This study designed an online massively multiple online role-playing card game that integrates a multi-dimensional scaffolding framework. The learning goal is to develop students' reading comprehension and contextual problem-solving skills. The game components include task cards (contextual problem-solving texts) and knowledge cards (corresponding to the learning content of the task cards). Students first need to read and analyze the text and learning information of the task cards and match the correct knowledge cards within a limited time. Multi-dimensional scaffolding provides students with three different dimensional clues in the game to help students complete the game tasks. Preliminary experimental results showed that students have a high degree of flow in this game activity. They think the game is easy to operate and will not cause excessive anxiety.

In multi-label classification, the imbalance between labels is often a concern. For a label that seldom occurs, the default threshold used to generate binarized predictions of that label is usually sub-optimal. However, directly tuning the threshold to optimize F-measure has been observed to overfit easily. In this work, we explain why this overfitting occurs. Then, we analyze the FBR heuristic, a previous technique proposed to address the overfitting issue. We explain its success but also point out some problems unobserved before. Then, we first propose a variant of the FBR heuristic that not only fixes the problems but is also more justifiable. Second, we propose a new technique based on smoothing the F-measure when tuning the threshold. We theoretically prove that, with proper parameters, smoothing results in desirable properties of the tuned threshold. Based on the idea of smoothing, we then propose jointly optimizing micro-F and macro-F as a lightweight alternative free from extra hyperparameters. Our methods are empirically evaluated on text and node classification datasets. The results show that our methods consistently outperform the FBR heuristic.

This paper aims to present novel constructions of Golay-ZCZ sequence sets with various set sizes and flexible lengths. A Golay-ZCZ sequence set is not only a Golay complementary set (GCS) but also a zero correlation zone (ZCZ) sequence set. Golay-ZCZ sequence sets have been employed in OFDM systems due to their low peak-to-average power ratios (PAPRs) which are upper bounded by the set sizes. Although several constructions of Golay-ZCZ sequence sets have been proposed in the literature, the known Golay-ZCZ sets cannot attain the theoretical upper bound on the ZCZ width for non-binary cases. Also, for the Golay-ZCZ sets constructed by generalized Boolean functions, parameters are limited to powers of two, such as set sizes, sequence lengths, and ZCZ widths. In this paper, constructions of Golay-ZCZ sequence sets based on the extended generalized Boolean functions are proposed, where set sizes, sequence lengths, and ZCZ widths are all flexible and not necessary to be power-of-two. Since the constructed Golay-ZCZ sets have various set sizes, their PAPR upper bounds can be tighter. In addition, the proposed Golay-ZCZ sequence sets can achieve the upper bound on the ZCZ width. Therefore, optimal and almost-optimal Golay-ZCZ sequence sets can be obtained by the proposed methods.

Strengthen by Artificial Intelligence (AI) and complexly integrated sensors, an autonomous mobile robot (AMR) is extensively applied in coping with various human resources tasks in indoor office environments. However, implementing an AMR system from scratch needs a strong Electric Engineer background due to the complexity of robot-controlling. Besides, Communication between robot-server, sensor, and client-service also increases the difficulty and time-cost in AMR developing. In this paper, the AMR system we proposed aims to be implemented by people without a EE background, and we will achieve this by employing Robot Operating System. Besides, the AMR system should work independently but still capable of responding to human requests. We will demonstrate a serverless could structure that includes the client-side service and integrate the edge-computing, which in charge of the immediacy-demanding job.

Online social connections allow users to share and spread information with others. Different users play various roles on information diffusion: some are influential within a community and some make information widely spread across communities. This paper aims to unveil the hidden relationships between nodes and information diffusion in the context of communities from the big Twitter diffusion data with a million-scale social network, as a complement to existing link-based diffusion analysis (e.g. the strength of weak ties). We identify six types of well-known community-aware roles of nodes, and examine their diffusion capability on propagating information within/across communities, using information flows via retweet and mention in Twitter. We find nodes acting as community bridges are exposed to more information and more dominative on spread information within/across communities. Community-aware roles are also validated via supervised learning to be effective for detecting the most diffusive information propagators.

Learning storytelling is beneficial for children's development. Various tools have been proposed to expand the set of materials children can use to compose their stories. However, most previous research focuses on enhancing the visual aspect of storytelling and underexplores the acoustic elements needed for children's stories. In this paper, Olegoru1, a sound composition tool in the form of magic gloves and soul stones, is proposed to augment children's storytelling when using physical objects. Children can create contextual and regional sound effects as well as event-based acoustics through speech, non-verbal and gestural interaction, and could potentially enable children to create more immersive story-worlds. To investigate the technical feasibility of such tool, a preliminary prototype was built that accepts a limited number of vocalized sound effects and vocabulary.

We in this paper explore a new data dissemination framework in Mobile P2P networks. Previous works in the literature usually elaborated upon the reduction of dissemination frequency in the network. However, many important and practical issues remain unresolved. First, the success of the system design usually relies on the support of other hardware components such as GPS, causing the extra power consumption. In addition, the property of the physical media used to make the ad-hoc network is not well discussed in the system. The system which assumes all users can access WiFi or 3G everywhere will limit the grow of the system popularity. Most importantly, the user preference is not considered, and each peer will receive and help to broadcast all messages whether the user is interested in. In this paper, we propose the MobiPAD framework, which the current stage concentrates on applications of data dissemination in student communities. The MobiPAD framework is built based on Bluetooth since Bluetooth is low power consumption and high penetration rate, and is suitable for students without the expensive 3G or WiFi accessibility. We also consider preference-aware disseminations to support various user preferences in the Mobile P2P network. The fairness issue is considered in the model, meaning that users who receive more interesting messages should contribute more message retransmission than users who wonder to receive few messages. The basic MobiPAD platform for student communities is also discussed in the paper, to show its possibility for further use.

Traditional routing protocols in wireless mesh networks establish a single data path between a source and a destination. However, the performance suffers from degradation and instability due to variation of wireless links and interference. Opportunistic routing has been proposed to overcome the problem by exploiting the broadcast nature of wireless links and multiple forwarders. Current opportunistic routing protocols with network coding improve the system performance but suffer from redundant packet transmissions because a source stops transmitting packets in the same segment until it receives an ACK message from the destination. In this paper, we propose a pipelined opportunistic routing protocol with network coding (PipelineOR) in which segments are transmitted in a pipelined manner. A source is allowed to send packets in the next segment without waiting for the ACK message from the destination. In addition, we present two mechanisms, namely ETX-based mechanism and DCounter-based mechanism, to suppress forwarders to transmit redundant packets. The simulation results show that the proposed PipelineOR outperforms MORE [7] and CodeOR [8] in terms of transmission time.

Within the ADDIE instruction design model, this paper reports the implementation phase and the evaluation phase of teaching introductory software engineering course framed in the cognitive apprenticeship approach. Twelve learning objectives are classified in a 2D Bloom framework and evaluated against the result of instructional implementation, demonstrating coherent linkage between objectives, instruction and assessment. Descriptive statistics show the coverage of cognitive apprenticeship and high degree of acceptance from students. Analysis of learning process profile also shows consistently expanding effects on various aspects of learners, especially on teamwork. Pedagogical improvement in terms of the time allocated to individual instruction activities is also polled.

The completion of SWEBOK and SE2004 marks a key milestone for the software engineering education community. It not only initiates issues of what to do next in the long run, but introduces some pragmatic issues such as customization and transition issues in the short term. Meanwhile, there is a lack of theoretical foundation with respect to instruction design and learning theory, which is also a shortage from an interdisciplinary perspective. Based upon experiences and lessons learned, we plan a course of introduction to software engineering based on the ADDIE instruction design model and cognitive apprenticeship enhanced with a situated business case and peer apprenticeship, along within a disciplined software process. We believe it can help combat both customization and transition barriers in the department of management information systems, not only by well-founded pedagogical design and implementation, but by being extensible to capstone projects.