With the development of mobile edge computing, task collaboration among edge nodes is an effective way to address the limited edge resources. The critical step in resource collaboration is obtaining and updating edge nodes eligible for collaboration. However, the dynamic nature of the mobile edge network makes acquiring an accurate and timely node state challenging. This paper introduces a Task-oriented Adaptive Distributed (TAD) node state exchange framework to improve network service quality and system energy efficiency. Two main components of the TAD framework are the Scheduled State Update (SSU) Algorithm, which periodically maintains state information, and the Task-driven Resource Discovery (TRD) Algorithm, which updates outdated information to ensure the network adapts to changes. In addition, the Adaptive Dynamic Counter (ADC) is designed to make the update frequency of SSU dynamically adjustable according to the task execution and node state. The framework significantly improves the task completion rate and reduces energy consumption. Compared to the existing state-of-the-art methods, the task completion rate demonstrates an improvement of up to 36.70%, while the energy consumption exhibits a reduction rate of up to 33.38%.

With the increasing number of devices, the demand for data computation is growing rapidly. In edge-cloud collaborative computing, tasks can be scheduled to servers as interdependent subtasks, enhancing performance through parallel computing. A task is executed in an executor, which must first initialize the runtime environment in a process called task startup. However, most existing research neglects the reuse of executors, leading to considerable delays during task startup. To address this issue, we model the edge-cloud collaborative task scheduling scenario considering executor reuse, task startup, and dependency relationships. We then formulate the dependent task scheduling problem with task startup. To meet real-time demands in edge-cloud collaborative computing, we propose ReflexPilot, an online task scheduling architecture featuring executor management. Building on this architecture, we introduce OTSA-PPO, a task scheduling algorithm based on Proximal Policy Optimization (PPO), and EMA, an advanced executor allocation algorithm. Under constraints of computational and communication resources, ReflexPilot leverages OTSA-PPO for online scheduling of dependent tasks based on current states, while EMA pre-creates and reuses executors to reduce the average task completion time. Extensive simulations demonstrate that ReflexPilot significantly reduces the average task completion time by 31% to 71% compared with existing baselines.

The rapid expansion of the Internet of Things (IoT) and communication technology has significantly increased the volume of complex data generated by terminal devices. This has created a demand for more efficient terminal devices with limited computing resources and battery energy. As a result, there is a need to investigate effective task offloading for many tasks with complex dependencies. This paper proposes a task offloading algorithm based on deep reinforcement learning and task offloading sequence to address the issue of dependent task offloading in end-edge-cloud collaboration scenarios. The algorithm introduces the topological structure of a directed acyclic graph (DAG) to represent task dependencies and determine task execution order based on task priorities. It uses a Markov Decision Process (MDP) to optimize and minimize latency and energy consumption for all terminal devices. Experimental results indicate that the proposed algorithm demonstrates better convergence and performance compared to baseline algorithms across various scenarios. It effectively reduces latency and energy consumption for all terminal devices, achieving cost reductions of 13.81%, 67.33%, and 81.04% compared to the three baseline algorithms, respectively. These results illustrate that our algorithm significantly outperforms the baselines under the specified conditions.

Embedded ethics education integrates ethical considerations into computer sciences courses in support of ethics-informed design, development, and deployment of technology. Scholarly assessment has demonstrated that such modules can influence students' attitudes about the relevance and importance of ethics to their work, as well as their perceived ability to tackle ethical issues in the workplace. In this paper, we report on a study that investigates whether embedded ethics modules have an impact beyond the classroom. Specifically, we examine whether embedded ethics modules influence students to learn more about ethics on their own, whether students are better able to recognize ethical issues when they enter the workplace for an industrial or research work experience, and whether they report that the modules they participated in helped them to navigate the ethical situations they encountered at work. While further assessment is needed to investigate these questions fully, our results suggest that embedded ethics modules can indeed have this kind of positive impact beyond the classroom.

Federated Learning (FL) is increasingly adopted for training ML models, driven by its ability to preserve data privacy and reduce communication costs. However, the limited availability of wireless bandwidth necessitates efficient client selection and bandwidth allocation. This paper addresses the challenges arising from non-IID data, heterogeneous computing capabilities, and varying communication conditions. We introduce a novel data quality evaluation criterion that comprehensively takes into consideration factors including data size, local data label skew, and the Age of Data. Based on this evaluation criterion, we propose a Joint Efficient Energy-constrained Client Selection and Adaptive Bandwidth Allocation (EECS-Apt) algorithm that leverages data quality, computing capabilities and communication conditions. The experimental results indicate that while satisfying the accuracy requirement, the proposed algorithm can significantly reduce delay by up to 87.4%, 60.7% and 36.8%, respectively, compared to: 1) Joint Energy-constrained Random Client Selection and Average Bandwidth Allocation (ERCS-Avg), 2) Joint Energy-constrained Delay-based Client Selection and Adaptive Bandwidth Allocation (EDCS-Apt), and 3) Reliable and Age-sensitive Client Selection and Adaptive Bandwidth Allocation (RACS-Apt).

Rapid advancements in Vehicle-to-Everything (V2X) and Mobile Edge Computing (MEC) have posed significant challenges for Mobile Devices (MDs) in managing complex tasks and addressing mobility effects. While MDs employ task offloading to mitigate resource constraints, the presence of task topological dependencies and mobility limitations diminishes its effectiveness, thereby impacting energy efficiency and service quality. In response, we propose the Energy-Efficient Topological Dependency and Data-aware Splittable Task Offloading (ETDS) framework. ETDS categorizes task data into stateless and stateful segments, enabling the offloading of stateless data, independent of topology constraints. Furthermore, ETDS optimizes task offloading timing and target locations, capitalizing on opportunistic offloading due to MD mobility, consequently reducing energy consumption associated with task offloading transmissions. Simulation results reveal that ETDS can significantly reduce MDs energy consumption by 30% to 63% when compared to traditional dependent task offloading schemes, demonstrating consistent performance across different MD speeds and real-world parameter configurations.

In common scenarios of cloud computing and edge computing, jobs are divided into tasks with dependencies, and task scheduling and computation are performed through containers. However, the cold start of containers significantly impedes the efficiency of short tasks. The existing research on cold starts faces challenges in addressing task scheduling with dependencies and may not fully exploit the distinct advantages offered by cloud and edge servers. On the other hand, there is limited research on using Deep Reinforcement Learning (DRL) to optimize container cold starts. Existing task scheduling algorithms based on DRL often struggle to handle scenarios with multiple jobs simultaneously. To reduce the job completion time of the system, this paper introduces a task scheduling algorithm based on DRL. By intelligently reusing containers and minimizing cold starts, the algorithm aims to simultaneously consider computing and communication resources, effectively leveraging the unique strengths of both cloud and edge servers to enhance job completion speed. The proposed architecture, comprising both Agent and Scheduler components, reduces the action space and enhances the ability to handle multiple jobs. Simulation results demonstrate that, compared to existing common algorithms, the proposed algorithm reduces the average job completion time by approximately 30%.

Embedding ethics modules in computer science (CS) courses is an approach to post-secondary ethics education that has been gaining traction. In contrast to dedicated courses on ethics in CS, embedding ethics modules into CS courses supports tight connections between ethical considerations and CS concepts, as well as enabling repeated exposure to ethics across multiple courses. Initial studies of the effectiveness of such modules suggest that this approach can increase both student interest in ethics and technology, and student self-efficacy towards incorporating ethical considerations in their computing work. Departments wishing to deploy embedded ethics (EE) modules need to decide how to invest resources, including class time, to maximize effectiveness while maintaining curriculum objectives. Such considerations include the number of EE module experiences a student has throughout their degree program, as well as the spacing of those experiences.

Abstract To improve the uniformity of one‐pixel width and continuity of the thinned edges, this paper proposes an edge thinning algorithm acting on grey‐scale edge images based on 24 self‐defined single‐pixel connection patterns. First, for binary or blurred grey‐scale gradient edge images, a distance–greyscale coupling algorithm is proposed to achieve gradient enhancement in the edge width direction. Then the elimination rules of noise points and gradient calculation method are given. Secondly, the marking rules of the first three pixels of each edge are given. The next pixel to be marked must meet that the new last three pixels belong to the 24 connection modes. Whether the qualified pixels are retained depends on its grey value and the local edge gradient. The algorithm is tested on four types of images. The results show that the proposed method can guarantee uniform, smooth, and connected one‐pixel‐wide lines that lie at the centre of the initial edges. The algorithm and the existing algorithms are performed on portrait image and four scenarios of the indoor datasets. Five evaluation indicators are statistically analyzed to prove the feasibility and effectiveness of the proposed algorithm.

With the application of new technologies in vehicles such as wireless communications, microelectronics, Artificial Intelligence (AI) and Autonomous Vehicles (AV), there are lots of embedded hardware and software in vehicles, and so cryptographic technologies such as block ciphers are also applied to the vehicle system to protect its security and privacy. In order to better find potential security vulnerabilities and improve security of the vehicle system which applied SIMON block cipher, in this paper, a novel Differential fault attack (DFA) method is presented. By inducing a random bit fault in$L^{T-4}$(the fourth round from the last), we first demonstrate the process of identifying the fault-induced position. Then, on the basis of the determined position, we describe how to retrieve three rounds of keys by inducing only one round of faults. For retrieving the SIMON family, compared with previous DFAs under the random bit fault model, the lowest numbers of fault-induced and fault locations (or rounds) are required by our attack. In particular, when the key words$m$is 2 or 3, the proposed attack only needs to induce one round of faults. Finally, we carry out simulation verification and a comparison to illustrate the correctness and effectiveness of the proposed attack.

Technology is shaping the way people live, work, and interact with each other, and graduates of our computer science programs increasingly find themselves designing algorithms and using data that raise ethical issues they may not be aware of or equipped to address. Courses that contemplate the role of technology in society have been a standard, but often optional, part of curricula for years. An emerging alternative is to embed ethical discussions as modules within CS courses. This approach offers the opportunity to tie ethical issues to technical content at the moment students learn it, and to have students engage with these issues repeatedly throughout their degree. However, little is known about the effect of embedded ethics education on students.