Hasil untuk "Vocational guidance. Career development"

Evangelos Ntouros, Ewoud J. J. Smeur

This paper develops a guidance control law based on a parametric Guiding Vector Field (GVF) and integrates it with a state-of-the-art acceleration and attitude control architecture for tailsitters. The resulting framework enables a direct comparison between traditional trajectory-tracking guidance and GVF-based path-following guidance using a realistic tailsitter model operating under windy conditions. Through extensive simulations, it is shown that for agile flight scenarios with wind and small initial position error, both guidance strategies achieve comparable tracking performance, indicating that the additional complexity introduced by the GVF formulation is not always justified. However, the GVF-based approach exhibits an advantage when initial deviation from the path is present, yielding smooth and well-behaved convergence toward the desired path. Two additional contributions support this evaluation. First, a modification of the parametric GVF is proposed that guarantees exponential stability of the tracking error dynamics for a single integrator system. Second, the differential flatness transform of a tailsitter vehicle is extended to account for explicit knowledge of the wind velocity vector.

Alon Wolf, Chen Katzir, Kfir Aberman et al.

Diffusion-based editing models have emerged as a powerful tool for semantic image and video manipulation. However, existing models lack a mechanism for smoothly controlling the intensity of text-guided edits. In standard text-conditioned generation, Classifier-Free Guidance (CFG) impacts prompt adherence, suggesting it as a potential control for edit intensity in editing models. However, we show that scaling CFG in these models does not produce a smooth transition between the input and the edited result. We attribute this behavior to the unconditional prediction, which serves as the guidance origin and dominates the generation at low guidance scales, while representing an arbitrary manipulation of the input content. To enable continuous control, we introduce Adaptive-Origin Guidance (AdaOr), a method that adjusts this standard guidance origin with an identity-conditioned adaptive origin, using an identity instruction corresponding to the identity manipulation. By interpolating this identity prediction with the standard unconditional prediction according to the edit strength, we ensure a continuous transition from the input to the edited result. We evaluate our method on image and video editing tasks, demonstrating that it provides smoother and more consistent control compared to current slider-based editing approaches. Our method incorporates an identity instruction into the standard training framework, enabling fine-grained control at inference time without per-edit procedure or reliance on specialized datasets.

Liraz Mudrik, Yaakov Oshman

In realistic pursuit-evasion scenarios, abrupt target maneuvers generate unavoidable periods of elevated uncertainty that result in estimation delays. Such delays can degrade interception performance to the point of causing a miss. Existing delayed-information guidance laws fail to provide a complete remedy, as they typically assume constant and known delays. Moreover, in practice they are fed by filtered estimates, contrary to these laws' foundational assumptions. We present an overarching strategy for tracking and interception that explicitly accounts for time-varying estimation delays. We first devise a guidance law that incorporates two time-varying delays, thereby generalizing prior deterministic formulations. This law is driven by a particle-based fixed-lag smoother that provides it with appropriately delayed state estimates. Furthermore, using semi-Markov modeling of the target's maneuvers, the delays are estimated in real-time, enabling adaptive adjustment of the guidance inputs during engagement. The resulting framework consistently conjoins estimation, delay modeling, and guidance. Its effectiveness and superior robustness over existing delayed-information guidance laws are demonstrated via an extensive Monte Carlo study.

María Marta Castro-Arce, Gabriela Chacón-Fonseca, Ángeles Sánchez-López

Objective: To analyze the influence of guidance work in pre-university settings on the development of comprehensive student well-being. Methodology: The research was performed in the Costa Rican Humanities School System (SCHC, its acronym in Spanish). It was carried out from a positivist and naturalistic paradigm, using a concurrent triangulation design with a non-probabilistic sample as a starting point. A total of 85 participants were selected based on the following criteria: representatives of SCHC executive directors and eleventh-year students. Results:Adolescence is influenced by a series of changes that affect the construction of identity and life plans. Therefore, the analysis of the information obtained confirms the functions that characterize the Guidance service in this educational modality and reflects the three dimensions of the comprehensive well-being circle that are prominent in guidance and collective interventions. Conclusions: To ensure the comprehensive well-being of students, the Guidance Department provides personalized services on various topics, such as the sense of identity and belonging, mental health, emotional regulation, decision-making, as well as an approach focused on adaptability in a transitional context, which facilitates understanding of the personal present and life design.

Tomoki Arita, Keisuke Okumura

Guidance is an emerging concept that improves the empirical performance of real-time, sub-optimal multi-agent pathfinding (MAPF) methods. It offers additional information to MAPF algorithms to mitigate congestion on a global scale by considering the collective behavior of all agents across the entire workspace. This global perspective helps reduce agents' waiting times, thereby improving overall coordination efficiency. In contrast, this study explores an alternative approach: providing local guidance in the vicinity of each agent. While such localized methods involve recomputation as agents move and may appear computationally demanding, we empirically demonstrate that supplying informative spatiotemporal cues to the planner can significantly improve solution quality without exceeding a moderate time budget. When applied to LaCAM, a leading configuration-based solver, this form of guidance establishes a new performance frontier for MAPF.

Ashok R Samrat, Swati Singh, Shashi Ranjan Kumar

This paper addresses the time-constrained interception of targets at a predetermined time with bounded field-of-view capability of the seeker-equipped interceptors. We propose guidance laws using the effective lead angle and velocity lead angles of the interceptor to achieve a successful interception of the target. The former scheme extends the existing two-dimensional guidance strategy to a three-dimensional setting. We have shown that such an extension may result in high-frequency switching in the input demand, which may degrade the interceptor's performance. To overcome the potential limitations of such a guidance strategy, we propose an elegant solution using the velocity lead angles and the range error with a backstepping technique. Using the velocity lead angles as virtual inputs, the effective lead angle profile is subsequently regulated to satisfy the seeker's field-of-view bound. Unlike the existing strategies, the proposed guidance strategy does not rely on the time-to-go estimate, which is an appealing feature of the design, as the time-to-go estimate may not always be available with high precision. We provide a theoretical analysis of the error variable and subsequently analytically derive the bounds on achievable impact times. Numerical simulations are performed to support the theoretical findings. The performance of the proposed guidance strategy is compared with that of an existing one, and it has been shown to yield better performance. Finally, a study on different choices of virtual inputs is also provided.

Kaiyu Song, Hanjiang Lai

Classifier-free guidance (CFG) succeeds in condition diffusion models that use a guidance scale to balance the influence of conditional and unconditional terms. A high guidance scale is used to enhance the performance of the conditional term. However, the high guidance scale often results in oversaturation and unrealistic artifacts. In this paper, we introduce a new perspective based on low-frequency signals, identifying the accumulation of redundant information in these signals as the key factor behind oversaturation and unrealistic artifacts. Building on this insight, we propose low-frequency improved classifier-free guidance (LF-CFG) to mitigate these issues. Specifically, we introduce an adaptive threshold-based measurement to pinpoint the locations of redundant information. We determine a reasonable threshold by analyzing the change rate of low-frequency information between prior and current steps. We then apply a down-weight strategy to reduce the impact of redundant information in the low-frequency signals. Experimental results demonstrate that LF-CFG effectively alleviates oversaturation and unrealistic artifacts across various diffusion models, including Stable Diffusion-XL, Stable Diffusion 2.1, 3.0, 3.5, and SiT-XL.

Lisha Li, Jingwen Hou, Weide Liu et al.

Facial Aesthetics Enhancement (FAE) aims to improve facial attractiveness by adjusting the structure and appearance of a facial image while preserving its identity as much as possible. Most existing methods adopted deep feature-based or score-based guidance for generation models to conduct FAE. Although these methods achieved promising results, they potentially produced excessively beautified results with lower identity consistency or insufficiently improved facial attractiveness. To enhance facial aesthetics with less loss of identity, we propose the Nearest Neighbor Structure Guidance based on Diffusion (NNSG-Diffusion), a diffusion-based FAE method that beautifies a 2D facial image with 3D structure guidance. Specifically, we propose to extract FAE guidance from a nearest neighbor reference face. To allow for less change of facial structures in the FAE process, a 3D face model is recovered by referring to both the matched 2D reference face and the 2D input face, so that the depth and contour guidance can be extracted from the 3D face model. Then the depth and contour clues can provide effective guidance to Stable Diffusion with ControlNet for FAE. Extensive experiments demonstrate that our method is superior to previous relevant methods in enhancing facial aesthetics while preserving facial identity.

Lohitvel Gopikannan, Shashi Ranjan Kumar, Abhinav Sinha

This paper presents a cooperative guidance strategy for the simultaneous interception of a constant-velocity, non-maneuvering target, addressing the realistic scenario where only a subset of interceptors are equipped with onboard seekers. To overcome the resulting heterogeneity in target observability, a fixed-time distributed observer is employed, enabling seeker-less interceptors to estimate the target state using information from seeker-equipped agents and local neighbors over a directed communication topology. Departing from conventional strategies that approximate time-to-go via linearization or small-angle assumptions, the proposed approach leverages deviated pursuit guidance where the time-to-go expression is exact for such a target. Moreover, a higher-order sliding mode consensus protocol is utilized to establish time-to-go consensus within a finite time. The effectiveness of the proposed guidance and estimation architecture is demonstrated through simulations.

Taehyeon Eum, Jieun Choi, Tae-Kyun Kim

Diffusion-based methods have achieved significant successes in T2I generation, providing realistic images from text prompts. Despite their capabilities, these models face persistent challenges in generating realistic human hands, often producing images with incorrect finger counts and structurally deformed hands. MGHanD addresses this challenge by applying multi-modal guidance during the inference process. For visual guidance, we employ a discriminator trained on a dataset comprising paired real and generated images with captions, derived from various hand-in-the-wild datasets. We also employ textual guidance with LoRA adapter, which learns the direction from `hands' towards more detailed prompts such as `natural hands', and `anatomically correct fingers' at the latent level. A cumulative hand mask which is gradually enlarged in the assigned time step is applied to the added guidance, allowing the hand to be refined while maintaining the rich generative capabilities of the pre-trained model. In the experiments, our method achieves superior hand generation qualities, without any specific conditions or priors. We carry out both quantitative and qualitative evaluations, along with user studies, to showcase the benefits of our approach in producing high-quality hand images.

Seyedmorteza Sadat, Otmar Hilliges, Romann M. Weber

Classifier-free guidance (CFG) is crucial for improving both generation quality and alignment between the input condition and final output in diffusion models. While a high guidance scale is generally required to enhance these aspects, it also causes oversaturation and unrealistic artifacts. In this paper, we revisit the CFG update rule and introduce modifications to address this issue. We first decompose the update term in CFG into parallel and orthogonal components with respect to the conditional model prediction and observe that the parallel component primarily causes oversaturation, while the orthogonal component enhances image quality. Accordingly, we propose down-weighting the parallel component to achieve high-quality generations without oversaturation. Additionally, we draw a connection between CFG and gradient ascent and introduce a new rescaling and momentum method for the CFG update rule based on this insight. Our approach, termed adaptive projected guidance (APG), retains the quality-boosting advantages of CFG while enabling the use of higher guidance scales without oversaturation. APG is easy to implement and introduces practically no additional computational overhead to the sampling process. Through extensive experiments, we demonstrate that APG is compatible with various conditional diffusion models and samplers, leading to improved FID, recall, and saturation scores while maintaining precision comparable to CFG, making our method a superior plug-and-play alternative to standard classifier-free guidance.

Philipp Vaeth, Alexander M. Fruehwald, Benjamin Paassen et al.

To sample from an unconditionally trained Denoising Diffusion Probabilistic Model (DDPM), classifier guidance adds conditional information during sampling, but the gradients from classifiers, especially those not trained on noisy images, are often unstable. This study conducts a gradient analysis comparing robust and non-robust classifiers, as well as multiple gradient stabilization techniques. Experimental results demonstrate that these techniques significantly improve the quality of class-conditional samples for non-robust classifiers by providing more stable and informative classifier guidance gradients. The findings highlight the importance of gradient stability in enhancing the performance of classifier guidance, especially on non-robust classifiers.

Sveinung Johan Ohrem

Aquaculture robotics is receiving increased attention and is subject to unique challenges and opportunities for research and development. Guidance, navigation and control are all important aspects for realizing aquaculture robotics solutions that can greatly benefit the industry in the future. Sensor technologies, navigation methods, motion planners and state control all have a role to play, and this paper introduces some technologies and methods that are currently being applied in research and industry before providing some examples of challenges that can be targeted in the future.

سحر رستمی, مرتضی انوشه, فرهاد درویشی سه تلانی et al.

این پژوهش به منظور تدوین مدل پارادایمی توسعه کارآفرینی در حوزه انرژی‌های تجدیدپذیر با رویکرد آینده‌پژوهی انجام شد. رویکرد حاکم بر پژوهش حاضر کیفی و از روش نظریه بنیانی استفاده شد. داده‌ها با استفاده از دو روش اسنادی و میدانی و با بهره‌گیری از پویش محیطی و روش‌های نمونه‌گیری هدفمند و نظری جمع‌آوری شد. متعاقبا از طریق مصاحبه نیمه ساختاریافته با 12 نفر از خبرگان اشباع نظری حاصل شد. سپس با کدگذاری باز، محوری و گزینشی، مدل توسعه کارآفرینی در حوزه انرژی‌های تجدیدپذیر تدوین شد. در این پژوهش از دو نوع زاویه‌بندی داده‌ای و زاویه-بندی تحلیل جهت افزایش اعتبار یافته‌های تحقیق استفاده شد. بنا بر نتایج پژوهش، توسعه کارآفرینی در حوزه انرژی‌های تجدیدپذیر، طیف وسیعی از اقدامات را در بر می‌گیرد که با رویکرد روش تحلیل لایه‌ای علت‌ها، در چهار سطح، اسطوره – استعاره، جهان‌بینی- گفتمان، نظام‌های اجتماعی و سطح لیتانی دسته‌بندی شد. همچنین بستر پتانسیل‌های منطقه، دیدگاه آحاد جامعه به عنوان شرایط زمینه‌ای و شرایط سیاسی – قانونی، مالی، فناورانه و دانش فنی به عنوان شرایط مداخله‌گر بر چهار لایه اقدامات تاثیرگذار است. در نهایت تضمین سلامتی انسان و محیط‌ زیست، ایجاد اشتغال، شکل-گیری کسب‌و‌کارهای نوین، رفاه اجتماعی و امنیت انرژی به عنوان مهمترین پیامدهای این اقدامات شناسایی شد. در راهبردهای آینده‌نگارانه بر اساس تحلیل لایه‌ای علت‌ها، تاکید اصلی بر الگوهای ذهنی است که چگونگی درک و پاسخگویی به دنیای پیرامون را تحت تاثیر قرار می‌دهند. بعد از این مرحله، باید به ایجاد تغییر در سیاستگذاری‌ها و قوانین اقدام شود و بر اساس آن ساختارها تغییر یابد.

مهدی نداف, سید جعفر موسوی

نظریه رفتار برنامه‌ریزی شده در برگیرنده متغیرهای انگیزشی مهمی است که بر قصد کارآفرینی افراد تاثیر گذاشته و این متغیرها خودشان، از جانب عوامل دیگری متاثر می‌شوند که در این پژوهش به دو عامل حمایت ادراکی دانشگاه و اشتیاق کارآفرینی پرداخته شده است. همچنین به نظر می‌رسد، تاثیر این دو عامل بر این متغیرهای انگیزشی، تحت تاثیر متغیر نیاز به خودمختاری (استقلال) قرار دارد و بر این اساس، مطالعه حاضر تدوین گردید. این پژوهش از منظر هدف، کاربردی و از حیث نحوه گردآوری داده‌ها، از نوع پژوهش‌های توصیفی محسوب می‌شود که به روش همبستگی-علی اجرا شده است. ابزار گردآوری داده، پرسشنامه طیف لیکرت است که با استفاده از شبکه اینترنت به صورت مجازی و با روش در دسترس و به صورت گلوله برفی، بین دانشجویان استان خوزستان توزیع شد و 447 پرسشنامه قابل استفاده مورد تجزیه و تحلیل قرار گرفت. یافته‌ها نشان دادند اشتیاق کارآفرینی و حمایت ادراکی دانشگاه به طور مستقیم و غیر مستقیم، با واسطه متغیرهای انگیزشی نظریه رفتار برنامه‌ریزی شده، بر قصد کارآفرینی مؤثر هستند. همچنین نقش تعدیل‌گری متغیر نیاز به خود مختاری در تاثیر اشتیاق کارآفرینی و حمایت ادراکی دانشگاه بر قصد کارآفرینی تایید گردید. در پایان، با توجه به گزاره‌های تحقیق، پیشنهادهایی ارایه گردید. در یک نمونه مهم، دانشگاه‌ها، مربیان، اساتید، اعضای هیات علمی و گروه‌های آموزشی باید با حمایت‌های مستمر آموزشی و مشوق‌های پژوهشی نظیر ارایه دوره‌‌های آموزشی، کارآموزی، کنفرانس‌ها و همایش‌های کارآفرینی و نظایر این موارد به خلق و توسعه یک اکوسیستم کارآفرینی و راه‌اندازی کسب و کار از سوی دانشجویان یاری رسانند.

سعید اشتیاقی, بهزاد شهرابی, فریدون آزما

هدف از این پژوهش شناسایی مؤلفه‌ها و شاخص‌های توسعه مسئولیت اجتماعی شرکتی براساس مولفه‌های مزیت رقابتی است که با تمرکز بر کارکردها و وظایف محوله به اتاق‌های بازرگانی بین‌المللی اجرا شده است. این تحقیق در دو مرحله انجام شده است. مرحله اول با کاربرد تکنیک دلفی فازی با هدف شناسایی شاخص‌های مسئولیت اجتماعی انجام شد. در این مرحله نمونه آماری شامل خبرگان دانشگاهی و مدیران عالی اتاق‌های بازرگانی به تعداد 30 نفر بودند که به شیوه هدفمند انتخاب شدند. در مرحله دوم رویکرد کمی جهت غربال و تایید مقوله‌های شناسایی شده، استفاده شد. نمونه آماری در این قسمت شامل کارشناسان اتاق‌های بازرگانی به تعداد 357 نفر تعیین شد و به روش تصادفی خوشه‌ای در بین استان‌های مختلف انتخاب شدند. یافته‌های این مطالعه نشان‌دهنده چهار بعد در قالب 12 مؤلفه و 46 شاخص برای مسئولیت اجتماعی شرکتی اتاق‌های بازرگانی بود. بعد زیست محیطی دارای چهار مؤلفه «پایداری»، «کارآمدی»، «شهرهای سبز» و «زیرساخت کارآمد»، بعد اخلاقی، دارای دو مؤلفه «پاسخگویی به ذینفعان» و «عدالت اجتماعی»، بعد اقتصادی دارای چهار مؤلفه «اقتصاد کارآفرین»، «اقتصاد رقابتی»، « اقتصاد تولید کننده» و «اقتصاد کارآمد» و بعد قانونی نیز دارای دو مؤلفه «رعایت الزامات در اسناد بالادستی» و «ایفای تعهدات نسبت به تمامی ذینفعان» می‌باشند. لذا پیشنهاد می‌گردد تا تمهیداتی بر اساس دانش و نوآوری‌های ارتباطی و اطلاعاتی جهت ارتقای مسئولیت اجتماعی شرکتی براساس مولفه‌های مزیت رقابتی اتاق‌های بازرگانی بین الملل صورت پذیرد.

Dina Zohrabi Alaee, Keegan Shea Tonry, Benjamin M. Zwickl

As students pursue a bachelor's degree in physics, they may ponder over which area to specialize in, such as theory, computation, or experiment. Often students develop preferences and dislikes, but it's unclear when this preference solidifies during their undergraduate experiences. To get a better understanding, we interviewed eighteen physics majors who were at different stages of their degree regarding their interest in theory, computation, and experimental methods. Out of the eighteen students, we chose to analyze only nine students who rated computation and theory the lowest. Our analysis did not include interest in experiment because the ratings were less negative. We used Social Cognitive Career Theory (SCCT) and Lucidchart to analyze students' responses and create individual graphical representations of the influences for each student. Through this, we uncovered how various factors such as learning experiences, self-efficacy, and outcome expectations influenced their low interest in a particular method. We found that lack of knowledge and experience is often the main reason why self-efficacy was lower. Students' lack of interest is also influenced by negative outcome expectations (e.g, math-intensive and a bad work-life balance) more than other SCCT factors. Our findings could help physics departments and educators identify positive and negative factors that could lead to a more motivating and inclusive physics curriculum.

زهره چناری, مرتضی رضایی زاده, قنبر محمدی الیاسی et al.

هدف پژوهش حاضر شناسایی و تبیین راه‌حل‌های ارتقای فرایند مربیگری در مدیریت استعدادهای سازمانی می‌باشد. چارچوب اصلی پژوهش کیفی است که جهت جمع‌آوری داده‌ها از ابزار مصاحبه نیمه‌ساختاریافته محقق ساخته استفاده شد. جامعه آماری پژوهش، متخصصان حوزه منابع انسانی و مربیگری می‌باشد. روش نمونه‌گیری پژوهش، هدفمند و از نوع گلوله برفی است. درحقیقت، داده‌های پژوهش از طریق مصاحبه با 15 نفر از متخصصان حوزه منابع انسانی و مربیگری به اشباع رسید. داده‌های حاصل از مصاحبه به روش کدگذاری اشتراوس و کوربین[1]  تحلیل شد. نتایج حاصل از تحلیل داده‌ها 10 مقوله کلی بود. این مقوله‌ها شامل «برقراری ارتباط موثر بین مدیر و کارکنان»، «ایجاد شناخت دقیق مدیر و کارکنان از یکدیگر»، «پیش شرطهای مربیگری در سازمان»، «تسهیل فرایند انتقادپذیری و انتقادگری مدیر و کارکنان»، «فرایند تسهیل‌گری و مواجهه‌گری کارکنان»، «تسهیل فرایند آموزش و توسعه مدیر و کارکنان»، «ایجاد باور به توانستن در مدیر و کارکنان»، «تسهیل فرایند اجرای بهتر تصمیم توسط کارکنان»، «بهبود سیستم ارزیابی عملکرد»، «افزایش همکاری مدیر و کارکنان» می‌شود. با توجه به نتایج پژوهش حاضر، پیشنهاد می‌شود در هر فرایند مربیگری از 10 مقوله مذکور جهت ارتقا و بهبود فرایند مربیگری استفاده شود.[1] Strauss & Corbin

Editorial Office

No abstract available.

Shang Li, Guixuan Zhang, Zhengxiong Luo et al.

Image downscaling and upscaling are two basic rescaling operations. Once the image is downscaled, it is difficult to be reconstructed via upscaling due to the loss of information. To make these two processes more compatible and improve the reconstruction performance, some efforts model them as a joint encoding-decoding task, with the constraint that the downscaled (i.e. encoded) low-resolution (LR) image must preserve the original visual appearance. To implement this constraint, most methods guide the downscaling module by supervising it with the bicubically downscaled LR version of the original high-resolution (HR) image. However, this bicubic LR guidance may be suboptimal for the subsequent upscaling (i.e. decoding) and restrict the final reconstruction performance. In this paper, instead of directly applying the LR guidance, we propose an additional invertible flow guidance module (FGM), which can transform the downscaled representation to the visually plausible image during downscaling and transform it back during upscaling. Benefiting from the invertibility of FGM, the downscaled representation could get rid of the LR guidance and would not disturb the downscaling-upscaling process. It allows us to remove the restrictions on the downscaling module and optimize the downscaling and upscaling modules in an end-to-end manner. In this way, these two modules could cooperate to maximize the HR reconstruction performance. Extensive experiments demonstrate that the proposed method can achieve state-of-the-art (SotA) performance on both downscaled and reconstructed images.