This paper revisits both the uplink and downlink two-user multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) systems. We present a novel concept of matrix decomposition called simultaneous generalized triangular decomposition (SGTD). The main idea is to simultaneously decompose two matrices into predetermined triangular forms while providing flexibility in designing the ratios of the diagonal elements. For uplink MIMO-NOMA, the proposed SGTD enables interference-free transmission between users by transforming MIMO channels into two triangular forms and successively decoding and canceling data streams. Unlike existing simultaneous diagonalization or triangularization approaches with fixed diagonal values, the proposed method provides flexibility in adjusting the diagonal elements of the triangular matrices. This adaptability makes it particularly advantageous for meeting specific design requirements, such as maximizing the weighted sum rate and ensuring stable transmission across each spatial stream. As an example, an optimized design is provided to maximize the weighted sum rate within the proposed framework. A similar approach is also proposed to develop a new precoding design for downlink MIMO-NOMA. Extensive simulations demonstrate that, for both uplink and downlink scenarios, the proposed scheme outperforms state-of-the-art MIMO-NOMA and orthogonal multiple access (OMA) systems.

Motivation Bots can alleviate the workload of instructors supporting students in large course Q&A platforms, but it's not clear whether students will be receptive to the use of automated assistants in this setting. Objectives We aim to observe student reactions when they encounter bot-generated follow-ups to Q&A board posts. We investigate the effect of revealing that a bot, rather than a human, is suggesting that the current post is a duplicate. Methods Our bot revealed or hid its bot identity when suggesting duplicate posts, with the condition selected randomly. We observed students' reactions in both conditions. A post-course survey was distributed to collect students' demographic data, previous experiences with bots, and attitudes toward our bot. Results We observed a slight increase in students' response rate when the bot hid its identity. We compared the positive response rate in both conditions and did not find evidence suggesting that students had less trust in bot-generated answers. From the survey, we only saw minimal direct evidence that students might mistrust the bot: 7 of 59 students reported worries about receiving an inaccurate bot-generated answer. Other students were concerned that they would not receive attention from an instructor. Discussion We did not find evidence that revealing the bot's identity has a negative impact on student reactions. However, future bot design should consider the emotional impact of deploying a bot as there may be negative emotional effects to receiving a bot-generated response.

In this paper, we present a driving license test APP system for traffic education. The system provides learning materials and the adaptive simulation quiz. Questions in the quiz are individually generated according to the user profile and the designed boosting algorithm. The experimental result revealed that our APP system showed statistically significant better score in the driving license exam.

Reading news is one of the most important learning activities in the education of the communication schools. Learners learn to improve the awareness and writing essay ability through observing contents inside the news. How to create the learning materials in a short time is still a gap between traditional communication education and information technology. In addition, the most challenge is that there embeds a huge amount of commercial ads and external links within a news page which usually mislead learners to read and explore further topics. To solve this, in this paper, we proposed a clean and pure news reading environment which makes it possible to touch up-to-date global news in our news corpus. It automatically crawls the daily news through the web without additional human effort. To preserve the news content, we also design a content extraction algorithm to keep the news content in the database is clean to read. Instructors can easily prepare and organize a set of news topics in this platform.

In general, video-based learning contains rich media information, but displaying an entire video linearly is time-consuming. As an alternative, video summarization techniques extract important content provides short but informative fragments. In this paper, a video learning platform (KVSUM: Keyword-based Video Summarization) is presented that integrates image processing, text summarization, and keyword extraction techniques. Without human annotators, the learning platform can process an input video and transform it into online learning materials automatically. The video frames are first split from a given video while the transcription is used to generate the text summary and keywords. In the current study, the video surrogates are composed of extracted keywords, text and video summaries, and video frames. In other words, KVSUM is able to provide both visual and verbal surrogates. In order to validate the effect of surrogates in the KVSUM, a comparison with another video surrogate, the fast forward (FF), to evaluate learners' comprehension to video contents. Sixty undergraduate students took part in examining two different video surrogates. The experimental results show that KVSUM had a more positive effect than FF in comprehension to videos. In terms of system usage and satisfaction, KVSUM is significantly more attractive than FF to learners.

Video-based concordancer can be used to provide a scenario that engage context information of studying novel words. Usually, a fixed number of contextual sentences are retrieved accompanying the keywords. However, there may be a lack of complete context for learners to comprehend the keywords in the videos. Few studies have discussed about how the videos are presented to assist learners to use the keywords appropriately, and lead learners to find relevant knowledge effectively. In this paper, a keyword-in-scene video concordance (KWIS), which recognizes the scenes in the videos and provides the scene-based clips, is proposed. Each video clip is tagged actual scene type information. Learners are able to query the KWIS system with keywords, phrases, or natural language sentences, and watch relevant scenario clips to understand where the conversation can be carried on. A pilot study was conducted to evaluate the proposed system. The result shows that there is a positive effect on students' comprehension of English phrases while using the system.

Abstract This article presents a bilingual video question answering (QA) system, namely BVideoQA, which allows users to retrieve Chinese videos through English or Chinese natural language questions. Our method first extracts an optimal one‐to‐one string pattern matching according to the proposed dense and longN‐gram match. On the basis of the matched string patterns, it gives a passage score based on our term‐weighting scheme. The main contributions of this approach to multimedia information retrieval literatures include: (a) development of a truly bilingual video QA system, (b) presentation of a robust bilingual passage retrieval algorithm to handle no‐word‐boundary languages such as Chinese and Japanese, (c) development of a large‐scale bilingual video QA corpus for system evaluation, and (d) comparisons of seven top‐performing retrieval methods under the fair conditions. The experimental studies indicate that our method is superior to other existing approaches in terms of precision and main rank reciprocal rates. When ported to English, encouraging empirical results also are obtained. Our method is very important to Asian‐like languages since the development of a word tokenizer is optional.

In this paper, we propose the English dialogue companion (EDC) system to help elementary school students learn second language, as English. Learners can practice conversation with the EDC system in English. In order to motivate the learners, we design the three learning activities to enhance the environment of English learning, which are the choice of learning companion phase, the conversation phase, and the teaching phase. We also conducted a pilot study to evaluate the EDC system. The results showed the subjects agreed that the system is positive and useful. For the most of learners, we found that the most favorite learning activity is to teach learning companion. In addition, the greater part of learners liked the same gender learning companion. And learners selected the learning companion depended on the learning companion is regards as a classmate or a teacher.

In this paper, we present a robust passage retrieval algorithm to extend the conventional text question answering (Q/A) to videos. Users interact with our videoQ/A system through natural language queries, while the top-ranked passage fragments with associated video clips are returned as answers. We compare our method with five of the high-performance ranking algorithms that are portable to different languages and domains. The experiments were evaluated with 75.3 h of Chinese videos and 253 questions. The experimental results showed that our method outperformed the second best retrieval model (language models) in relatively 1.43% in mean reciprocal rank (MRR) score and 11.36% when employing a Chinese word segmentation tool. By adopting the initial retrieval results from the retrieval models, our method yields an improvement of at least 5.94% improvement in MRR score. This makes it very attractive for the Asia-like languages since the use of a well-developed word tokenizer is unnecessary.

In this paper, the authors proposed a mobile video QA (Question Answering) system for online annotation and ubiquitous multimedia learning. Under the wireless and mobile environment, learners could interact with the system through natural language questions rather than traditional query-like text retrieval. The handheld device is not as well as traditional keyboard input, while the annotation is quite inefficient and inconvenient. To solve this, the authors designed a simple browsing and clicking model to enable learners annotating and auto-feedback as e-mail to learner's mail box. A pilot study was conducted to evaluate the preliminary experimental results for learning. The experimental results showed that the proposed system effectively engaged learners in multimedia video QA in handheld device where all subjects agreed that the system is positive and useful.

In this paper, the authors present a video content summarization for recommendation (called VCSR) system to auto-recommend suitable multimedia learning materials for learners. The VCSR system firstly extracts important content as summarization from input raw video data, while the generated summarization will be auto-routed to learners according to their profiles. Video captions are initially recognized using optical character recognition (OCR), then a set of key passages with corresponding frame images are extracted to form a video summary. The recommendation is achieved by calculating the relevance of the video summarization for each learner. Also, this paper indicates how the VCSR system effectively plays the intermediate role in a modern digital library.