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The channel structure of a proton-exchange membrane fuel cell (PEMFC) is very important for sustaining high performance with a reliable lifetime. There have been numerous computational studies to investigate the physics of electrochemical reactions with various flow field phenomena. Because straight channels reduce manufacturing costs, a straight channel was selected as the subject of a numerical study to investigate water removal and oxygen starvation in the channel. In this study, the liquid saturation distribution caused by the baffled obstacle geometry of a PEMFC with channels that were straight and parallel was analyzed in a computational study. Because baffled obstacles generate a vortex in the channel, the flow field structure improves the supply of reactants. When the operating pressure is reduced from 3 atm to 1 atm, the baffled obstacle structure interrupts the flow of reactant and product water so that the accumulation of liquid water increases from 0.75 to 0.86 in catalyst layer. As the number of baffled obstacles was increased from 13 to 19, the current density improved from 0.772 to 0.773 A/cm2 in the reference condition. The baffled obstacle geometry also affects the liquid water accumulation flow field. Results show that the design of the baffled obstacles in a channel should consider the number of baffled obstacles along with the baffled obstacle geometry such that water removal is accelerated. [ABSTRACT FROM AUTHOR]

In recent years, the world has witnessed the increased maturity of service-oriented computing. The mashup, as one of the typical service-based applications, aggregates contents from more than one source into a single user interface. Facing the rapid growth of the number of web services, choosing appropriate web services for different mashup sources plays an important issue in mashup development, when, in particular, the new mashup is developed from the scratch. To solve this cold start problem when creating new mashups, we propose a web Service Recommendation approach for Mashup creation based on Graph network, called SRMG. SRMG makes service recommendation based on service characteristics and historical usage. It first leverages Bidirectional Encoder Representations from Transformers, to intelligently discover mashups with similar functionalities based on specifications. Afterward, it employs GraphGAN to obtain representation vectors for mashups and services based on historical usage, and further obtains mashup preferences for each service based on representation vectors. Finally, the new mashup's preference for target services is derived from the preference of existing mashups that are similar to it. The extensive experiments on real datasets from ProgrammableWeb demonstrate that SRMG is superior to the state-of-the-art ones. [ABSTRACT FROM AUTHOR]

In software development, due to the demands from users or the limitations of time and resources, developers tend to adopt suboptimal solutions to achieve quick software development. In such a way, the released software usually involves not-quite-right code that is called technical debt, which will significantly decrease the quality of software and increase the maintenance cost. Recently, the concept of self-admitted technical debt (SATD) is proposed and refers to technical debt that is self-admitted by developers in code comments. Existing studies mainly focus on detecting technical debt by classifying code comments into either “SATD” or “non-SATD.” However, different types of SATD has different impacts on software maintenance and needs to be handled by different developers. Therefore, the detected SATD should be further classified so that developers can understand and remove technical debt better. In this article, we propose a new method based on eXtreme Gradient Boosting (XGBoost) to classify SATD into multiple classes. In our approach, we first preprocess the original code comments and adopt the easy data augmentation strategy to overcome the class unbalance problem. Then, chi-square is leveraged to select representative features from the textual feature set. Finally, we apply XGBoost to train a classifier and use the trained classifier to partition each comment into the corresponding class. We experimentally investigate the effectiveness of our approach on a public dataset, including 62 566 code comments from 10 open-source projects. Experimental results show that our approach achieves 56.66% in terms of macroaveraged precision, 59.07% in terms of macroaveraged recall, and 55.77% in terms of macroaveraged F-measure on average, and outperforms the natural language processing based method by 4.98%, 5.32%, and 3.17%, respectively. In addition, the experimental results also demonstrate that the data augmentation strategy is effective in improving the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

A methanol–steam reformer (MSR) can safely provide hydrogen-rich fuel for a fuel cell system. Since the operating temperature of an MSR is relatively low, convective heat transfer is typically used to provide thermal energy to the endothermic reactions in the MSR. In this study, the use of phase change heat transfer to provide thermal energy to the endothermic reactions was investigated, which enhanced the temperature uniformity longitudinally along the MSR. ANSYS Fluent® software was used to investigate the performance of the reforming reactions. A comparative analysis using sensible heat and latent heat as the heat supply sources was performed. Using latent heat as a heat source achieved a lesser temperature drop than sensible heat that was under 5.29 K in the outer pipe. Moreover, a sensitivity analysis of methanol–steam-reforming reactions that use phase change heat transfer in terms of the carbon ratio, gas hourly velocity (for the inner and outer pipes of the MSR), inlet temperature (inner and outer pipes), reactor length, and operating pressure (inner pipe) was performed. When the phase change energy of water vapor is used, the wall temperature of the MSR is conveniently controlled and is uniformly distributed along the channel (standard deviation: 0.81 K). Accordingly, the methanol conversion rate of an MSR that uses phase change energy is ~4% higher than that of an MSR that employs convective heat transfer. [ABSTRACT FROM AUTHOR]

Methanol reforming is a key method of producing hydrogen, an eco-friendly energy source. A feasible validation has been performed using previous experimental data. Present numerical model, validated using previously published experimental data, facilitates a factor-by-factor study of methanol reforming performance characteristics. In particular, the effects of inlet temperature of reformed gas and heat source gas, gas hourly space velocity (GHSV), reformer length, and reformer diameter are numerically analyzed. The effect of each parameter on reforming performance was presented by analyzing the average temperature of the reformed gas for each location inside the reformer. In addition, the inlet temperature of the heat source gas was more dominant in reforming performance than that of the reformed gas through sensitivity analysis. Finally, the performance was explained in terms of residence time, and the relationship between CO selectivity and residence time was presented. • A Numerical simulation of a shell and tube methanol reformer was performed. • The effect of operating conditions (temperature, GHSV) on reforming performance was analyzed. • The effect of geometric shapes of the reformer on reforming performance was analyzed. • The reforming performance is more sensitive to the inlet temperature of heat source gas than that of the reformed gas. [ABSTRACT FROM AUTHOR]

Predicting traffic flow represents a critical undertaking within the domain of Intelligent Transportation Systems (ITS), given its pivotal role in optimizing traffic management strategies. Nevertheless, this endeavor is fraught with challenges, stemming from the inherent complexity and dynamic nature of traffic systems. Existing methods still suffer from the following three main limitations: (i) modeling the spatio-temporal correlations in a static way, restricting the ability to learn dynamic correlations; (ii) only capturing short-term local temporal features while neglecting long-term temporal patterns; (iii) cannot learn the latent relationships between traffic nodes at different time steps. Therefore, we propose a novel method named InOutformer based on In -Time-Window self-attention and Out -Time-Window self-attention to predict traffic flow. This method can effectively capture both short-term local temporal features and model long-term global temporal patterns, while also capturing dynamic spatial correlations between nodes. Specifically, we first propose a novel time window partitioning mechanism to replace the conventional sliding time window approach, in order to capture dynamic temporal features. Second, two specially designed temporal self-attention modules are used to learn short-term local temporal features and model long-term global temporal patterns, respectively. Then, a 2D-attention method is devised to capture the spatial correlations of nodes at different time steps. Moreover, by exploiting transition flow data, the detrimental effects due to spatial information propagation delays are effectively mitigated. Extensive experiments on four real-world traffic datasets demonstrate that the proposed model InOutformer outperforms the baselines, including several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Code smell detection is one of the essential tasks in the field of software engineering. Identifying whether a code snippet has a code smell is subjective and varies by programming language, developer, and development method. Moreover, developers tend to focus on code smells that have a real impact on development and ignore insignificant ones. However, existing static code analysis tools and code smell detection approaches exhibit a high false positive rate in detecting code smells, which makes insignificant smells drown out those smells that developers value. Therefore, accurately reporting those actionable code smells that developers tend to spend energy on refactoring can prevent developers from getting lost in the sea of smells and improve refactoring efficiency. In this paper, we aim to detect actionable code smells that developers tend to refactor. Specifically, we first collect actionable and non-actionable code smells from projects with numerous historical versions to construct our datasets. Then, we propose a dual-stream model for fusion learning of code metrics and code semantics to detect actionable code smells. On the one hand, code metrics quantify the code's structure and even some rules or patterns, providing fundamental information for detecting code smells. On the other hand, code semantics encompass information about developers' refactoring tendencies, which prove valuable in detecting actionable code smells. Extensive experiments show that our approach can detect actionable code smells more accurately compared to existing approaches. • We provide a method to collect actionable code smells automatically. • We propose a dual-stream model for detecting and identifying actionable code smells. • We comprehensively evaluate our approach on the publicly available and collected datasets. • We provide several valuable suggestions for practitioners and a benchmark for identifying actionable code smells. [ABSTRACT FROM AUTHOR]

Choosing appropriate Web services to create new applications plays a significant role for service-based development. The general service recommendation approaches usually prefer to suggest popular and frequently used services. However, nowadays users are becoming increasingly interested in long tail services, which are novel but less concerned. Unfortunately, few of the existing service recommendation methods explicitly takes such long tail services into consideration, leading to the poor diversity and serendipity of recommendation results. Compared with the general service recommendation, the major challenges of recommending long tail services come from: (1) more sparse historical invocation records, and (2) lower quality of textual service description. To address the above challenges, in this paper, we put forward a method for Long tail Service Recommendation based on Cross-view and Contrastive learning (LSRCC). More specifically, we propose a text view that employs recursive neural network, multilayer perceptron and attention mechanism to learn the semantic representations of application and service in a more precise way. In addition, we present an invocation view that employs LightGCN (Light Graph Convolution Network) and local level contrastive learning to capture the implicit connections among applications, services and their tags. Finally, we introduce global level contrastive learning to further integrate the above two views for the long tail service recommendation. The extensive experiments on real-world datasets demonstrate that LSRCC outperforms the state-of-the-art baselines in terms of diversity and tail coverage, with the satisfactory precision and recall, in long tail service recommendation. • A long tail service recommendation method is proposed for application creation. • Contrastive learning is used globally and locally to capture implicit connection. • Double views are used to mitigate the problem of data sparsity and inferiority. • Extensive experiments on the real datasets show its superiority over baselines. [ABSTRACT FROM AUTHOR]

• A collective POI recommendation model is given for visiting initial and next POIs. • The initial POI recommendation enables a joint modeling of various factors. • The next POI recommendation applies transfer probability and context information. [Display omitted] With the popularity of mobile devices, location-based services, such as Foursquare and Facebook, have attracted increasing attention in recent years. Users share their interests anytime, anywhere with their friends on the social network. Point-of-Interest (POI) recommendation, as one of key services on Location-Based Social Networks (LBSNs), can effectively enhance users' experience especially when they travel in a new city. Previous studies have made great success on POI recommendation by employing geographical influence and user preference. However, it is believed that the human decision on where to visit is very complex and involves the comprehensive factors such as POI popularity, POI location, user trajectory, and time context. In this paper, we propose a collective POIs recommendation framework which leverages the individual latent preference and contextual information. Firstly, to recommend top-K initial POIs, a scoring prediction model is constructed, which considers the influence of similarity, popularity and location of POIs. Furthermore, a next POI recommendation model based on personalized transfer probability is proposed, and the initial POI recommendation is combined to calculate the user's score on the next POI. Extensive experiments based on real datasets collected from Foursquare demonstrate the proposed framework outperforms the state-of-art ones. [ABSTRACT FROM AUTHOR]

With the development of video and image processing technology, the field of video tampering forensics is facing enormous challenges. Specifically, as the fundamental basis of judicial forensics, passive forensics for object removal video forgery is particularly essential. To extract tampering traces in video more sufficiently, the author proposed a spatiotemporal trident network based on the spatial rich model (SRM) and 3D convolution (C3D), which provides three branches and can theoretically improve the detection and localization accuracy of tampered regions. Based on the spatiotemporal trident network, a temporal detector and a spatial locator were designed to detect and locate the tampered regions in the temporal and spatial domains of videos. For the temporal detector, 3D CNNs were employed in three branches as the encoders and a bidirectional long short-term memory (BiLSTM) as the decoder. For the spatial locator, a backbone network named C3D-ResNet12 was designed as the encoder of the three branches, and the region proposal networks (RPNs) were employed as the decoders in three branches. In addition, we optimized the loss functions of the above two algorithms based on focal loss and GIoU loss. The experimental results revealed the effectiveness of spatiotemporal detection and localization algorithms: for temporal forgery detection, the accuracy of the frame classification increased to 99+%; for spatial forgery localization, the successful localization rate of the tampered regions in forged frames reached 96+%, and the mean intersection over union of the located tampered regions and the real tampered regions reached 62+%. [ABSTRACT FROM AUTHOR]

A fuel cell air independent propulsion (AIP) system of underwater vehicle requires a hydrogen storage system. The methanol steam reforming system is a candidate of hydrogen storage which can produce hydrogen from chemical reaction. Different from reforming system for station fuel cell system, the methanol steam reformer (MSR) for underwater vehicle requires high-pressure operation. Since the longitudinal temperature uniformity is a core parameter of conversion efficiency of steam reforming system, this study is focused on computational analysis of phase change heat transfer through the annulus for methanol steam reforming reaction. The annulus MSR using phase change material was developed to improve the temperature uniformity. The simulation model is verified with safety and performance analysis code (SPACE). The performance parameters of MSR were flow arrangement, steam to carbon ratio (SCR), and gas hourly space velocity (GHSV). The results were analyzed in terms of the hydrogen yield, heat flux, liquid mass flow rate, and methanol conversion rate. The flow arrangement varied the methanol conversion rate to a minor extent of approximately 0.1% because wall temperature was maintained uniformly. In the case of SCR, the hydrogen yield at SCR 2.5 was 0.637 (dry basis), which was the highest yield rate. Also, if GHSV was increased, hydrogen yield decreased from 0.690 (dry basis) to 0.527 (dry basis). The heat transfer pattern was also analyzed and it was found that steam is interactively condensed along with the progress of the reforming reaction. • Methanol steam reforming reaction using the latent heat is analyzed numerically. • The two-phase flow model is validated with safety and performance analysis code. • Analyze the performance of the reformer using latent heat with various parameters. • Latent heat improves the temperature uniformity of endothermal reaction. [ABSTRACT FROM AUTHOR]

Service-based systems (SBSs) reuse existing loosely coupled Web services to provide value-added composite ones, which brings about much flexibility when the business changes frequently. The advent of automatic Web service composition technology allows system designers to quickly build SBSs without having to manually create process models. Despite the large number of strategies proposed so far, most of them compose Web services through the user-provided initial inputs and expected target outputs, which is not convenient for users to express their functional requirements. To address this issue, we allow users to employ keywords to represent key tasks of the composed Web services. To automatically compose Web services based on the given keywords, we study a new problem of keyword search in the AND/OR graph constructed through semantically matching input-output interfaces of existing related Web services. Due to the complexity of the problem, we propose a heuristic search approach, called UP-DFS. To improve the performance of UP-DFS, we further design two types of pruning strategies. The empirical study shows that our approach can efficiently generate a semantic input-output-based Web service composition that contains all the key tasks in the right order required by users while minimizing the number of services in the composition. [ABSTRACT FROM AUTHOR]

Abstract Forecasting the short-term speed of moving vehicles on roads plays a vital role on traffic control and trip planning, which however still remains a challenging task when the high accuracy is required. In this paper, we propose a novel approach to the short-term traffic speed forecasting, which takes into account the influence of different traffic attributes, such as traffic flow, traffic speed, road occupancy and traffic density, of adjacent roads on the traffic speed. In addition, in order to obtain the more accurate relation between traffic speed and traffic attributes, we employ the idea of piecewise correlation function and adopt the Jenks clustering method with dynamic programming to determine the segment intervals of relation. We validate our approach based on the real data collected from Wenzhou and Hangzhou, two large cities located in eastern China. The extensive experimental results show that, compared with the state-of-the-art approaches, our approach has the higher stability and accuracy, especially for 5-minute and 10-minute speed prediction. Highlights • We consider only traffic attributes most relevant to speed and also their weights. • We consider the differences of impacts on speed among adjacent roads. • We employ piecewise correlation function between traffic speed and attributes. • We adopt Jenks clustering with dynamic programming to determine segment intervals. • We validate our approach based on the real data collected from two large cities. [ABSTRACT FROM AUTHOR]

Graph convolutional networks are widely used in skeleton-based action recognition because of their good fitting ability to non-Euclidean data. While conventional multi-scale temporal convolution uses several fixed-size convolution kernels or dilation rates at each layer of the network, we argue that different layers and datasets require different receptive fields. We use multi-scale adaptive convolution kernels and dilation rates to optimize traditional multi-scale temporal convolution with a simple and effective self attention mechanism, allowing different network layers to adaptively select convolution kernels of different sizes and dilation rates instead of being fixed and unchanged. Besides, the effective receptive field of the simple residual connection is not large, and there is a great deal of redundancy in the deep residual network, which will lead to the loss of context when aggregating spatio-temporal information. This article introduces a feature fusion mechanism that replaces the residual connection between initial features and temporal module outputs, effectively solving the problems of context aggregation and initial feature fusion. We propose a multi-modality adaptive feature fusion framework (MMAFF) to simultaneously increase the receptive field in both spatial and temporal dimensions. Concretely, we input the features extracted by the spatial module into the adaptive temporal fusion module to simultaneously extract multi-scale skeleton features in both spatial and temporal parts. In addition, based on the current multi-stream approach, we use the limb stream to uniformly process correlated data from multiple modalities. Extensive experiments show that our model obtains competitive results with state-of-the-art methods on the NTU-RGB+D 60 and NTU-RGB+D 120 datasets. [ABSTRACT FROM AUTHOR]

Temporal action localisation is a challenging task, and video context is crucial to localisation actions. Most existing cases that incorporate temporal and semantic contexts into video features suffer from single contextual representation and blurred temporal boundaries. In this study, a multi‐temporal resolution pyramid structure model is proposed. Firstly, a temporal‐semantic context aggregation module (TSCF) is designed to assign different attention weights to temporal contexts and combine them with multi‐level semantics into video features. Secondly, for the problem of large differences in the time span between different actions in the video, a local‐global attention module is designed to combine local and global temporal dependencies for each temporal point to obtain a more flexible and robust representation of contextual relations. The redundant representation of the convolution kernel is reduced by modifying the convolution and the arithmetic power is redeployed at a microscopic granularity. To verify the effectiveness of the model, extensive experiments on three challenging datasets are performed. On THUMOS14, the best performance is obtained in IoU@0.3–0.6 with an average mAP of 47.02%. On ActivityNet‐1.3, an average mAP of 34.94% was obtained. On HACS, an average mAP of 28.46% was achieved. [ABSTRACT FROM AUTHOR]

Next activity prediction of business processes (BPs) provides valid execution information of ongoing (i.e., unfinished) process instances, which enables process executors to rationally allocate resources and detect process deviations in advance. Current researches on next activity prediction, however, concentrate mostly on model construction without in‐depth analysis of historical event logs. In this article, we are dedicated to proposing an approach to forecast the next activity effectively in BPs. After in‐depth analysis of historical event logs, three types of candidate activity attributes are defined and calculated as additional input for the prediction based on three essential elements, that is, frequent activity patterns, trace similarity and position information. Furthermore, we construct an effective hybrid prediction model combining the popular convolutional neural network (CNN) and bidirectional long short‐term memory (Bi‐LSTM) with self‐attention mechanism. Specifically, CNN is used to extract the temporal features before importing into Bi‐LSTM for accurate prediction, and self‐attention mechanism is applied to strengthen features that have decisive effects on the prediction results. Comparison experiments on four real‐life datasets demonstrate that our hybrid model with selected attributes achieves better performance on next activity prediction than single models, and improves the prediction accuracy by 2.98%, 6.05%, 2.70% and 5.26% on Helpdesk, Sepsis, BPIC2013 Incidents and BPIC2012O datasets than the state‐of‐the‐art methods, respectively. [ABSTRACT FROM AUTHOR]

As a new software design paradigm, microservices structure an application as a collection of services that are independently deployable and loosely coupled. A key step of migrating non‐microservices‐based systems to microservices‐based systems is the identification of microservices in the target application. Traditional approaches to identify microservices, however, usually suffer from lack of full automation and low effectiveness. This paper puts forward an expert system to identify microservices automatically from legacy systems by leveraging the similarity of RESTful APIs. The system consists of three major parts. The first part calculates the candidate topic similarity and the response message similarity of APIs, and the overall similarity is obtained through their combination. Afterwards, the second part constructs a graph of API similarities with API as the node and the overall similarity as the weight. The third part employs a graph‐based clustering algorithm to identify candidate microservices from the API similarity graph. Experiments conducted on open‐source projects demonstrate the effectiveness of our system. [ABSTRACT FROM AUTHOR]

Point-Of-Interest (POI) recommendation not only assists users to find their preferred places, but also helps businesses to attract potential customers. Recent studies have proposed many approaches to the POI recommendation. However, the lack of negative samples and the complexities of check-in contexts limit their effectiveness significantly. This paper focuses on the problem of context-specific POI recommendation based on the check-in behaviors recorded by Location-Based Social Network (LBSN) services, which aims at recommending a list of POIs for a user to visit at a given context (such as time and weather). Specifically, a bidirectional influence correlativity metric is proposed to measure the semantic feature of user check-in behavior, and a contextual smoothing method to effectively alleviate the problem of data sparsity. In addition, the check-in probability is computed based on the geographical distance between the user's home and the POI. Furthermore, to handle the problem of no negative feedback in LBSN, a weighted random sampling method is proposed based on contextual popularity. Finally, the recommendation results is obtained by utilizing Factorization Machine with Bayesian Personalized Ranking (BPR) loss. Experiments on a real dataset collected from Foursquare show that the proposed approach has better performance than others. [ABSTRACT FROM AUTHOR]

Soy protein hydrolysates with hydrolysis degree 4 (SPH4) and their xanthan gum (XG) blends were subjected to heat-shearing treatment, and the resulting microparticulated SPH4-XG complex (MSPH4/XG) was used as a fat replacer for producing low-fat (LF) ice cream. The effects of MSPH4/XG ratios and freezing times on the overrun, melting properties, thermal stability, texture, and sensory properties were studied. Results showed that the melting and sensory properties of LF ice creams were impacted by the microstructural parameters and their related factors. It was identified that the LF ice cream formed using MSPH4/XG (92:8) as a fat replacer and a freezing time of 21.25 min (hardness level 5) had better melting properties and the closest sensory characteristics to full-fat ice creams. Overall, this study showed that MSPH4/XG could stabilize the microstructural parameters during freezing, thus promoting the melting resistance and sensory properties of LF ice creams prepared using different freezing times. • Effects of MSPH4/XG ratios and freezing time on low-fat ice cream were studied. • A ratio of MSPH4/XG at 92:8 is optimal. • Freezing time of 21.25 min offers sensory quality ice cream. • LF92 ice cream had stable melting resistance when changing freezing time. • MSPH4/XG can stabilize ice cream microstructural parameters during freezing. [ABSTRACT FROM AUTHOR]

Distributed computing, such as Cloud, provides traditional workflow applications with completely new deployment architecture offering high performance and scalability. However, when executing the workflow tasks in a distributed computing environment, significant scheduling overheads are generated. Task clustering is a key technology to optimize process execution. Unreasonable task clustering can lead to the problems of runtime and dependency imbalance, which reduces the degree of parallelism during task execution. In order to solve the problem of runtime imbalance, we propose Runtime Balance Clustering Algorithm (RBCA), which employs the Backtracking approach to make the runtime of each cluster more balanced. In addition, to address the problem of dependency imbalance, we also propose Dependency Balance Clustering Algorithm (DBCA), which defines the dependency correlation to measure the similarity between tasks in terms of data dependencies. The tasks with high dependency correlation are clustered together so as to avoid the dependency imbalance to most extent. We conducted extensive experiments on the WorkflowSim platform and compared our algorithms with the existing task clustering algorithms. The results show that RBCA and DBCA significantly reduce the execution time of the whole workflow. • A runtime balance clustering algorithm (RBCA) is proposed to solve the problem of runtime imbalance. • A dependency balance clustering algorithm (DBCA) is proposed to solve the problem of data dependency imbalance. • A strategy of choosing RBCA or DBCA is proposed according to data transmission amount and task runtime. • The clustering algorithms are compared with existing algorithms through WorkflowSim with extensive experiments. [ABSTRACT FROM AUTHOR]

Performance tests can be automatically done on each commit version with a continuous integration system. After code changes are pushed to the repository, multiple benchmark tests are run to measure system's performance. Change point detection technology analyzes all these outcomes to identify commits that significantly change performance automatically. However, a considerable number of automatically detected change points are tagged as not being actionable (false positive). Typically, validating potential change points by hand is difficult, time-consuming, and thus creates a bottleneck in the testing process. Our work focuses on identifying what factors affect performance change point triagement and triaging whether a newly detected change point is true positive or not automatically. We start by extracting 34 features from four dimensions i.e., Configuration, Time Series, Version, and Context. We utilize a random forest classifier to triage the change points based on the proposed features. The results indicate that most of the proposed features differ significantly between true positive and false positive change points. On average, our model obtains an AUC of 0.881, which is statistically considerably better than two state-of-the-art approaches. We also look into the most important features for triagement and discover that version related features are the most essential factors. • We use the features from the four dimensions to build a richer set of features. • Our approach considers the triagement problem whenever a change point is detected, which is a just in time prediction. • To make the experiments closer to reality, temporal dependence is considered in the validation and experiments. [ABSTRACT FROM AUTHOR]

Business process compliance is an essential part of business process management, which saves organizations from penalties caused by non-compliant processes. However, current researches on business process compliance mainly focus on checking using general constraint rules that have been formalized without in-depth analysis of related regulatory documents and mostly involve extensive human efforts. In this paper, we aim to propose an automatic and interpretable compliance checking approach for design-time business processes. By combining deep learning and a natural language processing approach based on rule templates, we extract semantic information from regulatory documents at different granularities for subsequent compliance checking. In addition, we match appropriate rules to the process model and detect the degree of violation of the business process from three control-flow perspectives. The effectiveness of this method is validated on two real-world datasets. [ABSTRACT FROM AUTHOR]

Recently, deep neural networks are widely applied in recommender systems for their effectiveness in capturing/modeling users' preferences. Especially, the attention mechanism in deep learning enables recommender systems to incorporate various features in an adaptive way. Specifically, as for the next item recommendation task, we have the following three observations: 1) users' sequential behavior records aggregate at time positions ("time-aggregation"), 2) users have personalized taste that is related to the "time-aggregation" phenomenon ("personalized time-aggregation"), and 3) users' short-term interests play an important role in the next item prediction/recommendation. In this paper, we propose a new T ime-aware L ong- and S hort-term A ttention N etwork (TLSAN) to address those observations mentioned above. Specifically, TLSAN consists of two main components. Firstly, TLSAN models "personalized time-aggregation" and learn user-specific temporal taste via trainable personalized time position embeddings with category-aware correlations in long-term behaviors. Secondly, long- and short-term feature-wise attention layers are proposed to effectively capture users' long- and short-term preferences for accurate recommendation. Especially, the attention mechanism enables TLSAN to utilize users' preferences in an adaptive way, and its usage in long- and short-term layers enhances TLSAN's ability of dealing with sparse interaction data. Extensive experiments are conducted on Amazon datasets from different fields (also with different size), and the results show that TLSAN outperforms state-of-the-art baselines in both capturing users' preferences and performing time-sensitive next-item recommendation. [ABSTRACT FROM AUTHOR]

Point of Interest (POI) recommendation algorithms can help users find the POIs that they prefer, and they can also help merchants to find potential customers. However, most existing methods still have difficulties effectively utilizing the information in users' check-in data. Significantly, they ignore the intent behind the users' check-in behaviors, which limits the recommendation performance. In this paper, we propose an I ntent A ware G raph N eural N etwork-based model(IAGNN) to predict/recommend the next POI with which the target user may interact. Specifically, IAGNN first models the user's check-in behavior sequences as graphs and utilizes the information transmission mechanism of the graph neural network (GNN) to learn the feature vector representation (embedding) of POIs. Second, we devise a hierarchical attention network for capturing users' preferences adaptively. At the same time, we design a user intent-aware module based on disentangled representations to extract the user's intents. Finally, the user's preferences and their intents obtained by the user intent perception module are combined to recommend the POI for the user. Extensive evaluations are conducted on two real-world POI check-in datasets. The experimental results show that our proposed model IAGNN outperforms the baselines in terms of both recall and MRR. [ABSTRACT FROM AUTHOR]

Circular references keep objects in loops from being released even if they are no longer reachable. Programming languages provide various garbage collection strategies for reclaiming unused objects at runtime. For example, CPython is equipped with a tracing collector as a complement to the count-based collector to recycle circular garbage. However, runtime detection inevitably entails resource consumption. The overhead of circular reference detection can be reduced if the introduction of circular references can be avoided, or if the presence of circular references in code can be detected before the code is run. To reduce the occurrence of circular references, we provide a data-driven approach to discover the common patterns from dynamic data and then automatically detect circular references statically. First, we observe and characterize common circular reference patterns using a large-scale dataset collected from 671 open-source projects at runtime. With these patterns in mind, developers can be aware of where potential circular references might be coming from, leading to more robust Python codes and ultimately lightening the garbage collection workload, and also potentially altering their coding practices. Then, we design CRef , a pattern-based approach for detecting circular references in code. We investigate the effectiveness of CRef on 17 projects. Across all experimental projects, CRef achieves a precision of 0.95 and a recall of 0.98. • We conducted an empirical study on circular references in Python using real-world data collected from 671 open-source projects. • We manually investigate the most common circular reference patterns in Python projects considering the instance objects and their references. • We propose a pattern-based approach for detecting circular references which achieved a precision of 0.95 and a recall of 0.98 recall. [ABSTRACT FROM AUTHOR]

Traffic prediction aims to predict the future traffic state by mining features from history traffic information, and it is a crucial component for the intelligent transportation system. However, most existing traffic prediction methods focus on road segment prediction while ignore the fine-grainedlane-level traffic prediction. From observations, we found that different lanes on the same road segment have similar but not identical patterns of variation. Lane-level traffic prediction can provide more accurate prediction results for humans or autonomous driving systems to make appropriate and efficient decisions. In traffic prediction, the mining of spatial features is an important step and graph-based methods are effective methods. While most existing graph-based methods construct a static adjacent matrix, these methods are difficult to respond to spatio-temporal changes in time. In this paper, we propose a deep learning model for lane-level traffic prediction. Specifically, we take advantage of the graph convolutional network (GCN) with a data-driven adjacent matrix for spatial feature modeling and treat different lanes of the same road segment as different nodes. The data-driven adjacent matrix consists of the fundamental distance-based adjacent matrix and the dynamic lane correlation matrix. The temporal features are extracted with the gated recurrent unit (GRU). Then, we adaptively fuse spatial and temporal features with the gating mechanism to get the final spatio-temporal features for lane-level traffic prediction. Extensive experiments on a real-world dataset validate the effectiveness of our model. [ABSTRACT FROM AUTHOR]

In the edge computing environment, Internet of Things (IoT) application service providers can rent resources from edge servers to cache their service items such as datasets and code libraries, and thus significantly reducing the service request latency and the core network traffic. Since IoT service providers need to pay for the rented edge computing resources, it is essential to find a dynamical service caching strategy to minimize the service cost while optimizing the performance objective such as service latency reduction. However, most of the existing studies either overlooked the problem of collaborative service caching or failed to consider the system's long-term service cost and latency. In this paper, to address such a problem, we coordinate multiple edge servers to cache service items and formulate the collaborative service caching problem using a multi-agent multi-armed bandit model. Furthermore, we propose a utility-aware collaborative service caching (UACSC) scheme based on a multi-agent reinforcement learning. The UACSC scheme can coordinate multiple edge servers to make a dynamic joint caching decision, aiming at maximizing the system's long-term utility. To evaluate the performance of our proposed scheme, we implement four representative baseline algorithms and compare them with six different performance metrics. In addition, a real-world case study is also presented to demonstrate the effectiveness of the UACSC scheme. Comprehensive experimental results show that the UACSC scheme can effectively coordinate multiple edge servers to cache service items, and achieve higher service latency reduction and lower service cost compared with other baseline algorithms. • A new problem formulation for the dynamic collaborative service caching at the edge. • Aim to maximize the long-term system utility defined by the service cost and latency reduction. • A new multi-agent reinforcement learning based utility-aware service caching scheme. [ABSTRACT FROM AUTHOR]

Temporal action detection is a very important task in video understanding, aiming at predicting the start and end time boundaries of all action instances in an unedited video and their action classification. This task has been widely studied, especially after the transformer has been widely used in the field of vision. However, transformer model brings massive computational resource consumption when processing long sequence input data. At the same time, due to the ambiguity of the video action boundary, many proposal and instance-based methods cannot predict the video boundary accurately. Based on the above two points, we propose LGAFormer: a very concise model that combines the local self-attention mechanism with the global self-attention mechanism, using local self-attention in the shallow layer of the network to process short-range temporal data to model local representations while reducing a large amount of computational consumption, and using global self-attention in the deep layer of the network to model long-range temporal context. This allows our model to achieve a good balance between effectiveness and efficiency. And in terms of detection head, we combine the advantages of the segment feature and instance feature to predict action boundaries more accurately. Thanks to these two points, our method achieves comparable results on all three datasets (THUMOS14, ActivityNet 1.3, and EPIC-Kitchens 100). On THUMOS14, an average mAP of 67.7% was obtained. On ActivityNet 1.3, the best performance is obtained with an average mAP of 36.6%. On EPIC-Kitchens 100, an average mAP of 24.6% was achieved. [ABSTRACT FROM AUTHOR]

Next point-of-interest (POI) recommendation has emerged as an essential task in recommender systems with the rapid development of location-based social networks (LBSNs). It has a wide range of applications in smart cities for building personalized scenarios. Current research typically uses sequential relationships to mine user preferences; however, it fails to sufficiently explore the spatial dependence of check-ins and the multi-perspective information they contain. To this end, this study proposes a Multiple Active Region Aware Network (MARAN), a novel routine-aware model for the next POI recommendation that simultaneously captures the user's routine regularity and short-term preference changes from check-in records. The key to MARAN is its ability to decompose sophisticated user behavior into two parts. One is a stable routine part characterized by central-based graphs built from historical trajectories based on spatial aggregation. The other is an unstable preference part that obtains the user's recent changes from short-term trajectories. Moreover, a neighborhood-aware negative sampler based on adjacent areas was designed to alleviate spatial sparsity, that is, the imbalance between positive and negative samples during model training. Experiments on two real-world datasets demonstrated that MARAN outperformed state-of-the-art methods. • Next POI recommendation considering spatial aggregation. • User's routines are characterized by central-based graphs. • Users' recent preferences are obtained from short-term trajectories. • Dynamic negative sampling based on current position. [ABSTRACT FROM AUTHOR]

Mobile devices (MDs) and applications are receiving extensive popularity and attracting significant attention. Mobile applications, especially for artificial intelligence (AI) applications, require powerful computation-intensive resources. Hence, running all the AI applications on a single MD introduces high energy consumption and application delay, as it has limited battery capacity and computation resources. Fortunately, the emerging edge-cloud computing (ECC) architecture pushes the computation resource to both the network edge and remote cloud to cope with challenging AI applications. Although the advantage of ECC greatly benefits various mobile applications, data security remains an important open issue in this scenario, which has not been well studied. This paper focuses on the profit maximization (PM) problem for security-aware task offloading in an ECC environment, i.e., considering the tasks from MDs with different service demands, edge nodes should decide them to be processed on the edge node or the remote cloud with a security guarantee. Specifically, we first construct the security model to measure the time overhead for each task under various scenarios. We then formulate the PM problem by jointly considering the security demand and deadline constraints of tasks. Finally, we propose a genetic algorithm-based PM (GA-PM) algorithm, the coding strategy of which considers the task execution location and execution order. Moreover, the crossover and mutation operations are implemented based on the coding strategy. Extensive simulation experiments with various parameters varying demonstrate that our GA-PM can achieve better performance than all the comparison algorithms. • The security model is built to measure the execution time of tasks under different parameters. • A genetic algorithm (GA)-based PM algorithm is proposed to implement task offloading and obtain optimal profit. • A coding strategy is devised by considering the tasks execution location and execution order. • Extensive simulation results demonstrate that GA-PM algorithm achieves the optimal profit. [ABSTRACT FROM AUTHOR]

With the rapid development of Information Technology, there exist massive amounts of data available on the Internet, which result in a severe information overload problem. Especially, it becomes more and more challenging but necessary to help users find the contents or services that they really need. To address the problem mentioned above, recommender systems have been developed to exploit user's historical behavior data and provide personalized services for promoting customer experiences in many fields, such as Point of Interest (POI) applications, multimedia services, and e-commerce websites. Specifically, in POI recommendation, user's next check-in behaviors depend on both long- and short-term preferences. However, traditional recommendation methods often ignore the dynamic changes of user's short-term preferences over time, which limits their performance. Besides, many existing methods cannot fully exploit the complex correlations and transitions between POI in check-ins sequences. In this paper, we propose an A ttentive S equential model based on G raph N eural N etwork (ASGNN) for accurate next POI recommendation. Specifically, ASGNN firstly models user's check-in sequences as graphs and then use Graph Neural Networks (GNN) to learn the informative low-dimension latent feature vectors (embeddings) of POIs. Secondly, a personalized hierarchical attention network is adopted to exploit complex correlations between users and POIs in check-in sequences and capture user's long- and short-term preferences. Finally, we perform the next POI recommendation via leveraging user's long- and short-term preferences obtained from their behavior sequences with ASGNN. Extensive experiments are conducted on three real-world check-in datasets, and the results demonstrate that the proposed model ASGNN outperforms baselines, including some state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Traffic congestions in urban road traffic networks originate from some crowded road segments with crucial locations, and diffuse towards other parts of the urban road network creating further congestions. This behavior of road networks motivates the need to understand the influence of individual road segments on others in terms of congestion. In this paper, we investigate the problems of global influence ranking and local influence ranking of road segments. We propose an algorithm called RoadRank to compute the global influence scores of each road segment from their traffic measures, and rank them based on their overall influence. To identify the locally influential road segments, we also propose an extension called distributed RoadRank, based on road network partitions. We perform extensive experiments on real SCATS datasets of Melbourne. We found that the segments of Batman Avenue, Footscray Road, Punt Road, La Trobe Street, and Victoria Street, are highly influential in the early morning times, which are well known as congestion hotspots for both the network operators and the commuters. Our promising results and detailed insights demonstrate the efficacy of our method. [ABSTRACT FROM AUTHOR]

A methanol steam reformer converts methanol and steam into a hydrogen-rich mixture through an endothermic reaction. The methanol reformer is divided into a reaction section and a heat supply section that transfers thermal energy from 200 to 300 °C. This study presents the behavior of the methanol steam reforming reaction using the latent heat of the steam. A numerical analysis was separately conducted for two different regimes assuming constant heat flux conditions. A methanol steam reformer is an annulus structure that has a phase change heat transfer from an outer tube to an inner tube. Different from the steam zone temperature in the tube, the latent heat of steam condensation decreases, and there is a gradual between-wall temperature decrease along the longitudinal direction. Since the latent heat of steam condensation is very sensitive to the requested heat from the reformer, it is necessary to consider a refined design of a methanol reformer to obtain a large enough amount of heat by steam condensation. [ABSTRACT FROM AUTHOR]