Weaknesses: My main concerns are on the experiments. While the authors make effort to perform ablation analysis, I think there are still some important missing ablations to convince me that such BNN-powerd self-training scheme is better than classic ST: (1) The proposed method always uses smart sample selection strategy while the classic ST baseline in this paper does not select samples or just select them uniformly. It is very common for classic ST to select samples based on confidence scores, which can be class-dependent as well. Thus I feel that the comparison made with classic ST is not very fair. I would like to see the comparison between UST removing Conf and classic ST with confidence-based and class-dependent sample selection, or just replace the sample selection part in full UST with confidence-score-based selection to see what happens, otherwise I don't see any direct evidence to show that the BNN-powered "uncertainty-awareness" is better than simple confidence-score-based baseline.

User Stories record what must be built in projects that use agile practices. User Stories serve both to estimate effort, generally measured in Story Points, and to plan what should be done in a Sprint. Therefore, it is essential to train software engineers on how to create simple, easily readable, and comprehensive User Stories. For that reason, we designed, implemented, applied, and evaluated a web application called User Story Tutor (UST). UST checks the description of a given User Story for readability, and if needed, recommends appropriate practices for improvement. UST also estimates a User Story effort in Story Points using Machine Learning techniques. As such UST may support the continuing education of agile development teams when writing and reviewing User Stories. UST's ease of use was evaluated by 40 agile practitioners according to the Technology Acceptance Model (TAM) and AttrakDiff. The TAM evaluation averages were good in almost all considered variables. Application of the AttrakDiff evaluation framework produced similar good results. Apparently, UST can be used with good reliability. Applying UST to assist in the construction of User Stories is a viable technique that, at the very least, can be used by agile developments to complement and enhance current User Story creation.

Numerous works are proposed to improve or evaluate the capabilities of Large language models (LLMs) to fulfill user instructions. However, they neglect the possibility that user inputs may inherently contain incorrect information due to users' false beliefs or malicious intents. In this way, blindly adhering to users' false content will cause deception and harm. To address this problem, we propose a challenging benchmark consisting of Inductive Instructions (INDust) to evaluate whether LLMs could resist these instructions. The INDust includes 15K instructions across three categories: Fact-Checking Instructions, Questions based on False Premises, and Creative Instructions based on False Premises. Our experiments on several strong LLMs reveal that current LLMs can be easily deceived by INDust into generating misleading and malicious statements. Hence we employ Self-Critique prompting to encourage LLMs to not only critique themselves like in previous works but also the users, which show remarkable improvement in handling inductive instructions under both zero-shot and few-shot settings.

Artificial Intelligence improves business capabilities in various ways. Adnan Masood, PhD., Chief Architect – AI / ML at UST, explains how AI is utilised in the manufacturing industry and the benefits it brings to organisations such as transforming operations and reducing costs. What benefits does AI bring to manufacturing? As a collective and sometimes rather omniscient term, Artificial Intelligence (AI) includes the capabilities of learning systems that are perceived as intelligent by humans. AI and Machine Learning (ML) technologies have become top priorities in manufacturing since they allow firms to alter business models, invent operational paradigms to support those models, and monetise information to achieve higher levels of productivity.

Time series classification is a task that aims at classifying chronological data. It is used in a diverse range of domains such as meteorology, medicine and physics. In the last decade, many algorithms have been built to perform this task with very appreciable accuracy. However, applications where time series have uncertainty has been under-explored. Using uncertainty propagation techniques, we propose a new uncertain dissimilarity measure based on Euclidean distance. We then propose the uncertain shapelet transform algorithm for the classification of uncertain time series. The large experiments we conducted on state of the art datasets show the effectiveness of our contribution. The source code of our contribution and the datasets we used are all available on a public repository.