PeerJ Computer Science最新文献

In the ever-expanding digital landscape, the abundance of user-generated content on consumer platforms such as Booking and TripAdvisor offers a rich source of information for both travellers and hoteliers. Sentiment analysis, a fundamental research task of natural language processing (NLP) is used for mining sentiments and opinions within this vast reservoir of text reviews. A more specific type of sentiment analysis, i.e., aspect-based sentiment analysis (ABSA), is used when processing customer reviews is required. In ABSA, we aim to capture aspect-level sentiments and intricate relationships between various aspects within reviews. This article proposes a novel approach to ABSA by introducing a novel technique of word sense disambiguation (WSD) and integrating it with the Transformer architecture bidirectional encoder representations from Transformers (BERT) and graph convolutional networks (GCNs). The proposed approach resolves the intriguing ambiguities of the words and represents the review data as a complex graph structure, facilitating the modeling of intricate relationships between different aspects. The combination of bidirectional long short-term memory (BiLSTM) and GCN proves effective in capturing inter-dependencies among various aspects, providing a nuanced understanding of customer sentiments. The experiments are conducted on the RABSA dataset (an enhanced and richer hotel review data collection), and results demonstrate that our approach outperforms previous baselines, showcasing the effectiveness of integrating WSD in ABSA. Furthermore, an ablation study confirms the significant contribution of the WSD module to the overall performance. Moreover, we explore different similarity measures and find that cosine similarity yields the best results when identifying the real sense of a word in a given sentence using WordNet. The findings of our work and future work related to our work create lots of interest for people in the tourism and hospitality industry. This research gives another boost to the concept of the potential of NLP techniques in sentiment analysis. It emphasizes that if we combine the potential of NLP techniques along with state-of-the-art machine learning frameworks, we can shape the future of this field.

Republicans and Democrats practically everywhere have been demonstrating concerns about environmental conservation to achieve sustainable development goals (SDGs) since the turn of the century. To promote fuel (energy) savings and a reduction in the amount of carbon dioxide CO₂ emissions in several enterprises, actions have been taken based on the concepts described. This study proposes an environmentally friendly manufacturing system designed to minimize environmental impacts. Specifically, it aims to develop a sustainable manufacturing process that accounts for energy consumption and CO₂ emissions from direct and indirect energy sources. A multi-objective mathematical model has been formulated, incorporating financial and environmental constraints, to minimize overall costs, energy consumption, and CO₂ emissions within the manufacturing framework. The input model parameters for real-world situations are generally unpredictable, so a fuzzy multi-objective model will be developed as a way to handle it. The validity of the proposed ecological industrial design will be tested using a scenario-based approach. Results demonstrate the high reliability, applicability, and effectiveness of the proposed network when analyzed using the developed techniques.

In intertidal mudflat culture (IMC), the fishing efficiency and the degree of damage to nature have always been a pair of irreconcilable contradictions. To improve the efficiency of razor clam fishing and at the same time reduce the damage to the natural environment, in this study, a razor clam burrows dataset is established, and an intelligent razor clam fishing method is proposed, which realizes the accurate identification and counting of razor clam burrows by introducing the object detection technology into the razor clam fishing activity. A detection model called intertidal mudflat culture-You Only Look Once (IMC-YOLO) is proposed in this study by making improvements upon You Only Look Once version 8 (YOLOv8). In this study, firstly, at the end of the backbone network, the Iterative Attention-based Intrascale Feature Interaction (IAIFI) module module was designed and adopted to improve the model's focus on advanced features. Subsequently, to improve the model's effectiveness in detecting difficult targets such as razor clam burrows with small sizes, the head network was refactored. Then, FasterNet Block is used to replace the Bottleneck, which achieves more effective feature extraction while balancing detection accuracy and model size. Finally, the Three Branch Convolution Attention Mechanism (TBCAM) is proposed, which enables the model to focus on the specific region of interest more accurately. After testing, IMC-YOLO achieved mAP50, mAP50:95, and F1best of 0.963, 0.636, and 0.918, respectively, representing improvements of 2.2%, 3.5%, and 2.4% over the baseline model. Comparison with other mainstream object detection models confirmed that IMC-YOLO strikes a good balance between accuracy and numbers of parameters.

Uncrewed Aerial Systems (UASs) are widely implemented in safety-critical fields such as industrial production, military operations, and disaster relief. Due to the diversity and complexity of implementation scenarios, UASs have become increasingly intricate. The challenge of designing and implementing highly reliable UASs while effectively controlling development costs and improving efficiency has been a pressing issue faced by academia and industry. To address this challenge, this article aims to examine an integrated method for modeling, verification, and code generation for UASs. This article begins to utilize Architecture Analysis and Design Language (AADL) to model UASs, proposing generic UAS models. Then, formal specifications describe a system's safety properties and functions based on these models. Finally, this article introduces a method to generate flight controller codes for UASs based on the verified models. Experiments demonstrate its effectiveness in pinpointing potential vulnerabilities in UASs during the early design phase and generating viable flight controller codes from the verified models. The proposed approach can also improve the efficiency of designing and verifying high-reliability UASs.

As the business world shifts to the web and tremendous amounts of data become available on multilingual mobile applications, new business and research challenges and opportunities have been explored. This research aims to intensify the usage of data analytics, machine learning, and sentiment analysis of textual data to classify customers' reviews, feedback, and ratings of businesses in Jordan's food and restaurant industry. The main methods used in this research were sentiment polarity (to address the challenges posed by businesses to automatically apply text analysis) and bio-metric techniques (to systematically identify users' emotional states, so reviews can be thoroughly understood). The research was extended to deal with reviews in Arabic, dialectic Arabic, and English, with the main focus on the Arabic language, as the application examined (Talabat) is based in Jordan. Arabic and English reviews were collected from the application, and a new model was proposed to sentimentally analyze reviews. The proposed model has four main stages: data collection, data preparation, model building, and model evaluation. The main purpose of this research is to study the problem expressed above using a model of ordinal regression to overcome issues related to misclassification. Additionally, an automatic multi-language prediction approach for online restaurant reviews was proposed by combining the eXtreme gradient boosting (XGBoost) and particle swarm optimization (PSO) techniques for the ordinal regression of these reviews. The proposed PSO-XGB algorithm showed superior results when compared to support vector machine (SVM) and other optimization methods in terms of root mean square error (RMSE) for the English and Arabic datasets. Specifically, for the Arabic dataset, PSO-XGB achieved an RMSE value of 0.7722, whereas PSO-SVM achieved an RSME value of 0.9988.

Effective and efficient detection of pavement distress is very important for the normal use and maintenance of roads. To achieve this goal, a new road damage detection method based on YOLOv8 is proposed in this article. Firstly, omni-dimensional dynamic convolution (ODConv) block is employed to better grasp the complex and diverse features of damage objects by making dynamic adjustment according to the features of input images. Secondly, to extract the global and local feature information simultaneously to better improve the feature extraction ability of the model, BoTNet is added to the end of the backbone, which can combine the advantages of convolutional neural network (CNN) and Transformer. Finally, the coordinate attention mechanism (CA) is incorporated into the Neck section to make more accurate speculations and enhance detection accuracy further which can effectively mitigate irrelevant feature interference. The new proposed model is named OBC-YOLOv8 and the experimental results on the RDD2022-China dataset demonstrate its superiority compared with baselines, with 1.8% and 1.6% increases in mean average precision 50 (mAP@0.5) and F1-score, respectively.

The construction of a consumption pattern can realize the analysis of consumer characteristics and behaviors, identify the relationship between commodities, and provide technical support for commodity recommendation and market analysis. However the current studies on consumer behavior and consumption patterns are very limited, and most of them are based on market research data. This method of data collection has high cost, low data coverage, and lagging survey results. The algorithm proposed in this article analyzes purchasing data from e-commerce platforms and extracts short- and long-term consumption matrices of consumers. By further processing these two matrices and removing the difference in granularity in time and marginal substitution rate, these matrices are finally integrated to form one consumption pattern matrix that can describe the characteristics of consumer consumption behavior in a period of time. Extensive experiments on various domains demonstrate that our proposed method outperforms state-of-the-art baselines on synthetic and real-world datasets.

In efforts to better accommodate users, numerous researchers have endeavored to model customer behavior, seeking to comprehend how they interact with diverse items within online platforms. This exploration has given rise to recommendation systems, which utilize customer similarity with other customers or customer-item interactions to suggest new items based on the existing item catalog. Since these systems primarily focus on enhancing customer experiences, they overlook providing insights to sellers that could help refine the aesthetics of their items and increase their customer coverage. In this study, we go beyond customer recommendations to propose a novel approach: suggesting aesthetic feedback to sellers in the form of refined item images informed by customer-item interactions learned by a recommender system from multiple consumers. These images could serve as guidance for sellers to adapt existing items to meet the dynamic preferences of multiple users simultaneously. To evaluate the effectiveness of our method, we design experiments showcasing how changing the number of consumers and the class of item image used affect the change in preference score. Through these experiments, we found that our methodology outperforms previous approaches by generating distinct, realistic images with user preference higher by 16.7%, thus bridging the gap between customer-centric recommendations and seller-oriented feedback.

In this article, the robust model predictive control (RMPC) problem is investigated for a class of polytopic uncertain systems over high-rate networks whose signal exchanges are scheduled by the FlexRay protocol (FRP). During signal measurement, a high-rate network is applied to broadcast the data from the sensors to the controller efficiently. The FRP including the characteristics of event-triggered mechanism and the time-triggered mechanism is embedded into the high-rate network to regulate the data transmission in a circular period which can improve the flexibility of data transmission. With the aid of the Round-Robin and Try-Once-Discard protocols, a new expression of the measurement model is formulated by the use of certain data holding strategies. Subsequently, taking both high-rate networks and FRP into account, sufficient conditions are obtained by solving a time-varying terminal constraint set of an auxiliary optimization problem. In addition, an algorithm including both off-line and on-line parts is provided to find a sub-optimal solution. Lastly, two numerical simulations are carried out to substantiate the validity of the proposed RMPC strategy which is based on FRP and a high-rate network.

Entertainment robotics has garnered significant attention in recent years, with researchers focusing on developing robots capable of performing a variety of tasks, including magic, drawing, dancing, and music. This article presents our research on forming a musical band that includes both humanoid robots and human musicians, with the goal of achieving natural synchronization and collaboration during musical performances. We utilized two of our humanoid robots for this project: Polaris, a mid-sized humanoid robot, as the drummer, and Oscar, a Robotis-OP3 humanoid robot, as the keyboardist. The technical implementation incorporated essential components such as visual servoing, human-robot interaction, and Robot Operating System (ROS), enabling seamless communication and coordination between the humanoid robots and the human musicians. The success of this collaborative effort can be both seen and heard through the following YouTube link: https://youtu.be/pFOyt1KKCfY?feature=shared.