This study aims to provide a systematic review of existing research on the use of digital game-based learning (DGBL) technology for foreign language (English) in preschool and elementary schools. Digital game-based technology enables learning to become more interactive and inspirational, thereby enhancing language acquisition. The authors present a DGBL classification schema with four main components, design principles, language content, pedagogical factors, and feedback mechanisms, as guidelines for designing and implementing DGBL in foreign language learning. This survey classifies and filters 110 published articles from January 2010 to April 2022 based on the proposed DGBL schema adhering to the insertion and omission criteria, from which 50 studies focus on preschool and primary school students between the ages of 2 and 10 years. The findings indicate that DGBL influences motivation, creativity, and problem-solving ability. However, the synthesized literature also reveals challenges and issues in designing and developing DGBL systems based on novel technologies. The findings are fundamental for exploring the game classification schema for designing and implementing game-based learning to augment the trends of DBGL in foreign language learning and identify the possible issues and solutions for DGBL to lessen health concerns and technology challenges. The authors recommend emerging technologies and software engineering processes as possible solutions for continuity in DGBL research by enlightening the significance of evolving technologies such as artificial intelligence, blockchain, deep learning, AR/VR, and DevOps.

Convolutional neural networks (CNNs) were popular in ImageNet large scale visual recognition competition (ILSVRC 2012) because of their identification ability and computational efficiency. This paper proposes a palm vein recognition method based on CNN. The four main steps of palm vein recognition are image acquisition, image preprocessing, feature extraction, and matching. To reduce the processing steps in the recognition of palm vein images, a palm vein recognition method using a CNN is proposed. CNN is a deep learning network. Palm vein images are acquired using near-infrared light, under which the veins in the palm of the hand are relatively prominent. To obtain a good vein image, many previous methods used preprocessing to further enhance the image before using feature extraction to find feature matches for further comparison. In recent years, CNNs have been shown to have great advantages and have performed well in image classification. To reduce early-stage image processing, a CNN is used to classify and recognize palm vein images. The networks AlexNet and VGG depth CNN were trained to extract image features. The palm vein recognition rates by VGG-19, VGG-16, and AlexNet were 98.5%, 97.5%, and 96%, respectively.

Developing the means by which to discover RESTful Web APIs/services is crucial to the further development of this technology. Unfortunately, existing search engines for RESTful Web APIs/services provide only keyword-based or tag-based search functions. A failure to take into account the semantics and/or characteristics (e.g., their interface compatibility) greatly hampers the ability to find suitable APIs/services. In this study, we propose a novel approach to the discovery of RESTful Web API/services, referred to as Test-Oriented API Search with Semantic Interface Compatibility (TASSIC). This scheme involves expanding the terms of Web API/service documents based on DBpedia and WordNet. Unsuitable APIs/services are then filtered out using a four-step process, as follows: 1) calculation of semantic similarity between a set of candidate APIs/services and a user query, 2) analysis of interface compatibility between candidate APIs/services using the Hungarian Algorithm, 3) invocation of candidate APIs/services to verify functionality and availability, and 4) analysis of similarity between the actual response of the candidate services and the expected response specified in the user query. Experiment results demonstrate the efficacy of TASSIC, the accuracy of which is superior to that of existing methods.

Quality of service (QoS) time-series forecasting of web services has been studied for over a decade, and in recent years, this problem has been investigated in its multi-step-ahead version for the long-term rental and use of cloud computing. For multi-step-ahead QoS time-series forecasting problem, previous research has adopted single-predictor-based strategies and conventional time-series methods, such as autoregressive integrated moving average models and exponential smoothing, to solve this problem. However, this paper proposes the idea of applying genetic programming to evolve a set of multiple predictors, in which each predictor is dedicated to the forecasting task of a specific future time point. In our approach, two types of multiple predictors are proposed and tested, which are different from the consumed predictor inputs that drive each predictor to produce its QoS forecasting results. Furthermore, two techniques, namely, elite individual composition (EIC) and hybrid evolution, are proposed and applied to enhance the forecasting accuracy of our approach. Finally, based on a real-world QoS time series dataset, the proposed approach is validated and compared with conventional methods to demonstrate its superiority in terms of accuracy; in addition, the effectiveness and efficiency of the proposed approach and two techniques are also verified in the experiment.

This paper proposes a dynamic synchronous capture (DSC) algorithm to calculate the flow rate for an electromagnetic flowmeter. The characteristics of the DSC algorithm can accurately calculate the flow rate signal and efficiently convert an analog signal to upgrade the execution performance of a microcontroller unit (MCU). Furthermore, it can reduce interference from abnormal noise. It is extremely steady and independent of fluctuations in the flow measurement. Moreover, it can calculate the current flow rate signal immediately (m/s). The DSC algorithm can be applied to the current general MCU firmware platform without using DSP (Digital Signal Processing) or a high-speed and high-end MCU platform, and signal amplification by hardware reduces the demand for ADC accuracy, which reduces the cost.