Exploring the affordances of telepresence robots in foreign language learning

The importance of authentic communicative practices in foreign language (FL) learning has long been recognized. However, most FL learners lack adequate access to authentic communicative environments in the target language. In this article, we propose the use of telepresence robots as a potential solution to bridge this gap. Telepresence robots can be controlled by remote language learners online, enabling them to gain virtual access to authentic environments in the target language and to interact with native speakers in those environments in real time. In this exploratory study, three English learners and a native-speaker of American English participated in a campus tour activity using a telepresence robot. We examined the experience of our participants and the conversational features of their telepresence interactions through analyses of the interview data, field notes, and transcripts of conversations captured on video. Our findings show that telepresence robots have substantial potential for promoting FL learning by providing authentic communicative practice for remote language learners. The findings have useful implications for informing future research design

Moving P2P Live Streaming to Mobile and Ubiquitous Environment

Media streams distribution over a wired network to static hosts can be realized by Client/Server mode or Peer-to-Peer overlay networks. However, if the end hosts are mobile over heterogeneous wireless access networks, one needs to consider many operational issues such as network detection, handoff, join and leave latency, and desired level of quality of service, as well as caching. In the latest researches, one popular P2P live streaming system, called AnySee, over the wired network, has been deployed and widely used. Based on the AnySee system, this paper proposed and implemented one hybrid live streaming system, AnySee-Mobile, under wired and wireless environment. In the system, one wireless peer will be selected to act as an agent. One agent has two main functions, to request media from P2P overlay network as a normal peer, and to multicast media to WLAN as a multicast source. In this paper we study, how to elect one multicast agent in WLAN. Several experimentations have been made and proved that the system has good user experiences and performances

Empirical research on the evaluation model and method of sustainability of the open source ecosystem

The development of open source brings new thinking and production modes to software engineering and computer science, and establishes a software development method and ecological environment in which groups participate. Regardless of investors, developers, participants, and managers, they are most concerned about whether the Open Source Ecosystem can be sustainable to ensure that the ecosystem they choose will serve users for a long time. Moreover, the most important quality of the software ecosystem is sustainability, and it is also a research area in Symmetry. Therefore, it is significant to assess the sustainability of the Open Source Ecosystem. However, the current measurement of the sustainability of the Open Source Ecosystem lacks universal measurement indicators, as well as a method and a model. Therefore, this paper constructs an Evaluation Indicators System, which consists of three levels: The target level, the guideline level and the evaluation level, and takes openness, stability, activity, and extensibility as measurement indicators. On this basis, a weight calculation method, based on information contribution values and a Sustainability Assessment Model, is proposed. The models and methods are used to analyze the factors affecting the sustainability of Stack Overflow (SO) ecosystem. Through the analysis, we find that every indicator in the SO ecosystem is partaking in different development trends. The development trend of a single indicator does not represent the sustainable development trend of the whole ecosystem. It is necessary to consider all of the indicators to judge that ecosystem’s sustainability. The research on the sustainability of the Open Source Ecosystem is helpful for judging software health, measuring development efficiency and adjusting organizational structure. It also provides a reference for researchers who study the sustainability of software engineering

Deduplication-based Energy Effcient Storage System in Cloud Environment

In cloud computing, companies usually use high-end storage systems to guarantee the I/O performance of virtual machines (VM). These storage systems cost a lot of energy for their high performance. In this paper, we propose an EEVS, a deduplication-based energy efficiency storage system for VM storage. We firstly investigate some VM image files with general operating systems. With the analysis result, we find there are many redundant data blocks that bring extra energy cost VM storage. Therefore, in the EEVS, we design an online-deduplication mechanism to reduce these redundant data without service interruption, while traditional deduplication technology is used for offline backup. Based on the system design, we implement an EEVS with the existing cloud platform. Since this mechanism needs considerable computing resources, we design a deduplication selection algorithm such that the storage energy consumption is minimized for a given set of VMs with limited resources for deduplication. Experiment results in a para-virtualization environments of the EEVS show that energy consumption is reduced by even up to 66% with negligible performance degradation

Clinical significance of bladder training in preoperative localization of high-intensity focused ultrasound ablation of uterine fibroids

Objectives: High-intensity focused ultrasound (HIFU) is widely used to treat uterine fibroids. HIFU preoperative localization of uterine fibroids can be used to determine whether the patient is a suitable candidate for HIFU treatment. This study investigated the clinical significance of bladder training in improving the success rate of HIFU preoperative localization uterine fibroids. Material and methods: Our sample consists of patients who planned to undergo HIFU treatment in our hospital but who were failed in previous HIFU preoperative localization. They were recruited between July 2021 and April 2022, and randomly divided into experimental and control groups. A total of 150 patients were enrolled. Each group consisted of 75 patients. The patients in the experimental group adopted the procedure of drinking water multiple times and retaining urine. The training program lasted three days. The patients in the control group were required to keep regular drinking and urination habits without any special instructions or requirements. Results: There were no statistically significant differences between the two groups in maximum bladder capacity, residual urine volume of bladder, bladder filling levels, and bladder shape change. After bladder training, the maximum bladder capacity and the degree bladder shape change of the patients in the experimental group were improved significantly. The success rate of HIFU preoperative localization in the patients in the experimental group was significantly higher than that of the control group. Conclusions: Bladder training can effectively improve the success rate of HIFU preoperative localization of uterine fibroids

On Performance Debugging of Unnecessary Lock Contentions on Multicore Processors: A Replay-based Approach

Locks have been widely used as an effective synchronization mechanism among processes and threads. However, we observe that a large number of false inter-thread dependencies (i.e., unnecessary lock contentions) exist during the program execution on multicore processors, thereby incurring significant performance overhead. This paper presents a performance debugging framework, PERFPLAY, to facilitate a comprehensive and in-depth understanding of the performance impact of unnecessary lock contentions. The core technique of our debugging framework is trace replay. Specifically, PERFPLAY records the program execution trace, on the basis of which the unnecessary lock contentions can be identified through trace analysis. We then propose a novel technique of trace transformation to transform these identified unnecessary lock contentions in the original trace into the correct pattern as a new trace free of unnecessary lock contentions. Through replaying both traces, PERFPLAY can quantify the performance impact of unnecessary lock contentions. To demonstrate the effectiveness of our debugging framework, we study five real-world programs and PARSEC benchmarks. Our experimental results demonstrate the significant performance overhead of unnecessary lock contentions, and the effectiveness of PERFPLAY in identifying the performance critical unnecessary lock contentions in real applications.Comment: 18 pages, 19 figures, 3 table

Accelerating Backward Aggregation in GCN Training with Execution Path Preparing on GPUs

The emerging Graph Convolutional Network (GCN) has now been widely used in many domains, and it is challenging to improve the efficiencies of applications by accelerating the GCN trainings. For the sparsity nature and exploding scales of input real-world graphs, state-of-the-art GCN training systems (e.g., GNNAdvisor) employ graph processing techniques to accelerate the message exchanging (i.e. aggregations) among the graph vertices. Nevertheless, these systems treat both the aggregation stages of forward and backward propagation phases as all-active graph processing procedures that indiscriminately conduct computation on all vertices of an input graph. In this paper, we first point out that in a GCN training problem with a given training set, the aggregation stages of its backward propagation phase (called as backward aggregations in this paper) can be converted to partially-active graph processing procedures, which conduct computation on only partial vertices of the input graph. By leveraging such a finding, we propose an execution path preparing method that collects and coalesces the data used during backward propagations of GCN training conducted on GPUs. The experimental results show that compared with GNNAdvisor, our approach improves the performance of the backward aggregation of GCN trainings on typical real-world graphs by 1.48x~5.65x. Moreover, the execution path preparing can be conducted either before the training (during preprocessing) or on-the-fly with the training. When used during preprocessing, our approach improves the overall GCN training by 1.05x~1.37x. And when used on-the-fly, our approach improves the overall GCN training by 1.03x~1.35x

Review on the protective activity of osthole against the pathogenesis of osteoporosis

Osteoporosis (OP), characterized by continuous bone loss and increased fracture risk, has posed a challenge to patients and society. Long-term administration of current pharmacological agents may cause severe side effects. Traditional medicines, acting as alternative agents, show promise in treating OP. Osthole, a natural coumarin derivative separated from Cnidium monnieri (L.) Cusson and Angelica pubescens Maxim. f., exhibits protective effects against the pathological development of OP. Osthole increases osteoblast-related bone formation and decreases osteoclast-related bone resorption, suppressing OP-related fragility fracture. In addition, the metabolites of osthole may exhibit pharmacological effectiveness against OP development. Mechanically, osthole promotes osteogenic differentiation by activating the Wnt/β-catenin and BMP-2/Smad1/5/8 signaling pathways and suppresses RANKL-induced osteoclastogenesis and osteoclast activity. Thus, osthole may become a promising agent to protect against OP development. However, more studies should be performed due to, at least in part, the uncertainty of drug targets. Further pharmacological investigation of osthole in OP treatment might lead to the development of potential drug candidates