Looking for local events and promotions is a common need for most people during travel or moving to a new city. Similarly, delivering event messages to the right people is also a challenge for small businesses that se...
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The largest issue facing the retail industry today is product counterfeiting. Products that are counterfeit are just poor copies of authentic brands. Various techniques have been implemented to fight product counterfe...
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Let R be a set of objects. An object o ∈ R is an outlier, if there exist less than k objects in R whose distances to o are at most r. The values of k, r, and the distance metric are provided by a user at the run time...
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ISBN:
(纸本)1595933395
Let R be a set of objects. An object o ∈ R is an outlier, if there exist less than k objects in R whose distances to o are at most r. The values of k, r, and the distance metric are provided by a user at the run time. The objective is to return all outliers with the smallest I/O cost. This paper considers a generic version of the problem, where no information is available for outlier computation, except for objects' mutual distances. We prove an upper bound for the memory consumption which permits the discovery of all outliers by scanning the dataset 3 times. The upper bound turns out to be extremely low in practice, e.g., less than 1% of R. Since the actual memory capacity of a realistic DBMS is typically larger, we develop a novel algorithm, which integrates our theoretical findings with carefully-designed heuristics that leverage the additional memory to improve I/O efficiency. Our technique reports all outliers by scanning the dataset at most twice (in some cases, even once), and significantly outperforms the existing solutions by a factor up to an order of magnitude. Copyright 2006 ACM.
Insider threat detection (ITD) presents a significant challenge in cybersecurity, particularly within large and complex organizations. Traditionally, ITD has been overshadowed by the focus of external threats, resulti...
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ISBN:
(纸本)9798350362480
Insider threat detection (ITD) presents a significant challenge in cybersecurity, particularly within large and complex organizations. Traditionally, ITD has been overshadowed by the focus of external threats, resulting in less attention and development in this critical area. Conventional ITD approaches often rely heavily on event-driven approaches. On top of that, researchers developed various rule-based methods to conquer the tasks. Based on that, we often ignore the intrinsic temporal relationships that are naturally built in between events that occur in different moments. For instance, we may easily understand events with causality such as one anomalous event followed by another specific event to complete a malicious action;however, may not be aware of events that occur around 9 am every morning during working hours. In our opinion, we attempt to re-consider the temporal behavior to extract the information hidden in cyberspace activities. Specifically, some effective sentence embeddings can assist us in providing informative internal representations to summarize temporal behaviors in the temporal activity sequences to make the right judgment on insider threat detection. In this paper, we propose a novel methodology for insider threat detection that emphasizes temporal relationship modeling on top of already-matured event sequence analysis to effectively catch insider threats. The proposed approach leverages contrastive sentence embeddings to learn users' intentions in sequences, followed by the deployment of a user-level and event-level Contrastive Learning (euCL) model to incorporate temporal behaviors with user behavior embeddings. To validate the proposed methodology, we conduct extensive analyses and experiments using the publicly available CERT dataset. The results demonstrate the effectiveness and robustness of the proposed method in detecting insider threats and identifying malicious scenarios, highlighting its potential for enhancing cybersecurity measur
Human body action recognition (HBAR) is an important area of research in machine learning and image processing due to its vast range of applications. Similarly, estimating various components of human anatomy from RGB ...
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A Vehicular Ad Hoc Network (VANET) not only experiences highly mobile and frequently disconnected, but may also have to deal with rapid changes of network topologies, especially when accidents and road traffic jams ha...
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The paper proposes novel dynamic distribution-aware data dissemination (DDA) based Publish/Subscribe system for Vehicular Ad Hoc Networks(VANETs). Based on the characteristics of vehicles' moving in VANET, a notif...
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Everyone needs to be relaxed in some way. Previous studies show that listening to the combination of musical pieces and natural sounds effectively provide listeners with a relaxed feeling. In this study, we advocate a...
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In the past, several parallel processing approaches were proposed to speed up the execution of the ant colony optimization. The approaches for parallel runs or multiple colonies usually set the ant number in each run ...
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ISBN:
(纸本)9781450330084
In the past, several parallel processing approaches were proposed to speed up the execution of the ant colony optimization. The approaches for parallel runs or multiple colonies usually set the ant number in each run or each colony the same as that in sequential ant colony optimization. In this paper, we revisit the parallel ant colony optimization from a different viewpoint, by partitioning a whole colony into sub-groups and analyze its behavior. An algorithm is proposed and experiments are conducted on the data of a traveling salesman problem to show the effectiveness of the proposed approach. Copyright 2014 ACM.
An encryption scheme provides security against illegal duplication and manipulation of multimedia contents especially to digital images. In this paper a novel optimized partial image encryption scheme based on Particl...
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