In the social production system, image data are rapidly generated from almost all fields such as factories, hospitals, and transportation, promoting higher requirements for anomaly detection technologies, including low consumption, adaptability, accuracy. However, existing methods fragile to heterogeneous by complex systems tend require strong computing power resource support. To address above problems, a knowledge-driven framework is proposed, in which local feature enhancement method...

Embedding-based recommender systems rely on historical interactions to model users, which poses challenges for recommending new known as the user cold-start problem. Some approaches incorporate social networks deduce preferences based circles of users solve problem sparse features. However, such methods have difficulty distinguishing between superficial correlations and causal relationships in behaviors, leading inaccuracies predicting preferences. To address aforementioned issues, we...

Text anomaly detection is crucial for identifying spam, misinformation, and offensive language in natural processing tasks. Despite the growing adoption of embedding-based methods, their effectiveness generalizability across diverse application scenarios remain under-explored. To address this, we present TAD-Bench, a comprehensive benchmark designed to systematically evaluate approaches text detection. TAD-Bench integrates multiple datasets spanning different domains, combining...

Medical time series has been playing a vital role in real-world healthcare systems as valuable information monitoring health conditions of patients. Accurate classification for medical series, e.g., Electrocardiography (ECG) signals, can help early detection and diagnosis. Traditional methods towards rely on handcrafted feature extraction statistical methods; with the recent advancement artificial intelligence, machine learning deep have become more popular. However, existing often fail to...

Mobile cloud computing provides powerful and storage capacity on managing GPS big data by offloading vast workloads to remote clouds. For the mobile applications with urgent or communication deadline, it is necessary reduce workload transmission latency between devices This can be technically achieved expanding cloudlets that are moving co-located Access Points (APs). However, not-trivial place such movable efficiently enhance service for dynamic context-aware applications. In view of this...

With the advent of IoT (Internet Things) age, considerable web services are emerging rapidly in service communities, which places a heavy burden on target users' selection decisions. In this situation, various techniques, e.g., collaborative filtering (i.e., CF) is introduced recommendation to alleviate burden. However, traditional CF-based approaches often assume that historical user-service quality data centralized, while neglect distributed situation. Generally, involves inevitable...

Anomaly detection plays an increasingly important role in various fields for critical tasks such as intrusion cybersecurity, financial risk detection, and human health monitoring. A variety of anomaly methods have been proposed, a category based on the isolation forest mechanism stands out due to its simplicity, effectiveness, efficiency, e.g., iForest is often employed state-of-the-art detector real deployment. While majority forests use binary structure, framework LSHiForest has...

With the development of Internet Things (IoT) technology, a vast amount IoT data is generated by mobile applications from devices. Cloudlets provide paradigm that allows and to be offloaded devices cloudlets for processing storage through access points (APs) in Wireless Metropolitan Area Networks (WMANs). Since most relevant personal privacy, it necessary pay attention transmission security. However, still challenge realize goal optimizing time, energy consumption resource utilization with...

Anomaly detection is one of the crucial research topics in artificial intelligence, encompassing various fields such as health monitoring, network intrusion detection, and fraud financial transactions. Deep anomaly (DAD) methods are considered effective approaches for addressing complex problems. Among them, deep isolation forest have gained rapid development recently due to their simplicity parameter turning efficiency model training. The existing all based on representation learning, while...

Abstract Anomaly detection plays a crucial role in many Internet of Things (IoT) applications such as traffic anomaly for smart transportation and medical diagnosis healthcare. With the explosion IoT data, on data streams raises higher requirements real-time response strong robustness large-scale arriving at same time various application fields. However, existing methods are either slow or application-specific. Inspired by edge computing generic technique, we propose an isolation forest...

Anomaly detection is a significant but challenging data mining task in wide range of applications. Different domains usually use different ways to measure the characteristics and define anomaly types. As result, it big challenge develop versatile framework that can be universally applied with satisfactory performance most, if not all, In this article, we propose generic isolation forest based ensemble named EDBHiForest, which spaces arbitrary distance measures. It realized through embedding...

With the rapid resource requirements of Internet Things applications, cloud computing technology is regarded as a promising paradigm for provision. To improve efficiency and effectiveness services, it essential to fairness achieve energy savings. However, still challenge schedule virtual machines in an energy-efficient manner while taking into consideration fairness. In view this challenge, fair machine scheduling method applications designed article. Specifically, are analyzed formal way....

Anomaly detection is one of the most important data mining tasks in many real-life applications such as network intrusion for cybersecurity and medical diagnosis healthcare. In big era, these demand fast versatile anomaly capability to handle various types increasingly huge-volume data. However, existing methods are either slow due high computational complexity, or unable deal with complicated anomalies like local anomalies. this paper, we propose a novel method named OPHiForest use order...

Summary With the development of artificial intelligence, cloud‐edge computing and virtual reality, industrial design that originally depends on human imagination power can be transitioned to metaverse applications in smart manufacturing, which offloads services cloud edge platforms for enhancing quality service (QoS), considering inadequate terminal devices like sensors access points (APs). However, large overhead privacy exposure occur during data transmission cloud, while (ECDs) are at...

Recently, biometric identification has been extensively used for border control. Some face recognition systems have designed based on Internet of Things. But the rich personal information contained in images can cause severe privacy breach and abuse issues during process if a system compromised by insiders or external security attacks. Encrypting query image is state-of-the-art solution to protect an individual’s but incurs huge computational cost poses big challenge time-critical...

With the great success of deep neural networks (DNNs) in a variety fields, learning gains pioneering development anomaly detection. Although achieves good accuracy detection, it is troubled with long execution time and high memory consumption. These problems are associated inherent drawbacks learning, such as too many parameters training layers. To remedy above drawbacks, we try to explore an unsupervised non-neural network model for detection based on experience forest. In this paper,...

Anomaly detection is a longstanding and active research area that has many applications in domains such as finance, security, manufacturing. However, the efficiency performance of anomaly algorithms are challenged by large-scale, high-dimensional, heterogeneous data prevalent era big data. Isolation-based unsupervised novel effective approach for identifying anomalies It relies on idea few different from normal instances, thus can be easily isolated random partitioning. methods have several...

Recommendation systems are essential tools for suggesting items or information to users based on their preferences and behaviours, which have been widely applied in various online platforms services personalize user experiences, increase engagement, drive business growth. However, the security efficacy of recommendation can be compromised if input data is tainted by malicious users. One primary threats shilling attacks, pose great challenges handling types huge-volume with anomaly detection...

Through document-level relation extraction (RE), the analysis of global between entities in text is feasible, and more comprehensive accurate semantic information can be obtained. In RE, model needs to infer implicit relations two different sentences. To obtain information, existing methods mainly focus on exploring entity representations. However, they ignore correlations indivisibility relations, contexts. Furthermore, current only independently estimate cases predefined ignoring case "no...