Diverging assessment maintains a common question set for all students but varies the input data so that each student has a unique problem to solve. It is an approach in student assessment that offers a unique and authentic learning experience. Although such assessments have been implemented in computing courses, their effectiveness and students' perceptions in different contexts remain unexplored. In this paper, we investigate student perspectives on diverging assessment. We surveyed students in four courses across three different universities. Each surveyed student was enrolled in one of the four courses on networking, operation systems, digital forensics or ethical hacking. Each course featured at least one diverging assessment. The students' overall perceptions about diverging assessments and three different aspects of diverging assessment, namely authenticity, assessment-as-learning, and academic integrity, are surveyed, reported, and analyzed.

Nowadays, organisations in various industries are required to have sustainable planning and implementation of cybersecurity frameworks, where communication, analysis, and responses are crucial for various stakeholders, including cybersecurity experts, supply chain partners, and customers. The cybersecurity domain is a complex area with a very involved terminology and encompasses numerous security mechanisms to provide security services, which in turn thwart attacks and protect data and other resources. Unfortunately, there are significant inconsistencies and ambiguities in the use of cybersecurity terminology across the sector, and this hinders communication among cybersecurity professionals. In this paper, we review the most significant existing cybersecurity ontology studies with the help of a refined search strategy, data management, and continuous evaluation. This Systematic Literature Review identifies knowledge gaps and a need for a new cybersecurity ontology focusing on mapping standard terms and identifying semantic equivalences to leverage current ontologies and support practical applications.

This article presents Med Herb Lens, a mobile application prototype that uses artificial intelligence to identify medicinal plants. Med Herb Lens does not attempt to identify all plant species, rather, it focuses on plants used for medicinal purposes. The app combines a deep-learning-trained image classifier system with a dynamic, user-augmented knowledge base. EfficientNetB0 was used for model development, with model performance improved via transfer learning. The model was converted into TensorFlow Lite to facilitate real-time and offline inference on Android devices. A Firebase backend architecture is employed to facilitate user contributions, synchronise the dynamic knowledge base, and enable users to access the application in remote areas. The implementation of the proof-of-concept model with a small dataset (seven medicinal plant species) yielded high classification accuracy and inference times of under 400 milliseconds on average. The app could also facilitate crowdsourcing of imagery and metadata, while allowing for iterative model improvement and development, and preserving traditional botanical knowledge. This work suggests that AI-powered tools can bridge the gap between conventional medicine and technology and be implemented in low-resource settings, focusing on under-resourced communities.

This paper presents a study on the use of incremental and zero-shot learning for classifying Vietnamese medicinal plants using image analysis. Traditional machine learning methods often struggle with the constant emergence of new plant species and variability in appearances. Our methodology combines incremental learning, which continuously updates the model with new data while retaining prior knowledge, and zero-shot learning, which classifies unseen plant species by leveraging semantic similarities. Evaluated on a unique dataset from Vietnam, our approach shows improved adaptability, robustness, and reduced dependency on extensive labeled data, making it suitable for dynamic environments like medicinal plant identification.

In this experience paper, we introduce the concept of 'diverging assessments', process-based assessments designed so that they become unique for each student while all students see a common skeleton. We present experiences with diverging assessments in the contexts of computer networks, operating systems, ethical hacking, and software development. All the given examples allow the use of generative-AI-based tools, are authentic, and are designed to generate learning opportunities that foster students' meta-cognition. Finally, we reflect upon these experiences in five different courses across four universities, showing how diverging assessments enhance students' learning while respecting academic integrity.

Confluence is a novel non-Intersection over Union (IoU) alternative to Non-Maxima Suppression (NMS) in bounding box post-processing in object detection. It overcomes the inherent limitations of IoU-based NMS variants to provide a more stable, consistent predictor of bounding box clustering by using a normalized Manhattan Distance inspired proximity metric to represent bounding box clustering. Unlike Greedy and Soft NMS, it does not rely solely on classification confidence scores to select optimal bounding boxes, instead selecting the box which is closest to every other box within a given cluster and removing highly confluent neighboring boxes. Confluence is experimentally validated on the MS COCO and CrowdHuman benchmarks, improving Average Precision by 0.2--2.7% and 1--3.8% respectively and Average Recall by 1.3--9.3 and 2.4--7.3% when compared against Greedy and Soft-NMS variants. Quantitative results are supported by extensive qualitative analysis and threshold sensitivity analysis experiments support the conclusion that Confluence is more robust than NMS variants. Confluence represents a paradigm shift in bounding box processing, with potential to replace IoU in bounding box regression processes.

DNA sequencing is the process of determining the exact order of the nucleotide bases of an individual's genome in order to catalogue sequence variation and understand its biological implications. Whole-genome sequencing techniques produce masses of data in the form of short sequences known as reads. Assembling these reads into a whole genome constitutes a major algorithmic challenge. Most assembly algorithms utilise de Bruijn graphs constructed from reads for this purpose. A critical step of these algorithms is to detect typical motif structures in the graph caused by sequencing errors and genome repeats, and filter them out; one such complex subgraph class is a so-called superbubble. In this paper, we propose an O(n+m)-time algorithm to detect all superbubbles in a directed acyclic graph with n vertices and m (directed) edges, improving the best-known O(mlog⁡m)-time algorithm by Sung et al.

Given a positive value r, a distance-based range query returns the objects that lie within the distance r of the query location. In this paper, we focus on the distance-based range queries that continuously change their locations in a euclidean space. We present an efficient and effective monitoring technique based on the concept of a safe zone. The safe zone of a query is the area with a property that while the query remains inside it, the results of the query remain unchanged. Hence, the query does not need to be reevaluated unless it leaves the safe zone. Our contributions are as follows: 1) We propose a technique based on powerful pruning rules and a unique access order which efficiently computes the safe zone and minimizes the I/O cost. 2) We theoretically determine and experimentally verify the expected distance a query moves before leaving the safe zone and, for majority of queries, the expected number of guard objects. 3) Our experiments demonstrate that the proposed approach is close to optimal and is an order of magnitude faster than a naïve algorithm. 4) We also extend our technique to monitor the queries in a road network. Our algorithm is up to two order of magnitude faster than a naïve algorithm.

Given a positive value r, a circular range query returns the objects that lie within the distance r of the query location. In this paper, we study the circular range queries that continuously change their locations. We present an efficient and effective technique to monitor such moving range queries by utilising the concept of a safe zone. The safe zone of a query is the area with a property that while the query remains inside it, the results of the query remain unchanged. Hence, the query does not need to be re-evaluated unless it leaves the safe zone. The shape of the safe zone is defined by the so-called guard objects. The cost of checking whether a query lies in the safe zone takes k distance computations, where k is the number of the guard objects. Our contributions are as follows. 1) We propose a technique based on powerful pruning rules and a unique access order which efficiently computes the safe zone and minimizes the I/O cost. 2) To show the effectiveness of the safe zone, we theoretically evaluate the probability that a query leaves the safe zone within one time unit and the expected distance a query moves before it leaves the safe zone. Additionally, for the queries that have diameter of the safe zone less than its expected value multiplied by a constant, we also give an upper bound on the expected number of guard objects. This upper bound turns out to be a constant, that is, it does not depend either on the radius r of the query or the density of the objects. The theoretical analysis is verified by extensive experiments. 3) Our thorough experimental study demonstrates that our proposed approach is close to optimal and is an order of magnitude faster than a nai¿ve algorithm.

Let D={a 1 , . . ., a n } be a set of real numbers, and let $S\subset \{1,\ldots,n\}$. For an interval $I\subset \{1,\ldots,n\}$ we set SUM(I)=\sum_{i\in I}a_i$. In this paper we solve the following problem which has been asked in connection with security of statistical databases: Find a largest family B of subintervals of {1,. . .,n} so that knowing the value of SUM(I) for all $I\in {\bf B}$ does not enable one to calculate any element $a_i\in D$, where $i\in S$.

The usability of a statistical database is defined to be the ratio of the cardinality of the largest set of queries which can be answered without compromise to the total number of queries. In this paper, we present new results concerning the usability of secure statistical databases for general SUM, COUNT and MEAN queries, as well as for the corresponding range queries. We give the usability of these k-dimensional databases for all k/spl ges/1. The paper concludes with a discussion of the implications of our results.