达优高科 一、人工智能的定义与讨论
人工智能的定义与讨论
人工智能（AI）是当今科技领域中备受关注的热门话题，其在各个领域的应用和发展日益深入。而对于人工智能的定义和讨论也是众说纷纭，不同领域的专家学者对人工智能的看法和定位有所不同。
首先，我们可以从技术角度来定义人工智能。人工智能是一种技术，它利用计算机科学、数学和其他多个交叉学科的知识和方法，使计算机能够模拟人类的智能行为。人工智能技术包括机器学习、深度学习、自然语言处理等方面，通过这些技术的应用，计算机可以执行像人类一样的认知任务。
从应用的角度来看，人工智能可以用于各个领域，包括医疗、金融、交通、农业等。在医疗领域，人工智能可以用于辅助医生进行诊断和制定治疗方案；在金融领域，人工智能可以用于风险控制和智能投顾等。可以说，人工智能已经渗透到我们生活的方方面面。
然而，人工智能的发展也面临着一些挑战和争议。一方面，人工智能的发展可能导致一些就业岗位的下岗，引发社会的不稳定和不平等；另一方面，人工智能的应用也存在着隐私保护、伦理道德等方面的难题。因此，在讨论人工智能时，我们不仅需要关注其技术发展，还需要思考其对社会、经济和伦理的影响。
从学术角度看，人工智能的定义也有着多种解释。一些学者认为，人工智能是一种弱人工智能，即通过模拟人类的思维和行为来解决特定问题；而另一些学者则认为，人工智能应该是一种强人工智能，即能够拥有自我意识和创造力，达到甚至超过人类智能的水平。
无论是弱人工智能还是强人工智能，人工智能的发展都将引领科技的下一波变革。在这个信息爆炸的时代，人工智能可以帮助我们更好地处理和分析海量的数据，为人类社会的发展提供更多可能性。因此，人工智能的定义和讨论将伴随着技术的不断突破和创新而不断深化。
结论
综上所述，人工智能的定义和讨论是一个复杂多维的课题，需要从技术、应用、社会、伦理等多个角度来进行深入思考和探讨。只有在全社会的共同努力下，人工智能才能更好地造福于人类，引领科技的发展，推动社会的进步。
二、讨论讨论之类的词语？
当您需要进行讨论时，以下是一些常用的表达方式和词语：
1. 讨论：discuss, debate, talk about, exchange ideas
例如："我们需要讨论一下这个问题。" (We need to discuss this issue.)
2. 探讨：explore, delve into, investigate, examine
例如："我们需要探讨这个议题的各个方面。" (We need to explore all aspects of this issue.)
3. 辩论：argue, debate, dispute
例如："他们正在辩论这个问题的不同观点。" (They are arguing different viewpoints on this issue.)
4. 提出观点：put forward opinions, express viewpoints
例如："每个人都可以提出自己的观点。" (Everyone can put forward their opinions.)
5. 分析：analyze, examine, break down
例如："我们需要仔细分析这个问题的原因和影响。" (We need to analyze the causes and impacts of this issue.)
6. 交流意见：exchange opinions, share ideas
例如："我们应该互相交流意见，寻找解决办法。" (We should exchange opinions and find solutions together.)
7. 研讨会：workshop, seminar, symposium
例如："我们计划组织一个研讨会来讨论这个议题。" (We plan to organize a workshop to discuss this issue.)
8. 立场：standpoint, perspective, viewpoint
例如："每个人都有自己的立场和观点。" (Everyone has their own standpoint and viewpoint.)
在讨论中，请确保尊重他人的意见，保持礼貌和开放的态度，并尽量寻求共识和合作。
三、父子俩讨论ai人工智能写作
父子俩讨论ai人工智能写作
随着科技的不断发展，人工智能已经成为了当今社会最为热门的话题之一。而作为一个人工智能领域的热门话题，ai人工智能写作也成为了许多人的关注焦点。最近，父子俩在家中就这个话题展开了一场深入的讨论。 儿子是一个对ai人工智能写作充满热情的年轻人，他认为随着技术的不断进步，ai写作将会成为未来写作领域的主流。他相信，通过不断的研究和探索，人们将会开发出更加智能的写作工具，帮助人们更加高效地创作出更加优秀的作品。 父亲则对这个观点持有不同的看法。他认为，虽然科技的发展为写作带来了许多便利，但是人类的创造力是无法被替代的。他指出，在写作过程中，情感、思考和想象力是必不可少的，而这些是无法被机器所替代的。 于是，父子俩开始了一场激烈的讨论。他们讨论了ai写作的优势和劣势，探讨了人类创造力和机器智能之间的关系。在这个过程中，他们不仅深入了解了彼此的观点，还对ai人工智能写作有了更加全面的认识。 在讨论中，儿子强调了ai写作在效率上的优势。他指出，通过使用智能算法和机器学习技术，ai写作工具可以在短时间内生成大量的文本内容，从而大大节省了创作时间和精力。此外，ai写作工具还可以根据用户的反馈和需求进行不断的优化和改进，从而为用户提供更加个性化的写作体验。 而父亲则从人类创造力的角度出发，强调了情感、思考和想象力在写作中的重要性。他指出，这些因素是机器所无法模仿和替代的。他认为，在未来的写作领域中，人类仍然需要发挥自己的创造力，通过自己的思考和情感来创作出更加优秀的作品。 在这个讨论中，父子俩还探讨了ai写作的应用场景和未来发展。他们认为，随着技术的不断进步，ai写作将会在更多的领域得到应用，如新闻报道、广告文案、小说创作等。同时，他们也指出了ai写作目前存在的问题和挑战，如版权问题、信任问题等。 总的来说，这场讨论让父子俩对ai人工智能写作有了更加深入的认识。他们意识到，尽管科技的发展为写作带来了许多便利，但是人类创造力和想象力仍然是无法替代的。在未来，人们需要继续探索和尝试，将科技与创造力相结合，创造出更加优秀的作品。四、发展党员讨论是集中讨论还是逐一讨论？
个别吸收”，是发展党员必须坚持的原则之一。反映在工作程序上，就是逐个讨论和表决，这样可以使党组织和党员对发展对象有更充分的了解，也可以使参加支部大会的每个党员更明确地表明自己的意见。支部大会讨论两个及以上的人入党时，实行逐个讨论和表决，体现了发展党员工作的严肃性，也体现了尊重党员的民主权利，有利于保证新党员的质量，可以防止不具备党员条件的人在成批讨论通过中混入党内。
五、在党校学习 讨论课讨论什么？
回答如下：在党校学习中，讨论课讨论的内容通常包括党的理论、方针政策、重要会议文件、历史经验、时事热点等。通过讨论，可以深化对党的理论和政策的理解，提高思想认识和政治素养，增强党性观念和组织纪律意识，推动个人和党组织的发展。
同时，讨论课也是一个宣传与教育的平台，可以通过交流、互相借鉴、共同学习，提高党员干部的业务水平和工作能力。
六、激烈讨论和热烈讨论哪个正确？
热烈讨论是正确的。
要弄清激烈与热烈的区别。
激烈的意思是：剧烈；强烈（指言论、动作等）：战斗激烈，言辞激烈。
热烈的意思是：兴奋激动：热烈的掌声|小组会上发言很热烈。
由此可见，讨论要用热烈，大家积极参与。激烈多用在动作激烈的战斗、激烈的比赛等等。
供参考。
七、一道英语来讨论讨论？
Last week, we had very heated discussion on this topic.上周我们对这个主题进行了激烈的讨论。
时间是在上周是发生过的事所以have不能用原形要改为过去时态，heated 是形容词激烈的激昂的热烈的而heating是名词意思是加热 制热 供暖所以不能用。
八、讨论研究和研究讨论？
探讨、讨论、研讨三者为近义词，区别在于用法和语意强调的重点不同。
1、用法区别：探讨和讨论两个词一般用于“~某事”；研讨一词一般用于“~会”、“~建议”等。
2、语意重点区别：探讨指探索、探索讲求，强调探寻的过程；讨论是指就某一问题交换意见或进行辩论，强调解决问题的方法、形式；研讨是指研究讨论，强调前期研究的基础。
九、有没有讨论论文的，关于人工智能，机器人论文？
人工智能相关论文
【1】 Rollout Algorithms and Approximate Dynamic Programming for Bayesian Optimization and Sequential Estimation
标题：用于贝叶斯优化和序列估计的滚动算法和近似动态编程
作者：Dimitri Bertsekas链接：https://arxiv.org/abs/2212.07998摘要：We provide a unifying approximate dynamic programming framework that applies to a broad variety of problems involving sequential estimation. We consider first the construction of surrogate cost functions for the purposes of optimization, and we focus on the special case of Bayesian optimization, using the rollout algorithm and some of its variations. We then discuss the more general case of sequential estimation of a random vector using optimal measurement selection, and its application to problems of stochastic and adaptive control. We finally consider related search and sequential decoding problems, and a rollout algorithm for the approximate solution of the Wordle and Mastermind puzzles, recently developed in the paper [BBB22].我们提供了一个统一的近似动态编程框架，适用于涉及序列估计的各种问题。我们首先考虑为优化目的而构建代用成本函数，我们重点讨论贝叶斯优化的特殊情况，使用推出算法及其一些变化。然后，我们讨论了使用最优测量选择对随机矢量进行顺序估计的更一般的情况，以及它对随机和适应性控制问题的应用。最后，我们考虑了相关的搜索和顺序解码问题，以及最近在论文[BBB22]中开发的用于近似解决Wordle和Mastermind谜题的滚屏算法。
【2】 Intensional First Order Logic for Strong-AI Generation of Robots
标题：用于强人工智能机器人生成的扩展性一阶逻辑作者：Zoran Majkic链接：https://arxiv.org/abs/2212.07935摘要：Neuro-symbolic AI attempts to integrate neural and symbolic architectures in a manner that addresses strengths and weaknesses of each, in a complementary fashion, in order to support robust strong AI capable of reasoning, learning, and cognitive modeling. In this paper we consider the intensional First Order Logic (IFOL) as a symbolic architecture of modern robots, able to use natural languages to communicate with humans and to reason about their own knowledge with self-reference and abstraction language property. We intend to obtain the grounding of robot's language by experience of how it uses its neuronal architectures and hence by associating this experience with the mining (sense) of non-defined language concepts (particulars/individuals and universals) in PRP (Properties/Relations/propositions) theory of IFOL. We consider three natural language levels: The syntax of particular natural language (Italian, French, etc..), and two universal language properties: its semantic logic structure (based on virtual predicates of FOL and logic connectives), and its corresponding conceptual PRP structure which universally represents the composite mining of FOL formulae grounded on the robot's neuro system.神经符号人工智能试图以一种互补的方式整合神经和符号架构，解决各自的优势和劣势，以支持能够推理、学习和认知建模的强大人工智能。在本文中，我们考虑将广义一阶逻辑（IFOL）作为现代机器人的符号架构，能够使用自然语言与人类交流，并通过自我参照和抽象语言属性对自己的知识进行推理。我们打算通过机器人如何使用其神经元架构的经验来获得机器人语言的基础，从而将这种经验与IFOL的PRP（属性/关系/命题）理论中的非定义语言概念（特殊/个体和普遍）的挖掘（意义）联系起来。我们考虑三个自然语言层面。特定自然语言（意大利语、法语等）的语法，以及两个普遍的语言属性：其语义逻辑结构（基于FOL的虚拟谓词和逻辑连接词），以及其相应的概念性PRP结构，该结构普遍代表了基于机器人神经系统的FOL公式的复合挖掘。
【3】 Multi-Agent Reinforcement Learning with Shared Resources for Inventory Management
标题：带有共享资源的库存管理的多代理强化学习作者：Yuandong Ding, Mingxiao Feng, Guozi Liu, Wei Jiang, Chuheng Zhang, Li Zhao, Lei Song, Houqiang Li, Yan Jin, Jiang Bian链接：https://arxiv.org/abs/2212.07684摘要：In this paper, we consider the inventory management (IM) problem where we need to make replenishment decisions for a large number of stock keeping units (SKUs) to balance their supply and demand. In our setting, the constraint on the shared resources (such as the inventory capacity) couples the otherwise independent control for each SKU. We formulate the problem with this structure as Shared-Resource Stochastic Game (SRSG)and propose an efficient algorithm called Context-aware Decentralized PPO (CD-PPO). Through extensive experiments, we demonstrate that CD-PPO can accelerate the learning procedure compared with standard MARL algorithms.在本文中，我们考虑了库存管理（IM）问题，即我们需要对大量的库存单位（SKU）进行补货决策，以平衡它们的供应和需求。在我们的设定中，对共享资源（如库存容量）的约束使每个SKU的独立控制成为可能。我们将这种结构的问题表述为共享资源随机博弈（SRSG），并提出了一种高效的算法，称为上下文感知的分散式PPO（CD-PPO）。通过广泛的实验，我们证明CD-PPO与标准的MARL算法相比，可以加速学习过程。
【4】 Many-valued Argumentation, Conditionals and a Probabilistic Semantics for Gradual Argumentation
标题：多值论证、条件论和渐进论证的概率语义学作者：Mario Alviano, Laura Giordano, Daniele Theseider Dupré链接：https://arxiv.org/abs/2212.07523摘要：In this paper we propose a general approach to define a many-valued preferential interpretation of gradual argumentation semantics. The approach allows for conditional reasoning over arguments and boolean combination of arguments, with respect to a class of gradual semantics, through the verification of graded (strict or defeasible) implications over a preferential interpretation. As a proof of concept, in the finitely-valued case, an Answer set Programming approach is proposed for conditional reasoning in a many-valued argumentation semantics of weighted argumentation graphs. The paper also develops and discusses a probabilistic semantics for gradual argumentation, which builds on the many-valued conditional semantics.在本文中，我们提出了一种定义渐进式论证语义的多值优先解释的一般方法。该方法允许对论据和论据的布尔组合进行条件推理，就一类渐变语义而言，通过对优先解释的分级（严格或可忽略）含义的验证。作为概念的证明，在有限值的情况下，为加权论证图的多值论证语义中的条件推理提出了一种答案集编程方法。本文还发展并讨论了渐进式论证的概率语义，它建立在多值条件语义的基础上。
【5】 FlexiViT: One Model for All Patch Sizes
标题：FlexiViT: 一个模型适用于所有补丁尺寸作者：Lucas Beyer, Pavel Izmailov, Alexander Kolesnikov, Mathilde Caron, Simon Kornblith, Xiaohua Zhai, Matthias Minderer, Michael Tschannen, Ibrahim Alabdulmohsin, Filip Pavetic链接：https://arxiv.org/abs/2212.08013摘要：Vision Transformers convert images to sequences by slicing them into patches. The size of these patches controls a speed/accuracy tradeoff, with smaller patches leading to higher accuracy at greater computational cost, but changing the patch size typically requires retraining the model. In this paper, we demonstrate that simply randomizing the patch size at training time leads to a single set of weights that performs well across a wide range of patch sizes, making it possible to tailor the model to different compute budgets at deployment time. We extensively evaluate the resulting model, which we call FlexiViT, on a wide range of tasks, including classification, image-text retrieval, open-world detection, panoptic segmentation, and semantic segmentation, concluding that it usually matches, and sometimes outperforms, standard ViT models trained at a single patch size in an otherwise identical setup. Hence, FlexiViT training is a simple drop-in improvement for ViT that makes it easy to add compute-adaptive capabilities to most models relying on a ViT backbone architecture. 视觉变换器通过将图像切成斑块将其转换为序列。这些斑块的大小控制着速度/准确度的权衡，较小的斑块导致较高的准确度，但计算成本较高，但改变斑块大小通常需要重新训练模型。在本文中，我们证明了在训练时简单地随机化补丁大小会导致一组权重在广泛的补丁大小范围内表现良好，使得在部署时根据不同的计算预算定制模型成为可能。我们对所产生的模型进行了广泛的评估，我们称之为FlexiViT，其任务包括分类、图像-文本检索、开放世界检测、全景分割和语义分割，结论是它通常与在其他相同的设置中以单一补丁大小训练的标准ViT模型相匹配，有时甚至优于后者。因此，FlexiViT训练是对ViT的一个简单的改进，可以很容易地将计算适应能力添加到大多数依赖于ViT骨干结构的模型中。
【6】 Zero-Shot Learning for Joint Intent and Slot Labeling标题：用于联合意图和槽位标签的零样本学习作者：Rashmi Gangadharaiah, Balakrishnan Narayanaswamy链接：https://arxiv.org/abs/2212.07922摘要：It is expensive and difficult to obtain the large number of sentence-level intent and token-level slot label annotations required to train neural network (NN)-based Natural Language Understanding (NLU) components of task-oriented dialog systems, especially for the many real world tasks that have a large and growing number of intents and slot types. While zero shot learning approaches that require no labeled examples -- only features and auxiliary information -- have been proposed only for slot labeling, we show that one can profitably perform joint zero-shot intent classification and slot labeling. We demonstrate the value of capturing dependencies between intents and slots, and between different slots in an utterance in the zero shot setting. We describe NN architectures that translate between word and sentence embedding spaces, and demonstrate that these modifications are required to enable zero shot learning for this task. We show a substantial improvement over strong baselines and explain the intuition behind each architectural modification through visualizations and ablation studies.要获得大量的句子级别的意图和标记级别的槽位标签注释来训练基于神经网络（NN）的面向任务的对话系统的自然语言理解（NLU）组件是非常昂贵和困难的，特别是对于许多具有大量且不断增长的意图和槽位类型的现实世界任务。虽然不需要标记的例子--只有特征和辅助信息--的零点学习方法只被提出来用于槽的标记，但我们表明可以有利地进行零点意图分类和槽的联合标记。我们证明了捕捉意图和槽之间的依赖关系的价值，以及在零次拍摄的语篇中不同槽之间的依赖关系。我们描述了在词和句子嵌入空间之间转换的NN架构，并证明这些修改是实现这一任务的零点学习所必需的。我们展示了对强基线的实质性改进，并通过可视化和消减研究解释了每个架构修改背后的直觉。
【7】 Manifestations of Xenophobia in AI Systems标题：人工智能系统中的仇外心理表现作者：Nenad Tomasev, Jonathan Leader Maynard, Iason Gabriel链接：https://arxiv.org/abs/2212.07877摘要：Xenophobia is one of the key drivers of marginalisation, discrimination, and conflict, yet many prominent machine learning (ML) fairness frameworks fail to comprehensively measure or mitigate the resulting xenophobic harms. Here we aim to bridge this conceptual gap and help facilitate safe and ethical design of artificial intelligence (AI) solutions. We ground our analysis of the impact of xenophobia by first identifying distinct types of xenophobic harms, and then applying this framework across a number of prominent AI application domains, reviewing the potential interplay between AI and xenophobia on social media and recommendation systems, healthcare, immigration, employment, as well as biases in large pre-trained models. These help inform our recommendations towards an inclusive, xenophilic design of future AI systems.仇外心理是边缘化、歧视和冲突的主要驱动因素之一，但许多著名的机器学习（ML）公平性框架未能全面衡量或减轻由此产生的仇外心理伤害。在这里，我们旨在弥补这一概念上的差距，帮助促进人工智能（AI）解决方案的安全和道德设计。我们对仇外心理的影响进行了分析，首先确定了不同类型的仇外伤害，然后将这一框架应用于一些著名的人工智能应用领域，回顾了人工智能和仇外心理在社交媒体和推荐系统、医疗、移民、就业以及大型预训练模型中的潜在相互作用。这些都有助于为我们对未来人工智能系统的包容性、排外性的设计提供建议。
【8】 Population Template-Based Brain Graph Augmentation for Improving One-Shot Learning Classification标题：基于群体模板的脑图增强，提高单次学习分类能力作者：Oben Özgür, Arwa Rekik, Islem Rekik链接：https://arxiv.org/abs/2212.07790摘要：The challenges of collecting medical data on neurological disorder diagnosis problems paved the way for learning methods with scarce number of samples. Due to this reason, one-shot learning still remains one of the most challenging and trending concepts of deep learning as it proposes to simulate the human-like learning approach in classification problems. Previous studies have focused on generating more accurate fingerprints of the population using graph neural networks (GNNs) with connectomic brain graph data. Thereby, generated population fingerprints named connectional brain template (CBTs) enabled detecting discriminative bio-markers of the population on classification tasks. However, the reverse problem of data augmentation from single graph data representing brain connectivity has never been tackled before. In this paper, we propose an augmentation pipeline in order to provide improved metrics on our binary classification problem. Divergently from the previous studies, we examine augmentation from a single population template by utilizing graph-based generative adversarial network (gGAN) architecture for a classification problem. We benchmarked our proposed solution on AD/LMCI dataset consisting of brain connectomes with Alzheimer's Disease (AD) and Late Mild Cognitive Impairment (LMCI). In order to evaluate our model's generalizability, we used cross-validation strategy and randomly sampled the folds multiple times. Our results on classification not only provided better accuracy when augmented data generated from one sample is introduced, but yields more balanced results on other metrics as well.收集神经系统疾病诊断问题的医疗数据所面临的挑战，为具有稀缺样本数量的学习方法铺平了道路。由于这个原因，一次性学习仍然是深度学习中最具挑战性和趋势性的概念之一，因为它提议在分类问题中模拟类似人类的学习方法。以前的研究集中在使用图神经网络（GNN）与连接体脑图数据生成更准确的群体指纹。因此，生成的人群指纹被命名为连接脑模板（CBTs），能够在分类任务中检测出人群的鉴别性生物标记。然而，从代表大脑连接的单一图数据中进行数据增强的反向问题以前从未被解决过。在本文中，我们提出了一个扩增管道，以便为我们的二元分类问题提供更好的指标。与之前的研究不同，我们通过利用基于图的生成对抗网络（gGAN）架构，对单一群体模板的分类问题进行增强。我们在由阿尔茨海默病（AD）和晚期轻度认知障碍（LMCI）的大脑连接体组成的AD/LMCI数据集上对我们提出的解决方案进行了基准测试。为了评估我们模型的普适性，我们使用了交叉验证策略，并对褶皱进行了多次随机采样。我们的分类结果不仅在引入由一个样本产生的增强数据时提供了更好的准确性，而且在其他指标上也产生了更均衡的结果。
【9】 A New Deep Boosted CNN and Ensemble Learning based IoT Malware Detection
标题：一种新的基于深度提升的CNN和集合学习的物联网恶意软件检测方法作者：Saddam Hussain Khan, Wasi Ullah (Department of Computer Systems Engineering, University of Engineering and Applied Science, Swat, Pakistan)链接：https://arxiv.org/abs/2212.08008摘要：Security issues are threatened in various types of networks, especially in the Internet of Things (IoT) environment that requires early detection. IoT is the network of real-time devices like home automation systems and can be controlled by open-source android devices, which can be an open ground for attackers. Attackers can access the network, initiate a different kind of security breach, and compromises network control. Therefore, timely detecting the increasing number of sophisticated malware attacks is the challenge to ensure the credibility of network protection. In this regard, we have developed a new malware detection framework, Deep Squeezed-Boosted and Ensemble Learning (DSBEL), comprised of novel Squeezed-Boosted Boundary-Region Split-Transform-Merge (SB-BR-STM) CNN and ensemble learning. The proposed S.T.M. block employs multi-path dilated convolutional, Boundary, and regional operations to capture the homogenous and heterogeneous global malicious patterns. Moreover, diverse feature maps are achieved using transfer learning and multi-path-based squeezing and boosting at initial and final levels to learn minute pattern variations. Finally, the boosted discriminative features are extracted from the developed deep SB-BR-STM CNN and provided to the ensemble classifiers (SVM, M.L.P., and AdaboostM1) to improve the hybrid learning generalization. The performance analysis of the proposed DSBEL framework and SB-BR-STM CNN against the existing techniques have been evaluated by the IOT_Malware dataset on standard performance measures. Evaluation results show progressive performance as 98.50% accuracy, 97.12% F1-Score, 91.91% MCC, 95.97 % Recall, and 98.42 % Precision. The proposed malware analysis framework is helpful for the timely detection of malicious activity and suggests future strategies.安全问题在各种类型的网络中都受到威胁，特别是在物联网（IoT）环境中，需要早期检测。物联网是由家庭自动化系统等实时设备组成的网络，可以由开源的安卓设备控制，这对攻击者来说是一个开放的场所。攻击者可以访问网络，启动不同的安全漏洞，并破坏网络控制。因此，及时发现越来越多的复杂恶意软件攻击是确保网络保护可信度的挑战。在这方面，我们开发了一个新的恶意软件检测框架，即深度挤压提升和集合学习（DSBEL），由新颖的挤压提升边界-区域分割-变换-合并（SB-BR-STM）CNN和集合学习组成。拟议的S.T.M.块采用多路径扩张卷积、边界和区域操作来捕捉同质和异质的全球恶意模式。此外，利用转移学习和基于多路径的挤压和提升，在初始和最终层面实现多样化的特征图，以学习微小的模式变化。最后，从开发的深度SB-BR-STM CNN中提取提升的判别特征，并提供给集合分类器（SVM、M.L.P.和AdaboostM1）以提高混合学习的通用性。拟议的DSBEL框架和SB-BR-STM CNN相对于现有技术的性能分析已经通过IOT_Malware数据集的标准性能指标进行了评估。评估结果显示，准确率为98.50%，F1分数为97.12%，MCC为91.91%，召回率为95.97%，精确度为98.42%。拟议的恶意软件分析框架有助于及时检测恶意活动，并提出了未来的策略。
人机交互相关论文
【1】 DOPAMINE: Doppler frequency and Angle of arrival MINimization of tracking Error for extended reality标题：DOPAMINE: 多普勒频率和到达角 延伸现实的跟踪误差最小化作者：Andrea Bedin, Alexander Marinšek, Shaghayegh Shahcheraghi, Nairy Moghadas Gholian, Liesbet Van der Perre链接：https://arxiv.org/abs/2212.07764摘要：In this paper, we investigate how Joint Communication And Sensing (JCAS) can be used to improve the Inertial Measurement Unit (IMU)- based tracking accuracy of eXtended Reality (XR) Head-Mounted Displays (HMDs). Such tracking is used when optical and InfraRed (IR) tracking is lost, and its lack of accuracy can lead to disruption of the user experience. In particular, we analyze the impact of using doppler-based speed estimation to aid the accelerometer-based position estimation, and Angle of Arrival (AoA) estimation to aid the gyroscope-based orientation estimation. Although less accurate than IMUs for short times in fact, the JCAS based methods require one fewer integration step, making the tracking more sustainable over time. Based on the proposed model, we conclude that at least in the case of the position estimate, introducing JCAS can make long lasting optical/IR tracking losses more sustainable.在本文中，我们研究了如何利用联合通信和传感（JCAS）来改善基于惯性测量单元（IMU）的扩展现实（XR）头戴式显示器（HMD）的跟踪精度。当光学和红外（IR）跟踪丢失时，就会使用这种跟踪，而其缺乏准确性会导致用户体验的中断。特别是，我们分析了使用基于多普勒的速度估计来帮助基于加速度计的位置估计，以及使用到达角（AoA）估计来帮助基于陀螺仪的方向估计的影响。虽然在短时间内的准确度不如IMU，但基于JCAS的方法需要较少的整合步骤，使跟踪随着时间的推移更加持久。基于所提出的模型，我们得出结论，至少在位置估计的情况下，引入JCAS可以使长期的光学/红外跟踪损失更加持续。
【2】 Improving Developers' Understanding of Regex Denial of Service Tools through Anti-Patterns and Fix Strategies标题：通过反模式和修复策略提高开发人员对拒绝服务工具的认识作者：Sk Adnan Hassan, Zainab Aamir, Dongyoon Lee, James C. Davis, Francisco Servant链接：https://arxiv.org/abs/2212.07979摘要：Regular expressions are used for diverse purposes, including input validation and firewalls. Unfortunately, they can also lead to a security vulnerability called ReDoS (Regular Expression Denial of Service), caused by a super-linear worst-case execution time during regex matching. Due to the severity and prevalence of ReDoS, past work proposed automatic tools to detect and fix regexes. Although these tools were evaluated in automatic experiments, their usability has not yet been studied; usability has not been a focus of prior work. Our insight is that the usability of existing tools to detect and fix regexes will improve if we complement them with anti-patterns and fix strategies of vulnerable regexes. We developed novel anti-patterns for vulnerable regexes, and a collection of fix strategies to fix them. We derived our anti-patterns and fix strategies from a novel theory of regex infinite ambiguity - a necessary condition for regexes vulnerable to ReDoS. We proved the soundness and completeness of our theory. We evaluated the effectiveness of our anti-patterns, both in an automatic experiment and when applied manually. Then, we evaluated how much our anti-patterns and fix strategies improve developers' understanding of the outcome of detection and fixing tools. Our evaluation found that our anti-patterns were effective over a large dataset of regexes (N=209,188): 100% precision and 99% recall, improving the state of the art 50% precision and 87% recall. Our anti-patterns were also more effective than the state of the art when applied manually (N=20): 100% developers applied them effectively vs. 50% for the state of the art. Finally, our anti-patterns and fix strategies increased developers' understanding using automatic tools (N=9): from median "Very weakly" to median "Strongly" when detecting vulnerabilities, and from median "Very weakly" to median "Very strongly" when fixing them.正则表达式被用于多种用途，包括输入验证和防火墙。不幸的是，它们也会导致一种叫做ReDoS（正则表达式拒绝服务）的安全漏洞，这种漏洞是由正则表达式匹配过程中的超线性最坏情况下的执行时间引起。由于ReDoS的严重性和普遍性，过去的工作提出了自动工具来检测和修复REGEXES。尽管这些工具在自动实验中得到了评估，但它们的可用性还没有被研究过；可用性还没有成为先前工作的重点。我们的见解是，如果我们用反模式和易受攻击的词组的修复策略来补充现有的检测和修复词组的工具，其可用性将会提高。我们开发了新的反模式，用于易受攻击的词组，并开发了一系列的修复策略来修复它们。我们的反模式和修复策略来自于一个新的关于词组无限模糊性的理论--这是一个容易受到ReDoS攻击的词组的必要条件。我们证明了我们理论的合理性和完整性。我们评估了我们的反模式的有效性，包括在自动实验和手动应用时。然后，我们评估了我们的反模式和修复策略在多大程度上改善了开发者对检测和修复工具结果的理解。我们的评估发现，我们的反模式对一个大型的词组数据集（N=209,188）是有效的。100%的精度和99%的召回率，提高了50%的精度和87%的召回率。我们的反模式在手动应用（N=20）时也比现有技术水平更有效：100%的开发者有效地应用它们，而现有技术水平只有50%。最后，我们的反模式和修复策略提高了开发者使用自动工具的理解能力（N=9）：在检测漏洞时，从中位数 "非常弱 "到中位数 "强"；在修复漏洞时，从中位数 "非常弱 "到中位数 "非常强"。
【3】 Beyond the Metaverse: XV (eXtended meta/uni/Verse)标题：元宇宙之外：XV（eXtended meta/omni/uni/Verse）作者：Steve Mann, Yu Yuan, Tom Furness, Joseph Paradiso, Thomas Coughlin链接：https://arxiv.org/abs/2212.07960摘要：We propose the term and concept XV (eXtended meta/omni/uni/Verse) as an alternative to, and generalization of, the shared/social virtual reality widely known as ``metaverse''. XV is shared/social XR. We, and many others, use XR (eXtended Reality) as a broad umbrella term and concept to encompass all the other realities, where X is an ``anything'' variable, like in mathematics, to denote any reality, X ∈ \{physical, virtual, augmented, \ldots \} reality. Therefore XV inherits this generality from XR. We begin with a very simple organized taxonomy of all these realities in terms of two simple building blocks: (1) physical reality (PR) as made of ``atoms'', and (2) virtual reality (VR) as made of ``bits''. Next we introduce XV as combining all these realities with extended society as a three-dimensional space and taxonomy of (1) ``atoms'' (physical reality), (2) ``bits'' (virtuality), and (3) ``genes'' (sociality). Thus those working in the liminal space between Virtual Reality (VR), Augmented Reality (AR), metaverse, and their various extensions, can describe their work and research as existing in the new field of XV. XV includes the metaverse along with extensions of reality itself like shared seeing in the infrared, ultraviolet, and shared seeing of electromagnetic radio waves, sound waves, and electric currents in motors. For example, workers in a mechanical room can look at a pump and see a superimposed time-varying waveform of the actual rotating magnetic field inside its motor, in real time, while sharing this vision across multiple sites.我们提出了XV（eXtended meta/omni/uni/Verse）这一术语和概念，作为广泛称为 "metaverse "的共享/社会虚拟现实的替代和概括。XV是共享/社会XR。我们和许多其他人使用XR（eXtended Reality）作为一个广泛的伞状术语和概念，以包括所有其他的现实，其中X是一个 "任何东西 "的变量，就像在数学中，表示任何现实，X∈{物理、虚拟、增强、ldots }现实。因此，XV从XR继承了这种一般性。我们以两个简单的构件开始对所有这些现实进行一个非常简单的有组织的分类：（1）由 "原子 "组成的物理现实（PR），以及（2）由 "比特 "组成的虚拟现实（VR）。接下来，我们介绍XV，将所有这些现实与扩展的社会结合起来，作为一个三维空间和分类法：（1）"原子"（物理现实），（2）"比特"（虚拟性），和（3）"基因"（社会性）。因此，那些在虚拟现实（VR）、增强现实（AR）、元空间及其各种扩展之间的边缘空间工作的人，可以把他们的工作和研究描述为存在于XV这个新领域中。XV包括元空间以及现实本身的延伸，如在红外线、紫外线中的共享视觉，以及对电磁波、声波和电机中的电流的共享视觉。例如，机械室里的工人可以看一个泵，并实时看到其电机内实际旋转磁场的叠加时间变化波形，同时在多个地点共享这一视觉。
【4】 Synthesizing Research on Programmers' Mental Models of Programs, Tasks and Concepts -- a Systematic Literature Review标题：综合研究程序员对程序、任务和概念的心理模型--系统的文献回顾作者：Ava Heinonen, Bettina Lehtelä, Arto Hellas, Fabian Fagerholm链接：https://arxiv.org/abs/2212.07763摘要：Programmers' mental models represent their knowledge and understanding of programs, programming concepts, and programming in general. They guide programmers' work and influence their task performance. Understanding mental models is important for designing work systems and practices that support programmers. Although the importance of programmers' mental models is widely acknowledged, research on mental models has decreased over the years. The results are scattered and do not take into account recent developments in software engineering. We analyze the state of research into programmers' mental models and provide an overview of existing research. We connect results on mental models from different strands of research to form a more unified knowledge base on the topic. We conducted a systematic literature review on programmers' mental models. We analyzed literature addressing mental models in different contexts, including mental models of programs, programming tasks, and programming concepts. Using nine search engines, we found 3678 articles (excluding duplicates). 84 were selected for further analysis. Using the snowballing technique, we obtained a final result set containing 187 articles. We show that the literature shares a kernel of shared understanding of mental models. By collating and connecting results on mental models from different fields of research, we uncovered some well-researched aspects, which we argue are fundamental characteristics of programmers' mental models. This work provides a basis for future work on mental models. The research field on programmers' mental models still faces many challenges rising from a lack of a shared knowledge base and poorly defined constructs. We created a unified knowledge base on the topic. We also point to directions for future studies. In particular, we call for studies that examine programmers working with modern practices and tools.程序员的心理模型代表了他们对程序、编程概念和一般编程的知识和理解。它们指导程序员的工作，并影响他们的任务表现。理解心理模型对于设计支持程序员的工作系统和实践非常重要。尽管程序员心智模式的重要性被广泛认可，但多年来对心智模式的研究却在减少。这些研究结果是分散的，没有考虑到软件工程的最新发展。我们分析了对程序员心理模型的研究状况，并对现有的研究进行了概述。我们把来自不同研究领域的关于心理模型的结果联系起来，以形成一个关于该主题的更统一的知识库。我们对程序员的心理模型进行了系统的文献回顾。我们分析了不同背景下的心理模型的文献，包括程序的心理模型、编程任务和编程概念。使用九个搜索引擎，我们找到了3678篇文章（不包括重复的）。挑选了84篇进行进一步分析。使用滚雪球技术，我们得到了一个包含187篇文章的最终结果集。我们表明，这些文献分享了对心理模型的共同理解的内核。通过整理和连接来自不同研究领域的关于心理模型的结果，我们发现了一些经过充分研究的方面，我们认为这些是程序员心理模型的基本特征。这项工作为未来关于心理模型的工作提供了一个基础。关于程序员心理模型的研究领域仍然面临着许多挑战，这些挑战来自于缺乏一个共享的知识库和定义不清的结构。我们创建了一个关于这个主题的统一的知识库。我们还指出了未来研究的方向。特别是，我们呼吁对使用现代实践和工具的程序员的研究。
【5】 Tensions Between the Proxies of Human Values in AI标题：人工智能中人类价值的代名词之间的紧张关系作者：Teresa Datta, Daniel Nissani, Max Cembalest, Akash Khanna, Haley Massa, John P. Dickerson链接：https://arxiv.org/abs/2212.07508摘要：Motivated by mitigating potentially harmful impacts of technologies, the AI community has formulated and accepted mathematical definitions for certain pillars of accountability: e.g. privacy, fairness, and model transparency. Yet, we argue this is fundamentally misguided because these definitions are imperfect, siloed constructions of the human values they hope to proxy, while giving the guise that those values are sufficiently embedded in our technologies. Under popularized methods, tensions arise when practitioners attempt to achieve each pillar of fairness, privacy, and transparency in isolation or simultaneously. In this position paper, we push for redirection. We argue that the AI community needs to consider all the consequences of choosing certain formulations of these pillars -- not just the technical incompatibilities, but also the effects within the context of deployment. We point towards sociotechnical research for frameworks for the latter, but push for broader efforts into implementing these in practice.出于减轻技术潜在有害影响的动机，人工智能界已经制定并接受了某些责任制支柱的数学定义：如隐私、公平和模型透明度。然而，我们认为这从根本上来说是错误的，因为这些定义是不完善的，是他们希望代理的人类价值的孤立的构造，同时也给这些价值充分嵌入我们的技术打上了幌子。在流行的方法下，当从业者试图孤立地或同时实现公平、隐私和透明的每个支柱时，就会出现紧张。在这份立场文件中，我们推动了方向性的改变。我们认为，人工智能界需要考虑选择这些支柱的某些形式的所有后果--不仅仅是技术上的不相容性，还有在部署背景下的影响。我们指出，社会技术研究是为了建立后者的框架，但也要推动更广泛的努力在实践中实施这些框架。
机器翻译由DeepL翻译生成，仅供参考。
十、人工智能时代下,底特律变人引发的社会讨论
在科技飞速发展的今天,人工智能技术已经渗透到我们生活的方方面面。其中,底特律变人这部科幻电影引发了人们对人工智能的广泛讨论。这部作品不仅展现了人工智能技术的发展前景,也引发了人们对人机关系、伦理道德等诸多问题的思考。让我们一起探讨一下这部作品所引发的社会热议。
人工智能技术的发展与应用
近年来,随着人工智能技术的不断进步,其在各个领域的应用也越来越广泛。从自动驾驶汽车到智能家居,再到医疗诊断,人工智能正在深刻地改变着我们的生活方式。底特律变人这部作品生动地描绘了人工智能技术在未来社会中的应用场景,让观众对人工智能的发展前景有了更直观的认识。
在电影中,人类制造出了拥有高度智能的仿生人,他们可以完成各种复杂的工作,并且与人类无异。这种人机融合的场景,让人们不禁思考,未来人工智能技术会如何影响人类社会的发展。
人机关系与伦理道德问题
底特律变人还引发了人们对人机关系和伦理道德问题的广泛讨论。在电影中,人类与仿生人之间的矛盾冲突时有发生,这让人们思考,人工智能技术的发展是否会威胁到人类的地位和价值。
同时,电影也探讨了人工智能系统的自主性和道德判断能力。仿生人是否应该拥有自主意识和独立思考的能力?他们的行为是否应该受到人类的道德约束?这些问题都引发了人们的深思。
此外,电影还涉及到人工智能技术在社会中的应用问题,比如仿生人是否应该取代人类从事某些工作,这可能会导致大规模失业等社会问题。这些问题都值得我们认真思考和讨论。
结语
总的来说,底特律变人这部作品不仅展现了人工智能技术的发展前景,也引发了人们对人机关系、伦理道德等诸多问题的深入思考。随着人工智能技术的不断进步,这些问题将会变得越来越重要。我们需要更多地关注人工智能技术的发展,并积极探讨如何在技术进步的同时,维护人类的价值和尊严。
感谢您阅读这篇文章,希望通过这篇文章,您能够更好地了解人工智能时代下,底特律变人引发的社会讨论。让我们一起为人机和谐发展贡献自己的力量。
