This paper proposes a deep reinforcement learning-based power management method for mobile devices. By learning the load characteristics of the device under different usage scenarios and considering the influence of network conditions on power consumption, the CPU and GPU frequencies are dynamically adjusted for multiple application scenarios. At the same time, a “SLIDER” adjustment strategy is proposed, and combined with the system default adjustment strategy, which reduces the difficulty of adjustment and more fully utilizes the middle adjustable frequency of the CPU. The proposed method reduces power consumption by 5.3%-18% compared to state-of-the-art method.

Abstract Recently, many studies incorporate external knowledge into character-level feature based models to improve the performance of Chinese relation extraction. However, these methods tend to ignore the internal information of the Chinese character and cannot filter out the noisy information of external knowledge. To address these issues, we propose a mixture-of-view-experts framework (MoVE) to dynamically learn multi-view features for Chinese relation extraction. With both the internal and external knowledge of Chinese characters, our framework can better capture the semantic information of Chinese characters. To demonstrate the effectiveness of the proposed framework, we conduct extensive experiments on three real-world datasets in distinct domains. Experimental results show consistent and significant superiority and robustness of our proposed framework. Our code and dataset will be released at: https://gitee.com/tmg-nudt/multi-view-of-expert-for-chinese-relation-extraction

A parallel task can always be modelled as a directed acyclic graph (DAG), where sequential instruction blocks are modelled as vertices and data dependencies or resource constraints are modelled as edges. We propose a new federated scheduling algorithm for arbitrary-deadline sporadic DAG tasks, assuming that the exact structures of DAG tasks are unknown before runtime. Federated scheduling algorithms are a class of algorithms that can efficiently schedule DAG tasks by assigning several processors exclusively to each task. Existing studies have shown the advantages of federated scheduling, which include increasing the analytical schedulability and minimising the scheduling overhead. We are particularly focused on the scheduling of any task with a deadline longer than its release period; in this case, multiple jobs generated by the task could run concurrently. For such tasks, our algorithm is different from most federated scheduling algorithms in that it assigns dedicated processors to each job instead of letting jobs released by the same task share processors. The main idea is to increase the analytical schedulability by avoiding interference between jobs. The simulation results show that our algorithm outperforms existing algorithms when the exact structures of tasks are unknown before runtime.

Browser is a key application for mobile devices and its power management is significant given that mobile devices are power-sensitive. Currently, dynamic voltage and frequency scaling (DVFS) and energy-aware scheduling (EAS) techniques have been implemented in mobile devices for energy savings. However, it is still challenging to achieve an energy-efficient mobile browser due to the varied content of webpages that need different resources to fetch, parser, render, etc. An ideal power governor should adjust CPU frequency dynamically according to webpage characteristics, but the current governor is configured statically and webpage-agnostic. To address the above issues, we propose KylinTune, an energy-efficient model for mobile browsers. The KylinTune is based on Deep-Q Network (DQN), a reinforcement learning technique. KylinTune learns from the browser runtime and adjusts CPU frequency to an optimal execution speed for a specific webpage based on EAS. We apply KylinTune to the Chromium browser on Google Pixel2 XL and evaluate it on the top 100 popular websites. Experimental results show that KylinTune achieves 14.51%–24% energy savings in different loading environments, with trivial quality of service (QoS) degradation.

Adversarial attacks expose the vulnerability of deep neural networks. Compared to image adversarial attacks, textual adversarial attacks are more challenging due to the discrete nature of texts. Recent synonym-based methods achieve the current state-of-the-art results. However, these methods introduce new words against the original text, leading to that humans easily perceive the difference between the adversarial example and the original text. Motivated by the fact that humans are usually unaware of chaotic word order in some cases, we propose exchange-attack (EA), a concise and effective word-level textual adversarial attack model. Specifically, the EA model generates adversarial examples by exchanging words of the original text itself according to the contributions that these words make regarding classification results. Intuitively, the smaller the distance between the two exchanged words, the more difficult the chaotic word order to be perceived by humans. We thus take the word distance into consideration when generating the chaotic word orders. Extensive experiments on several text classification data sets show that the EA model consistently outperforms the selected baselines in terms of averaged after-attack accuracy, modification rate, query number, and semantic similarity. And human evaluation results reveal that humans difficultly perceive the adversarial examples generated by the EA model. In addition, quantitative and qualitative analyses further validate the effectiveness of the EA model, including that the generated adversarial examples are grammatically correct and semantically preserved.

A number of scheduling algorithms have been proposed for real-time parallel tasks modeled as Directed Acyclic Graphs (DAGs). Many of them focus on scheduling DAG tasks with implicit deadlines. Fewer studies have considered DAG tasks with constrained deadlines or arbitrary deadlines. In this study, we propose a scheduling strategy based on fluid scheduling theory and we target DAG tasks with constrained or arbitrary deadlines. We prove that the proposed algorithm has a capacity augmentation bound of 1/2(1++((1+)24/m)) when scheduling multiple DAG tasks with constrained deadlines, in which m is the number of processors and is the maximum ratio of task period to deadline. This value is lower than the current best result +2((1+-1/m)(1-1/m)). We also prove that a capacity augmentation bound of 1/2(1+2+((1+2)242/m)) is guaranteed by our algorithm in the case of scheduling multiple DAG tasks with deadlines greater than periods. To the best of our knowledge, this is the first capacity augmentation bound that has been proven for scheduling multiple DAG tasks with deadlines greater than periods. Our experiments show that our algorithm outperforms the state of the art scheduling algorithms in the percentage of schedulable task sets.