Training Procedure Research Articles

Knowledge Graph Guided Neural Machine Translation with Dynamic Reinforce-selected Triples

Previous methods incorporating knowledge graphs (KGs) into neural machine translation (NMT) adopt a static knowledge utilization strategy, that introduces many useless knowledge triples and makes the useful triples difficult be utilized by NMT. To address this problem, we propose a KG guided NMT model with dynamic reinforce-selected triples. The proposed methods could dynamically select the different useful knowledge triples for different source sentences. Specifically, the proposed model contains two components: 1) knowledge selector, that dynamically selects useful knowledge triples for a source sentence, and 2) knowledge guided NMT (KgNMT), that utilizes the selected triples to guide the translation of NMT. Meanwhile, to overcome the non-differentiable problem and guide the training procedure, we propose a policy gradient strategy to encourage the model to select useful triples and improve the generation probability of gold target sentence. Various experimental results show that the proposed method can significantly outperform the baseline models in both translation quality and handling the entities.

Occupational Injuries and Its Determinants Among Cement Factory Workers: A Cross Sectional Study

Abstract Introduction: Cement manufacturing industry workers face inevitable circumstances and are unintentionally exposed to different injuries at the workplace. The purpose of this study was to establish the pattern of injuries and their related risk factors. Materials and Methods: This cross-sectional study involved 360 workers from different departments within three cement factories in Chhattisgarh, India. Results: 55% of employees had either abrasion (30%) or dust in their eyes (23%). Workers in open areas (unadjusted odds ratio [UOR] = 3.28 [2.08–5.18]) and those who do not have training in personal protective equipment (UOR = 2.19 [1.42–3.39]) were more likely to get injured. Conclusions: A high number of workers suffered from injuries, as reported by this study; therefore, there is a need for thorough training and safety procedures.

Strategies for Assessing Autistic-Like Behaviors in Mice.

Autism spectrum disorder (ASD) is a neurobiologically complex condition with a heterogeneous genetic etiology. Clinically, ASD is diagnosed by social communication impairments and restrictive or repetitive behaviors, such as hand flapping or lining up objects. These behavioral patterns can be reliably observed in mouse models with ASD-linked genetic mutations, making them highly useful tools for studying the underlying cellular and molecular mechanisms in ASD. Understanding how genetic changes affect the neurobiology and behaviors observed in ASD will facilitate the development of novel targeted therapeutic compounds to ameliorate core behavioral impairments. Our lab has employed several protocols encompassing well-described training and testing procedures that reflect a wide range of behavioral deficits related to ASD. Here, we detail two assays to study the core features of ASD in mouse models: self-grooming (a measure of repetitive behavior) and the three-chamber social interaction test (a measure of social interaction approach and preference for social novelty).

Linear Parameter Varying and Reinforcement Learning Approaches for Trajectory Tracking Controller of Autonomous Vehicles

This research focuses on controlling the motion trajectory of autonomous vehicles by using a combination of two high-performance control methods: Linear Parameter Varying (LPV) and Reinforcement Learning (RL). First, a single-track motion model is researched and developed with coordinate systems to determine the car's motion trajectory through signals from GPS. Then, the LPV control method is used to design a controller to control the car's motion trajectory. Reinforcement learning method with detailed training procedures is used to combine with the advantages of LPV controller. Finally, the simulation results are evaluated in the time domain through the use of specialized CarSim software, which clearly demonstrates the superiority of the research method.

IMRA/SRS Delphi consensus on international standards for common core components of robotic surgical training design

Robotic surgery has expanded internationally at pace. There are multiple local robotic training pathways but there is inconsistency in standardisation of core common components for curricula internationally. A framework is required to define key objectives that can be implemented across robotic training ecosystems. This Delphi consensus aimed to provide recommendations for core considerations in robotic training design across diverse training environments internationally. A literature search was performed and an international steering committee (AG, KL, JW, HM, TC) proposed key components for contemporary robotic training design and a modified Delphi approach was used to gather stakeholder opinion. The outcomes were then discussed at a face-to-face international expert consensus at the IMRA educational session at the Society of Robotic Surgery (SRS) meeting and final voting was conducted on outstanding items. Stakeholders included robotic surgeons, proctors, trainees and robotic surgical training providers. There was consensus achieved in 139 statements organised into 15 themes. There was 100% agreement that standardised themes in robotic curricula may improve patient safety. Key take-home messages include—training curricula should be multiplatform, non-technical skills are an important component of a robotic curriculum as well as console and bedside skills, clinically relevant performance metrics should be used for assessment where available, the reliance on cadaveric and live animal models should be reduced as high-fidelity synthetic models emerge, and stepwise component training is useful for advanced procedural training. These consensus recommendations are intended to guide design of fit for purpose contemporary robotic surgical curricula. Integration of these components into robotic training pathways internationally is recommended.

Advanced fine-tuning procedures to enhance DNN robustness in visual coding for machines

Video Coding for Machines (VCM) is gaining momentum in applications like autonomous driving, industry manufacturing, and surveillance, where the robustness of machine learning algorithms against coding artifacts is one of the key success factors. This work complements the MPEG/JVET standardization efforts in improving the resilience of deep neural network (DNN)-based machine models against such coding artifacts by proposing the following three advanced fine-tuning procedures for their training: (1) the progressive increase of the distortion strength as the training proceeds; (2) the incorporation of a regularization term in the original loss function to minimize the distance between predictions on compressed and original content; and (3) a joint training procedure that combines the proposed two approaches. These proposals were evaluated against a conventional fine-tuning anchor on two different machine tasks and datasets: image classification on ImageNet and semantic segmentation on Cityscapes. Our joint training procedure is shown to reduce the training time in both cases and still obtain a 2.4% coding gain in image classification and 7.4% in semantic segmentation, whereas a slight increase in training time can bring up to 9.4% better coding efficiency for the segmentation. All these coding gains are obtained without any additional inference or encoding time. As these advanced fine-tuning procedures are standard-compliant, they offer the potential to have a significant impact on visual coding for machine applications.

Educational research on medical residency programs in Chile: a scoping review and analysis of the impact of the new accreditation policy

BackgroundAccrediting medical specialties programs are expected to influence and standardize training program quality, align curriculum with population needs, and improve learning environments. Despite global agreement on its necessity, methods vary widely. In the Chilean context, a recent new accreditation criteria includes research productivity in relation to educational research on resident programs, so we aimed to define it. What is the profile of publications in educational research produced by Chilean medical specialty residency programs in the last five years? Based on these results, we intend to analyze the potential impact of the new accreditation policy on medical specialty programs in Chile.MethodsWe performed a preliminary bibliometric search to identify the use of the term “resident” in literature. After that, we conducted a literature search, using a six-step approach to scoping reviews, including the appraisal of the methodological quality of the articles.ResultsBetween 2019 and 2023, an average of 6.2 articles were published yearly (19%). The bibliometric analysis revealed that the dominant thematic area of the journals was clinical, accounting for 78.1%. Most articles focused on residents (84.38%), with only two articles including graduates as participants. One university was responsible for 62.50% of the articles and participated in all multicenter studies (9.38%). Surgical specialties produced 15 research articles focused on procedural training using simulation. Psychiatry was the second most productive specialty, with 5 articles (15.63%) covering standardized patients, well-being, and mental health assessment. The most frequent research focus within residency programs over the five-year period was teaching and learning methodologies, with 19 articles representing almost 60% of the total analyzed.ConclusionsResearch on medical education in Chile’s postgraduate residency programs is limited, with most studies concentrated in a few universities. The new accreditation criteria emphasize educational research, posing challenges for many institutions to meet higher standards. Understanding unexplored areas in educational research and learning from successful programs can enhance research productivity and align efforts with accreditation expectations. Continuous evaluation and new research on residents’ satisfaction, skills acquisition, and well-being are needed to ensure training quality and accountability.

Simulation-based procedural training – a pilot programme that delivers training in a novel way

Simulation-based procedural training – a pilot programme that delivers training in a novel way

DAMM for the detection and tracking of multiple animals within complex social and environmental settings

Accurate detection and tracking of animals across diverse environments are crucial for studying brain and behavior. Recently, computer vision techniques have become essential for high-throughput behavioral studies; however, localizing animals in complex conditions remains challenging due to intra-class visual variability and environmental diversity. These challenges hinder studies in naturalistic settings, such as when animals are partially concealed within nests. Moreover, current tools are laborious and time-consuming, requiring extensive, setup-specific annotation and training procedures. To address these challenges, we introduce the 'Detect-Any-Mouse-Model' (DAMM), an object detector for localizing mice in complex environments with minimal training. Our approach involved collecting and annotating a diverse dataset of single- and multi-housed mice in complex setups. We trained a Mask R-CNN, a popular object detector in animal studies, to perform instance segmentation and validated DAMM’s performance on a collection of downstream datasets using zero-shot and few-shot inference. DAMM excels in zero-shot inference, detecting mice and even rats, in entirely unseen scenarios and further improves with minimal training. Using the SORT algorithm, we demonstrate robust tracking, competitive with keypoint-estimation-based methods. Notably, to advance and simplify behavioral studies, we release our code, model weights, and data, along with a user-friendly Python API and a Google Colab implementation.

Training school psychologists to conduct culturally informed Functional Behavior Assessment interviews.

Exclusionary discipline is a racialized mechanism through which schools systematically remove racially and ethnically minoritized youth from the learning environment. Although the development of Functional Behavior Assessments (FBAs) and linked behavior support plans have been identified as an alternative practice, school psychologists often do not ask questions about the cultural and contextual factors that may influence students' behavior during FBA interviews. Therefore, training is warranted. Unfortunately, staff often have limited time and resources to devote to professional development; therefore, it is critical to ensure that training procedures that require more time offer better outcomes than those that require fewer resources. The present study compared the effectiveness of high intensity training (e.g., behavioral skills training) versus a low intensity training procedure (e.g., self-training) procedures on school psychologists' ability to facilitate simulated FBA interviews grounded in cultural humility with caregivers before and after exposure to one of two training conditions. Findings from regression analyses indicated that self-training adequately prepared school psychologists to adhere to the questions on the protocol, however, behavioral skills training improved the quality of interview delivery. Implications for future research and training are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Enhancing integration of administrative databases in South Africa's HIV program: Validation of record linkage using non-representative gold standard

IntroductionLinked administrative data are widely used in epidemiology to capture patient data across multiple databases. Linkage error rates, critical to measure linkage performance, are rarely reported due to difficulty in obtaining representative gold standard. We propose a training and validation approach for linkage procedures that yield unbiased performance estimates even with a non-representative gold standard. MethodsWe linked patient records from two non-deduplicated databases for HIV monitoring in South Africa, TIER.Net and NHLS laboratory database, using a network-based probabilistic linkage and deduplication approach. National IDs (gold standard) were available for a non-representative minority of records (10%). We calculated sensitivity (Sen, share of true matches identified by the algorithm) and positive predictive value (PPV, share of algorithm-identified matches that were true matches). We adjusted for bias due to informative missingness in National IDs using inverse probability weights to break the link between missingness and match probability. Results111,755 record pairs were considered. National IDs were not missing completely at random. Match probabilities for National ID record pairs exhibited substantially less uncertainty (mid-range match probabilities), inflating Sen and PPV. Before bias correction, Sen and PPV were estimated at 97.0% and 97.8% respectively. After bias correction for missing National IDs, Sen and PPV were estimated at 95.7% and 96.6%. Failure to address this bias understated the overlinkage rate (100% - PPV) by 35% and the underlinkage rate (100% - Sen) by 30%. ConclusionFailure to adjust for informative missingness in the gold standard may lead to biased validation metrics and over/underconfidence in linked data.

Comparison of In-person VS remote learning modalities for ultrasound-guided knee arthrocentesis training using formalin-embalmed cadavers

ObjectivesThis study aims to compare the efficacy of remote versus in-person training strategies to teach ultrasound guided knee arthrocentesis using formalin embalmed cadavers.Methods30 first-year medical student participants were randomly assigned to remote or in-person training groups. Pre- and post- training surveys were used to evaluate participant’s self-confidence in their ability to perform the procedure. Participants were asked to watch a 30-minute training video and then attend a skills training workshop. The workshops consisted of 20 min of hands-on instruction followed by a skills assessment.ResultsFollowing training, participant self-confidence increased significantly across all survey items in both groups (p = 0.0001). No significant changes in participant self-confidence were detected between the groups. Skills and knowledge-related metrics did not differ significantly between the groups with the exception of the “knowledge of instruments” variable.ConclusionsOur data suggests that remote ultrasound-guided procedure training, although logistically complex, is a viable alternative to traditional in-person learning techniques even for a notoriously hands on skill like ultrasound guided knee arthrocentesis. Novice first-year medical student operators in the remote-training group were able to significantly increase their confidence and demonstrate competency in a manner statistically indistinguishable from those trained in-person. These results support the pedagogical validity of using remote training to teach ultrasound guided procedures which could have implications in rural and global health initiatives where educational resources are more limited.

Optimization of machine learning model training procedure on multi-gpu systems to enhance cyber security in telecommunication networks

This paper analyzes the optimization features of machine learning (ML) model training procedures using multi-GPU systems to enhance cyber security in telecommunication networks. A key aspect of the study is the use of data parallelism, which allows the distribution of the training load across multiple GPUs, significantly reducing training time and improving model accuracy-critical factors for rapid threat detection in cyberspace. A novel approach for optimizing data batch size using Mutual Information (MI) is proposed, which harmonizes the utilization of computational resources with the information content of the training data. MI helps to determine the optimal data batch size that minimizes training errors and improves model accuracy without a significant increase in training time. Experimental results demonstrate the substantial advantages of multi-GPU configurations compared to single-GPU setups, providing faster training and improved model accuracy. It was particularly emphasized that MI-guided batch size tuning significantly outperforms traditional manual tuning methods, ensuring higher validation accuracy and reducing training time. The study showed that the MI-based approach is an effective tool for addressing the problem of optimizing ML model training processes in real-world scenarios where cyber security is critical. The proposed methods allow ML models to train faster and more accurately identify potential threats, making them particularly relevant for telecommunication networks where a rapid response to new threats in real time is required. The implementation of modern computational technologies such as multi-GPU systems and MI-optimized training enhances the efficiency and accuracy of machine learning models. This, in turn, improves cyber security measures and ensures a more reliable defence of telecommunication networks against malicious attacks. It is noted that the proposed approaches can be adapted not only for cyber security but also for other areas where high model accuracy and fast training are important. Future research prospects include the development of new machine learning methods, particularly deep neural networks, the exploration of alternative computational architectures such as quantum computing or distributed systems, and their integration into real-time systems. Special attention should be paid to the ethical aspects of implementing automated cyber security systems, particularly in preventing bias in algorithms and ensuring fairness in their application.

A-Speak: Augmentative and alternative communication application for Thai individuals with complex communication needs

Background: Augmentative and alternative communication (AAC) is the approach that enhances communication competence in individuals with complex communication needs. With the advancement of technology, there are varieties of AAC applications with colored-graphic symbols and speech output, improving communication’s intelligibility compared to low-tech AAC systems. However, those AAC applications had some features that were not entirely suitable for Thai users, such as symbol appearance, speech intonation, etc. Objective: This study aimed to develop the first version of the Thai AAC application, A-Speak, based on Thai culture, lexicon, and intonation and remove other constraints that other AAC applications had, such as variation in voice-output age and gender. The proficiency of A-Speak regarding communication functions was also examined. Materials and methods: The participants comprised 15 individuals with cerebral palsy and complex communication needs. The participants were trained to use the A-Speak application, installed on a tablet, to communicate. The training procedures consisted of 3 phases: Phase 1: Train to select icons; Phase 2: Shift to different categories; and Phase 3: Use A-Speak to communicate. The researchers trained the participants to achieve adequate operational skills (i.e., Phases 1 and 2) before beginning Phase 3. In Phases 1 and 2, switches were employed to facilitate participants with limited mobility to operate A-Speak by finger. The researchers also taught the participants’ caregivers to continue training them at home. The researchers collected the participants’ communication abilities regarding communication functions in the recorded form. The data was reported into code numbers according to communication proficiency. Results: After receiving A-Speak training, all participants showed improvement in their communication abilities across a variety of communication functions. Participants showed significant progress in 10 out of 12 communication functions. The communication function in which participants exhibited the most improvement was explaining skills, whereas the communication function that showed the least development was storytelling skills. Conclusion: A-Speak AAC application reduced the constraints that possibly influenced communication intelligibility in the Thai language. Nevertheless, A-Speak still had a few drawbacks that required to be corrected to increase the productivity of this program. The findings indicated that participants gained communication skills through A-Speak as a means of communication. Keywords: A-Speak, augmentative and alternative communication, application, High-tech AAC

Advancing sentiment classification through a population game model approach

Computational Sentiment Analysis involves the automation of human emotion comprehension by categorizing sentiments as positive, negative, or neutral. In the contemporary digital environment, the extensive volume of social media content presents significant challenges for manual analysis, thereby necessitating the development and implementation of automated analytical tools. To address the limitations of existing techniques, which heavily rely on machine learning and extensive dataset pre-training, we propose an innovative unsupervised approach for sentiment classification. This novel methodology is grounded in game theory concepts, particularly the population game model, offering a promising solution by circumventing the need for extensive training procedures. We extract two textual features from review comments, namely context score and emotion score. Leveraging lexicon databases and numeric scores, this cognitive mathematical framework is language-independent. Competitive results are demonstrated across various domains (hotels, restaurants, electronic devices, etc.), and the efficacy of the proposed work is validated in two languages (English and Hindi). The highest accuracy recorded for the English domain dataset is 89%, while electronic Hindi reviews attain an 84% accuracy rate. The proposed model exhibits domain and language independence, validated through statistical analyses confirming the significance of the findings. The framework demonstrates noteworthy rationality and coherence in its outcomes.

Robust speech command recognition in challenging industrial environments

Speech is among the main forms of communication between humans and robots in industrial settings, being the most natural way for a human worker to issue commands. However, the presence of pervasive and loud environmental noise poses significant challenges to the adoption of Speech-Command Recognition systems onboard manufacturing robots; indeed, they are expected to perform in real time on hardware with limited computational capabilities and also to be robust and accurate in such complex environments. In this paper, we propose an innovative system based on an End-to-End architecture with a Conformer backbone. Our system is specifically designed to achieve high accuracy in noisy industrial environments and to guarantee a minimal computational burden to meet stringent real-time requirements while running on computing devices that are embedded in robots. In order to increase the generalization capability of the system, the training procedure is driven by a Curriculum Learning strategy combined with dynamic data augmentation techniques, that progressively increase the complexity of input samples by increasing the noise during the training phase. We have conducted extensive experimentation to assess the effectiveness of our system, using a dataset composed of more than 50,000 samples, of which about 2,000 have been acquired during the daily operations of a Stellantis Italian factory. The results confirm the suitability of the proposed approach to be adopted in a real industrial environment; indeed, it is able to achieve, on both English and Italian commands, an accuracy higher than 90%, maintaining a compact model size (the network is 1.81 MB) and running in real-time on an industrial embedded device (namely 41ms over an NVIDIA Xavier NX).

Nurses’ knowledge of venous blood collection: problems and ways of their solution

The article presents the results of surveys of nurses about their knowledge of the basics of venous blood collection. The identified problems are primarily because nurses do not always perceive the pre-analytical stage as an essential part of laboratory diagnostics, the results of which determine the further fate of patients. Thus, additional training in phlebotomy procedure is necessary to acquire and improve professional competencies when performing the service “Blood collection from peripheral vein”.

Leveraging body-worn camera footage to assess the effects of training on officer communication during traffic stops.

Can training police officers on how to best interact with the public actually improve their interactions with community members? This has been a challenging question to answer. Interpersonal aspects of policing are consequential but largely invisible in administrative records commonly used for evaluation. In this study, we offer a solution: body-worn camera footage captures police-community interactions and how they might change as a function of training. Using this footage-as-data approach, we consider changes in officers' communication following procedural justice training in Oakland, CA, USA, one module of which sought to increase officer-communicated respect during traffic stops. We applied natural language processing tools and expert annotations of traffic stop recordings to detect whether officers enacted the five behaviors recommended in this module. Compared with recordings of stops that occurred prior to the training, we find that officers employed more of these techniques in posttraining stops; officers were more likely to express concern for drivers' safety, offer reassurance, and provide explicit reasons for the stop. These methods demonstrate the promise of a footage-as-data approach to capture and affect change in police-community interactions.

Investigating the use of signal detection information in supervised learning-based image denoising with consideration of task-shift.

Recently, learning-based denoising methods that incorporate task-relevant information into the training procedure have been developed to enhance the utility of the denoised images. However, this line of research is relatively new and underdeveloped, and some fundamental issues remain unexplored. Our purpose is to yield insights into general issues related to these task-informed methods. This includes understanding the impact of denoising on objective measures of image quality (IQ) when the specified task at inference time is different from that employed for model training, a phenomenon we refer to as "task-shift." A virtual imaging test bed comprising a stylized computational model of a chest X-ray computed tomography imaging system was employed to enable a controlled and tractable study design. A canonical, fully supervised, convolutional neural network-based denoising method was purposely adopted to understand the underlying issues that may be relevant to a variety of applications and more advanced denoising or image reconstruction methods. Signal detection and signal detection-localization tasks under signal-known-statistically with background-known-statistically conditions were considered, and several distinct types of numerical observers were employed to compute estimates of the task performance. Studies were designed to reveal how a task-informed transfer-learning approach can influence the tradeoff between conventional and task-based measures of image quality within the context of the considered tasks. In addition, the impact of task-shift on these image quality measures was assessed. The results indicated that certain tradeoffs can be achieved such that the resulting AUC value was significantly improved and the degradation of physical IQ measures was statistically insignificant. It was also observed that introducing task-shift degrades the task performance as expected. The degradation was significant when a relatively simple task was considered for network training and observer performance on a more complex one was assessed at inference time. The presented results indicate that the task-informed training method can improve the observer performance while providing control over the tradeoff between traditional and task-based measures of image quality. The behavior of a task-informed model fine-tuning procedure was demonstrated, and the impact of task-shift on task-based image quality measures was investigated.

Mobile robot navigation based on intrinsic reward mechanism with TD3 algorithm

Deep reinforcement learning methods have been applied to mobile robot navigation to find the optimal path to the target. The rewards are usually given when the task is completed, which may lead to the local optima during the training procedure. It seriously affects the training efficiency and navigation performance of the mobile robot. To this end, this paper proposes an intrinsic reward mechanism with intrinsic curiosity module and randomness enhanced module, combining the TD3 (twin-delayed deep deterministic policy gradient) reinforcement learning algorithm for mobile robot navigation. It effectively resolves the issue of slow convergence caused by sparse rewards in continuous action spaces. It also encourages mobile robots to explore unknown areas and reduces the occurrence of local optima. The experimental results show that the proposed navigation method significantly improves the training efficiency of mobile robots. Out of 1000 test episodes, only 3 exceeded the maximum step limit. This approach significantly reduces the occurrence of local optima. Furthermore, it increases the success rate to an impressive 83.5%, outperforms the existing navigation methods.