Edge Computing通过同时且连续执行延迟敏感的机器学习（ML）应用程序来启用智能物联网的系统。这些基于边缘的机器学习系统通常是电池供电的（即能量限制的）。他们使用具有不同计算性能的异质资源（例如CPU，GPU和/或FPGA）来满足ML应用程序的延迟约束。面临的挑战是，就这些系统的能量和延迟约束分配了在异质边缘计算系统（HEC）上对不同ML应用程序的请求。为此，我们研究和分析资源分配解决方案，这些解决方案可以在考虑能量限制的同时增加准时任务完成率。重要的是，我们研究了边缘友好的（轻巧）多目标映射启发式方法，这些启发式启发式方法不会偏向于特定的应用程序类型以实现目标；取而代之的是，启发式方法在其映射决策中考虑了同一ML应用程序中的“公平性”。绩效评估表明，根据潜伏期和能源目标，尤其是在低至中等请求的到达率方面，提出的启发式胜诉率优于异质系统中广泛使用的启发式方法。我们观察到准时任务完成率提高了8.9％，节能提高了12.6％，而没有在边缘系统上施加任何明显的开销。

基于深度学习的（DL）申请越来越受欢迎，并以前所未有的步伐推动。虽然正在进行许多研究工作以增强深度神经网络（DNN） - DL应用的核心 - 云和边缘系统中这些应用的实际部署挑战，它们对应用程序的可用性的影响并未充分调查。特别是，部署不同虚拟化平台的影响由云和边缘提供的DL应用程序的可用性（在端到端（E2E）推理时间）仍然是一个打开的问题。重要的是，资源弹性（通过放大），CPU固定和处理器类型（CPU VS GPU）配置已经显示在虚拟化开销上有影响力。因此，本研究的目标是研究这些潜在决定的部署选项对E2E性能的影响，从而实现了DL应用程序的可用性。为此，我们在改变处理器配置时，我们测量四种流行的执行平台（即，裸机，虚拟机（VM），容器和容器中的裸机，虚拟机（VM），容器和容器）的影响（扩展，CPU固定）和处理器类型。本研究揭示了一系列有趣的，有时是反向直观的发现，可以用作云解决方案架构师的最佳实践，以便在各种系统中有效地部署DL应用程序。值得注意的发现是，解决方案架构师必须了解DL应用特征，特别是它们的预处理和后处理要求，能够最佳选择和配置执行平台，确定使用GPU，并决定有效扩展范围。

由于长距离，照明变化，有限的用户合作和移动科目，虹膜分割和定位在不受约束环境中具有挑战性。为了解决这个问题，我们介绍了一个U-Net，具有预先培训的MobileNetv2深神经网络方法。我们使用MobileNetv2的预先训练的权重，用于想象成数据集，并在虹膜识别和本地化域上进行微调。此外，我们推出了一个名为Kartalol的新数据集，以更好地评估虹膜识别方案中的检测器。为了提供域适应，我们可以在Casia-Iris-Asia，Casia-Iris-M1和Casia-Iris-Africa和Casia-Iris-Africa和我们的数据集中微调MobileNetv2模型。我们还通过执行左右翻转，旋转，缩放和亮度来增强数据。我们通过迭代所提供的数据集中的图像来选择二进制掩码的二值化阈值。沿着Kartalol DataSet，Casia-Iris-Asia，Casia-Iris-M1，Casia-Iris-M1，Casia-Iris-M1，Casia-Iris-M1，Casia-Iris-M1，Casia-Iris-M1培训。实验结果强调了我们的方法在基于移动的基准上超越了最先进的方法。代码和评估结果在https://github.com/jalilnkh/kartalol-nir -isl2021031301上公开可用。

Code generation from text requires understanding the user's intent from a natural language description (NLD) and generating an executable program code snippet that satisfies this intent. While recent pretrained language models (PLMs) demonstrate remarkable performance for this task, these models fail when the given NLD is ambiguous due to the lack of enough specifications for generating a high-quality code snippet. In this work, we introduce a novel and more realistic setup for this task. We hypothesize that ambiguities in the specifications of an NLD are resolved by asking clarification questions (CQs). Therefore, we collect and introduce a new dataset named CodeClarQA containing NLD-Code pairs with created CQAs. We evaluate the performance of PLMs for code generation on our dataset. The empirical results support our hypothesis that clarifications result in more precise generated code, as shown by an improvement of 17.52 in BLEU, 12.72 in CodeBLEU, and 7.7\% in the exact match. Alongside this, our task and dataset introduce new challenges to the community, including when and what CQs should be asked.

In data-driven systems, data exploration is imperative for making real-time decisions. However, big data is stored in massive databases that are difficult to retrieve. Approximate Query Processing (AQP) is a technique for providing approximate answers to aggregate queries based on a summary of the data (synopsis) that closely replicates the behavior of the actual data, which can be useful where an approximate answer to the queries would be acceptable in a fraction of the real execution time. In this paper, we discuss the use of Generative Adversarial Networks (GANs) for generating tabular data that can be employed in AQP for synopsis construction. We first discuss the challenges associated with constructing synopses in relational databases and then introduce solutions to those challenges. Following that, we organized statistical metrics to evaluate the quality of the generated synopses. We conclude that tabular data complexity makes it difficult for algorithms to understand relational database semantics during training, and improved versions of tabular GANs are capable of constructing synopses to revolutionize data-driven decision-making systems.

Hawkes processes have recently risen to the forefront of tools when it comes to modeling and generating sequential events data. Multidimensional Hawkes processes model both the self and cross-excitation between different types of events and have been applied successfully in various domain such as finance, epidemiology and personalized recommendations, among others. In this work we present an adaptation of the Frank-Wolfe algorithm for learning multidimensional Hawkes processes. Experimental results show that our approach has better or on par accuracy in terms of parameter estimation than other first order methods, while enjoying a significantly faster runtime.

Graph neural networks have shown to learn effective node representations, enabling node-, link-, and graph-level inference. Conventional graph networks assume static relations between nodes, while relations between entities in a video often evolve over time, with nodes entering and exiting dynamically. In such temporally-dynamic graphs, a core problem is inferring the future state of spatio-temporal edges, which can constitute multiple types of relations. To address this problem, we propose MTD-GNN, a graph network for predicting temporally-dynamic edges for multiple types of relations. We propose a factorized spatio-temporal graph attention layer to learn dynamic node representations and present a multi-task edge prediction loss that models multiple relations simultaneously. The proposed architecture operates on top of scene graphs that we obtain from videos through object detection and spatio-temporal linking. Experimental evaluations on ActionGenome and CLEVRER show that modeling multiple relations in our temporally-dynamic graph network can be mutually beneficial, outperforming existing static and spatio-temporal graph neural networks, as well as state-of-the-art predicate classification methods.

The Longest Common Subsequence (LCS) is the problem of finding a subsequence among a set of strings that has two properties of being common to all and is the longest. The LCS has applications in computational biology and text editing, among many others. Due to the NP-hardness of the general longest common subsequence, numerous heuristic algorithms and solvers have been proposed to give the best possible solution for different sets of strings. None of them has the best performance for all types of sets. In addition, there is no method to specify the type of a given set of strings. Besides that, the available hyper-heuristic is not efficient and fast enough to solve this problem in real-world applications. This paper proposes a novel hyper-heuristic to solve the longest common subsequence problem using a novel criterion to classify a set of strings based on their similarity. To do this, we offer a general stochastic framework to identify the type of a given set of strings. Following that, we introduce the set similarity dichotomizer ($S^2D$) algorithm based on the framework that divides the type of sets into two. This algorithm is introduced for the first time in this paper and opens a new way to go beyond the current LCS solvers. Then, we present a novel hyper-heuristic that exploits the $S^2D$ and one of the internal properties of the set to choose the best matching heuristic among a set of heuristics. We compare the results on benchmark datasets with the best heuristics and hyper-heuristics. The results show a higher performance of our proposed hyper-heuristic in both quality of solutions and run time factors.

Cross-domain graph anomaly detection (CD-GAD) describes the problem of detecting anomalous nodes in an unlabelled target graph using auxiliary, related source graphs with labelled anomalous and normal nodes. Although it presents a promising approach to address the notoriously high false positive issue in anomaly detection, little work has been done in this line of research. There are numerous domain adaptation methods in the literature, but it is difficult to adapt them for GAD due to the unknown distributions of the anomalies and the complex node relations embedded in graph data. To this end, we introduce a novel domain adaptation approach, namely Anomaly-aware Contrastive alignmenT (ACT), for GAD. ACT is designed to jointly optimise: (i) unsupervised contrastive learning of normal representations of nodes in the target graph, and (ii) anomaly-aware one-class alignment that aligns these contrastive node representations and the representations of labelled normal nodes in the source graph, while enforcing significant deviation of the representations of the normal nodes from the labelled anomalous nodes in the source graph. In doing so, ACT effectively transfers anomaly-informed knowledge from the source graph to learn the complex node relations of the normal class for GAD on the target graph without any specification of the anomaly distributions. Extensive experiments on eight CD-GAD settings demonstrate that our approach ACT achieves substantially improved detection performance over 10 state-of-the-art GAD methods. Code is available at https://github.com/QZ-WANG/ACT.

Continuous behavioural authentication methods add a unique layer of security by allowing individuals to verify their unique identity when accessing a device. Maintaining session authenticity is now feasible by monitoring users' behaviour while interacting with a mobile or Internet of Things (IoT) device, making credential theft and session hijacking ineffective. Such a technique is made possible by integrating the power of artificial intelligence and Machine Learning (ML). Most of the literature focuses on training machine learning for the user by transmitting their data to an external server, subject to private user data exposure to threats. In this paper, we propose a novel Federated Learning (FL) approach that protects the anonymity of user data and maintains the security of his data. We present a warmup approach that provides a significant accuracy increase. In addition, we leverage the transfer learning technique based on feature extraction to boost the models' performance. Our extensive experiments based on four datasets: MNIST, FEMNIST, CIFAR-10 and UMDAA-02-FD, show a significant increase in user authentication accuracy while maintaining user privacy and data security.