No abstract available.

The online technical Q&A site Stack Overflow (SO) is popular among developers to support their coding and diverse development needs. To address shortcomings in API official documentation resources, several research works have thus focused on augmenting official API documentation with insights (e.g., code examples) from SO. The techniques propose to add code examples/insights about APIs into its official

Android developers heavily use reflection in their apps for legitimate reasons. However, reflection is also significantly used for hiding malicious actions. Unfortunately, current state-of-the-art static analysis tools for Android are challenged by the presence of reflective calls, which they usually ignore. Thus, the results of their security analysis, e.g., for private data leaks, are incomplete

High performance is a critical factor to achieve and maintain the success of a software system. Performance anomalies represent the performance degradation issues (e.g., slowing down in system response times) of software systems at run-time. Performance anomalies can cause a dramatically negative impact on users’ satisfaction. Prior studies propose different approaches to detect anomalies by analyzing

A number of product-line analysis approaches lift analyses such as type checking, model checking, and theorem proving from the level of single programs to the level of product lines. These approaches share concepts and mechanisms that suggest an unexplored potential for reuse of key analysis steps and properties, implementation, and verification efforts. Despite the availability of taxonomies synthesizing

Blockchain offers a distributed ledger to record data collected from Internet of Thing (IoT) devices as immutable and tamper-proof transactions and securely shared among authorized participants in a Peer-to-Peer (P2P) network. Despite the growing interest in using blockchain for securing IoT systems, there is a general lack of systematic research and comprehensive review of the design issues on the

Search-based techniques have been successfully used to automate test case generation. Such approaches allocate a fixed search budget to generate test cases aiming at maximizing code coverage. The search budget plays a crucial role; due to the hugeness of the search space, the higher the assigned budget, the higher the expected coverage. Code components have different structural properties that may

UI (User Interface) is an essential factor influencing users’ perception of an app. However, it is hard for even professional designers to determine if the UI is good or not for end-users. Users’ feedback (e.g., user reviews in the Google Play) provides a way for app owners to understand how the users perceive the UI. In this article, we conduct an in-depth empirical study to analyze the UI issues

Static bug detection has shown its effectiveness in detecting well-defined memory errors, e.g., memory leaks, buffer overflows, and null dereference. However, modern software systems have a wide variety of vulnerabilities. These vulnerabilities are extremely complicated with sophisticated programming logic, and these bugs are often caused by different bad programming practices, challenging existing

Android, the most popular mobile operating system, has attracted millions of users around the world. Meanwhile, the number of new Android malware instances has grown exponentially in recent years. On the one hand, existing Android malware detection systems have shown that distilling the program semantics into a graph representation and detecting malicious programs by conducting graph matching are able

Malware detection at scale in the Android realm is often carried out using machine learning techniques. State-of-the-art approaches such as DREBIN and MaMaDroid are reported to yield high detection rates when assessed against well-known datasets. Unfortunately, such datasets may include a large portion of duplicated samples, which may bias recorded experimental results and insights. In this article

With the rapid development of mobile app ecosystem, mobile apps have grown greatly popular. The explosive growth of apps makes it difficult for users to find apps that meet their interests. Therefore, it is necessary to recommend user with a personalized set of apps. However, one of the challenges is data sparsity, as users’ historical behavior data are usually insufficient. In fact, user’s behaviors

Temporal networks representing a stream of timestamped edges are seemingly ubiquitous in the real world. However, the massive size and continuous nature of these networks make them fundamentally challenging to analyze and leverage for descriptive and predictive modeling tasks. In this work, we propose a general framework for temporal network sampling with unbiased estimation. We develop online, single-pass

Traditional network embedding primarily focuses on learning a continuous vector representation for each node, preserving network structure and/or node content information, such that off-the-shelf machine learning algorithms can be easily applied to the vector-format node representations for network analysis. However, the learned continuous vector representations are inefficient for large-scale similarity

Network embedding is a method of learning a low-dimensional vector representation of network vertices under the condition of preserving different types of network properties. Previous studies mainly focus on preserving structural information of vertices at a particular scale, like neighbor information or community information, but cannot preserve the hierarchical community structure, which would enable

In this article, we aim to develop a unified view of causal and non-causal feature selection methods. The unified view will fill in the gap in the research of the relation between the two types of methods. Based on the Bayesian network framework and information theory, we first show that causal and non-causal feature selection methods share the same objective. That is to find the Markov blanket of

Benchmarking unsupervised outlier detection is difficult. Outliers are rare, and existing benchmark data contains outliers with various and unknown characteristics. Fully synthetic data usually consists of outliers and regular instances with clear characteristics and thus allows for a more meaningful evaluation of detection methods in principle. Nonetheless, there have only been few attempts to include

Katz centrality is a fundamental concept to measure the influence of a vertex in a social network. However, existing approaches to calculating Katz centrality in a large-scale network are unpractical and computationally expensive. In this article, we propose a novel method to estimate Katz centrality based on graph sampling techniques, which object to achieve comparable estimation accuracy of the state-of-the-arts

Granger causality is a fundamental technique for causal inference in time series data, commonly used in the social and biological sciences. Typical operationalizations of Granger causality make a strong assumption that every time point of the effect time series is influenced by a combination of other time series with a fixed time delay. The assumption of fixed time delay also exists in Transfer Entropy

Online reviews and ratings play an important role in shaping the purchase decisions of customers in e-commerce. Many researches have been done to make proper recommendations for users, by exploiting reviews, ratings, user profiles, or behaviors. However, the dynamic evolution of user preferences and item properties haven’t been fully exploited. Moreover, it lacks fine-grained studies at the aspect

This article introduces a novel task-independent sampler for attributed networks. The problem is important because while data mining tasks on network content are common, sampling on internet-scale networks is costly. Link-trace samplers such as Snowball sampling, Forest Fire, Random Walk, and Metropolis–Hastings Random Walk are widely used for sampling from networks. The design of these attribute-agnostic

Recently, network embedding has received a large amount of attention in network analysis. Although some network embedding methods have been developed from different perspectives, on one hand, most of the existing methods only focus on leveraging the plain network structure, ignoring the abundant attribute information of nodes. On the other hand, for some methods integrating the attribute information

This study was to analyze the diagnostic value of magnetic resonance imaging (MRI) for gastric cancer (GC) lesions and the treatment effect of complete laparoscopic radical resection (CLSRR). A malignant tumor recognition algorithm was constructed in this study based on the backprojection (BP) and support vector machine (SVM), which was named BPS. 78 GC patients were divided into an experimental group

The complexity of the socio-economic environment requires the involvement of a group of decision makers in the decision-making process. They can contribute the diversity of knowledge and experience that allow all the important aspects of the decision-making problem to be taken into account. In the aggregation process, the weights of the decision makers play an important role. In this paper we have

This paper employs some strategies for improving service quality in a congested network consisting of facilities and customers. The quality is measured considering both customers’ travel distances and congestion delays. The strategies include opening new facilities, increasing the service capacities, and incorporating multiple backup services for customers, empowering the network to distribute the

The distributed permutation flowshop scheduling problem (DPFSP) has attracted a lot of attention in recent years, as current production systems frequently operate multiple factories at once. Due to the practical relevance of mixed no-idle flowshop, we address an extension of the DPFSP where there are identical factories, each one with a mixed no-idle flowshop environment. In a mixed no-idle flowshop

With the progress of technology, the multi-agent system is successfully applied in many applications. In this paper, we investigate the problem of multi-agent system reconnaissance mission scheduling, which is the core of the reconnaissance decision support system and can be modeled as an extension of Multi-Mode Multi-Skill Resource-Constrained Project Scheduling Problem. Three objectives are considered

As remanufacturing may reduce production costs and energy consumption, an increasing number of companies are devoting attention to remanufacturing. This study investigates the impact of the remanufacturing strategy on the manufacturer, considering customer prejudice and accurate response. We characterize the optimal production and pricing decisions for new and remanufactured products in a two-stage

Fully automatic manufacturing systems have received wide attention and are being adopted rapidly in manufacturing industry with the application of Internet of Things. How to coordinate correlated operations efficiently has become a prominent challenge for many practitioners. In this regard, this paper considers an optimal control issue arising in a fully automatic manufacturing system based on Internet

Flourishing e-commerce has prompted manufacturers to design different electronic channel structures for consumers' electronic shopping needs. As a result, the study of e-commerce channel configuration decision has become an important research topic. Toward this end, we establish three-stage sequential game models and investigate a manufacturer’s electronic channel configuration strategy from the perspective

Context: Memory error vulnerabilities have been consequential and several well-known, open-source memory error vulnerability detectors exist, built on static and/or dynamic code analysis. Yet there is a lack of assessment of such detectors based on rigorous, quantitative accuracy and efficiency measures while not being limited to specific application domains. Objective: Our study aims to assess and

Facial alignment is an essential task for many higher level facial analysis applications, such as animation, human activity recognition and human - computer interaction. Although the recent availability of big datasets and powerful deep-learning approaches have enabled major improvements on the state of the art accuracy, the performance of current approaches can severely deteriorate when dealing with

In the recent years, Convolutional Neural Network-based object detection has experienced impressive progress. Despite these improvements, the performance on the small object detection leaves much to be desired, small objects are often missed or wrongly detected. The reasonable explanation is that the supervisory signals on the small objects are insufficient, and through the analysis of loss distribution

Alcohol Use Disorder (AUD) is a chronic relapsing brain disease characterized by excessive alcohol use, loss of control over alcohol intake, and negative emotional states under no alcohol consumption. The key factor in successful treatment of AUD is the accurate diagnosis for better medical and therapy management. Conventionally, for individuals to be diagnosed with AUD, certain criteria as outlined

Automatic sleep stage mymargin classification is of great importance to measure sleep quality. In this paper, we propose a novel attention-based deep learning architecture called AttnSleep to classify sleep stages using single channel EEG signals. This architecture starts with the feature extraction module based on multi-resolution convolutional neural network (MRCNN) and adaptive feature recalibration

Machine-learning techniques are suitably employed for gait-event prediction from only surface electromyographic (sEMG) signals in control subjects during walking. Nevertheless, a reference approach is not available in cerebral-palsy hemiplegic children, likely due to the large variability of foot-floor contacts. This study is designed to investigate a machine-learning-based approach, specifically developed

Motor evoked potential (MEP), which was elicited by transcranial magnetic stimulation (TMS), has been widely used to detect corticospinal projection from TMS cortical site to trunk muscles. It can help to find the stimulation hotspot in the scalp. However, it fails to precisely describe coordinated activities of trunk muscle groups with only single-channel myoelectric signal. In this study, we aimed

The World Wide Web serves as an excellent platform for information dissemination. Educational institutions such as universities are utilizing the web medium to reach their target audience. In the post-Covid-19 scenario, the web medium has obtained increased significance as it has become the primary access channel to reach these institutions. In this backdrop, it becomes essential to analyze the accessibility

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It is hard to know what to say about the year that has just passed. The turmoil that so many of us have experienced both personally and professionally has been unprecedented. As universities, colleges, and schools closed their doors, educators, educational designers, and education researchers came to the fore to pivot traditional learning experiences online in record time. We have seen remarkable innovation

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Dual-energy computed tomography (DECT) is of great clinical significance because of its material identification and quantification capacity. Although DECT measures attenuation using two different spectra, the anatomical structure of the low- and high-energy CT images are consistent with each other and the images are also correlated in the energy domain, resulting in significant information redundancy

Interactive Narrative is an emerging application of automated planning, in which a planning domain is used to generate a consistent chain of narrative actions that constitute a plot structure. The task of creating narrative planning domains has been identified as a bottleneck which is hampering further development of the field. This stems from the difficulties faced by humans authoring such planning

Clustering is an unsupervised classification technique used to form groups of unlabeled data sets called clusters. K-means algorithm is a popular clustering algorithm in which random cluster centers are initially taken. The cluster centers are randomly picked in most of the clustering techniques for which the results obtained by these techniques might be compromised. In clustering techniques, the cluster

Due to the explosive growth trend of cross-border e-commerce, a series of problems such as high transportation costs and insufficient publicity are to be solved urgently by researching on supply chain from both decision-making and coordination perspectives. Under the four-incomplete cooperative Stackelberg decision-making modes of cross-border e-commerce supply chain members, the transportation cost

The aim of this paper is to present a design of a metaheuristic search called improved monkey algorithm (MA+) that provides a suitable solution for the optimization problems. The proposed algorithm has been renewed by a new method using random perturbation (RP) into two control parameters (p1 and p2) to solve a wide variety of optimization problems. A novel RP is defined to improve the control parameters

With the rapid development of the Internet, network attacks often occur, and network security is widely concerned. Searching for practical security risk assessment methods is a research hotspot in the field of network security. Network attack graph model is an active detection technology for the attack path. From the perspective of the attacker, it simulated the whole network attack scenario and then

From computer vision and speech recognition to forecasting trajectories in autonomous vehicles, deep learning approaches are at the forefront of so many domains. Deep learning models are developed using plethora of high-level, generic frameworks and libraries. Running those models on the mobile devices require hardware-aware optimizations and in most cases converting the models to other formats or

Federated learning has emerged as a popular paradigm for collaboratively training a model from data distributed among a set of clients. This learning setting presents, among others, two unique challenges: how to protect privacy of the clients' data during training, and how to ensure integrity of the trained model. We propose a two-pronged solution that aims to address both challenges under a single

Cyclic loading affects the long-term response of geostructures build on natural soils. Soft soils are particularly susceptible to the development of large deformations, induced by the repetitive nature of loading. A new viscoplastic cyclic accumulation model is presented, which is an hierarchical extension of the Creep-SClay1S model, to model the long-term permanent deformation resulting from undrained

Progressive failure is common in many natural clays of strain-softening behavior and may affect the bearing capacity of foundations placed on such soils. The progressive failure might be insignificant for small structures where the mobilized strains are generally uniform within the failed soil mass. This places a demerit to the applications of knowledge obtained from physical model tests to prototype

We develop algorithms capable of tackling robust black-box optimisation problems, where the number of model runs is limited. When a desired solution cannot be implemented exactly the aim is to find a robust one, where the worst case in an uncertainty neighbourhood around a solution still performs well. To investigate improved methods we employ an automatic generation of algorithms approach: Grammar-Guided

We study a chemical tanker scheduling problem in which a multi-compartment chemical tanker picks up chemicals (within specific time windows) and delivers them to their destinations. Additional safety and ship balancing requirements related to the compartment storage need to be met. We refer to this problem as the single ship pick-up and delivery problem with pick-up time windows, tank allocations and