Three-Dimensional Image Reconstruction for Virtual Talent Training Scene

In real training, the training conditions are often undesirable, and the use of equipment is severely limited. These problems can be solved by virtual practical training, which breaks the limit of space, lowers the training cost, while ensuring the training quality. However, the existing methods work poorly in image reconstruction, because they fail to consider the fact that the environmental perception of actual scene is strongly regular by nature. Therefore, this paper investigates the three-dimensional (3D) image reconstruction for virtual talent training scene. Specifically, a fusion network model was deigned, and the deep-seated correlation between target detection and semantic segmentation was discussed for images shot in two-dimensional (2D) scenes, in order to enhance the extraction effect of image features. Next, the vertical and horizontal parallaxes of the scene were solved, and the depth-based virtual talent training scene was reconstructed three dimensionally, based on the continuity of scene depth. Finally, the proposed algorithm was proved effective through experiments.

Database Tuning using Natural Language Processing

Introduction. We have seen significant advances in the state of the art in natural language processing (NLP) over the past few years [20]. These advances have been driven by new neural network architectures, in particular the Transformer model [19], as well as the successful application of transfer learning approaches to NLP [13]. Typically, training for specific NLP tasks starts from large language models that have been pre-trained on generic tasks (e.g., predicting obfuscated words in text [5]) for which large amounts of training data are available. Using such models as a starting point reduces task-specific training cost as well as the number of required training samples by orders of magnitude [7]. These advances motivate new use cases for NLP methods in the context of databases.

A Federated Learning Approach to Anomaly Detection in Smart Buildings

Internet of Things (IoT) sensors in smart buildings are becoming increasingly ubiquitous, making buildings more livable, energy efficient, and sustainable. These devices sense the environment and generate multivariate temporal data of paramount importance for detecting anomalies and improving the prediction of energy usage in smart buildings. However, detecting these anomalies in centralized systems is often plagued by a huge delay in response time. To overcome this issue, we formulate the anomaly detection problem in a federated learning setting by leveraging the multi-task learning paradigm, which aims at solving multiple tasks simultaneously while taking advantage of the similarities and differences across tasks. We propose a novel privacy-by-design federated learning model using a stacked long short-time memory (LSTM) model, and we demonstrate that it is more than twice as fast during training convergence compared to the centralized LSTM. The effectiveness of our federated learning approach is demonstrated on three real-world datasets generated by the IoT production system at General Electric Current smart building, achieving state-of-the-art performance compared to baseline methods in both classification and regression tasks. Our experimental results demonstrate the effectiveness of the proposed framework in reducing the overall training cost without compromising the prediction performance.

Radiography image analysis using cat swarm optimized deep belief networks

Radiography images are widely utilized in the health sector to recognize the patient health condition. The noise and irrelevant region information minimize the entire disease detection accuracy and computation complexity. Therefore, in this study, statistical Kolmogorov–Smirnov test has been integrated with wavelet transform to overcome the de-noising issues. Then the cat swarm-optimized deep belief network is applied to extract the features from the affected region. The optimized deep learning model reduces the feature training cost and time and improves the overall disease detection accuracy. The network learning process is enhanced according to the AdaDelta learning process, which replaces the learning parameter with a delta value. This process minimizes the error rate while recognizing the disease. The efficiency of the system evaluated using image retrieval in medical application dataset. This process helps to determine the various diseases such as breast, lung, and pediatric studies.

Turnover Intention in Non-Governmental Organisation (NGOs): A Case Study of Recreation Society at Kuantan, Pahang

The hidden cost caused by employee turnover is inevitable. In filling in the vacancy of a position, the causes of turnover will affect the cycling systems. Low turnover rate is essential to an organization’s human resources department as it shows an excellent indicator of budgeting and training cost. This study aims to examine the significant relationship between the factors namely organizational commitment, job stress, job satisfaction and reward satisfaction with turnover intention among committee members in recreation society at Kuantan, Pahang. The sample size of respondents from this society selected in Kuantan, Pahang is 169 from 316 of the population sizes. Stratified random sampling technique was chosen in achieving the objectives of this study because it is a systematic data collection method. The data for this study was collected from the questionnaires distributed to the respondents by using Google form. Altogether, there are four independent variables and one dependent variable. Statistical Package for Social Science version 23 (SPSS) was employed to perform data analysis of the study. The study discovered that organizational commitment, job stress, job satisfaction, and reward satisfaction have significant relationships with turnover intention. These factors like organizational commitment, job stress, job satisfaction and reward Satisfaction are suitable in predicting and analysing organizational behaviour regarding human resource management. Hence, it is crucial to study turnover intentions in organizational management. Keywords: Non-Governmental Organisation, Committees Members, Turnover Intention, Organisational Behaviour

Transfer Learning-Based Algorithms for the Detection of Fatigue Crack Initiation Sites: A Comparative Study

The identification of fatigue crack initiation sites (FCISs) is routinely performed in the field of engineering failure analyses; this process is not only time-consuming but also knowledge-intensive. The emergence of convolutional neural networks (CNNs) has inspired numerous innovative solutions for image analysis problems in interdisciplinary fields. As an explorative study, we trained models based on the principle of transfer learning using three state-of-the-art CNNs, namely VGG-16, ResNet-101, and feature pyramid network (FPN), as feature extractors, and a faster R-CNN as the backbone to establish models for FCISs detection. The models showed application-level detection performance, with the highest precision reaching up to 95.9% at a confidence threshold of 0.6. Among the three models, the ResNet model exhibited the highest accuracy and lowest training cost. The performance of the FPN model closely followed that of the ResNet model with an advantage in terms of the recall.

Peningkatan Manajemen Usaha Pada Pembenihan Ikan ‘Kolam As Syifa’ Cindai Alus

The purpose of the community partnership program activities: improving business management through bookkeeping and report training (logbooks, cash books, inventory, and balance sheets) and increase knowledge to assess business viability through profit analysis training, Cost-Revenue Ratio, Payment Period, and Break-Even Point. The methods applied are 1. the stage of the situation and condition of the partners, 2. the stage of preparation of all materials and materials for training activities, 3. the stage of counseling and management training (training on preparing and presenting reports), analytical training according to final needs, and 4. the stage of evaluation. Community partnership program activities to improve business management for hatcheries at the As Syifa Pond Business provide increased understanding and skills of fish breeders about professional business management through increasing ability in compiling simple financial reports/bookkeeping and in analyzing business financially. This increase in understanding and skills is not only felt for current operations but also business development in the future.Keywords: management, business, bookkeeping, feasibility, As Syifa Abstrak Tujuan kegiatan PKM adalah: perbaikan manajemen usaha melalui pelatihan penyusunan pembukuan dan laporan keuangan (log book, buku kas, buku persediaan dan neraca) dan meningkatkan pengetahuan untuk menilai kelayakan usaha melalui pelatihan menganalisis keuntungan, Revenue Cost Ratio, Payback Period dan Break Even Point. Metode kegiatan yang diterapkan adalah: 1. tahap analisis situasi dan kondisi mitra, 2. tahap persiapan semua bahan dan materi untuk kegiatan pelatihan, 3. tahap penyuluhan dan pelatihan manajemen usaha (pelatihan pembuatan dan penyajian laporan keuangan), pelatihan menganalisis kelayakan usaha secara finasial, dan 4. tahap evaluasi PKM. Kegiatan PKM perbaikan manajemen usaha terhadap pembenih ikan pada Usaha Kolam As Syifa memberikan peningkatan pemahaman dan keterampilan pembenih ikan tentang manajemen usaha yang profesional melalui peningkatan kemampuan dalam menyusun laporan keuangan/pembukuan sederhana dan dalam menganalisis kelayakan usaha secara finansial. Peningkatan pemahaman dan keterampilan ini bukan hanya dirasakan untuk operasional saat ini, tetapi juga untuk pengembangan usaha di masa depan.Kata kunci: manajemen, usaha, pembukuan, kelayakan, As Syifa

Building heterogeneous ensembles by pooling homogeneous ensembles

Heterogeneous ensembles consist of predictors of different types, which are likely to have different biases. If these biases are complementary, the combination of their decisions is beneficial and could be superior to homogeneous ensembles. In this paper, a family of heterogeneous ensembles is built by pooling classifiers from M homogeneous ensembles of different types of size T. Depending on the fraction of base classifiers of each type, a particular heterogeneous combination in this family is represented by a point in a regular simplex in M dimensions. The M vertices of this simplex represent the different homogeneous ensembles. A displacement away from one of these vertices effects a smooth transformation of the corresponding homogeneous ensemble into a heterogeneous one. The optimal composition of such heterogeneous ensemble can be determined using cross-validation or, if bootstrap samples are used to build the individual classifiers, out-of-bag data. The proposed heterogeneous ensemble building strategy, composed of neural networks, SVMs, and random trees (i.e. from a standard random forest), is analyzed in a comprehensive empirical analysis and compared to a benchmark of other heterogeneous and homogeneous ensembles. The achieved results illustrate the gains that can be achieved by the proposed ensemble creation method with respect to both homogeneous ensembles and to the tested heterogeneous building strategy at a fraction of the training cost.

Deep Reinforcement Learning for Task Offloading and Power Allocation in UAV-assisted MEC System

Mobile edge computing (MEC) can provide computing services for mobile users (MUs) by offloading computing tasks to edge clouds through wireless access networks. Unmanned aerial vehicles (UAVs) are deployed as supplementary edge clouds to provide effective MEC services for MUs with poor wireless communication condition. In this paper, a joint task offloading and power allocation (TOPA) optimization problem is investigated in UAV-assisted MEC system. Since the joint TOPA problem has a strong non-convex characteristic, a method based on deep reinforcement learning is proposed. Specifically, the joint TOPA problem is modeled as Markov decision process. Then, considering the large state space and continuous action space, a twin delayed deep deterministic policy gradient algorithm is proposed. Simulation results show that the proposed scheme has lower smoothing training cost than other optimization methods.

Network Intrusion Detection Algorithm Combined with Group Convolution Network and Snapshot Ensemble

In order to adapt to the rapid development of network technology and network security detection in different scenarios, the generalization ability of the classifier needs to be further improved and has the ability to detect unknown attacks. However, the generalization ability of a single classifier is limited to dealing with class imbalance, and the previous ensemble methods inevitably increase the training cost. Therefore, in this paper, a novel network intrusion detection algorithm combined with group convolution is proposed to improve the generalization performance of the model. The basic classifier uses group convolution with symmetric structure instead of ordinary convolution neural network, which is trained by the cyclic cosine annealing learning rate. Through snapshot ensemble, the generalization ability of the integration model is improved without increasing the training cost. The effectiveness of this method is proved on NSL-KDD and UNSW-NB15 datasets compared to six other ensemble methods, the classification accuracy can achieve 85.82% and 80.38%, respectively.