残疾人在医疗保健，就业和政府政策等各个领域的各种复杂的决策过程中受到各种复杂的决策。这些环境通常已经不透明他们影响的人并缺乏充分的残疾观点代表，它迅速采用人工智能（AI）技术来用于数据分析以告知决策，从而增加因不当或不公平的算法而造成的伤害风险增加。本文介绍了一个通过残疾镜头进行严格检查AI数据分析技术的框架，并研究了AI技术设计师选择的残疾定义如何影响其对残疾分析对象的影响。我们考虑了三种残疾的概念模型：医学模型，社会模型和关系模型；并展示在每个模型下设计的AI技术如何差异很大，以至于与彼此不相容和矛盾。通过讨论有关医疗保健和政府残疾福利中AI分析的常见用例，我们说明了技术设计过程中的特定考虑因素和决策点，这些因素和决策点影响了这些环境中的电力动态和包容性，并有助于确定其对边缘化或支持的方向。我们提出的框架可以作为对AI技术的深入批判性检查的基础，并开发用于残疾相关的AI分析的设计实践。

Network intrusion detection systems (NIDSs) play an important role in computer network security. There are several detection mechanisms where anomaly-based automated detection outperforms others significantly. Amid the sophistication and growing number of attacks, dealing with large amounts of data is a recognized issue in the development of anomaly-based NIDS. However, do current models meet the needs of today's networks in terms of required accuracy and dependability? In this research, we propose a new hybrid model that combines machine learning and deep learning to increase detection rates while securing dependability. Our proposed method ensures efficient pre-processing by combining SMOTE for data balancing and XGBoost for feature selection. We compared our developed method to various machine learning and deep learning algorithms to find a more efficient algorithm to implement in the pipeline. Furthermore, we chose the most effective model for network intrusion based on a set of benchmarked performance analysis criteria. Our method produces excellent results when tested on two datasets, KDDCUP'99 and CIC-MalMem-2022, with an accuracy of 99.99% and 100% for KDDCUP'99 and CIC-MalMem-2022, respectively, and no overfitting or Type-1 and Type-2 issues.

With the growth of residential rooftop PV adoption in recent decades, the problem of 1 effective layout design has become increasingly important in recent years. Although a number 2 of automated methods have been introduced, these tend to rely on simplifying assumptions and 3 heuristics to improve computational tractability. We demonstrate a fully automated layout design 4 pipeline that attempts to solve a more general formulation with greater geometric flexibility that 5 accounts for shading losses. Our approach generates rooftop areas from satellite imagery and uses 6 MINLP optimization to select panel positions, azimuth angles and tilt angles on an individual basis 7 rather than imposing any predefined layouts. Our results demonstrate that although several common 8 heuristics are often effective, they may not be universally suitable due to complications resulting 9 from geometric restrictions and shading losses. Finally, we evaluate a few specific heuristics from the 10 literature and propose a potential new rule of thumb that may help improve rooftop solar energy 11 potential when shading effects are considered.

Cross entropy loss has served as the main objective function for classification-based tasks. Widely deployed for learning neural network classifiers, it shows both effectiveness and a probabilistic interpretation. Recently, after the success of self supervised contrastive representation learning methods, supervised contrastive methods have been proposed to learn representations and have shown superior and more robust performance, compared to solely training with cross entropy loss. However, cross entropy loss is still needed to train the final classification layer. In this work, we investigate the possibility of learning both the representation and the classifier using one objective function that combines the robustness of contrastive learning and the probabilistic interpretation of cross entropy loss. First, we revisit a previously proposed contrastive-based objective function that approximates cross entropy loss and present a simple extension to learn the classifier jointly. Second, we propose a new version of the supervised contrastive training that learns jointly the parameters of the classifier and the backbone of the network. We empirically show that our proposed objective functions show a significant improvement over the standard cross entropy loss with more training stability and robustness in various challenging settings.

Masader（Alyafeai等，2021）创建了一种元数据结构，用于分类阿拉伯NLP数据集。但是，开发一种简单的方法来探索这种目录是一项艰巨的任务。为了为探索目录的用户和研究人员提供最佳体验，必须解决一些设计和用户体验的挑战。此外，用户与网站的交互可能提供了一种简单的方法来改善目录。在本文中，我们介绍了Masader Plus，该网络接口供用户浏览masader。我们演示了数据探索，过滤和简单的API，该API允许用户从后端检查数据集。可以使用此链接https://arbml.github.io/masader探索masader plus。可以在此处找到的视频录制说明界面的录制https://www.youtube.com/watch?v=setDlseqchk。

近年来，知识蒸馏（KD）被认为是模型压缩和加速度的关键解决方案。在KD中，通过最大限度地减少两者的概率输出之间的分歧，一项小学生模型通常从大师模型中培训。然而，如我们实验中所示，现有的KD方法可能不会将老师的批判性解释知识转移给学生，即两种模型所做的预测的解释并不一致。在本文中，我们提出了一种新颖的可解释的知识蒸馏模型，称为XDistillation，通过该模型，解释信息都从教师模型转移到学生模型。 Xdistillation模型利用卷积的自动统计学器的想法来近似教师解释。我们的实验表明，由Xdistillation培训的模型优于传统KD方法的那些不仅在预测准确性的术语，而且对教师模型的忠诚度。

基于全面的生物识别是一个广泛的研究区域。然而，仅使用部分可见的面，例如在遮盖的人的情况下，是一个具有挑战性的任务。在这项工作中使用深卷积神经网络（CNN）来提取来自遮盖者面部图像的特征。我们发现，第六和第七完全连接的层，FC6和FC7分别在VGG19网络的结构中提供了鲁棒特征，其中这两层包含4096个功能。这项工作的主要目标是测试基于深度学习的自动化计算机系统的能力，不仅要识别人，还要对眼睛微笑等性别，年龄和面部表达的认可。我们的实验结果表明，我们为所有任务获得了高精度。最佳记录的准确度值高达99.95％，用于识别人员，99.9％，年龄识别的99.9％，面部表情（眼睛微笑）认可为80.9％。

新颖的检测方法识别不代表模型训练集的样本，从而标记误导性预测并在部署时间带来更大的灵活性和透明度。但是，该领域的研究仅考虑了离线环境中的新颖性检测。最近，在计算机视觉社区中，应用程序越来越多，应用程序需要更灵活的框架 - 持续学习 - 在该框架中，代表新域，新类或新任务的新数据在不同的时间点可用。在这种情况下，新颖性检测变得越来越重要，有趣且具有挑战性。这项工作确定了这两个问题之间的关键联系，并研究了持续学习环境下的新颖性检测问题。我们制定了持续的新颖性检测问题，并提出了基准，在该基准中，我们比较了不同持续学习设置下的几种新颖性检测方法。我们表明，持续学习会影响新颖性检测算法的行为，而新颖性检测可以确定持续学习者的行为的见解。我们进一步提出了基准并讨论可能的研究方向。我们认为，这两个问题的耦合是将视觉模型付诸实践的有前途的方向。

Artificial neural networks thrive in solving the classification problem for a particular rigid task, acquiring knowledge through generalized learning behaviour from a distinct training phase. The resulting network resembles a static entity of knowledge, with endeavours to extend this knowledge without targeting the original task resulting in a catastrophic forgetting. Continual learning shifts this paradigm towards networks that can continually accumulate knowledge over different tasks without the need to retrain from scratch. We focus on task incremental classification, where tasks arrive sequentially and are delineated by clear boundaries. Our main contributions concern (1) a taxonomy and extensive overview of the state-of-the-art; (2) a novel framework to continually determine the stability-plasticity trade-off of the continual learner; (3) a comprehensive experimental comparison of 11 state-of-the-art continual learning methods and 4 baselines. We empirically scrutinize method strengths and weaknesses on three benchmarks, considering Tiny Imagenet and large-scale unbalanced iNaturalist and a sequence of recognition datasets. We study the influence of model capacity, weight decay and dropout regularization, and the order in which the tasks are presented, and qualitatively compare methods in terms of required memory, computation time and storage.

A continual learning agent learns online with a non-stationary and never-ending stream of data. The key to such learning process is to overcome the catastrophic forgetting of previously seen data, which is a well known problem of neural networks. To prevent forgetting, a replay buffer is usually employed to store the previous data for the purpose of rehearsal. Previous works often depend on task boundary and i.i.d. assumptions to properly select samples for the replay buffer. In this work, we formulate sample selection as a constraint reduction problem based on the constrained optimization view of continual learning. The goal is to select a fixed subset of constraints that best approximate the feasible region defined by the original constraints. We show that it is equivalent to maximizing the diversity of samples in the replay buffer with parameters gradient as the feature. We further develop a greedy alternative that is cheap and efficient. The advantage of the proposed method is demonstrated by comparing to other alternatives under the continual learning setting. Further comparisons are made against state of the art methods that rely on task boundaries which show comparable or even better results for our method.