There is a great deal of research supporting the application of student- generated questions (SGQ) to English curriculums, and recent studies have found L2 learners to benefit further from an elaborated SGQ approach — contextualized SGQ (cSGQ), which leverages the provision of a context for students to base on their generated questions. Previous research has also proved the advantages of making revision on English learner’s writing proficiency but its relevance to English grammar learning is yet to be exploited. Thus, the present study aimed to investigate the effect of cSGQ learning tasks combined with the practice of making revision of the given context on L2 learners’ English grammar learning performance. A pre- and post-test quasi-experimental research design was adopted, and 88 university students enrolling in freshman English class were invited to participate. The results showed that students in the cSGQ-revision group outperformed those not given the opportunity to make revision in both linguistic knowledge and pragmatic understanding.

Software-Defined Networking (SDN) enables flexible network resource allocations for traffic engineering, but at the same time the scalability problem becomes more serious since traffic is more difficult to be aggregated. Those crucial issues in SDN have been studied for unicast but have not been explored for multicast traffic, and addressing those issues for multicast is more challenging since the identities and the number of members in a multicast group can be arbitrary. In this paper, therefore, we propose a new multicast tree for SDN, named Branch-aware Steiner Tree (BST). The BST problem is difficult since it needs to jointly minimize the numbers of the edges and the branch nodes in a tree, and we prove that it is NP-Hard and inapproximable within k, which denotes the number of group members. We further design an approximation algorithm, called Branch Aware Edge Reduction Algorithm (BAERA), to solve the problem. Simulation results demonstrate that the trees obtained by BAERA are more bandwidth-efficient and scalable than the shortest-path trees and traditional Steiner trees. Most importantly, BAERA is computation-efficient to be deployed in SDN since it can generate a tree on massive networks in small time.

How to accelerate tag identification is an important issue in Radio Frequency Identification (RFID) systems. In some cases, the RFID reader repeatedly identifies the same tags since these tags always stay in its communication range. An anticollision protocol, called the adaptive query splitting protocol (AQS), was proposed to handle these cases. This protocol reserves information obtained from the last process of tag identification so that the reader can quickly identify these staying tags again. This paper proposes two blocking protocols, a couple-resolution blocking protocol (CRB) and an enhanced couple-resolution blocking protocol (ECRB), based on AQS. CRB and ECRB not only have the above-mentioned capability as AQS but also use the blocking technique, which prohibits unrecognized tags from colliding with staying tags, to reduce the number of collisions. Moreover, CRB adopts a couple-resolution technique to couple staying tags by simultaneously transmitting two ID prefixes from the reader, while ECRB allows the reader to send only one ID prefix to interrogate a couple of staying tags. Thus, they only need half time to identify staying tags. We formally analyze the identification delay of CRB and ECRB in the worst and average cases. Our analytic and simulation results show that they obviously outperform AQS, and ECRB needs less transmitted bits than CRB.

Automatic motion generation for digital character under the real-time user control is a challenging problem for computer graphic research and virtual environment applications such as on-line games. The present study introduces a methodology to generate a glove puppet animation which is controlled by a new input device, called the Smart Glove, capturing the hand motion. An animation system is proposed to generate the puppet animation based on the procedural animation and motion capture data from Smart Glove. As the control of the puppet character in the animation takes into account the design of Smart Glove and the operation of the puppet in reality, the physical hand motion can either activate the designed procedural animation through motion recognition or tune the parameters of the procedural animation to build the new puppet motion, which allows the direct user control on the animation. The potential application and improvement of the current animation system are also discussed.

This paper addresses how to provide the proportional delay differentiation (PDD) in a wireless LAN (WLAN) and proposes a cross-layer fine-tuning scheduling (CFS) scheme with the goal to maintain PDD among all wireless stations while improving performance in a WLAN. CFS additionally considers the time-varying channel capacity to schedule packets, finely tunes the contention window, and properly arbitrates the backoff time. Also, it operates in a fully distributed manner among all stations and in a cross- layer approach in each station. The simulation results demonstrate that the CFS scheme can provide more satisfactory PDD and higher performance in a WLAN, compared with 802.11e.