Applying petri nets to model SCORM learning sequence specification in collaborative learning

[[abstract]]With the rapid development of Internet technology and Web-based education, distance learning provides a novel learning style, which is different from traditional education. In order to adapt different teaching strategies in accordance to individual students' abilities in a distance learning environment, system directed navigation of students was proposed in a distance learning standard called SCORM (sharable content object reference model). We introduce the distance-learning color Petri net (DCPN), applying the features of Petri nets, to decrease the complexity of the sequencing definition model in the SCORM 2004 specification. We thus construct a sequencing framework for various instructional strategies by piecing DPCN subnets together.[[notice]]補正完畢[[conferencetype]]國際[[conferencetkucampus]]淡水校園[[conferencedate]]20050328~20050330[[booktype]]紙本[[conferencelocation]]Taipei, Taiwa

Integrating SPC Table Formative Assessment with SCORM

[[abstract]]SCORM (Sharable Content Object Reference Model) is one of the international e-learning specifications, which provides sharable content, compatible run-time environment and learning profile. SCORM supports accessibility, adaptability, affordability, durability, interoperability and reusability. People can share their own learning content with each other and learn things on the Internet. SCORM learning content can run on the SCORM learning management system. However SCORM do not have a complete evaluation mechanism. The SP chart represents students and problems. It is a tool to analyze the relationship between students and their answers to test problems. In this paper, we integrate the SP chart into SCORM as a formative assessment and add course as the third dimension to strengthen SCORM assessment. The developed tool can be used for SCORM assessment in three perspectives: student-problem, course-problem, and student-problem. So any test problem set and the student performance can be thoroughly examined. The tool was applied to a class and the empirical results are presented in this paper.[[notice]]補正完畢[[booktype]]紙本[[booktype]]電子版[[countrycodes]]SG

Recovery of trace organic pollutants by solvent extraction and freeze concentration

The objective of this investigation was to evaluate the use of batch type solvent extraction and freeze concentration in recovering trace organic pollutants from water. The work was performed using a simplified water system containing known concentrations of phenol, and natural water systems collected from three sources (Meramec Spring, Gasconade River near Jerome, and Missouri River in Jefferson City) with different levels of pollution. The simplified water system was used to evaluate the effect of the number of extractions, solvent to sample ratio, extraction time, initial organic concentration, pH, and turbidity on solvent extraction; and the effect of volumetric concentration and flash freezing on freeze concentration. The natural water systems were employed to evaluate the practical application of the method, and emphasis was placed on the selection and sequence of solvents, pH adjustment, and effect of turbidity. Benzene and chloroform were the solvents used. The proper selection of solvents and the solvent to sample ratio were the most important factors in the solvent extraction method; the number of sequential extractions and pH adjustment were also important variables. Serial extraction with chloroform and benzene yielded a larger recovery at natural pH than extraction with benzene and chloroform; and extraction with chloroform sequentially at pH 4 and 10 produced a greater recovery than extraction with benzene. The concentration of trace organics in spring and river water was subject to significant seasonal variation. The efficiency of phenol recovery by freeze concentration depended on the volumetric concentration ratio and almost complete recovery was obtained at ratios ranging from 6 to 9 --Abstract, page ii

A Fringe Center Detection Technique Based on a Sub-Pixel Resolution, and Its Applications Using Sinusoidal Gratings

A common problem in optical profilometry is the accuracy in locating fringe centers. This paper presents an accurate fringe center detection technique based on sub-pixel resolution using the fringe projection method. An optimum reconstruction filter is developed which has low sensitivity to noise. In fringe center detection, computer simulation results of using one-pixel and sub-pixel resolutions are compared. The detection technique is then applied to radius measurement of cylindrical objects and surface profile measurement of diffuse objects. The experimental results thus obtained through the proposed optimum reconstruction filter show significant improvement in measurement accuracy

A Fringe Center Detection Technique Based on a Sub-Pixel Resolution, and Its Applications Using Sinusoidal Gratings

A common problem in optical profilometry is the accuracy in locating fringe centers. This paper presents an accurate fringe center detection technique based on sub-pixel resolution using the fringe projection method. An optimum reconstruction filter is developed which has low sensitivity to noise. In fringe center detection, computer simulation results of using one-pixel and sub-pixel resolutions are compared. The detection technique is then applied to radius measurement of cylindrical objects and surface profile measurement of diffuse objects. The experimental results thus obtained through the proposed optimum reconstruction filter show significant improvement in measurement accuracy

LLM4TS: Two-Stage Fine-Tuning for Time-Series Forecasting with Pre-Trained LLMs

In this work, we leverage pre-trained Large Language Models (LLMs) to enhance time-series forecasting. Mirroring the growing interest in unifying models for Natural Language Processing and Computer Vision, we envision creating an analogous model for long-term time-series forecasting. Due to limited large-scale time-series data for building robust foundation models, our approach LLM4TS focuses on leveraging the strengths of pre-trained LLMs. By combining time-series patching with temporal encoding, we have enhanced the capability of LLMs to handle time-series data effectively. Inspired by the supervised fine-tuning in chatbot domains, we prioritize a two-stage fine-tuning process: first conducting supervised fine-tuning to orient the LLM towards time-series data, followed by task-specific downstream fine-tuning. Furthermore, to unlock the flexibility of pre-trained LLMs without extensive parameter adjustments, we adopt several Parameter-Efficient Fine-Tuning (PEFT) techniques. Drawing on these innovations, LLM4TS has yielded state-of-the-art results in long-term forecasting. Our model has also shown exceptional capabilities as both a robust representation learner and an effective few-shot learner, thanks to the knowledge transferred from the pre-trained LLM

SeGA: Preference-Aware Self-Contrastive Learning with Prompts for Anomalous User Detection on Twitter

In the dynamic and rapidly evolving world of social media, detecting anomalous users has become a crucial task to address malicious activities such as misinformation and cyberbullying. As the increasing number of anomalous users improves the ability to mimic normal users and evade detection, existing methods only focusing on bot detection are ineffective in terms of capturing subtle distinctions between users. To address these challenges, we proposed SeGA, preference-aware self-contrastive learning for anomalous user detection, which leverages heterogeneous entities and their relations in the Twittersphere to detect anomalous users with different malicious strategies. SeGA utilizes the knowledge of large language models to summarize user preferences via posts. In addition, integrating user preferences with prompts as pseudo-labels for preference-aware self-contrastive learning enables the model to learn multifaceted aspects for describing the behaviors of users. Extensive experiments on the proposed TwBNT benchmark demonstrate that SeGA significantly outperforms the state-of-the-art methods (+3.5\% ~ 27.6\%) and empirically validate the effectiveness of the model design and pre-training strategies. Our code and data are publicly available at https://github.com/ying0409/SeGA.Comment: AAAI 2024 Main Trac