ABSTRACT Transformer methods have revolutionized software requirements classification by combining advanced natural language processing to accurately understand and categorize requirements. While traditional methods like Doc2Vec and TF‐IDF are useful, they often fail to capture the deep contextual relationships and subtle meanings inherent in textual data. Transformer models possess unique strengths and weaknesses, impacting their ability to capture various aspects of the data. Consequently, relying on a single model can lead to suboptimal feature representations, limiting the overall performance of the classification task. To address this challenge, our study introduces an innovative BET‐BiLSTM (balanced ensemble transformers using Bi‐LSTM) model. This model combines the strengths of five transformer–based models BERT, RoBERTa, XLNet, GPT‐2, and T5 through weighted averaging ensemble, resulting in a sophisticated and resilient feature set. By employing data balancing techniques, we ensure a well‐distributed representation of features, addressing the issue of class imbalance. The BET‐BiLSTM model plays a crucial role in the classification process, achieving an impressive accuracy of 96%. Moreover, the practical applicability of this model is validated through its successful implementation on three publicly available unlabeled datasets and one additional labeled dataset. The model significantly improved the completeness and reliability of these datasets by accurately predicting labels for previously unclassified requirements. This makes our approach a powerful tool for large‐scale requirements analysis and classification tasks, outperforming traditional single‐model methods and showcasing its real‐world effectiveness.

In the field of edge computing, the increasing demand for high Quality of Service (QoS), particularly in dynamic multimedia streaming applications (e.g., Augmented Reality/Virtual Reality and online gaming), has prompted the need for effective solutions. Nevertheless, adopting an edge paradigm grounded in distributed computing has exacerbated the issue of tail latency. Given a limited variety of multimedia services supported by edge servers and the dynamic nature of user requests, employing traditional queuing methods to model tail latency in distributed edge computing is challenging, substantially exacerbating head-of-line (HoL) blocking. In response to this challenge, we have developed a learning-based scheduling method to mitigate the overall tail latency, which adaptively selects appropriate edge servers for execution as incoming distributed tasks vary with unknown size. To optimize the utilization of the edge computing paradigm, we leverage the Laplace transform to theoretically derive an upper bound for the response time of edge servers. Subsequently, we integrate this upper bound into reinforcement learning to facilitate tail-learning and enable informed decisions for autonomous distributed scheduling. The experiment results demonstrate the efficiency in reducing tail latency compared to existing methods.

Existing pedestrian attribute recognition (PAR) algorithms adopt pre-trained CNN (e.g., ResNet) as their backbone network for visual feature learning, which might obtain sub-optimal results due to the insufficient employment of the relations between pedestrian images and attribute labels. In this paper, we formulate PAR as a vision-language fusion problem and fully exploit the relations between pedestrian images and attribute labels. Specifically, the attribute phrases are first expanded into sentences, and then the pre-trained vision-language model CLIP is adopted as our backbone for feature embedding of visual images and attribute descriptions. The contrastive learning objective connects the vision and language modalities well in the CLIP-based feature space, and the Transformer layers used in CLIP can capture the long-range relations between pixels. Then, a multi-modal Transformer is adopted to fuse the dual features effectively and feed-forward network is used to predict attributes. To optimize our network efficiently, we propose the region-aware prompt tuning technique to adjust very few parameters (i.e., only the prompt vectors and classification heads) and fix both the pre-trained VL model and multi-modal Transformer. Our proposed PAR algorithm only adjusts 0.75% learnable parameters compared with the fine-tuning strategy. It also achieves new state-of-the-art performance on both standard and zero-shot settings for PAR, including RAPv1, RAPv2, WIDER, PA100K, and PETA-ZS, RAP-ZS datasets. The source code and pre-trained models will be released onhttps://github.com/Event-AHU/OpenPAR.

The development of cloud computing delivery models inspires the emergence of cloud-native computing. Cloud-native computing, as the most influential development principle for web applications, has already attracted increasingly more attention in both industry and academia. Despite the momentum in the cloud-native industrial community, a clear research roadmap on this topic is still missing. As a contribution to this knowledge, this article surveys key issues during the life cycle of cloud-native applications, from the perspective of services. Specifically, we elaborate on the research domains by decoupling the life cycle of cloud-native applications into four states: building, orchestration, operation, and maintenance. We also discuss the fundamental necessities and summarize the key performance metrics that play critical roles during the development and management of cloud-native applications. We highlight the key implications and limitations of existing works in each state. The challenges, future directions, and research opportunities are also discussed.

Multi-modal salient object detection (MSOD) aims to boost saliency detection performance by integrating visible sources with depth or thermal infrared ones. Existing methods generally design different fusion schemes to handle certain issues or challenges. Although these fusion schemes are effective at addressing specific issues or challenges, they may struggle to handle multiple complex challenges simultaneously. To solve this problem, we propose a novel adaptive fusion bank that makes full use of the complementary benefits from a set of basic fusion schemes to handle different challenges simultaneously for robust MSOD. We focus on handling five major challenges in MSOD, namely center bias, scale variation, image clutter, low illumination, and thermal crossover or depth ambiguity. The fusion bank proposed consists of five representative fusion schemes, which are specifically designed based on the characteristics of each challenge, respectively. The bank is scalable, and more fusion schemes could be incorporated into the bank for more challenges. To adaptively select the appropriate fusion scheme for multi-modal input, we introduce an adaptive ensemble module that forms the adaptive fusion bank, which is embedded into hierarchical layers for sufficient fusion of different source data. Moreover, we design an indirect interactive guidance module to accurately detect salient hollow objects via the skip integration of high-level semantic information and low-level spatial details. Extensive experiments on three RGBT datasets and seven RGBD datasets demonstrate that the proposed method achieves the outstanding performance compared to the state-of-the-art methods.

Vehicle Re-IDentification (Re-ID) aims to retrieve the most similar images with a given query vehicle image from a set of images captured by non-overlapping cameras, and plays a crucial role in intelligent transportation systems and has made impressive advancements in recent years. In real-world scenarios, we can often acquire the text descriptions of target vehicle through witness accounts, and then manually search the image queries for vehicle Re-ID, which is time-consuming and labor-intensive. To solve this problem, this paper introduces a new fine-grained cross-modal retrieval task called text-to-image vehicle re-identification, which seeks to retrieve target vehicle images based on the given text descriptions. To bridge the significant gap between language and visual modalities, we propose a novel Multi-scale multi-view Cross-modal Alignment Network (MCANet). In particular, we incorporate view masks and multi-scale features to align image and text features in a progressive way. In addition, we design the Masked Bidirectional InfoNCE (MB-InfoNCE) loss to enhance the training stability and make the best use of negative samples. To provide an evaluation platform for text-to-image vehicle re-identification, we create a Text-to-Image Vehicle Re-Identification dataset (T2I VeRi), which contains 2465 image-text pairs from 776 vehicles with an average sentence length of 26.8 words. Extensive experiments conducted on T2I VeRi demonstrate MCANet outperforms the current state-of-art (SOTA) method by 2.2% in rank-1 accuracy.

Since the standard license plate of large vehicle is easily affected by occlusion and stain, the traffic management department introduces the enlarged license plate at the rear of the large vehicle to assist license plate recognition. However, current researches regards standard license plate recognition and enlarged license plate recognition as independent tasks, and do not take advantage of the complementary benefits from the two types of license plates. In this work, we propose a new computer vision task called collaborative license plate recognition, aiming to leverage the complementary advantages of standard and enlarged license plates for achieving more accurate license plate recognition. To achieve this goal, we propose an Association Enhancement Network (AENet), which achieves robust collaborative licence plate recognition by capturing the correlations between characters within a single licence plate and enhancing the associations between two license plates. In particular, we design an association enhancement branch, which supervises the fusion of two licence plate information using the complete licence plate number to mine the association between them. To enhance the representation ability of each type of licence plates, we design an auxiliary learning branch in the training stage, which supervises the learning of individual license plates in the association enhancement between two license plates. In addition, we contribute a comprehensive benchmark dataset called CLPR, which consists of a total of 19,782 standard and enlarged licence plates from 24 provinces in China and covers most of the challenges in real scenarios, for collaborative license plate recognition. Extensive experiments on the proposed CLPR dataset demonstrate the effectiveness of the proposed AENet against several state-of-the-art methods.

As the number of mobile and IoT devices grows, an edge network faces considerable difficulty in collaborative service provision. Computing and bandwidth resources in edge environments are restricted and unevenly distributed. Besides, video streaming requests from users may vary over time. These factors pose significant challenges for users to acquire a consistent Quality of Experience (QoE). Previous studies concentrate on a resource allocation problem with a single objective: maximizing total QoE for all users. Few studies have focused on a fairness issue for each user's QoE in an edge environment. This work designs a resource allocation algorithm by allocating computing and bandwidth resources fairly for all users. We leverage the relationships between entities in an edge network to model the graph embeddings of each entity using a graph neural network. Our proposed Path-embedding Learning (PEL) method learns from the network topology comprised of links with bandwidth resources and nodes with computing resources to generate feature representations of workload migration paths. Our proposed Fairness-ensure Backward Propagation (FBP) scheme can guarantee fairness for users. Our theoretical derivation reveals that the proposed algorithm achieves fairness with approximate Pareto optimality. Simulation results demonstrate that the algorithm can obtain better QoE than existing algorithms.

Influence maximization (IM) problem, which selects a subset of nodes from a social network to maximize the influence spread, appeals to numerous scholars. Since the IM problem is NP-hard, it is still an arduous task to achieve good results in terms of influence spread and running time at the same time. This article proposes a novel search space reduction strategy-based progressive evolutionary algorithm (SSR-PEA) for solving IM problems effectively and efficiently. In SSR-PEA, a novel search space reduction strategy (SSR) in the light of the power-law distribution of the social network is designed to reduce the computational overheads, which eliminates a great deal of less influential nodes in a sensible way. After that, we propose a progressive evolutionary framework based on SSR, where the$k$-element individual is optimized on the basis of the ($k$-1)-element individual to speed up the optimal solution search process. Experimental results on ten real-world networks demonstrate that the proposed algorithm SSR-PEA can achieve 98% of the influence spread achieved by cost-effective lazy forward (CELF) on average, and its running time is two or even three orders of magnitude shorter. Thus, SSR-PEA strikes a better balance between effectiveness and efficiency.

Edge caching has emerged as a prominent paradigm for supporting the needs of contents by pushing storage functionalities to ratio access network (RAN) and alleviates the strain of core network. However, current mainstream small base station (SBS) caching can not adapt to system dynamics due to fixed infrastructures. Mobile network operators (MNOs) attempt to explore the potential of vehicles as content carries to cope with time-varied and location-based demands resulted by mobile environment and improve both of hit rate and QoS (delivery rate) for users’ file requests. Nevertheless, vehicular caching trading policy between MNO and content providers (CPs) arriving in unknown sequence still remains complicated to design since selfish and private CPs may be vicious to contend for limited vehicular capacity to obtain larger profits. To bridge this gap and provide economic insights into vehicular caching, we focus on caching leasing between MNO and CPs and propose a general cache valuation and online pricing framework to realize incentive compatibility, individual rationality, privacy protection and computational efficiency with the target of social welfare maximization. To the best of our knowledge, this is the first work to study the cache trading pattern of vehicular networks. Experimental results based on the ONE simulator substantiate the usefulness and superiority of our scheme in term of social welfare maximization.

Edge computing is booming as a promising paradigm to extend service provisioning from the centralized cloud to the network edge. Benefit from the development of serverless computing, an edge server can be configured as a carrier of limited serverless functions, in the way of deploying Docker runtime and Kubernetes engine. Meanwhile, an application generally takes the form of directed acyclic graphs (DAGs), where vertices represent dependent functions and edges represent data traffic. The status quo of minimizing the completion time (a.k.a. makespan) of the application motivates the study on optimal function placement. However, current approaches lose sight of proactively splitting and mapping the traffic to the logical data paths between the heterogeneous edge servers, which could affect the makespan significantly. To remedy that, we propose an algorithm, termed as Dependent Function Embedding (DPE), to get the optimal edge server for each function to execute and the moment it starts executing. DPE finds the best segmentation of each data traffic by exquisitely solving several infinity norm minimization problems. DPE is theoretically verified to achieve the global optimality. Extensive experiments on Alibaba cluster trace show that DPE significantly outperforms two baseline algorithms in makespan by 43.19% and 40.71%, respectively.

Edge computing, a fast evolving computing paradigm, has spawned a variety of new system architectures and computing methods discussed in both academia and industry. Edge servers are directly deployed near users' equipment or devices owned by telecommunications companies. This allows for offloading computing tasks of various devices nearby to edge servers. Due to the shortage of computing resources in edge computing networks, they are often not as sufficient as the computing resources in a cloud computing center. This leads to the problem of service load imbalance once the load in the edge computing network increases suddenly. To solve the problem of “load evacuation” in edge environments, we introduce a strategy when the number of service requests for mobile devices or IoT devices increases rapidly within a short period of time. Therefore, to prevent poor QoS in edge computing, service load should be migrated to other edge servers to reduce the overall delay of these service requests. In this article, we have introduced a strategy with two stages during the burst load evacuation. Based on an optimal routing search at the dispatching stage, tasks will be migrated from the server in which the burst load occurs to other servers as soon as possible. Subsequently, with the assistance of the remote server and edge servers, these tasks are processed with the highest efficiency through the proposed parallel structure at the scheduling stage. Finally, we conduct numerical experiments to clarify the superiority of our algorithm in an edge environment simulation.

Context: Design pattern detection is a very important research on software reuse, which can greatly help software maintenance and reconstruction. At present, many researchers have invested in this work and proposed a variety of methods for detection. Method: This paper extends the graph matching technology based on the past and proposes a new method that combines network analysis and structural matching for detection. First, we use network level analysis to obtain important nodes, then use the neighborhood path matching algorithm to match the pattern instance. Result: We describe the detection of five patterns on four open source systems, then analyze and compare with the other three methods, for achieving high precision and recall, which demonstrates that our method is effective. Conclusion: Using combining network analysis with structural matching can well detect these pattern instances, and these instances are also especially important for future software refactoring.

Background: Seeking an appropriate architecture for a software design is always a challenge in recent decades. Although microservices as a lightweight architecture style is claimed that can improve the current practices with several characteristics, many practices are based upon the different circumstances and reflect the variant effects. An empirical inquiry brings us a systematic insight into the industrial practices on microservices. Objective: This study is to investigate the gap between the ideal visions and real industrial practices on microservices and what benefits we can gain from the industrial experiences. Method: We carried out a series of industrial interviews with thirteen different types of companies. The collected data were then codified according to the defined qualitative methods. Results: We characterized the gaps between the typical characteristics accepted in the community and the industrial practices of microservices. Furthermore, the compromise between benefits and sufferings of microservices around these nine dimensions were also investigated. Conclusion: We confirmed the benefits of the microservices that can be obtained from practice as well as their possible pains that need to be addressed with extra expense from experiences. Besides, some outlined pains, e.g., organizational transformation, decomposition, distributed monitoring, and bug localization, may inspire researchers to conduct the further research.

Mobile Edge Computing has already become a new paradigm to reduce the latency in data transmission for resource-limited mobile devices by offloading computation tasks onto edge servers. However, for mobility-aware computation-intensive services, existing offloading strategies cannot handle the offloading procedure properly because of the lack of collaboration among edge servers. A data stream application is partitionable if it can be presented by a directed acyclic dataflow graph, which makes cross-edge collaboration possible. In this paper, we propose a cross-edge computation offloading (CCO) framework for partitionable applications. The transmission, execution and coordination cost, as well as the penalty for task failure, are considered. An online algorithm based on Lyapunov optimization is proposed to jointly determine edge site-selection and energy harvesting without priori knowledge. By stabilizing the battery energy level of each mobile device around a positive constant, the proposed algorithm can obtain asymptotic optimality. The-oretical analysis about the complexity and the effectiveness of the proposed framework is provided. Experimental results based on a real-life dataset corroborate that CCO can achieve superior performance compared with benchmarks where crossedge collaboration is not allowed.

Cloud computing can provide a more cost-effective way to deploy scientific workflows than traditional distributed computing environments such as cluster and grid. Due to the large size of scientific datasets, data placement plays an important role in scientific cloud workflow systems for improving system performance and reducing data transfer cost. Traditional task-level data placement strategy only considers shared datasets within individual workflows to reduce data transfer cost. However, it is obvious that task-level strategy is not necessarily good enough for the situation of multiple workflows at the workflow level. In this paper, a novel workflow-level data placement model is constructed, which regards multiple workflows as a whole. Then, a two-stage data placement strategy is proposed which first pre-allocates initial datasets to proper datacenters during workflow build-time stage, and then dynamically distributes newly generated datasets to appropriate datacenters during runtime stage. Both stages use an efficient discrete particle swarm optimization algorithm to place flexible-location datasets. Comprehensive experiments demonstrate that our workflow-level data placement strategy can be more cost-effective than its task-level counterpart for data-sharing scientific cloud workflows.

Cloud computing is an emerging business computing model whose basic idea is to transmit all kinds of resources through the Internet such as storage resources, computing resources, bandwidth and so forth. So users do not need to purchase a large of computing systems to manage their business, on the contrary they only need to purchase the resources according to their needs in order to reduce the cost greatly. Due to the flexibility, convenience and low maintenance cost of cloud computing, more and more service providers choose to deploy their services to the cloud. However, under the influence of uncertain factors such as manufacturing technology, the price and delivery time of virtual machine also been changed. Because of these variations, it is difficult for users to select the appropriate cloud servers at a minimum cost, which will lead to a decline in user experience. To solve the problems above, this paper analyzes the performance of different Ali Cloud servers and proposes a server selection strategy according to the size of the whole instance and the required time of completing the workflow, so as to make the whole workflow instances execution costs as little as possible in the regulations. We also provide experimental results to demonstrate the effectiveness of our selection strategies.

Due to fast development of e-commerce, logistics, network and cloud computing, many businesses have changed their traditional production and sale patterns. This brings big opportunities and challenges to logistics. Among the process of logistics, delivery is a key issue. This paper mainly focuses on logistics scheduling based on cloud business workflows. A constrained Dijkstra algorithm is proposed to select an optimal route for logistics transportation with various parameters. We then put forward a distribution logistics scheduling algorithm to solve tracking large numbers of expresses based on business workflows. The simulation indicates the effectiveness of our two novel algorithms.

Context: A previous study has shown that software developers who are experienced with using design patterns hold some conflicting opinions about three of the more popular design patterns: Facade, Singleton and Visitor. Aim: To identify the characteristics of these three patterns that have caused them to generate such differing views.

Context. Although research in software engineering largely seeks to improve the practices and products of software development, many practices are based upon codification of expert knowledge, often with little or no underpinning from objective empirical evidence. Software design patterns seek to codify expert knowledge to share experience about successful design structures. Objectives. To investigate how extensively the use of software design patterns has been subjected to empirical study and what evidence is available about how and when their use can provide an effective mechanism for knowledge transfer about design. Method. We conducted a systematic literature review in the form of a mapping study, searching the literature up to the end of 2009 to identify relevant primary studies about the use of the 23 patterns catalogued in the widely referenced book by the “Gang of Four.” These studies were then categorized according to the forms of study employed, the patterns that were studied, as well as the context within which the study took place. Results. Our searches identified 611 candidate papers. Applying our inclusion/exclusion criteria resulted in a final set of 10 papers that described 11 instances of “formal” experimental studies of object-oriented design patterns. We augmented our analysis by including seven “experience” reports that described application of patterns using less rigorous observational forms. We report and review the profiles of the empirical evidence for those patterns for which multiple studies exist. Conclusions. We could not identify firm support for any of the claims made for patterns in general, although there was some support for the usefulness of patterns in providing a framework for maintenance, and some qualitative indication that they do not help novices learn about design. For future studies we recommend that researchers use case studies that focus upon some key patterns, and seek to identify the impact that their use can have upon maintenance.