Advanced Training Techniques Research Articles

Generative spatiotemporal image exploitation for datacenter traffic prediction

The tremendous growth rate of global internet traffic in past years increases the importance of traffic prediction for network operators to ensure seamless Quality of Service (QoS) with proactive traffic engineering. Even minor anomalies in traffic management can lead to service disruptions that affect a vast user base, necessitating highly accurate traffic predictions. While recent studies have exploited Deep Learning for accurate traffic predictions, most of them have targeted mobile network traffic and they often fall short in delivering precise long-range predictions and effective spatiotemporal feature extraction from single-stream time-series data. This research addresses these limitations by proposing a Convolutional Recurrent Generative Adversarial Network (CoRe-GAN) consisting of generator and discriminator neural networks for high-accuracy traffic prediction. The generator with Convolutional Long Short-Term Memory (ConvLSTM) model effectively captures intricate features, whereas the discriminator utilizes a Convolutional Neural Network (CNN) to train the generator through feedback. Moreover, advanced training techniques like fact forcing and feature matching increase the learning convergence rate, avoid mode collapse, and amplify prediction accuracy of CoRe-GAN. The evaluation with Pangyo Network Dataset (PND) and synthetic Intrusion Detection Dataset (IDD) confirms CoRe-GAN superiority. The results show that it outperforms ConvLSTM models with an average 20% and 16% lower Mean Square Error (MSE) with PND and IDD traffic data, respectively.

Advancements in object detection: From machine learning to deep learning paradigms

The evolution of object detection from traditional machine learning approaches to advanced deep learning techniques marks a significant milestone in the field of computer vision. Initially, object detection relied on algorithms such as Support Vector Machines (SVMs) and decision trees, leveraging handcrafted features like Histogram of Oriented Gradients (HOG) and Scale-Invariant Feature Transform (SIFT) for classification and recognition tasks. However, these methods exhibited limitations in scalability and adaptability to complex environments. The breakthrough came with the adoption of Convolutional Neural Networks (CNNs), which transformed the landscape by automating feature extraction, thereby enhancing detection accuracy and efficiency. Subsequent innovations in network architectures, such as R-CNN, YOLO, and SSD, have continually refined object detection capabilities, optimizing both speed and precision. This paper examines the progression of object detection technologies, focusing on the impact of deep learning models and the optimization of network structures. It also delves into the quantitative analysis of model performance, highlighting the role of data augmentation and advanced training techniques in overcoming real-world detection challenges. Through this exploration, the paper aims to provide comprehensive insights into the current state and future directions of object detection techniques.

Benchmarking Llama 3 70B for Code Generation: A Comprehensive Evaluation

This study benchmarks the capabilities of Llama 3 70B, a 70-billion parameter large language model (LLM), for code generation tasks. To effectively train and fine-tune this massive model, we integrate PyTorch Fully Sharded Data Parallel (FSDP) [1], [2] for distributed training and Quantized Low-Rank Adaptation (Q-LoRA) [7] for efficient fine-tuning. We address challenges associated with distributed training, including communication overhead and synchronization complexities, through optimization strategies like gradient accumulation, optimizer state sharding, and mixed precision training. Additionally, we employ advanced training techniques such as Curriculum Learning, Dynamic Batch Sizing, and Adaptive Optimization Algorithms to enhance model performance and training efficiency. Our primary focus is evaluating the performance of the fine-tuned Llama 3 70B model on two widely-recognized code generation benchmarks: HumanEval [8] and MBPP [9]. HumanEval assesses the model's ability to translate natural language problem descriptions into functionally correct code, while MBPP evaluates its proficiency in solving complex programming problems by generating accurate Python code. We present detailed performance results on these benchmarks, analyzing the model's strengths and limitations in various code generation scenarios. Furthermore, we compare the impact of our training and fine-tuning methodologies on scalability, memory efficiency, and training speed, demonstrating the feasibility and efficiency of our approach. This benchmark study offers valuable insights for researchers and practitioners exploring the application of LLMs for code generation. It provides a comprehensive evaluation of Llama 3 70B's capabilities, sheds light on the effectiveness of various training and fine-tuning techniques, and emphasizes the importance of rigorous benchmark evaluation in driving progress within this rapidly evolving field.

Prediction of Sleep Stages Via Deep Learning Using Smartphone Audio Recordings in Home Environments: Model Development and Validation

The growing public interest and awareness regarding the significance of sleep is driving the demand for sleep monitoring at home. In addition to various commercially available wearable and nearable devices, sound-based sleep staging via deep learning is emerging as a decent alternative for their convenience and potential accuracy. However, sound-based sleep staging has only been studied using in-laboratory sound data. In real-world sleep environments (homes), there is abundant background noise, in contrast to quiet, controlled environments such as laboratories. The use of sound-based sleep staging at homes has not been investigated while it is essential for practical use on a daily basis. Challenges are the lack of and the expected huge expense of acquiring a sufficient size of home data annotated with sleep stages to train a large-scale neural network. This study aims to develop and validate a deep learning method to perform sound-based sleep staging using audio recordings achieved from various uncontrolled home environments. To overcome the limitation of lacking home data with known sleep stages, we adopted advanced training techniques and combined home data with hospital data. The training of the model consisted of 3 components: (1) the original supervised learning using 812 pairs of hospital polysomnography (PSG) and audio recordings, and the 2 newly adopted components; (2) transfer learning from hospital to home sounds by adding 829 smartphone audio recordings at home; and (3) consistency training using augmented hospital sound data. Augmented data were created by adding 8255 home noise data to hospital audio recordings. Besides, an independent test set was built by collecting 45 pairs of overnight PSG and smartphone audio recording at homes to examine the performance of the trained model. The accuracy of the model was 76.2% (63.4% for wake, 64.9% for rapid-eye movement [REM], and 83.6% for non-REM) for our test set. The macro F1-score and mean per-class sensitivity were 0.714 and 0.706, respectively. The performance was robust across demographic groups such as age, gender, BMI, or sleep apnea severity (accuracy 73.4%-79.4%). In the ablation study, we evaluated the contribution of each component. While the supervised learning alone achieved accuracy of 69.2% on home sound data, adding consistency training to the supervised learning helped increase the accuracy to a larger degree (+4.3%) than adding transfer learning (+0.1%). The best performance was shown when both transfer learning and consistency training were adopted (+7.0%). This study shows that sound-based sleep staging is feasible for home use. By adopting 2 advanced techniques (transfer learning and consistency training) the deep learning model robustly predicts sleep stages using sounds recorded at various uncontrolled home environments, without using any special equipment but smartphones only.

Self-Reported Training and Supplementation Practices Between Performance-Enhancing Drug-User Bodybuilders Compared with Natural Bodybuilders.

Li, J, Davies, TB, and Hackett, DA. Self-reported training and supplementation practices between performance-enhancing drug-user bodybuilders compared with natural bodybuilders. J Strength Cond Res 37(5): 1079-1088, 2023-This study aimed to examine whether the training and supplementation practices differ between performance-enhancing drug (PED)-using bodybuilders (BB) and natural BB. One hundred eighty-seven competitive male bodybuilders with a median age of 27.0 years completed an online survey. Of this sample, 40 respondents reported using PED (PED-user) and 147 respondents reported to be natural. Compared with natural BB, PED-user BB reported greater off-season body weight ( p < 0.001) and weight loss before a competition ( p < 0.001). In the off-season, PED-user BB performed a greater number of exercises per muscle group ( p < 0.001), number of repetition maximum (RM) per set ( p < 0.01), and less recovery between the sets ( p < 0.01). During the precompetition phase, the natural BB increased their number of RM; however, the PED-user BB still reported using a greater number of RM per set ( p = 0.02), exercises per muscle group ( p < 0.001), and less recovery time between the sets ( p < 0.01). Both the PED-user BB and natural BB reported greater aerobic exercise frequency ( p < 0.001) and session duration ( p < 0.001), although PED-user BB performed a greater number of aerobic exercise sessions ( p = 0.04) and at a higher intensity ( p < 0.01). Advanced training techniques, including negatives, partial repetitions, preexhaustion sets, and timed repetitions, were more popular among PED-user BB ( p < 0.05). Creatine monohydrate usage was more popular among natural BB ( p < 0.001), whereas branched-chain and essential amino acids were more popular among PED-user BB ( p ≤ 0.001). Findings suggest that PED-user BB perform more metabolically demanding resistance training sessions, more strenuous aerobic training during the precompetition phase, and may have different supplementation preferences compared with natural BB.

WE-227. Nontraditional advanced training techniques for neuromonitorists

WE-227. Nontraditional advanced training techniques for neuromonitorists

Tech Optimization in Cybersecurity Defenses by Advanced ML Methods: The Use Case of Volleyball Industry.

Individual and team performance can be improved by utilizing “smart” devices and applications that are connected through networks. In sports, the Internet of Things (IoT) refers to all of the “smart” devices and applications linked through networks to reduce injuries to the bare minimum, develop advanced training techniques, and apply analytical advanced sports improvement methodologies to improve sports performance in general. The Internet of Things (IoT) in sports is closely related to the objective of both security and privacy in sports, which has become a topic of crucial concern for the sports business in recent years, as evidenced by the adoption of IoT in sports years. For this reason, security flaws can have catastrophic consequences, including the disclosure of personal data, the manipulation of statistical findings, the harming of organizations' reputations, and enormous financial losses for the sporting organization. One or more of the consequences, as previously mentioned, is related to sports organizations and the athletes who are members of those organizations, and they have a direct impact on the corresponding set of sports-related, medical-related, and paramedical enterprises, specifically those that provide specialized sports equipment and associated services. A critical need to detect and quantify threats has long been recognized to better support decision-making when adopting or constructing a safe and reliable sports Internet-of-Things infrastructure, which is becoming increasingly common. Using advanced machine learning algorithms, this research provides a methodology for technology optimization in cybersecurity defenses that is then used in a unique case study utilizing volleyball players to demonstrate its effectiveness. In conjunction with a Monte Carlo optimization technique, an upgraded variant of fuzzy cognitive maps (FCM) is presented in greater detail. This model is utilized for a specific scenario of risk identification of volleyball industry, assessment, and optimization for IoT sports networks.

Weed Identification by Single-Stage and Two-Stage Neural Networks: A Study on the Impact of Image Resizers and Weights Optimization Algorithms.

The accurate identification of weeds is an essential step for a site-specific weed management system. In recent years, deep learning (DL) has got rapid advancements to perform complex agricultural tasks. The previous studies emphasized the evaluation of advanced training techniques or modifying the well-known DL models to improve the overall accuracy. In contrast, this research attempted to improve the mean average precision (mAP) for the detection and classification of eight classes of weeds by proposing a novel DL-based methodology. First, a comprehensive analysis of single-stage and two-stage neural networks including Single-shot MultiBox Detector (SSD), You look only Once (YOLO-v4), EfficientDet, CenterNet, RetinaNet, Faster Region-based Convolutional Neural Network (RCNN), and Region-based Fully Convolutional Network (RFCN), has been performed. Next, the effects of image resizing techniques along with four image interpolation methods have been studied. It led to the final stage of the research through optimization of the weights of the best-acquired model by initialization techniques, batch normalization, and DL optimization algorithms. The effectiveness of the proposed work is proven due to a high mAP of 93.44% and validated by the stratified k-fold cross-validation technique. It was 5.8% improved as compared to the results obtained by the default settings of the best-suited DL architecture (Faster RCNN ResNet-101). The presented pipeline would be a baseline study for the research community to explore several tasks such as real-time detection and reducing the computation/training time. All the relevant data including the annotated dataset, configuration files, and inference graph of the final model are provided with this article. Furthermore, the selection of the DeepWeeds dataset shows the robustness/practicality of the study because it contains images collected in a real/complex agricultural environment. Therefore, this research would be a considerable step toward an efficient and automatic weed control system.

Earf-YOLO: An Efficient Attention Receptive Field Model for Recognizing Symbols of Zhuang Minority Patterns

As for recognizing Zhuang minority pattern symbols, current recognition models often cause high computational overhead and low accuracy since Zhuang minority pattern symbols have large feature vectors and some complex features. In this paper, we present the efficient attention receptive field you only look once (Earf-YOLO), a new scheme to address those problems. Firstly, a global-local-transformer (GLocalT) structure is proposed, through which other control systems are introduced into the axial self-attention module, and global-local training strategies are also designed. The structure can use other control systems to compensate for the lost feature information along the height, width, and channel axes. The global-local training strategy can encode long-term dependencies between features and reduce local information loss, fully illustrating that the structure has high feature expression ability. Besides, strength receptive field block (SRFB) is suggested to use the dilated convolution to control the receptive field’s eccentricity and enrich the feature information of the receptive field during its training. With more branches, it can better extract multiscale features, enrich the feature space of the convolution block, and reparametrize multibranch during prediction to fuse them into the main branch, all of which contribute to the improvement of the model performance. Finally, some advanced training techniques are adopted to enhance the detection effect further. In the end, comparative experiments are conducted on the datasets of Zhuang pattern symbols and PASCAL VOC, whose results indicate that the AP and FPS of the suggested model reach their highest values, manifesting its high efficiency.

A systematic approach to athletic development

Since the dawn of social media, sports performance professionals have had the ability to share ideas and display training methodologies to anyone across the globe. Research problem/aim: The problem with this connectedness is much of this information is baseless. Coaches, athletes and parents are too often misinformed, confused, and duped by fad exercise programs and gimmicks that put can puts their athletes under too much stress (physical and emotional) too fast, putting them at risk of injury. Findings: In order to be successful on the sporting field, athletes need to be able to make it to the playing field first. Sports performance specialists need to focus on long term development not just pushing their athletes to the limit. Athletes need a structured training progression that builds a solid foundation of strength, endurance, and coordination to give them the tools to be successful in the weight room before placing them under a loaded bar or implementing advanced training techniques. With athletes at any level, high school, collegiate, or professional, even the most talented of athletes on the field may not necessarily have a very strong background in the weight room. Conclusion: A systematic pattern of athletic development would allow adaptation in fundamental movement patterns and develops requisite physical qualities, and allow the athletes to advance safely and effectively.

A convolutional autoencoder-based approach with batch normalization for energy disaggregation

Non-intrusive loading monitoring (NILM) is a load analyzing algorithm that performs the energy dis-aggregation of power load for the smart meter technology. NILM is a highly valuable application due to its cost effectiveness, but it is a very challenging research because the noisy low-level features are not easily distinguishable when multiple appliances are used together. This paper proposes a deep learning-based scheme, named the CAEBN-HC, to address this issue. The proposed CAEBN-HC is designed based on the one-dimensional convolutional neural networks (1D-CNN) autoencoder and uses advanced training techniques, particularly the batch normalization (BN) and hill climbing (HC) algorithm to solve the NILM problem. The 1D-CNN autoencoder is used to extract the temporal features, and the BN is used to re-adjust the output distribution of each layer to prevent the gradient vanishing or explosion problem in the training process. In addition, the HC is used to perform the hyperparameter tuning. The NILM problem is first modeled as a regression problem, and the proposed method can predict the target signal correctly. To validate the effectiveness of the proposed scheme, the REDD appliance and power usage dataset is applied as a benchmark for performance comparison. Results showed that the proposed CAEBN-HC performed the best when compared with the LSTM and the conventional convolutional autoencoder (CAE) scheme without batch normalization and hyperparameter optimization.

Neural Architecture Search by Estimation of Network Structure Distributions

The influence of deep learning is continuously expanding across different domains, and its new applications are ubiquitous. The question of neural network design thus increases in importance, as traditional empirical approaches are reaching their limits. Manual design of network architectures from scratch relies heavily on trial and error, while using existing pretrained models can introduce redundancies or vulnerabilities. Automated neural architecture design is able to overcome these problems, but the most successful algorithms operate on significantly constrained design spaces, assuming the target network to consist of identical repeating blocks. While such approach allows for faster search, it does so at the cost of expressivity. We instead propose an alternative probabilistic representation of a whole neural network structure under the assumption of independence between layer types. Our matrix of probabilities is equivalent to the population of models, but allows for discovery of structural irregularities, while being simple to interpret and analyze. We construct an architecture search algorithm, inspired by the estimation of distribution algorithms, to take advantage of this representation. The probability matrix is tuned towards generating high-performance models by repeatedly sampling the architectures and evaluating the corresponding networks, while gradually increasing the model depth. Our algorithm is shown to discover non-regular models which cannot be expressed via blocks, but are competitive both in accuracy and computational cost, while not utilizing complex dataflows or advanced training techniques, as well as remaining conceptually simple and highly extensible.

INTEGRATION OF TEAMWORK SKILLS IN TEACHING IN ORDER TO MEET THE LEARNING OUTCOMES IN THE CDIO SYLLABUS: APPLICATION TO TECHNOLOGY AND ENGINEERING

With the goal of fundamental and comprehensive innovation of higher education towards shifting the educational process from mainly equipped with the knowledge to the complete development of learners’ competencies and qualities, universities have made many innovation initiatives to meet the increasing demands for higher education. To implement the above objectives, one of the innovations is to apply the CDIO approach as a standard framework for developing training programs, advanced training techniques to meet social needs and response, and international quality standards. Improving teaching capacity for teachers through the selection of teaching methods integrated between teaching professional knowledge with personal skills and interactive skills compatible with output standards and methods evaluate the effectiveness. Teamwork skills are a CDR on skills in CDIO syllabus, so it is necessary to implement teaching, organizing and evaluating group activities. The paper focused on the organization and method of implementing group activities in each class when implementing the training program by the CDIO approach.

Repeated Bouts of Advanced Strength Training Techniques: Effects on Volume Load, Metabolic Responses, and Muscle Activation in Trained Individuals.

This study investigated the effects of advanced training techniques (ATT) on muscular responses and if performing a second training session would negatively affect the training stimulus. Eleven strength-trained males performed a traditional strength training session (TST) and four different ATT: pre-exhaustion A (PE-A), pre-exhaustion B (PE-B), forced repetitions (FR), and super-set (SS). On day 1, SS produced lower volume load than TST, FR, and PE-B (−16.0%, p ≤ 0.03; −14.9, p ≤ 0.03 and −18.2%, p ≤ 0.01, respectively). On day 2, SS produced lower volumes than all the other ATT (−9.73–−18.5%, p ≤ 0.03). Additionally, subjects demonstrated lower perceived exertion on day 1 compared to day 2 (6.5 ± 0.4 AU vs. 8.7 ± 0.3 AU, p = 0.0001). For blood lactate concentration [La-] on days 1 and 2, [La-] after the tenth set was the highest compared to all other time points (baseline: 1.7 ± 0.2, fifth-set: 8.7 ± 1.0, tenth-set 9.7 ± 0.9, post-5 min: 8.7 ± 0.7 mmol∙L−1, p ≤ 0.0001). Acute muscle swelling was greater immediately and 30-min post compared to baseline (p ≤ 0.0001). On day 2, electromyography (EMG) amplitude on the clavicular head of the pectoralis major was lower for SS than TST, PE-A, and PE-B (−11.7%, p ≤ 0.01; −14.4%, p ≤ 0.009; −20.9%, p = 0.0003, respectively). Detrimental effects to the training stimulus were not observed when ATT (besides SS) are repeated. Strength trained individuals can sustain performance, compared to TST, when they are using ATT in an acute fashion. Although ATT have traditionally been used as a means to optimize metabolic stress, volume load, and neuromuscular responses, our data did not project differences in these variables compared to TST. However, it is important to note that different ATT might produce slight changes in volume load, muscle excitation, and fluid accumulation in strength-trained individuals from session to session.

The Effects of Breakdown Set Resistance Training on Muscular Performance and Body Composition in Young Men and Women.

Breakdown (BD) training has been advocated by multiple commercial and academic publications and authors, seemingly as a result of the acute hormonal and muscle activation responses it produces. However, there is a relative dearth of research that has empirically considered this advanced method of resistance training (RT) over a chronic intervention while appropriately controlling other RT variables. The present study considered 36 male and female participants divided into 3 groups: BD (n = 11), heavy-load breakdown (n = 14), and traditional (n = 11), performing full-body RT programs 2 times per week for 12 weeks. No significant between-group differences were identified for change in absolute muscular endurance for chest press, leg press, or pull-down exercises or for body composition changes. Effect sizes for absolute muscular endurance changes were large for all groups and exercises (0.86-2.74). The present study supports previous research that the use of advanced training techniques stimulates no greater muscular adaptations when compared with performing more simplified RT protocols to momentary muscular failure.

Multimedia And Other Advanced TrainingTechniques

Individualization of training has resulted in designing specific advanced training techniques such as simulators and multimedia telematics. Simulator: Training for crane and gantry crane drivers by simulators has increased considerably the level of competence at a lower cost than traditional the spot training in a shorter period with a better level of safety. Telemactics multimedia European project Safety-net The Port of Le Havre and IPER are the French representatives amongst 13 partners from 7 countries in the European project SAFETY-NET. The 4 industrial sectors involved are : MARITIME, PORT, offshore oil exploration and rail. The project aims at demonstrating the feasibility of training on the work place by telematics. Each Competent Development Module (CDM) has three components : a training component, a knowledge assessment and a performance assessment. The technology for distance learning includes CDROM, INTERNET, VISIO CONFERENCE. An appraisal shows a potential of 3500 learners located on 30 European competence development sites. Extending the network world-wide is expected through satellite broadcast. 1 Tomorrow's technology already available for today's training Training gantry crane drivers by SIMULATOR Distance learning by INTERNET Transactions on the Built Environment vol 36, © 1998 WIT Press, www.witpress.com, ISSN 1743-3509 J4 ivianume engineering ana rons Le Havre Port Authority Engineering Department is involved in maritime public works and assistance abroad, therefore it provides the following-up training. Port training is organised by IPER either in Le Havre or in-company in foreign countries. Short courses aim at training port managers and higher executives. The seminars are conducted both in French and English. Additionally a Master Degree on international transport and logistics is offered to students over one year. Efficiency in training has always been a genuine target keeping abreast with cost reduction. Organising small group sessions on a specific content was the first step in meeting the needs of port human resources. Experience has led to go further into individualisation and has resulted in designing specific advanced training techniques such as simulators and multimedia telematics. 1.1 Training sessions on a simulation system for gantry crane operators In order to train container gantry crane operators or to improve their driving these machines, the Port of Le Havre Authority has equipped with a simulation system of operation of gantry cranes. With this kind of equipment, the Port of Le Havre Authority can train its own staff but it can also organise training sessions intended for the staff of other French or foreign Port Authority. 1.1.1 The training equipment The simulation system is made up of a series of equipment grouped together in the premises of the Training Centre of the Port of Le Havre Authority. A first room shelters a driving cab, raised on hydraulic jacks, which places the operator in driving conditions, the video projection and the electric and hydraulic equipment simulating the movements. The physical environment of the cab is that of a real driving cab of one of the latest gantry cranes. A second room shelters all the driving and monitoring equipment of the simulation system.