrolled.In 15(44.1%)cases,the target lesion was in contact with the prostatic capsule at multiparametric magnetic resonance imaging(Wheeler grade L2)while in 19(55.9%)cases extracapsular extension was detected(Wheeler grade L3).3D AAR guided biopsies were negative in all pathological tumour stage 2(pT2)patients while they revealed the presence of cancer in 14 cases in the pT3 cohort(14/16;87.5%).PSM rates were 0%and 7.1%in the pathological stages pT2 and pT3(<3 mm,Gleason score 3),respectively.Conclusion:With the proposed 3D AAR system,it is possible to correctly identify the lesion’s location on the NVB in 87.5%of pT3 patients and perform a 3D-guided tailored nerve-sparing even in locally advanced diseases,without compromising the oncological safety in terms of PSM rates.

An Examination of Computer Science and Internet Technologies in Addressing Educational Inequities and Societal Psychological Concerns:A Literature Review from the Perspectives of 5G,Artificial Intelligence,and Augmented/Virtual Reality

This study comprehensively reviews the literature to deeply explore the role of computer science and internet technologies in addressing educational inequality and socio-psychological issues,with a particular focus on applications of 5G,artificial intelligence(AI),and augmented/virtual reality(AR/VR).By analyzing how these technologies are reshaping learning and their potential to ameliorate educational disparities,the study reveals challenges present in ensuring educational equity.The research methodology includes exhaustive reviews of applications of AI and machine learning,the Internet of Things and wearable technologies integration,big data analytics and data mining,and the effects of online platforms and social media on socio-psychological issues.Besides,the study discusses applications of these technologies in educational inequality and socio-psychological problem-solving through the lens of 5G,AI,and AR/VR,while also delineating challenges faced by these emerging technologies and future outlooks.The study finds that while computer science and internet technologies hold promise to bridge academic divides and address socio-psychological problems,the complexity of technology access and infrastructure,lack of digital literacy and skills,and critical ethical and privacy issues can impact widespread adoption and efficacy.Overall,the study provides a novel perspective to understand the potential of computer science and internet technologies in ameliorating educational inequality and socio-psychological issues,while pointing to new directions for future research.It also emphasizes the importance of cooperation among educational institutions,technology vendors,policymakers and researchers,and establishing comprehensive ethical guidelines and regulations to ensure the responsible use of these technologies.

Multiple Data Augmentation Strategy for Enhancing the Performance of YOLOv7 Object Detection Algorithm

The object detection technique depends on various methods for duplicating the dataset without adding more images.Data augmentation is a popularmethod that assists deep neural networks in achieving better generalization performance and can be seen as a type of implicit regularization.Thismethod is recommended in the casewhere the amount of high-quality data is limited,and gaining new examples is costly and time-consuming.In this paper,we trained YOLOv7 with a dataset that is part of the Open Images dataset that has 8,600 images with four classes(Car,Bus,Motorcycle,and Person).We used five different data augmentations techniques for duplicates and improvement of our dataset.The performance of the object detection algorithm was compared when using the proposed augmented dataset with a combination of two and three types of data augmentation with the result of the original data.The evaluation result for the augmented data gives a promising result for every object,and every kind of data augmentation gives a different improvement.The mAP@.5 of all classes was 76%,and F1-score was 74%.The proposed method increased the mAP@.5 value by+13%and F1-score by+10%for all objects.

Emotional intelligence in professional nursing practice:A concept review using Rodgers's evolutionary analysis approach

Background:Knowledge around emotional intelligence originated in the 1990s from research regarding thoughts,emotions and abilities.The concept of emotional intelligence has evolved over the last 25 years;however,the understanding and use is still unclear.Despite this,emotional intelligence has been a widely-considered concept within professions such as business,management,education,and within the last 10 years has gained traction within nursing practice.Aims and objectives:The aim of this concept review is to clarify the understanding of the concept emotional intelligence,what attributes signify emotional intelligence,what are its antecedents,consequences,related terms and implications to advance nursing practice.Method:A computerized search was guided by Rodger's evolutional concept analysis.Data courses included:CINAHL,PyschINFO,Scopus,EMBASE and ProQuest,focusing on articles published in Canada and the United Stated during 1990e2017.Results:A total of 23 articles from various bodies of disciplines were included in this integrative concept review.The analysis reveals that there are many inconsistencies regarding the description of emotional intelligence,however,four common attributes were discovered:self-awareness,self-management,social awareness and social/relationship management.These attributes facilitate the emotional well-being among advance practice nurses and enhances the ability to practice in a way that will benefit patients,families,colleagues and advance practice nurses as working professionals and as individuals.Conclusion:The integration of emotional intelligence is supported within several disciplines as there is consensus on the impact that emotional intelligence has on job satisfaction,stress level,burnout and helps to facilitate a positive environment.Explicit to advance practice nursing,emotional intelligence is a concept that may be central to nursing practice as it has the potential to impact the quality of patient care and outcomes,decision-making,critical thinking and overall the well-being of practicing nurses.

Synergy Model of Artificial Intelligence and Augmented Reality in the Processes of Exploitation of Energy Systems

The article presents a fragment of research and development, which objective was to develop technical tools and methodology to improve exploitation processes of energy systems. The author＇s model includes synergy of artificial intelligence and augmented reality. This solution, which combines modem technologies in order to improve the activities related to the continuity of energy supply, and reduce costs associated with the time needed to carry out exploitation activities and employment of qualified staff, is presented. This paper presents both theoretical foundations as well as the development of technical systems. The characteristics of exploitation processes of energy systems and possible technical conditions, as well as factors characterizing them, are discussed. The physical and software structures of the system and individual modules, as well as dependencies connecting them are demonstrated. The dependencies between physical and logical elements during the exploitation processes of energy systems, that determine decisions related to the evaluation of technical states and related activities are described. The advantages and limitations of the developed model which connects methods of data processing and analysis, interactive visualization processes and possible areas of application are as well discussed in detailed.

Clinical use of augmented reality,mixed reality,three-dimensionalnavigation and artificial intelligence in liver surgery

A precise knowledge of intra-parenchymal vascular and biliary architecture and the location of lesions in relation to the complex anatomy is indispensable to perform liver surgery.Therefore,virtual three-dimensional(3D)-reconstruction models from computed tomography/magnetic resonance imaging scans of the liver might be helpful for visualization.Augmented reality,mixed reality and 3Dnavigation could transfer such 3D-image data directly into the operation theater to support the surgeon.This review examines the literature about the clinical and intraoperative use of these image guidance techniques in liver surgery and provides the reader with the opportunity to learn about these techniques.Augmented reality and mixed reality have been shown to be feasible for the use in open and minimally invasive liver surgery.3D-navigation facilitated targeting of intraparenchymal lesions.The existing data is limited to small cohorts and description about technical details e.g.,accordance between the virtual 3D-model and the real liver anatomy.Randomized controlled trials regarding clinical data or oncological outcome are not available.Up to now there is no intraoperative application of artificial intelligence in liver surgery.The usability of all these sophisticated image guidance tools has still not reached the grade of immersion which would be necessary for a widespread use in the daily surgical routine.Although there are many challenges,augmented reality,mixed reality,3Dnavigation and artificial intelligence are emerging fields in hepato-biliary surgery.

Role of artificial intelligence in hepatobiliary and pancreatic surgery

Over the past decade,enhanced preoperative imaging and visualization,improved delineation of the complex anatomical structures of the liver and pancreas,and intra-operative technological advances have helped deliver the liver and pancreatic surgery with increased safety and better postoperative outcomes.Artificial intelligence(AI)has a major role to play in 3D visualization,virtual simulation,augmented reality that helps in the training of surgeons and the future delivery of conventional,laparoscopic,and robotic hepatobiliary and pancreatic(HPB)surgery;artificial neural networks and machine learning has the potential to revolutionize individualized patient care during the preoperative imaging,and postoperative surveillance.In this paper,we reviewed the existing evidence and outlined the potential for applying AI in the perioperative care of patients undergoing HPB surgery.

Artificial Intelligence Enabled Apple Leaf Disease Classification for Precision Agriculture

Precision agriculture enables the recent technological advancements in farming sector to observe,measure,and analyze the requirements of individual fields and crops.The recent developments of computer vision and artificial intelligence(AI)techniques find a way for effective detection of plants,diseases,weeds,pests,etc.On the other hand,the detection of plant diseases,particularly apple leaf diseases using AI techniques can improve productivity and reduce crop loss.Besides,earlier and precise apple leaf disease detection can minimize the spread of the disease.Earlier works make use of traditional image processing techniques which cannot assure high detection rate on apple leaf diseases.With this motivation,this paper introduces a novel AI enabled apple leaf disease classification(AIE-ALDC)technique for precision agriculture.The proposed AIE-ALDC technique involves orientation based data augmentation and Gaussian filtering based noise removal processes.In addition,the AIE-ALDC technique includes a Capsule Network(CapsNet)based feature extractor to generate a helpful set of feature vectors.Moreover,water wave optimization(WWO)technique is employed as a hyperparameter optimizer of the CapsNet model.Finally,bidirectional long short term memory(BiLSTM)model is used as a classifier to determine the appropriate class labels of the apple leaf images.The design of AIE-ALDC technique incorporating theWWO based CapsNetmodel with BiLSTM classifier shows the novelty of the work.Awide range of experiments was performed to showcase the supremacy of the AIE-ALDC technique.The experimental results demonstrate the promising performance of the AIEALDC technique over the recent state of art methods.

An Overview and Perspectives On Bidirectional Intelligence: Lmser Duality, Double IA Harmony,and Causal Computation

Advances on bidirectional intelligence are overviewed along three threads,with extensions and new perspectives.The first thread is about bidirectional learning architecture,exploring five dualities that enable Lmser six cognitive functions and provide new perspectives on which a lot of extensions and particularlly flexible Lmser are proposed.Interestingly,either or two of these dualities actually takes an important role in recent models such as U-net,ResNet,and Dense Net.The second thread is about bidirectional learning principles unified by best yIng-yAng(IA)harmony in BYY system.After getting insights on deep bidirectional learning from a bird-viewing on existing typical learning principles from one or both of the inward and outward directions,maximum likelihood,variational principle,and several other learning principles are summarised as exemplars of the BYY learning,with new perspectives on advanced topics.The third thread further proceeds to deep bidirectional intelligence,driven by long term dynamics(LTD)for parameter learning and short term dynamics(STD)for image thinking and rational thinking in harmony.Image thinking deals with information flow of continuously valued arrays and especially image sequence,as if thinking was displayed in the real world,exemplified by the flow from inward encoding/cognition to outward reconstruction/transformation performed in Lmser learning and BYY learning.In contrast,rational thinking handles symbolic strings or discretely valued vectors,performing uncertainty reasoning and problem solving.In particular,a general thesis is proposed for bidirectional intelligence,featured by BYY intelligence potential theory(BYY-IPT)and nine essential dualities in architecture,fundamentals,and implementation,respectively.Then,problems of combinatorial solving and uncertainty reasoning are investigated from this BYY IPT perspective.First,variants and extensions are suggested for AlphaGoZero like searching tasks,such as traveling salesman problem(TSP)and attributed graph matching(AGM)that are turned into Go like problems with help of a feature enrichment technique.Second,reasoning activities are summarized under guidance of BYY IPT from the aspects of constraint satisfaction,uncertainty propagation,and path or tree searching.Particularly,causal potential theory is proposed for discovering causal direction,with two roads developed for its implementation.

Effect of Data Augmentation of Renal Lesion Image by Nine-layer Convolutional Neural Network in Kidney CT

Artificial Intelligence(AI)becomes one hotspot in the field of the medical images analysis and provides rather promising solution.Although some research has been explored in smart diagnosis for the common diseases of urinary system,some problems remain unsolved completely A nine-layer Convolutional Neural Network(CNN)is proposed in this paper to classify the renal Computed Tomography(CT)images.Four group of comparative experiments prove the structure of this CNN is optimal and can achieve good performance with average accuracy about 92.07±1.67%.Although our renal CT data is not very large,we do augment the training data by affine,translating,rotating and scaling geometric transformation and gamma,noise transformation in color space.Experimental results validate the Data Augmentation(DA)on training data can improve the performance of our proposed CNN compared to without DA with the average accuracy about 0.85%.This proposed algorithm gives a promising solution to help clinical doctors automatically recognize the abnormal images faster than manual judgment and more accurately than previous methods.

Using of artificial intelligence:Current and future applications in colorectal cancer screening

Significant developments in colorectal cancer screening are underway and include new screening guidelines that incorporate considerations for patients aged 45 years,with unique features and new techniques at the forefront of screening.One of these new techniques is artificial intelligence which can increase adenoma detection rate and reduce the prevalence of colonic neoplasia.

Uniqueness and Reproducibility of Semantic Intelligence: New Approach to the Notion of Self-Organization

A novel notion of self-organization whose major property is that it brings about the execution of semantic intelligence as spontaneous physico-chemical processes in an unspecified ever-changing non-uniform environment is introduced. Its greatest advantage is that the covariance of causality encapsulated in any piece of semantic intelligence is provided with a great diversity of its individuality viewed as the properties of the current response and its reproducibility viewed as causality encapsulated in any of the homeostatic patterns. Alongside, the consistency of the functional metrics, which is always Euclidean, with any metrics of the space-time renders the proposed notion of self-organization ubiquitously available.

Augmenting Android Malware Using Conditional Variational Autoencoder for the Malware Family Classification

Android malware has evolved in various forms such as adware that continuously exposes advertisements,banking malware designed to access users’online banking accounts,and Short Message Service(SMS)malware that uses a Command&Control(C&C)server to send malicious SMS,intercept SMS,and steal data.By using many malicious strategies,the number of malware is steadily increasing.Increasing Android malware threats numerous users,and thus,it is necessary to detect malware quickly and accurately.Each malware has distinguishable characteristics based on its actions.Therefore,security researchers have tried to categorize malware based on their behaviors by conducting the familial analysis which can help analysists to reduce the time and cost for analyzing malware.However,those studies algorithms typically used imbalanced,well-labeled open-source dataset,and thus,it is very difficult to classify some malware families which only have a few number of malware.To overcome this challenge,previous data augmentation studies augmented data by visualizing malicious codes and used them for malware analysis.However,visualization of malware can result in misclassifications because the behavior information of the malware could be compromised.In this study,we propose an android malware familial analysis system based on a data augmentation method that preserves malware behaviors to create an effective multi-class classifier for malware family analysis.To this end,we analyze malware and use Application Programming Interface(APIs)and permissions that can reflect the behavior of malware as features.By using these features,we augment malware dataset to enable effective malware detection while preserving original malicious behaviors.Our evaluation results demonstrate that,when a model is created by using only the augmented data,a macro-F1 score of 0.65 and accuracy of 0.63%.On the other hand,when the augmented data and original malware are used together,the evaluation results show that a macro-F1 score of 0.91 and an accuracy of 0.99%.

DEBRA: On the Unsupervised Learning of Concept Hierarchies from (Literary) Text

With this work, we introduce a novel method for the unsupervised learning of conceptual hierarchies, or concept maps as they are sometimes called, which is aimed specifically for use with literary texts, as such distinguishing itself from the majority of research literature on the topic which is primarily focused on building ontologies from a vast array of different types of data sources, both structured and unstructured, to support various forms of AI, in particular, the Semantic Web as envisioned by Tim Berners-Lee. We first elaborate on mutually informing disciplines of philosophy and computer science, or more specifically the relationship between metaphysics, epistemology, ontology, computing and AI, followed by a technically in-depth discussion of DEBRA, our dependency tree based concept hierarchy constructor, which as its name alludes to, constructs a conceptual map in the form of a directed graph which illustrates the concepts, their respective relations, and the implied ontological structure of the concepts as encoded in the text, decoded with standard Python NLP libraries such as spaCy and NLTK. With this work we hope to both augment the Knowledge Representation literature with opportunities for intellectual advancement in AI with more intuitive, less analytical, and well-known forms of knowledge representation from the cognitive science community, as well as open up new areas of research between Computer Science and the Humanities with respect to the application of the latest in NLP tools and techniques upon literature of cultural significance, shedding light on existing methods of computation with respect to documents in semantic space that effectively allows for, at the very least, the comparison and evolution of texts through time, using vector space math.

OBE理念与多元智能理论在应用型本科院校实践教学改革中的应用研究

随着经济全球化的深入发展和科技的日新月异,社会对人才的需求日益多元化。实践教学是高校教学体系的重要组成部分,对培养学生的实践能力和创新精神至关重要。该文首先对应用型本科院校实践环节存在的问题进行了分析,然后基于OBE理念和多元智能理论,分别从教学目标、教师层面、学生层面、评估体系等方面提出了具体的改革措施,为应用型本科院校实践教学改革提供理论支撑。

基于样本增强的列车卫星定位伪距欺骗检测方法

基于卫星导航的列车自主定位是列车控制系统等铁路关键装备的重要技术方向。然而,列车卫星定位面临诸多挑战,除信号可视性问题和多径效应之外,来自系统外部的蓄意欺骗等干扰攻击,会对定位功能及性能产生直接威胁。为此,本文以基于全球导航卫星系统(GNSS)的列车定位面临的伪距欺骗这一典型干扰模式为对象,研究并提出一种基于样本增强的伪距欺骗主动检测方法。该方法运用Wasserstein生成式对抗网络(WGAN)解决受欺骗干扰样本数据不均衡问题,利用扩充的数据集训练检测模型,并引入自注意力(SA)机制优化来自不同接收机输入特征之间的相对位置关系,采用生成式对抗学习思想形成一套完整的列车卫星定位伪距欺骗干扰检测方案。由列车卫星定位欺骗干扰注入测试结果可知,提出的方法能够充分运用生成式对抗网络思想解决受欺骗样本的典型受限问题,融合自注意力机制所得检测性能显著优于载噪比检测和代表性机器学习算法等常规检测方案;对建模样本未覆盖特征具备良好的适应能力,具有更优的检测精度和鲁棒性,在多个伪距欺骗干扰模式数据集上测试所得F1分数均超过0.99。该方法在欺骗干扰检测性能方面的优势能够为众多卫星导航系统铁路应用提供有力支撑,为有效防范卫星定位在信息安全层面的攻击入侵提供了有利条件。

人工智能哲学视域中“人的本质”观念变革

对“人的本质”的认识总是被置入“人与他者”的关系中进行考量。他者作为一个变量,构成了对“人的本质”的认知参照。当前,人工智能作为新的他者出现,促使人的本质的理解方式在方法论层面发生变革。但是,人工智能不是一般意义上的他者,而是有可能充当人的本质的最佳认知参照。人工智能与人本身具有内在本质关联,而不只是人的心智的放大和延伸;同时,人工智能又是人所能创制的最复杂的造物,在特定意义上能够作为人的完成形态。因此,以人工智能为参照,对人的本质的认识可以实现从“人体解剖”到“人工智能体解剖”的方法论变革。这种变革并不否认在人与动物的对照中获得对人的本质规定性的理解,但它更强调应该在重新界定人与动物关系的基础上,把个人作为自然界中的一个独特种类即人类动物进而将之与人工智能相对照。这种对照表明,我是一个人,个人在根本上是人类动物,而人类动物与人工智能及其他动物的本质差异就在于,人类动物不但能够理解意义,而且能够为本无意义的宇宙赋予意义。质言之,人是宇宙中已知的唯一赋义者。

基于语义上下文感知的文本数据增强方法研究

在文本分类任务中,数据的质量和数量对分类模型的性能有着重要影响,而在现实场景中获取大规模标记数据往往是昂贵和困难的。数据增强作为一种解决数据匮乏问题的低成本方法,已在各种深度学习和机器学习任务中取得了显著效果。由于文本语言具有离散性,在语义保留的条件下进行数据增强具有一定困难。因此,提出基于语义上下文感知的数据增强方法,采用由WordNet 3.0中的词义定义(Gloss)和预训练模型BERT进行整合的Gloss选择模型,进一步识别上下文中目标词(尤其是多义词)的实际词义;然后根据下一个句子预测策略,将目标词的实际词义与被遮盖目标词的句子结合为一个句子对,使用掩码语言模型对句子对进行预测采样;最后计算语义文本相似度,并在三个基准分类数据集上对文中方法进行验证。实验结果表明,提出的方法在语义保留条件下,与选取的基线数据增强方法相比,在三个数据集的平均准确率指标上都有所提升,证明了文中方法的有效性。

基于AR技术的高档数控机床运维平台设计研究

为响应中国制造业智能转型需求,针对传统二维交互界面的数控机床运维系统在人机交互过程中容易造成信息认知偏差、维修错误等问题,研究探讨了AR技术的优势及其运用于高档数控机床运维领域的可能性,构建了一套基于AR技术的高档数控机床运维平台框架,并以Unity3D为载体,设计开发了基于AR技术的高档数控机床运维平台。实现了运维人员和数控机床之间面对面的AR人机交互,提升了数控机床运维的高效及智能管理效率。

人工智能时代的电影导演:生存环境、艺术观念与创作原则——电影工业美学“接着讲”的“作者”之维

人工智能时代电影导演面临生存挑战,需要导演在被动接受中主动适应。电影工业美学导演维度的研究也需要在人工智能时代“接着讲”。秉承“人机一体”的创作观,并以此观念构思作品,应该是人工智能时代的导演观。探索一种人工智能“算法内生存”的创作路径,应该是人工智能时代导演的创作方向:一方面,导演在遵循作者情感的同时,必须尊重人工智能的数据理性,进而在感性、情感与理性、数据之间折衷、平衡;另一方面,导演在积极发挥创造性想象的基础上,必须合理利用人工智能高效完成人类指示的底层机制,进而在借助数据算法、人工智能实现想象的同时,不断扩展想象。