Building an accurate model of travel behaviour based on individuals' characteristics and built environment attributes is of importance for policy-making and transportation planning. Recent experiments with big data and Machine Learning (ML) algorithms toward a better travel behaviour analysis have mainly overlooked socially disadvantaged groups. Accordingly, in this study, we explore the travel behaviour responses of low-income individuals to transit investments in the Greater Toronto and Hamilton Area, Canada, using statistical and ML models. We first investigate how the model choice affects the prediction of transit use by the low-income group. This step includes comparing the predictive performance of traditional and ML algorithms and then evaluating a transit investment policy by contrasting the predicted activities and the spatial distribution of transit trips generated by vulnerable households after improving accessibility. We also empirically investigate the proposed transit investment by each algorithm and compare it with the city of Brampton's future transportation plan. While, unsurprisingly, the ML algorithms outperform classical models, there are still doubts about using them due to interpretability concerns. Hence, we adopt recent local and global model-agnostic interpretation tools to interpret how the model arrives at its predictions. Our findings reveal the great potential of ML algorithms for enhanced travel behaviour predictions for low-income strata without considerably sacrificing interpretability.

通过为患者启用远程医疗服务，远程医疗有助于促进医疗专业人员的机会。随着必要的技术基础设施的出现，这些服务已逐渐流行。自从Covid-19危机开始以来，远程医疗的好处就变得更加明显，因为人们在大流行期间倾向于亲自探望医生。在本文中，我们专注于促进医生和患者之间的聊天课程。我们注意到，随着对远程医疗服务的需求的增加，聊天体验的质量和效率可能至关重要。因此，我们为医学对话开发了一种智能的自动反应生成机制，该机制可帮助医生有效地对咨询请求做出反应，尤其是在繁忙的课程中。我们探索了9个月内收集的医生和患者之间的900,000多个匿名的历史在线信息。我们实施聚类算法，以确定医生最常见的响应，并相应地手动标记数据。然后，我们使用此预处理数据来训练机器学习算法以生成响应。所考虑的算法有两个步骤：过滤（即触发）模型，以滤除不可行的患者消息和一个响应发生器，以建议成功通过触发阶段的响应前3位医生响应。该方法为Precision@3提供了83.28 \％的精度，并显示出其参数的鲁棒性。

Automated Program Repair (APR) is defined as the process of fixing a bug/defect in the source code, by an automated tool. APR tools have recently experienced promising results by leveraging state-of-the-art Neural Language Processing (NLP) techniques. APR tools such as TFix and CodeXGLUE combine text-to-text transformers with software-specific techniques are outperforming alternatives, these days. However, in most APR studies the train and test sets are chosen from the same set of projects. In reality, however, APR models are meant to be generalizable to new and different projects. Therefore, there is a potential threat that reported APR models with high effectiveness perform poorly when the characteristics of the new project or its bugs are different than the training set's(Domain Shift). In this study, we first define and measure the domain shift problem in automated program repair. Then, we then propose a domain adaptation framework that can adapt an APR model for a given target project. We conduct an empirical study with three domain adaptation methods FullFineTuning, TuningWithLightWeightAdapterLayers, and CurriculumLearning using two state-of-the-art domain adaptation tools (TFix and CodeXGLUE) and two APR models on 611 bugs from 19 projects. The results show that our proposed framework can improve the effectiveness of TFix by 13.05% and CodeXGLUE by 23.4%. Another contribution of this study is the proposal of a data synthesis method to address the lack of labelled data in APR. We leverage transformers to create a bug generator model. We use the generated synthetic data to domain adapt TFix and CodeXGLUE on the projects with no data (Zero-shot learning), which results in an average improvement of 5.76% and 24.42% for TFix and CodeXGLUE, respectively.

Numerous models have tried to effectively embed knowledge graphs in low dimensions. Among the state-of-the-art methods, Graph Neural Network (GNN) models provide structure-aware representations of knowledge graphs. However, they often utilize the information of relations and their interactions with entities inefficiently. Moreover, most state-of-the-art knowledge graph embedding models suffer from scalability issues because of assigning high-dimensional embeddings to entities and relations. To address the above limitations, we propose a scalable general knowledge graph encoder that adaptively involves a powerful tensor decomposition method in the aggregation function of RGCN, a well-known relational GNN model. Specifically, the parameters of a low-rank core projection tensor, used to transform neighborhood entities in the encoder, are shared across relations to benefit from multi-task learning and incorporate relations information effectively. Besides, we propose a low-rank estimation of the core tensor using CP decomposition to compress the model, which is also applicable, as a regularization method, to other similar linear models. We evaluated our model on knowledge graph completion as a common downstream task. We train our model for using a new loss function based on contrastive learning, which relieves the training limitation of the 1-N method on huge graphs. We improved RGCN performance on FB15-237 by 0.42% with considerably lower dimensionality of embeddings.

In a spoofing attack, an attacker impersonates a legitimate user to access or tamper with data intended for or produced by the legitimate user. In wireless communication systems, these attacks may be detected by relying on features of the channel and transmitter radios. In this context, a popular approach is to exploit the dependence of the received signal strength (RSS) at multiple receivers or access points with respect to the spatial location of the transmitter. Existing schemes rely on long-term estimates, which makes it difficult to distinguish spoofing from movement of a legitimate user. This limitation is here addressed by means of a deep neural network that implicitly learns the distribution of pairs of short-term RSS vector estimates. The adopted network architecture imposes the invariance to permutations of the input (commutativity) that the decision problem exhibits. The merits of the proposed algorithm are corroborated on a data set that we collected.

Dexterous and autonomous robots should be capable of executing elaborated dynamical motions skillfully. Learning techniques may be leveraged to build models of such dynamic skills. To accomplish this, the learning model needs to encode a stable vector field that resembles the desired motion dynamics. This is challenging as the robot state does not evolve on a Euclidean space, and therefore the stability guarantees and vector field encoding need to account for the geometry arising from, for example, the orientation representation. To tackle this problem, we propose learning Riemannian stable dynamical systems (RSDS) from demonstrations, allowing us to account for different geometric constraints resulting from the dynamical system state representation. Our approach provides Lyapunov-stability guarantees on Riemannian manifolds that are enforced on the desired motion dynamics via diffeomorphisms built on neural manifold ODEs. We show that our Riemannian approach makes it possible to learn stable dynamical systems displaying complicated vector fields on both illustrative examples and real-world manipulation tasks, where Euclidean approximations fail.

In this paper, we increase the availability and integration of devices in the learning process to enhance the convergence of federated learning (FL) models. To address the issue of having all the data in one location, federated learning, which maintains the ability to learn over decentralized data sets, combines privacy and technology. Until the model converges, the server combines the updated weights obtained from each dataset over a number of rounds. The majority of the literature suggested client selection techniques to accelerate convergence and boost accuracy. However, none of the existing proposals have focused on the flexibility to deploy and select clients as needed, wherever and whenever that may be. Due to the extremely dynamic surroundings, some devices are actually not available to serve as clients in FL, which affects the availability of data for learning and the applicability of the existing solution for client selection. In this paper, we address the aforementioned limitations by introducing an On-Demand-FL, a client deployment approach for FL, offering more volume and heterogeneity of data in the learning process. We make use of the containerization technology such as Docker to build efficient environments using IoT and mobile devices serving as volunteers. Furthermore, Kubernetes is used for orchestration. The Genetic algorithm (GA) is used to solve the multi-objective optimization problem due to its evolutionary strategy. The performed experiments using the Mobile Data Challenge (MDC) dataset and the Localfed framework illustrate the relevance of the proposed approach and the efficiency of the on-the-fly deployment of clients whenever and wherever needed with less discarded rounds and more available data.

Channel Attention reigns supreme as an effective technique in the field of computer vision. However, the proposed channel attention by SENet suffers from information loss in feature learning caused by the use of Global Average Pooling (GAP) to represent channels as scalars. Thus, designing effective channel attention mechanisms requires finding a solution to enhance features preservation in modeling channel inter-dependencies. In this work, we utilize Wavelet transform compression as a solution to the channel representation problem. We first test wavelet transform as an Auto-Encoder model equipped with conventional channel attention module. Next, we test wavelet transform as a standalone channel compression method. We prove that global average pooling is equivalent to the recursive approximate Haar wavelet transform. With this proof, we generalize channel attention using Wavelet compression and name it WaveNet. Implementation of our method can be embedded within existing channel attention methods with a couple of lines of code. We test our proposed method using ImageNet dataset for image classification task. Our method outperforms the baseline SENet, and achieves the state-of-the-art results. Our code implementation is publicly available at https://github.com/hady1011/WaveNet-C.

准确的交通预测对于智能运输系统至关重要。尽管许多深度学习模型已经达到了最新的1小时交通预测，但长期交通预测跨越多小时仍然是一个重大挑战。此外，大多数现有的深度学习流量预测模型都是黑匣子，提出了与解释性和解释性有关的其他挑战。我们开发了图形金字塔自动构造（X-GPA），这是一种基于注意力的空间 - 速率图神经网络，使用了新型金字塔自相关注意机制。它可以从图表上的长时间序列中学习，并提高长期流量预测准确性。与几种最先进的方法相比，我们的模型可以实现高达35％的长期流量预测准确性。 X-GPA模型的基于注意力的分数提供了基于交通动态的空间和时间解释，这些解释会改变正常与高峰时段的流量以及工作日与周末流量的变化。

深神经网络（DNN）已成为许多应用程序域（包括基于Web的服务）的重要组成部分。这些服务需要高吞吐量和（接近）实时功能，例如，对用户的请求做出反应或反应，或者按时处理传入数据流。但是，DNN设计的趋势是朝着具有许多层和参数的较大模型，以实现更准确的结果。尽管这些模型通常是预先训练的，但是在如此大的模型中，计算复杂性仍然相对显着，从而阻碍了低推断潜伏期。实施缓存机制是用于加速服务响应时间的典型系统工程解决方案。但是，传统的缓存通常不适合基于DNN的服务。在本文中，我们提出了一种端到端自动化解决方案，以根据其计算复杂性和推理延迟来提高基于DNN的服务的性能。我们的缓存方法采用了DNN模型和早期出口的自我介绍的思想。提出的解决方案是一种自动化的在线层缓存机制，如果提前出口之一中的高速缓存模型足够有信心，则可以在推理时间提早退出大型模型。本文的主要贡献之一是，我们将该想法实施为在线缓存，这意味着缓存模型不需要访问培训数据，并且仅根据运行时的传入数据执行，使其适用于应用程序使用预训练的模型。我们的实验在两个下游任务（面部和对象分类）上结果表明，平均而言，缓存可以将这些服务的计算复杂性降低到58 \％（就FLOPS计数而言），并将其推断潜伏期提高到46 \％精度低至零至零。