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Policing the polycrisis: Liberal democratic state terror and other contradictions
(2024-11-12) Rakete, Emmy
The pleasant daydream of capitalist social democracy has now ended, taking with it the possibility of a negotiated class detente. Capitalism has spent the last 50 years repeatedly colliding with external limits, which it has had to convert into merely internal barriers to avert terminal crisis. The stagflation crisis, the global financial crisis, and the crisis of social reproduction are just a few of the potential apocalypses warded off by the ruling class. This process of deferral has saved capitalism’s skin so far, but only by introducing new internal contradictions into capitalism that it must again defer, resolve, or be killed by. For the duration of the neoliberal period, the bourgeoisie has increasingly relied upon the repressive state apparatus to manage these contradictions. Cops, courts, and cages provide the capitalist state with both the directly repressive violence and the indirectly ideological violence that it depends upon for its continued existence. The qualitative transformation of the prison systems of social democracy into the mass incarceration of neoliberalism is one index by which we can register the increasing illiberalism of liberalism. Capitalism must both immiserate on a massive scale and imprison anyone who behaves miserably. Activists and organisers who threaten this intolerable system, like the student Palestine solidarity encampments, are met with police terror. Far from banishing crisis, this dependence upon the ‘justice’ system to incapacitate opposition constitutes a new potentially fatal internal contradiction. Capitalism can live now only so long as it continues to incarcerate, and by so doing it creates an enormous wasteful, futile, loathsome prison regime that teeters constantly on the brink of collapse and on which its whole legitimacy is now staked. The struggle against mass incarceration should be an immediate tactical priority for the communist movement.
Is assessment for learning really assessment?
(2024-11-30) Brown, Gavin
Test Item Analysis: Removing bad items before score estimation
(2024-11-26) Brown, Gavin
Analysis of test questions before score creation is a key psychometric process to ensure that the score best estimates a person’s ability. Classical test theory (CTT) creates test scores based on the block of items in a test. CTT usually removes items with too high or too low difficulty and removes items with zero or negative discrimination to create total scores. Item response theory (IRT) estimates item characteristics on a latent ability scale independent of which combination of items are in a test. IRT estimates item difficulty, item discrimination, so poorly performing items can be removed. There are three main IRT models, so the differences between the Rasch, 2PL, and 3PL models will be discussed. This workshop demonstrates how to obtain CTT and IRT values for any dichotomously score test containing MCQ or short answer questions. We will also look at using the AIC index to evaluate which model best fits the data. The workshop uses the free software R and the free RStudio interface. You will need your own device with R and R Studio. Please install the following packages before the workshop (psych, ltm, mirt).
Teacher assessment literacy in digital environments
(2024-10-17) Brown, Gavin
Teacher assessment literacy in practice
(2024-10-09) Brown, Gavin
STSPL-SSC: Semi-Supervised Few-Shot Short Text Clustering with Semantic text similarity Optimized Pseudo-Labels
(Association for Computational Linguistics, 2024) Nie, Wenhua; Deng, Lin; Liu, Chang-Bo; JialingWei, JialingWei; Han, Ruitong; Zheng, Haoran
This study introduces the Semantic Textual Similarity Pseudo-Label Semi-Supervised Clustering (STSPL-SSC) framework. The STSPL-SSC framework is designed to tackle the prevalent issue of scarce labeled data by combining a Semantic Textual Similarity Pseudo-Label Generation process with a Robust Contrastive Learning module. The process begins with employing k-means clustering on embeddings for initial pseudo-Label allocation. Then we use a Semantic Text Similarity-enhanced module to supervise the secondary clustering of pseudo-labels using labeled data to better align with the real clustering centers. Subsequently, an Adaptive Optimal Transport (AOT) approach fine-tunes the pseudo-labels. Finally, a Robust Contrastive Learning module is employed to foster the learning of classification and instance-level distinctions, aiding clusters to better separate. Experiments conducted on multiple real-world datasets demonstrate that with just one label per class, clustering performance can be significantly improved, outperforming state-of-the-art models with an increase of 1-6% in both accuracy and normalized mutual information, approaching the results of fully-labeled classification.
Investigating Ethical Learning Approaches in Higher Education Students
(2024-11-04) Henning, Marcus
This presentation reviews 11 journal articles, co-written by the presenter, on students’ self-reported ethical learning practices in Africa, Asia, and New Zealand. Using self-report questionnaires, ethical dilemma assessments, and qualitative commentaries, the studies identified five key areas: rationale for engagement, incidence and typologies, acceptability, consequences, and strategies. The implications of these findings will be discussed.
Lake microbial communities are not resistant or resilient to repeated large-scale natural pulse disturbances
(Copernicus Publications, 2021-03-04) Brasell, Katie; Howarth, Jamie; Pearman, John; Fitzsimons, Sean; Pochon, Xavier; Zaiko, Anastasija; Simon, Kevin; Vandergoes, Marcus; Wood, Susanna
Opportunities to study and understand community level responses to extreme natural pulse disturbances in unaltered ecosystems are rare. Lake sediment records that span thousands of years can contain well resolved sediment pulses, triggered by earthquakes. These paleo-records provide a means to study repeated pulse disturbance and the processes of resistance (insensitivity to disturbance) and ecological resilience (capacity to regain structure, function and process). In this study, DNA preserved in lake sediment layers was extracted from a sediment core from a lake in a near-natural catchment. Metabarcoding and inferred functions were used to assess the lake microbial community over the past 1,100 years – a period that included four major earthquakes. Microbial community composition and function differed significantly between highly perturbed (postseismic, c. 50 yrs) phases directly after the earthquakes and more stable (interseismic, c. 260 yr) phases, indicating a lack of community resistance to natural pulse disturbances. A decoupling between community structure and function in successive postseismic phases suggest potential functional redundancy in the community. Significant differences in composition and function in successive interseismic phases demonstrates the communities are not resilient to large scale natural pulse disturbances. The clear difference in structure and function, and high number of indicator taxa in the fourth interseismic phase likely represents a regime shift, possibly due to the two-fold increase in sediment and terrestrial biospheric organic carbon fluxes recorded following the fourth earthquake. Large pulse disturbances that enhance sediment inputs into lake systems may produce an underappreciated mechanism that destabilises lake ecosystem processes.
Developing a hybrid-built pre-hardened alloy steel for injection moulding tools using the laser powder bed fusion process
(Elsevier, 2024-10) Chan, Yuk Lun Simon; Xu, Xun; Diegel, Olaf
Hybrid additive-subtractive manufacturing has been adopted as a cost-effective alternative for manufacturing plastic injection moulding tools with conformal-cooled inserts created by fusing powder and wrought material. This article reports the development of a hybrid power-wrought pre-hardened alloy steel to supplement the current material choice for fabricating injection mould inserts using this advanced manufacturing strategy. In this study, MS1 (maraging 300) steel powder was additively deposited onto pre-machined wrought Nimax steel to form a hybrid alloy material. The mechanical and microstructural properties of the fusion-bonded interface were examined. Microstructural observation revealed a 280 μm thick interfacial region consisting of a homogenous mixing of powder and substrate materials. As a result of solid solution strengthening within the region, tensile tests established robust powder-substrate bonding with tensile ruptures occurring well away from the interface. The as-built hybrid-alloy steel possessed excellent mechanical properties, with 1200 MPa in ultimate tensile strength, 12.4 % in elongation at fracture and 39 HRC (Nimax)/42 HRC (MS1) in hardness. The overall results suggested that hybrid MS1-wrought Nimax steel is a suitable pre-hardened material for manufacturing durable and high-performance injection mould inserts as part of a cost-effective hybrid additive-subtractive manufacturing strategy.
Atmospheric turbulence as seen by a moving object
(2024-09-04) Richards, Peter; Kay, Nicholas; Norris, Stuart
SKGSum: Structured Knowledge-Guided Document Summarization
(2024-01-01) Wang, Q; Wang, R; Zhao, K; Amor, R; Liu, B; Liu, J; Zheng, X; Huang, Z
A summary structure is inherent to certain types of texts according to the Genre Theory of Linguistics. Such structures aid readers in efficiently locating information within summaries. However, most existing automatic summarization methods overlook the importance of summary structure, resulting in summaries that emphasize the most prominent information while omitting essential details from other sections. While a few summarizers recognize the importance of summary structure, they rely heavily on the predefined labels of summary structures in the source document and ground truth summaries. To address these shortcomings, we developed a Structured Knowledge-Guided Summarization (SKGSum) and its variant, SKGSum-W, which do not require structure labels. Instead, these methods rely on a set of automatically extracted summary points to generate summaries. We evaluate the proposed methods using three real-world datasets. The results indicate that our methods not only improve the quality of summaries, in terms of ROUGE and BERTScore, but also broaden the types of documents that can be effectively summarized.
Unveiling diplomatic narratives: Analyzing United Nations Security Council debates through metaphorical cognition
(2024-07-24) Mao, Rui; Zhang, Tianwei; Liu, Qian; Amir, Hussain; Erik, Cambria
The United Nations Security Council (UNSC) is entrusted with the responsibility of safeguarding global peace and security. Prominent global security concerns will be deliberated upon, and viewpoints will be presented within the UNSC. Analyzing the cognitive patterns from UNSC debates helps scholars gain insights into the intricacies of international relations and diplomatic discourse. In this study, our focus lies in the cognitive analysis of debates held within the UNSC. We employ metaphors and their associated concept mappings as a methodological tool to dissect the cognitive nuances present in the debates, spanning from January 1995 to December 2020. To undertake this extensive analysis from a large volume of documents, we leverage MetaPro, a state-of-the-art computational metaphor processing system to obtain the concept mappings of metaphors. We analyze cognitive variations by temporal and geographical variables. We also demonstrate the correlation between metaphor-reflected cognition and diplomatic behavior, and their recursive influence, based on large sample research. Our major finding highlights the mutual impacts of metaphorical cognition and voting behavior at the UN.
EmpathyEar: An Open-source Avatar Multimodal Empathetic Chatbot
(Association for Computational Linguistics, 2024) Fei, Hao; Zhang, Han; Wang, Bin; Liao, Lizi; Liu, Qian; Cambria, Erik
This paper introduces EmpathyEar, a pioneering open-source, avatar-based multimodal empathetic chatbot, to fill the gap in traditional text-only empathetic response generation (ERG) systems. Leveraging the advancements of a large language model, combined with multimodal encoders and generators, EmpathyEar supports user inputs in any combination of text, sound, and vision, and produces multimodal empathetic responses, offering users, not just textual responses but also digital avatars with talking faces and synchronized speeches. A series of emotion-aware instruction-tuning is performed for comprehensive emotional understanding and generation capabilities. In this way, EmpathyEar provides users with responses that achieve a deeper emotional resonance, closely emulating human-like empathy. The system paves the way for the next emotional intelligence, for which we open-source the code for public access.
Faithful Logical Reasoning via Symbolic Chain-of-Thought
(Association for Computational Linguistics, 2024) Xu, Jundong; Fei, Hao; Pan, Liangming; Liu, Qian; Lee, Mong-Li; Hsu, Wynne
While the recent Chain-of-Thought (CoT) technique enhances the reasoning ability of large language models (LLMs) with the theory of mind, it might still struggle in handling logical reasoning that relies much on symbolic expressions and rigid deducing rules. To strengthen the logical reasoning capability of LLMs, we propose a novel Symbolic Chain-of-Thought, namely SymbCoT, a fully LLM-based framework that integrates symbolic expressions and logic rules with CoT prompting. Technically, building upon an LLM, SymbCoT 1) first translates the natural language context into the symbolic format, and then 2) derives a step-by-step plan to solve the problem with symbolic logical rules, 3) followed by a verifier to check the translation and reasoning chain. Via thorough evaluations on 5 standard datasets with both First-Order Logic and Constraint Optimization symbolic expressions, SymbCoT shows striking improvements over the CoT method consistently, meanwhile refreshing the current state-of-the-art performances. We further demonstrate that our system advances in more faithful, flexible, and explainable logical reasoning. To our knowledge, this is the first attempt at combining symbolic expressions and rules into CoT for logical reasoning with LLMs. Code is open at https://github.com/Aiden0526/SymbCoT.
MetaPro 2.0: Computational Metaphor Processing on the Effectiveness of Anomalous Language Modeling
(Association for Computational Linguistics, 2024) Mao, Rui; He, Kai; Ong, Claudia; Liu, Qian; Cambria, Erik
Metaphor interpretation is a difficult task in natural language understanding. The development of relevant techniques in this domain is slow, mostly because of the lack of large annotated datasets and effective pre-trained language models (PLMs) for metaphor learning. Thus, we propose a large annotated dataset and a PLM for the metaphor interpretation task. Our foundation model is based on a novel anomalous language modeling (ALM) method, which we benchmark with comparable PLM baselines on the new dataset, finding that it largely improves model performance on metaphor identification and interpretation.
PanoSent: A Panoptic Sextuple Extraction Benchmark for Multimodal Conversational Aspect-based Sentiment Analysis
(ACM, 2024-10-28) Luo, Meng; Fei, Hao; Li, Bobo; Wu, Shengqiong; Liu, Qian; Poria, Soujanya; Cambria, Erik; Lee, Mong-Li; Hsu, Wynne
While existing Aspect-based Sentiment Analysis (ABSA) has received extensive effort and advancement, there are still gaps in defining a more holistic research target seamlessly integrating multimodality, conversation context, fine-granularity, and also covering the changing sentiment dynamics as well as cognitive causal rationales. This paper bridges the gaps by introducing a multimodal conversational ABSA, where two novel subtasks are proposed: 1) Panoptic Sentiment Sextuple Extraction, panoramically recognizing holder, target, aspect, opinion, sentiment, rationale from multi-turn multi-party multimodal dialogue. 2) Sentiment Flipping Analysis, detecting the dynamic sentiment transformation throughout the conversation with the causal reasons. To benchmark the tasks, we construct PanoSent, a dataset annotated both manually and automatically, featuring high quality, large scale, multimodality, multilingualism, multi-scenarios, and covering both implicit & explicit sentiment elements. To effectively address the tasks, we devise a novel Chain-of-Sentiment reasoning framework, together with a novel multimodal large language model (namely Sentica) and a paraphrase-based verification mechanism. Extensive evaluations demonstrate the superiority of our methods over strong baselines, validating the efficacy of all our proposed methods. The work is expected to open up a new era for the ABSA community, and thus all our codes and data are open at https://PanoSent.github.io/.
Numerical Simulation of Milk Droplet Drying Process
(University of Queensland Library, 2020-12-11) Sefidan, Ali Mohammadi; Sellier, Mathieu; Hewett, James; Willmott, Geoff; Becker, Sid
Spray drying is a commonly used method in various industries to rapidly produce a dry powder from a liquid or slurry by removing the solvent from suspension within a high temperature gas. This method is preferred for producing milk powder in the dairy industry to extend the shelf-life of milk as well as making it easier for transportation. During this process, some of the partially wet milk droplets deposit on the chamber's wall surface which is linked to the onset of biofouling. The main problem with biofouling is the reduced efficiency of the drying process, and can also be a serious health and safety hazard. Therefore, it is essential to improve the process of milk powder production in order to reduce the cost of production. A comprehensive CFD simulation of a spray dryer should couple droplet drying, non-isothermal airflow, droplet tracking and moisture transport submodels. This paper is focused on the key submodel, droplet drying, to find out how droplet mass and surface temperature are sensitive to the surrounding air temperature (Tg) and velocity (Vg). An implicit finite volume approach has been implemented to solve the governing equations to track the droplet's morphological evolution, moisture content and temperature distribution.
Discovery of imidazo [4,5-c]pyridin-2-ones as selective inhibitors of DNA-dependent protein kinase and effective radiosensitizers
(American Association for Cancer Research (AACR), 2024-01-09) Hay, Michael P; Hong, Cho R; Liew, Lydia P; Dickson, Benjamin D; Wong, Way W; O'Brien-Gortner, Sophia F; Airey, Rebecca; Jaiswal, Jagdish; Wilson, William R; Jamieson, Stephen M
Inhibition of the repair of radiation-induced DNA double strand breaks (DSB) offers the potential to sensitize tumors to radiation therapy. The dominant role of non-homologous end-joining in the repair of radiation-induced DSBs indicates that DNA-dependent protein kinase (DNA-PK) is an excellent target for the development of radiosensitizers. We report the discovery of a new chemical class of kinase inhibitor based on the imidazo[4,5-c]pyridin-2-one scaffold. The class was developed by scaffold-hopping from the pan phosphoinositide 3-kinsase (PI3K) and PI3K-like kinase (PIKK) inhibitor dactolisib. Iterative development culminated in the identification of SN39536 as a low nM DNA-PK inhibitor with excellent selectivity. Further modification led to the discovery of the more potent and more selective SN40905. Both SN39536 and SN40905 were effective inhibitors of DNA-PK kinase activity in a biochemical assay with substantial selectivity for DNA-PK across the PIKK and PI3K families. Both compounds selectively inhibited growth of HAP1 PRKDC wild-type cells when combined with radiation, but not the corresponding PRKDC-/- cells. SN39536 and SN40905 were effective radiosensitizers of colorectal carcinoma (HCT116), non-small cell lung cancer (H460, H1299, and A549), pancreatic (BxPC-3, PANC-1, and MiaPaCa-2) and head and neck squamous cell carcinoma (FaDu and UT-SCC-74B) cells determined using a clonogenic survival endpoint. Both SN39536 and SN40905 displayed high oral bioavailability. When administered PO at a range of non-toxic doses, both SN39536 and SN40905 provided significant additional tumor cell killing of HCT116, UT-SCC-74B, and BxPC-3 tumor xenografts in combination with a single (13 Gy) radiation treatment determined by ex vivo clonogenic survival assays. SN39536 also provided substantial tumor growth inhibition of HCT116 tumor xenografts in combination with RAD (10 Gy). SN39536 and SN40905 represent a new, potent, and selective class of DNA-PK inhibitors with significant potential as radiosensitizers for the treatment of human cancers.
Integrating Global Perspectives into the Curriculum
(2024-05-04) Ormond, Barbara
Curricula, across the world, aim to define what society values in education. This presentation investigates how global perspectives may be valued and included in curriculum design and how curricula can be future focussed with the intention of preparing students for a world that is unpredictable and in a constant state of change. Designing curricula to promote social justice, universal conceptual understandings, interdisciplinarity, and teacher agency are potential ways in which global connectivity can be enhanced through well-considered curricula design.
Artificial Intelligence and Healthcare
(2024-07-30) Yogarajan, Vithya

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