Analyzing the impact of storage shortage on data availability in decentralized online social networks

Maintaining data availability is one of the biggest challenges in decentralized online social networks (DOSNs). The existing work often assumes that the friends of a user can always contribute to the sufficient storage capacity to store all data. However, this assumption is not always true in today’s online social networks (OSNs) due to the fact that nowadays the users often use the smart mobile devices to access the OSNs. The limitation of the storage capacity in mobile devices may jeopardize the data availability. Therefore, it is desired to know the relation between the storage capacity contributed by the OSN users and the level of data availability that the OSNs can achieve. This paper addresses this issue. In this paper, the data availability model over storage capacity is established. Further, a novel method is proposed to predict the data availability on the fly. Extensive simulation experiments have been conducted to evaluate the effectiveness of the data availability model and the on-the-fly prediction

Fine-grained boundary recognition in wireless ad hoc and sensor networks by topological methods

Location-free boundary recognition is crucial and critical for many fundamental network functionalities in wireless ad hoc and sensor networks. Previous designs, often coarse-grained, fail to accurately locate boundaries, especially when small holes exist. To address this issue, we propose a fine-grained boundary recognition approach using connectivity information only. This algorithm accurately discovers inner and outer boundary cycles without using location information. To the best of our knowledge, this is the first design being able to determinately locate all hole boundaries no matter how small the holes are. Also, this distributed algorithm does not rely on high node density. We formally prove the correctness of our design, and evaluate its effectiveness through extensive simulations. Categories and Subject Descriptor

Risk Intelligence: Making Profit from Uncertainty in Data Processing System

In extreme scale data processing systems, fault tolerance is an essential and indispensable part. Proactive fault tolerance scheme (such as the speculative execution in MapReduce framework) is introduced to dramatically improve the response time of job executions when the failure becomes a norm rather than an exception. Efficient proactive fault tolerance schemes require precise knowledge on the task executions, which has been an open challenge for decades. To well address the issue, in this paper we design and implement RiskI, a profile-based prediction algorithm in conjunction with a riskaware task assignment algorithm, to accelerate task executions, taking the uncertainty nature of tasks into account. Our design demonstrates that the nature uncertainty brings not only great challenges, but also new opportunities. With a careful design, we can benefit from such uncertainties. We implement the idea in Hadoop 0.21.0 systems and the experimental results show that, compared with the traditional LATE algorithm, the response time can be improved by 46% with the same system throughput

One Adapter for All Programming Languages? Adapter Tuning for Code Search and Summarization

As pre-trained models automate many code intelligence tasks, a widely used paradigm is to fine-tune a model on the task dataset for each programming language. A recent study reported that multilingual fine-tuning benefits a range of tasks and models. However, we find that multilingual fine-tuning leads to performance degradation on recent models UniXcoder and CodeT5. To alleviate the potentially catastrophic forgetting issue in multilingual models, we fix all pre-trained model parameters, insert the parameter-efficient structure adapter, and fine-tune it. Updating only 0.6\% of the overall parameters compared to full-model fine-tuning for each programming language, adapter tuning yields consistent improvements on code search and summarization tasks, achieving state-of-the-art results. In addition, we experimentally show its effectiveness in cross-lingual and low-resource scenarios. Multilingual fine-tuning with 200 samples per programming language approaches the results fine-tuned with the entire dataset on code summarization. Our experiments on three probing tasks show that adapter tuning significantly outperforms full-model fine-tuning and effectively overcomes catastrophic forgetting.Comment: Accepted to the 45th International Conference on Software Engineering (ICSE 2023

Review of Knowledge-Enhanced Pre-trained Language Models

The knowledge-enhanced pre-trained language models attempt to use the structured knowledge stored in the knowledge graph to strengthen the pre-trained language models, so that they can learn not only the general semantic knowledge from the free text, but also the factual entity knowledge behind the text. In this way, the enhanced models can effectively solve downstream knowledge-driven tasks. Although this is a promising research direction, the current works are still in the exploratory stage, and there is no comprehensive summary and systematic arrangement. This paper aims to address the lack of comprehensive reviews of this direction. To this end, on the basis of summarizing and sorting out a large number of relevant works, this paper firstly explains the background information from three aspects: the reasons, the advantages, and the difficulties of introducing knowledge, summarizes the basic concepts involved in the knowledge-enhanced pre-trained language models. Then, it discusses three types of knowledge enhancement methods: using knowledge to expand input features, using knowledge to modify model architecture, and using knowledge to constrain training tasks. Finally, it counts the scores of various knowledge enhanced pre-trained language models on several evaluation tasks, analyzes the performance, the current challenges, and possible future directions of knowledge-enhanced pre-trained language models

TZDKS: A New TrustZone-based Dual-CriticalitySystem with Balanced Performance

Many mixed-criticality systems are composed of a RTOS (Real-Time Operating System) and a GPOS (General Purpose Operating System), and we define them as mixed-time-sensitive systems. Complexity, isolation, real-time latency, and overhead are the main metrics to evaluate such a mixed-time-sensitive system (MTSS). These metrics may conflict with each other, so it is difficult for them to be consistently optimized. Most existing implementations only optimize part of the above metrics but not all. As the first contribution, this paper provides a detailed analysis of performance influencing factors which are exerted by various runtime mechanisms of existing MTSSs. We figure out the difference in performance across system designs, including task switch, memory management, interrupt handling, and resource isolation. We propose the philosophy of utilizing TrustZone characteristics to optimize various mechanisms in MTSS. The second contribution is to propose a TrustZone-based solution - termed TZDKS - for MTSS. Appropriate utilization of TrustZone extensions helps TZDKS to implement (i) virtualization environment for GPOS and RTOS, (ii) high efficient task switch, memory access, interrupt handling and device access which are verified by experiments. Therefore, TZDKS can achieve a full-scale balance amongst aforementioned metrics