Hasil untuk "Vocational guidance. Career development"

Zhengyi Guo, Wenpin Tang, Renyuan Xu

We study conditional generation in diffusion models under hard constraints, where generated samples must satisfy prescribed events with probability one. Such constraints arise naturally in safety-critical applications and in rare-event simulation, where soft or reward-based guidance methods offer no guarantee of constraint satisfaction. Building on a probabilistic interpretation of diffusion models, we develop a principled conditional diffusion guidance framework based on Doob's h-transform, martingale representation and quadratic variation process. Specifically, the resulting guided dynamics augment a pretrained diffusion with an explicit drift correction involving the logarithmic gradient of a conditioning function, without modifying the pretrained score network. Leveraging martingale and quadratic-variation identities, we propose two novel off-policy learning algorithms based on a martingale loss and a martingale-covariation loss to estimate h and its gradient using only trajectories from the pretrained model. We provide non-asymptotic guarantees for the resulting conditional sampler in both total variation and Wasserstein distances, explicitly characterizing the impact of score approximation and guidance estimation errors. Numerical experiments demonstrate the effectiveness of the proposed methods in enforcing hard constraints and generating rare-event samples. The code of the numerical experiments can be found at https://github.com/ZhengyiGuo2002/CDG_Finance.

Apurv Varshney, Lily M. Turkstra, Jiaxin Su et al.

Navigation aids are central to immersive virtual reality (VR) experiences that involve physical locomotion. Their effectiveness depends not only on how much spatial information they provide, but also on how directly that information supports movement decisions. We compared three common guidance techniques for immersive VR wayfinding: a directional arrow, a minimap, and a compass. In a controlled room-scale VR study with 42 participants completing 1008 trials, participants navigated to target landmarks in a time-pressured maze with reduced visibility and forced route replanning. Across behavioral and eye-tracking measures, arrow guidance produced the strongest navigation performance, minimap guidance yielded intermediate performance, and compass cues performed worst, suggesting that during immersive locomotion users benefit from guidance that can be interpreted rapidly while moving. These results suggest that in demanding immersive locomotion tasks, interfaces that translate spatial information directly into actionable movement cues can outperform richer but more interpretive spatial representations. Our findings highlight the importance of designing XR navigation interfaces that minimize the cognitive translation between spatial information and movement decisions.

Helmot Zelloth

This paper has been inspired by Tony Watts’ pioneering work which explored the relationship between VET and career guidance. This relationship features traditionally ambiguous and not very clearcut definitions. His helpful distinction between career guidance ‘prior to’ and ‘within’ VET opened new perspectives for analysis. Career guidance cannot only serve as an ‘eye opener’ to stimulate VET demand, but also as a ‘change agent’ to improve VET supply. However, career guidance has come under attack for being ‘VET-blind’ and has been criticised for being inadequately sensitive to VET. Similarly VET needs to consider the relevance of career guidance. It is this potential for a reciprocal interaction between VET and career guidance that this paper focuses on.

Dehao Tao, Guangjie Liu, Weizheng et al.

While Large Language Models (LLMs) exhibit strong linguistic capabilities, their reliance on static knowledge and opaque reasoning processes limits their performance in knowledge intensive tasks. Knowledge graphs (KGs) offer a promising solution, but current exploration methods face a fundamental trade off: question guided approaches incur redundant exploration due to granularity mismatches, while clue guided methods fail to effectively leverage contextual information for complex scenarios. To address these limitations, we propose Guidance Graph guided Knowledge Exploration (GG Explore), a novel framework that introduces an intermediate Guidance Graph to bridge unstructured queries and structured knowledge retrieval. The Guidance Graph defines the retrieval space by abstracting the target knowledge' s structure while preserving broader semantic context, enabling precise and efficient exploration. Building upon the Guidance Graph, we develop: (1) Structural Alignment that filters incompatible candidates without LLM overhead, and (2) Context Aware Pruning that enforces semantic consistency with graph constraints. Extensive experiments show our method achieves superior efficiency and outperforms SOTA, especially on complex tasks, while maintaining strong performance with smaller LLMs, demonstrating practical value.

Jinmin He, Kai Li, Yifan Zang et al.

Multi-task reinforcement learning endeavors to efficiently leverage shared information across various tasks, facilitating the simultaneous learning of multiple tasks. Existing approaches primarily focus on parameter sharing with carefully designed network structures or tailored optimization procedures. However, they overlook a direct and complementary way to exploit cross-task similarities: the control policies of tasks already proficient in some skills can provide explicit guidance for unmastered tasks to accelerate skills acquisition. To this end, we present a novel framework called Cross-Task Policy Guidance (CTPG), which trains a guide policy for each task to select the behavior policy interacting with the environment from all tasks' control policies, generating better training trajectories. In addition, we propose two gating mechanisms to improve the learning efficiency of CTPG: one gate filters out control policies that are not beneficial for guidance, while the other gate blocks tasks that do not necessitate guidance. CTPG is a general framework adaptable to existing parameter sharing approaches. Empirical evaluations demonstrate that incorporating CTPG with these approaches significantly enhances performance in manipulation and locomotion benchmarks.

Mkpoikanke S. Otu, Maximus M. Sefotho

Background: The high level of non-employability among graduates could be attributed to insufficient career coaching programmes in higher education institutions. Objectives: This study examined whether rational emotive career coaching (RECC) increases graduates’ employability skills. The study also explored the effect of RECC on acquiring employability skills by gender. Methods: A group-randomised trial (GRT) design was used to examine the outcomes of a training programme in Enugu State aimed at improving career beliefs and employability skills. A total of 158 participants were randomised into either the control or treatment groups. The Career Belief Patterns Scale, Version 2, and the Employability Skills Acquisition Scale (ESAS) were used to collect data. A pretest was administered during the first week, followed by a post-test after the eighth week, followed by a follow-up one month after the intervention ended. Data were analysed using multivariate analysis of covariance (MANCOVA). Results: According to the results obtained, participants in the RECC intervention acquired significant employability skills. Graduates’ employability skills acquisition does not appear to be affected by gender and treatments. Conclusion: This study concludes that RECC enhances graduates’ employability skills effectively. Graduating students who acquire the skills to identify and dispute their irrational beliefs are empowered to make informed career choices and acquire skills that are highly valued today. Contribution: This article contributes to the growing body of research supporting rational emotive coaching as a valuable intervention for graduates seeking a competitive edge in the job market.

Julie Sikin Bhanji Jynge, Ingrid Bårdsdatter Bakke, Tristram Hooley

Using Q-method, this article explores how experience with integrated guidance frames practitioners’ pedagogic stances. Integrated guidance is an approach to delivering career guidance that combines face-to-face and digital approaches. Through statistical analysis of participants’ Q-sorts and qualitative interpretation of the results, we identify three groups of participants with different philosophies about integrated guidance and, consequently, different strategies and approaches to guidance. All groups recognise that blended learning pedagogy is useful in career guidance and believe that digital information, guidance tools and platforms can benefit clients in their career learning. However, there are also differences between the groups. The first group (enthusiasts) view the digital environment positively and are confident about their ability to adapt and apply emerging technologies in guidance. The second group (human connectors) prefer face-to-face approaches, especially for clients with low digital skills; they view the digital environment as potentially hostile and have concerns about their abilities to adapt to new guidance technologies. The third group (critical pragmatists) are confident in using digital technologies for guidance but believe that the digital environment can be hostile while recognising its potential as a site for their clients’ career development. These different groups are theorised and display three distinct pedagogic stances on integrated guidance. Abstrakt Ved bruk av Q-metode, utforsker denne artikkelen hvordan erfaring med integrert karriereveiledning former praktikernes pedagogiske standpunkt til karrierelæring. Integrert karriereveiledning er en tilnærming til karriereveiledning som kombinerer ansikt-til-ansikt og digitale strategier. Gjennom statistisk analyse av deltakernes Q-sorteringer og kvalitativ tolkning av resultatene identifiserer vi tre grupper av deltakere med ulike tanker om integrert karriereveiledning, og følgelig ulike strategier og tilnærminger til veiledning. Alle gruppene er enige i at blandet læring-pedagogikken er nyttig i karriereveiledning, og tror at digitale informasjonskanaler, veiledningsverktøy og plattformer kan være til nytte for veisøkerne i deres karrierelæring. Det er imidlertid også forskjeller mellom gruppene. Den første gruppen (entusiaster) ser positivt på det digitale miljøet og er trygge på sin evne til å tilpasse seg og anvende nye teknologier i veiledningen. Den andre gruppen (relasjonsorienterte) foretrekker å gi veiledning ansikt-til-ansikt, spesielt i arbeid med veisøkere med lave digitale ferdigheter. De ser på det digitale miljøet som potensielt fiendtlig og viser bekymring for sine egne evner til å tilpasse seg nye veiledningsteknologier. Den tredje gruppen (kritiske pragmatikere) er trygge på bruken av digitale teknologier for veiledning, men ser at de digitale miljøene kan være utrygge. Samtidig anerkjenner de potensialet som digital teknologi har for veisøkeres karriereutvikling. Gruppene har altså forskjellige innfallsvinkler til sin praksis som kan forstås som tre distinkt ulike pedagogiske holdninger til integrert karriereveiledning. Kaiserortus: Integrert karriereveiledning; digital; Q-metode; karriereveiledning; pedagogisk standpunkt

رقیه زاهدیان تجنکی, سید مجتبی مجاوریان, سید علی حسینی یکانی

هدف از این مطالعه بررسی نقش مکان استقرار در کنار سایر عوامل اثرگذار بر وضعیت بهکارگیری ظرفیت بالقوه صنایع تبدیلی و تکمیلی کشاورزی فعال در استان مازندران میباشد. داده‌های تحقیق از طریق تکمبل 230 پرسشنامه در سال 98-1397 از مدیران واحدهای تبدیلی کشاورزی در مازندران جمعآوری شد. روش نمونهگیری خوشه‌ای با فن انتساب انجام گرفت. واحدهای مورد مطالعه براساس سطح بهرهبرداری از ظرفیت بالقوه به سه گروه ضعیف، متوسط و کامل تقسیم و برای تعیین عوامل اثرگذار از الگوی لوجیت ترتیبی دوسطحی استفاده شده است. همچنین در این تحقیق از نرم‌افزار Stata 14 برای تجزیه و تحلیل داده‌ها استفاده شده است. یافتهها نشان دادند سهم عامل مکان در تغییرات سطوح بهرهبرداری از ظرفیت صنایع کشاورزی، بهطور متوسط 45/10 درصد است. با توجه به تغییرات سهم مکان میتوان نتیجه گرفت ویژگیهای فردی (داخلی واحد) 61 تا 87 درصد از تغییرات بهرهبرداری از ظرفیت بالقوه واحد را تبیین میکنند. متغیرهای عمر واحد، تحصیلات مدیر، اندازه واحد، نحوه تأمین مواد اولیه و میزان سرمایه اولیه برای احداث اثر منفی بر سطح بهکارگیری از ظرفیت بالقوه صنایع دارند. نتایج این تحقیق نشان داد تمرکز بر مشخصات داخلی واحدها بیشتر از مکان‌یابی برای افزایش کارایی صنایع تبدیلی و تکمیلی کشاورزی استان مازندران موثر است، از این رو پیشنهادهایی از قبیل برگزاری دوره‌های آموزشی، ارائه تسهیلات، حمایت از صنایع تبدیلی کوچک، اولویت‌بندی صدور مجوز براساس نوع مالکیت و نوع فعالیت ارائه گردید.

Seyedmorteza Sadat, Manuel Kansy, Otmar Hilliges et al.

Classifier-free guidance (CFG) has become the standard method for enhancing the quality of conditional diffusion models. However, employing CFG requires either training an unconditional model alongside the main diffusion model or modifying the training procedure by periodically inserting a null condition. There is also no clear extension of CFG to unconditional models. In this paper, we revisit the core principles of CFG and introduce a new method, independent condition guidance (ICG), which provides the benefits of CFG without the need for any special training procedures. Our approach streamlines the training process of conditional diffusion models and can also be applied during inference on any pre-trained conditional model. Additionally, by leveraging the time-step information encoded in all diffusion networks, we propose an extension of CFG, called time-step guidance (TSG), which can be applied to any diffusion model, including unconditional ones. Our guidance techniques are easy to implement and have the same sampling cost as CFG. Through extensive experiments, we demonstrate that ICG matches the performance of standard CFG across various conditional diffusion models. Moreover, we show that TSG improves generation quality in a manner similar to CFG, without relying on any conditional information.

Yutian Liu, Wenjun Ke, Jianguo Wei

Handwritten mathematical expression recognition (HMER) is challenging in image-to-text tasks due to the complex layouts of mathematical expressions and suffers from problems including over-parsing and under-parsing. To solve these, previous HMER methods improve the attention mechanism by utilizing historical alignment information. However, this approach has limitations in addressing under-parsing since it cannot correct the erroneous attention on image areas that should be parsed at subsequent decoding steps. This faulty attention causes the attention module to incorporate future context into the current decoding step, thereby confusing the alignment process. To address this issue, we propose an attention guidance mechanism to explicitly suppress attention weights in irrelevant areas and enhance the appropriate ones, thereby inhibiting access to information outside the intended context. Depending on the type of attention guidance, we devise two complementary approaches to refine attention weights: self-guidance that coordinates attention of multiple heads and neighbor-guidance that integrates attention from adjacent time steps. Experiments show that our method outperforms existing state-of-the-art methods, achieving expression recognition rates of 60.75% / 61.81% / 63.30% on the CROHME 2014/ 2016/ 2019 datasets.

Shuxian Bi, Wenjie Wang, Hang Pan et al.

Recommender systems mainly tailor personalized recommendations according to user interests learned from user feedback. However, such recommender systems passively cater to user interests and even reinforce existing interests in the feedback loop, leading to problems like filter bubbles and opinion polarization. To counteract this, proactive recommendation actively steers users towards developing new interests in a target item or topic by strategically modulating recommendation sequences. Existing work for proactive recommendation faces significant hurdles: 1) overlooking the user feedback in the guidance process; 2) lacking explicit modeling of the guiding objective; and 3) insufficient flexibility for integration into existing industrial recommender systems. To address these issues, we introduce an Iterative Preference Guidance (IPG) framework. IPG performs proactive recommendation in a flexible post-processing manner by ranking items according to their IPG scores that consider both interaction probability and guiding value. These scores are explicitly estimated with iteratively updated user representation that considers the most recent user interactions. Extensive experiments validate that IPG can effectively guide user interests toward target interests with a reasonable trade-off in recommender accuracy. The code is available at https://github.com/GabyUSTC/IPG-Rec.

Arwen Bradley, Preetum Nakkiran

We investigate the theoretical foundations of classifier-free guidance (CFG). CFG is the dominant method of conditional sampling for text-to-image diffusion models, yet unlike other aspects of diffusion, it remains on shaky theoretical footing. In this paper, we disprove common misconceptions, by showing that CFG interacts differently with DDPM (Ho et al., 2020) and DDIM (Song et al., 2021), and neither sampler with CFG generates the gamma-powered distribution $p(x|c)^γp(x)^{1-γ}$. Then, we clarify the behavior of CFG by showing that it is a kind of predictor-corrector method (Song et al., 2020) that alternates between denoising and sharpening, which we call predictor-corrector guidance (PCG). We prove that in the SDE limit, CFG is actually equivalent to combining a DDIM predictor for the conditional distribution together with a Langevin dynamics corrector for a gamma-powered distribution (with a carefully chosen gamma). Our work thus provides a lens to theoretically understand CFG by embedding it in a broader design space of principled sampling methods.

Mengfei Xia, Nan Xue, Yujun Shen et al.

Classifier-Free Guidance (CFG), which combines the conditional and unconditional score functions with two coefficients summing to one, serves as a practical technique for diffusion model sampling. Theoretically, however, denoising with CFG \textit{cannot} be expressed as a reciprocal diffusion process, which may consequently leave some hidden risks during use. In this work, we revisit the theory behind CFG and rigorously confirm that the improper configuration of the combination coefficients (\textit{i.e.}, the widely used summing-to-one version) brings about expectation shift of the generative distribution. To rectify this issue, we propose ReCFG with a relaxation on the guidance coefficients such that denoising with \method strictly aligns with the diffusion theory. We further show that our approach enjoys a \textbf{\textit{closed-form}} solution given the guidance strength. That way, the rectified coefficients can be readily pre-computed via traversing the observed data, leaving the sampling speed barely affected. Empirical evidence on real-world data demonstrate the compatibility of our post-hoc design with existing state-of-the-art diffusion models, including both class-conditioned ones (\textit{e.g.}, EDM2 on ImageNet) and text-conditioned ones (\textit{e.g.}, SD3 on CC12M), without any retraining. Code is available at \href{https://github.com/thuxmf/recfg}{https://github.com/thuxmf/recfg}.

Andres Pulido, Kyle Volle, Kristy Waters et al.

This paper presents a novel guidance law for target tracking applications where the target motion model is unknown and sensor measurements are intermittent due to unknown environmental conditions and low measurement update rate. In this work, the target motion model is represented by a transformer neural network and trained by previous target position measurements. This transformer motion model serves as the prediction step in a particle filter for target state estimation and uncertainty quantification. The particle filter estimation uncertainty is utilized in the information-driven guidance law to compute a path for the mobile agent to travel to a position with maximum expected entropy reduction (EER). The computation of EER is performed in real-time by approximating the information gain from the predicted particle distributions relative to the current distribution. Simulation and hardware experiments are performed with a quadcopter agent and TurtleBot target to demonstrate that the presented guidance law outperforms two other baseline guidance methods.

Hongzhi Zang, Yulun Zhang, He Jiang et al.

We study the problem of optimizing a guidance policy capable of dynamically guiding the agents for lifelong Multi-Agent Path Finding based on real-time traffic patterns. Multi-Agent Path Finding (MAPF) focuses on moving multiple agents from their starts to goals without collisions. Its lifelong variant, LMAPF, continuously assigns new goals to agents. In this work, we focus on improving the solution quality of PIBT, a state-of-the-art rule-based LMAPF algorithm, by optimizing a policy to generate adaptive guidance. We design two pipelines to incorporate guidance in PIBT in two different ways. We demonstrate the superiority of the optimized policy over both static guidance and human-designed policies. Additionally, we explore scenarios where task distribution changes over time, a challenging yet common situation in real-world applications that is rarely explored in the literature.

سمیه پرنیان, صدیقه امینیان, رضا یازرلو et al.

امروزه بیکاری از چالش‌های مهم در کشورهای در حال توسعه است، از این‌رو صاحب‌نظران معتقدند ذهنیت کارآفرینانه یکی از عواملی است که می‌تواند به حل چالش بیکاری کمک شایانی بکند. بنابراین هدف تحقیق حاضر طراحی الگوی ذهنیت کارآفرینانه دانش‌آموختگان با رویکرد ساختاری تفسیری بوده است. روش تحقیق از نوع کاربردی و توصیفی- همبستگی و جامعه آماری شامل 10 نفر از خبرگان دانشگاهی و کارآفرینان موفق استان سیستان و بلوچستان بوده است. برای شناسایی مولفه‌های ذهنیت کارآفرینانه دانش‌آموختگان از مصاحبه با خبرگان و برای طراحی الگو از روش مدل‌سازی ساختاری تفسیری با نرم‌افزار اکسل استفاده شده است. نتایج نشان داد الگوی ذهنیت کارآفرینانه دانش‌آموختگان دارای 4 سطح است که مولفه‌های (شناسایی فرصت‌های ناشی از تغییرات فناوری، توانایی استفاده از فرصت‌های بالقوه، توانایی ارائه ایده‌های جدید، ایجاد فضا جهت بکارگیری فرصت‌های کارآفرینانه، خودکارآمدی کارآفرینانه) در سطح اول و مولفه‌های (نگرش کارآفرینانه، تاب‌آوری، کنترل درونی، انگیزه، اعتماد به نفس، ریسک‌پذیری، الهام بخشی) در سطح دوم و مولفه‌های (نیاز به موفقیت، آینده‌نگری، تمایل به آزادی عمل) در سطح سوم و مولفه آموزش کارآفرینی در سطح چهارم قرار دارند و مولفه آموزش کارآفرینی در خوشه مستقل، مولفه توانایی استفاده از فرصت‌های بالقوه در خوشه وابسته و مولفه‌های (شناسایی فرصت‌های ناشی از تغییرات فناوری، توانایی ارائه ایده‌های جدید، نگرش کارآفرینانه، ایجاد فضا جهت بکارگیری فرصت‌های کارآفرینانه، خودکارآمدی کارآفرینانه، تاب‌آوری، کنترل درونی، انگیزه، اعتماد به نفس، نیاز به موفقیت، آینده‌نگری، ریسک‌پذیری، تمایل به آزادی عمل، الهام بخشی) در خوشه پیوندی قرار گرفتند.

V.V. Rubtsov, G.G. Saitgalieva, O.A. Denisova et al.

<p>The article provides an analysis of the current state of inclusive higher education in Russia based on the results of the implementation of the Interdepartmental Comprehensive Action Plan to ensure accessibility of vocational education for people with disabilities for 2016-2018 (approved by the Government of the Russian Federation on May 23, 2016 N 3467p-P8).According to this Plan a set of measures was implemented to help ensure that persons with disabilities receive high-quality higher education. The development and adoption of this document marked the beginning of a new stage in the formation and development of inclusive higher education in the Russian Federation, which determined the goals, objectives and ways to achieve the main indicators of creating an accessible educational environment in Russian universities. Today in the Russian Federation a new Interdepartmental Comprehensive Action Plan has been developed and approved to increase the accessibility of secondary vocational and higher education for people with disabilities and limited health capabilities, including career guidance and employment of these persons. It was approved by the Deputy Prime Minister of the Russian Federation Tatyana Golikova No. 14000p-P8 (hereinafter referred to as the Interdepartmental Comprehensive Plan, ICP, VO, SPO). The 2021 ICP was formed by the Russian Ministry of Education and Science together with the Russian Ministry of Education, the Russian Ministry of Labor, other federal executive bodies and public organizations. The activities of the Interdepartmental Comprehensive Plan bring together more than 25 performers. The development of this Plan, as a strategic planning document based on the principles of succession and continuity, taking into account the stages and results of the implementation of previously adopted documents and largely determines the main directions for the development of inclusive higher education.</p>

نادیا هاشمی, حسن درویش

هدف: هدف از پژوهش حاضر شناسایی ابعاد و مولفه های تعادل کار- زندگی کارکنان شرکت ملی گاز ایران می باشد. روش: پژوهش حاضر از نظر هدف کاربردی توسعه ای و از نظر روش پژوهش کیفی محسوب می شود. شناسائی مؤلفه‏های مدل و تدوین آن با روش تحلیل مضمون و روش دلفی(3 راند) انجام شد. جهت جمع آوری داده ها از مصاحبه‏های نیمه ساختاریافته استفاده شد، نتایج حاصل پس از پیاده سازی، به صورت سطر به سطر بررسی، مفهوم پردازی، مقوله بندی و سپس، بر اساس مشابهت، ارتباط مفهومی و ویژگی های مشترک به مضمون هایی بین اصلی و فرعی تقسیم شد. با بررسی مصاحبه ها، شناسه های اولیه ایجاد شد و سپس شناسه گذاری های مصاحیه ها انجام شد. یافته ‎ها: با شناخت مضمونها با توجه به نتایج حاصل از مصاحبه 124 عامل در تعادل کار و زندگی کارکنان شرکت ملی گاز ایران شناسایی شد.نتیجه‏گیری: در این تحقیق، عوامل مؤثر بر تعادل کار خانواده براساس برنامه های نقشه راه اصلاح نظام اداری کشور کارکنان شرکت ملی گاز ایران، بصورت7 مولفه و 37 شاخص (ساختار دولت: شامل4 شاخص، توسعه دولت الکترونیک و هوشمندسازی: شامل2 شاخص، مدیریت سرمایه انسانی: شامل 9 شاخص، عوامل و فناوری‏های مدیریتی: شامل 6 شاخص، توسعه فرهنگ سازمانی: شامل 7 شاخص، نظارت و ارزیابی : شامل 5 شاخص، صیانت از حقوق مردم و سلامت اداری: شامل 4 شاخص) مورد تأیید قرار گرفت.

Henry J. Nichols

Background: Numerous policies and initiatives of South African Department of Basic Education mandate principals to ensure inclusive school spaces to support and cater for lesbian, gay, bisexual, transgender, intersex, and queer or questioning (LGBTIQ) learners. Yet, heterosexual and cisgender youth are still valorised by principals as the only gender and sexual category in schools. Objectives: Drawing on the social justice leadership theory, this article aimed to add to the conversation on how school principals in their leadership positions can enable a safe and inclusive schooling space for learners with diverse gender identities and sexual orientations. Methods: Principals are mandated to collaborate with the community and parents such as the School Governing Body (SGB), thus the attitudes and responses of these principals through the narratives of parents of LGBTIQ children were examined. As part of a qualitative study, individual interviews were conducted with six parents in the Free State and Gauteng provinces of South Africa, and the data were analysed thematically. Results: The results of this study showed that the principals did not comply with any of these policy requirements or responsibilities and willingly ignored them. Conclusion: Expanding LGBTIQ content in educational leadership training is a necessary step to convince school leaders that LGBTIQ awareness and inclusion are necessary for creating a positive and inclusive schooling climate. Contribution: This study provided reasons for principals to disrupt normative ideas of gender and sexual diversity and the effect of their silence and ignorance.

Yunqi Zhu, Xuebing Yang, Yuanyuan Wu et al.

This study presents three deidentified large medical text datasets, named DISCHARGE, ECHO and RADIOLOGY, which contain 50K, 16K and 378K pairs of report and summary that are derived from MIMIC-III, respectively. We implement convincing baselines of automated abstractive summarization on the proposed datasets with pre-trained encoder-decoder language models, including BERT2BERT, T5-large and BART. Further, based on the BART model, we leverage the sampled summaries from the train set as prior knowledge guidance, for encoding additional contextual representations of the guidance with the encoder and enhancing the decoding representations in the decoder. The experimental results confirm the improvement of ROUGE scores and BERTScore made by the proposed method, outperforming the larger model T5-large.