Lund University Website Evaluation: Focus on homepage and English research pages

The present universities have their own websites to achieve academic goals, and for this reason, the process of maintaining a high quality and effective website is vital for a university to strengthen its unpredictable creativity and entrepreneurialism. The aim of this study was to develop and validate university website usability, quality and performance, especially focuses on English homepage and research pages. In addition, we will develop a model of how to evaluate university website. Specific objectives were to identify major usability issues and provide a foundation for future development work. The web evaluation methods adopted during the study fall into three major classes: usability testing, user feedback and usage data. Results indicated that English information on website is incomplete, layout and design of the English homepage need to be improved, and the quality of the English research pages varied dramatically. Some web pages were of high standard, enabling quick access to current research and reinforcing the university’s brand as a high quality university conducting world’s leading research. Other web pages were a usability disaster, giving poor user satisfaction and negatively affecting the credibility of the university. The study recommends making an improvement in content, design, layout and new technology, it is necessary to work closely with the faculties and institutes on a ‘case by case’ basis, and finally to improve site performance

Enhancing JPEG Steganography using Iterative Adversarial Examples

Convolutional Neural Networks (CNN) based methods have significantly improved the performance of image steganalysis compared with conventional ones based on hand-crafted features. However, many existing literatures on computer vision have pointed out that those effective CNN-based methods can be easily fooled by adversarial examples. In this paper, we propose a novel steganography framework based on adversarial example in an iterative manner. The proposed framework first starts from an existing embedding cost, such as J-UNIWARD in this work, and then updates the cost iteratively based on adversarial examples derived from a series of steganalytic networks until achieving satisfactory results. We carefully analyze two important factors that would affect the security performance of the proposed framework, i.e. the percentage of selected gradients with larger amplitude and the adversarial intensity to modify embedding cost. The experimental results evaluated on three modern steganalytic models, including GFR, SCA-GFR and SRNet, show that the proposed framework is very promising to enhance the security performances of JPEG steganography

Biomedical Entity Recognition by Detection and Matching

Biomedical named entity recognition (BNER) serves as the foundation for numerous biomedical text mining tasks. Unlike general NER, BNER require a comprehensive grasp of the domain, and incorporating external knowledge beyond training data poses a significant challenge. In this study, we propose a novel BNER framework called DMNER. By leveraging existing entity representation models SAPBERT, we tackle BNER as a two-step process: entity boundary detection and biomedical entity matching. DMNER exhibits applicability across multiple NER scenarios: 1) In supervised NER, we observe that DMNER effectively rectifies the output of baseline NER models, thereby further enhancing performance. 2) In distantly supervised NER, combining MRC and AutoNER as span boundary detectors enables DMNER to achieve satisfactory results. 3) For training NER by merging multiple datasets, we adopt a framework similar to DS-NER but additionally leverage ChatGPT to obtain high-quality phrases in the training. Through extensive experiments conducted on 10 benchmark datasets, we demonstrate the versatility and effectiveness of DMNER.Comment: 9 pages content, 2 pages appendi

Enhancing Deep Knowledge Tracing with Auxiliary Tasks

Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interactions with intelligent tutoring systems. Recent studies have applied multiple types of deep neural networks to solve the KT problem. However, there are two important factors in real-world educational data that are not well represented. First, most existing works augment input representations with the co-occurrence matrix of questions and knowledge components\footnote{\label{ft:kc}A KC is a generalization of everyday terms like concept, principle, fact, or skill.} (KCs) but fail to explicitly integrate such intrinsic relations into the final response prediction task. Second, the individualized historical performance of students has not been well captured. In this paper, we proposed \emph{AT-DKT} to improve the prediction performance of the original deep knowledge tracing model with two auxiliary learning tasks, i.e., \emph{question tagging (QT) prediction task} and \emph{individualized prior knowledge (IK) prediction task}. Specifically, the QT task helps learn better question representations by predicting whether questions contain specific KCs. The IK task captures students' global historical performance by progressively predicting student-level prior knowledge that is hidden in students' historical learning interactions. We conduct comprehensive experiments on three real-world educational datasets and compare the proposed approach to both deep sequential KT models and non-sequential models. Experimental results show that \emph{AT-DKT} outperforms all sequential models with more than 0.9\% improvements of AUC for all datasets, and is almost the second best compared to non-sequential models. Furthermore, we conduct both ablation studies and quantitative analysis to show the effectiveness of auxiliary tasks and the superior prediction outcomes of \emph{AT-DKT}.Comment: Accepted at WWW'23: The 2023 ACM Web Conference, 202

Study on Construction Resource Optimization and Uncertain Risk of Urban Sewage Pipe Network

With considering sewage pipe network upgrading projects in the “villages” in cities, the optimization of construction resources and the assessment of delay risks could be achieved. Based on the schedule-cost hypothetical theory, the mathematical model with constraint indicators was established to obtain the expression of optimal resource input, and conclude the method to analyze the schedule uncertainties. The analysis showed that cyclical footage of pipe could be regarded as a relatively fixed value, and the cost can be regarded as a function that depending on the number of working teams. The optimal number of teams and the optimal schedule occurred when the minimum total cost achieved. In the case of insufficient meteorological data, the Monte Carlo simulation method and uncertainty analysis method can be applied to assess the impact of rainfall on the total construction period, correspondingly the probability of such risk could be derived. The calculation showed that the risk of overdue completion varied significantly according to the construction starting time. It was necessary to take rainfall risk into consideration and make corresponding strategies and measures

The RNA m6A writer METTL3 in tumor microenvironment: emerging roles and therapeutic implications

The tumor microenvironment (TME) is a heterogeneous ecosystem comprising cancer cells, immune cells, stromal cells, and various non-cellular components, all of which play critical roles in controlling tumor progression and response to immunotherapies. Methyltransferase-like 3 (METTL3), the core component of N6-methyladenosine (m6A) writer, is frequently associated with abnormalities in the m6A epitranscriptome in different cancer types, impacting both cancer cells and the surrounding TME. While the impact of METTL3 on cancer cells has been extensively reviewed, its roles in TME and anti-cancer immunity have not been comprehensively summarized. This review aims to systematically summarize the functions of METTL3 in TME, particularly its effects on tumor-infiltrating immune cells. We also elaborate on the underlying m6A-dependent mechanism. Additionally, we discuss ongoing endeavors towards developing METTL3 inhibitors, as well as the potential of targeting METTL3 to bolster the efficacy of immunotherapy