Abstract Chromatic adaptation refers to the sensing and preprocessing of spectral composition incident light on retina, it is important for color‐image recognition. It challenging apply sensing, memory, processing functions color images via same physical process using complementary metal–oxide–semiconductor technology because redundant data detection, complicated signal conversion processes, requirement additional memory modules. Inspired by highly efficient chromatic human a 2D...

Unavoidable noise in real-world categorical data presents significant challenges to existing outlier detection methods because they normally fail separate noisy values from outlying values. Feature subspace-based inevitably mix when retaining an entire feature a may contain both and Pattern-based are based on frequency easily misled by values, resulting many faulty patterns. This paper introduces novel unsupervised framework termed OUVAS, its parameter-free instantiation RHAC explore...

Multi-view outlier detection recently attracted rapidly growing attention with the development of multi-view learning. Although promising performance demonstrated, we observe that identifying outliers in data is still a challenging task due to complicated characteristics data. Specifically, an effective method should be able handle (1) different types outliers; (2) two or more views; (3) samples without clusters; (4) high dimensional Unfortunately, little known about how these four issues...

Class incremental learning needs to deal with a dynamic environment where data class appears incrementally, it is challenge learn new knowledge while preserving what has already been learned. On the other hand, due limited storage of online scenario, algorithm usually obstructed frequently scan or simply store all historical data, another reduce for algorithm. Few existing work have addressed above challenges simultaneously. In this paper, we propose Fisher Discriminant Analysis Random...

Causal-based alert correlation is one of the mainstream techniques to detect multi-step threat behaviors. However, because large-scale network generates high-speed alerts and type distribution in dataflow changes over time, it challenging increase generality, scalability reduce overhead for causal method. In this paper, we propose a novel general, scalable low-overhead method, called GSLAC. GSLAC first presents "dispatch-aggregate" scheme based online framework employs general method diverse...

Anomaly detection plays a crucial role in the field of machine learning, as it involves constructing models capable identifying abnormal samples that deviate from expected patterns, using unlabeled or normal samples. In recent years, there has been growing interest integrating anomaly into image processing to tackle challenges related target detection, particularly when dealing with limited sample availability. This paper introduces novel fully connected network model enhanced memory...

Frequency hopping (FH) communication signal is usually statistically independent from common jamming signals. Blind source separation (BSS) or component analysis (ICA) can be introduced to separate the useful FH Through separation, signals are suppressed and quality of object improved. However, BSS suffered inherent permutation ambiguity, which makes it difficult select multiple separated According frequency spectrum characteristics signal, a time-frequency (TF) information based method for...

Fine-tuning on agent-environment interaction trajectory data holds significant promise for surfacing generalized agent capabilities in open-source large language models (LLMs). In this work, we introduce AgentBank, by far the largest tuning collection featuring more than 50k diverse high-quality trajectories which comprises 16 tasks covering five distinct skill dimensions. Leveraging a novel annotation pipeline, are able to scale annotated and generate dataset with minimized difficulty bias....

Outlier detection for categorical data is very important in many practical scenarios, such as intrusion detection, fraud early of diseases, etc. However, there no inherent difference measure data. The differences are hidden complex attribute value relationships. Existing methods do not properly handle the internal relationship and external attributes, resulting low accuracy outlier detection.This paper proposes a novel unsupervised method based on Multi-Hierarchy Attribute Relationship...

Feature selection places an important role in improving the performance of outlier detection, especially for noisy data. Existing methods usually perform feature and scoring separately, which would select subsets that may not optimally serve leading to unsatisfying performance. In this paper, we propose detection ensemble framework with embedded (ODEFS), address issue. Specifically, each random sub-sampling based learning component, ODEFS unifies into a pairwise ranking formulation learn are...

Talent similarity calculation is an important and useful research problem for precise talent search in online recruitment. However, the semantic information time-aware factor resume make it challenging to obtain effective similarity. In this paper, we proposed a analysis based method, termed TSRA. TSRA mainly learns between two key parts resume, i.e., skills work experiences. The pre-trained language model firstly employed generate word embeddings catch resume. A greedy matching approach...

Network anomaly detection is important for detecting and reacting to the presence of network attacks. In this paper, we propose a novel method effectively leverage features in anomalies, named FDEn, consisting flow-based Feature Derivation (FD) prior knowledge incorporated Ensemble models (En<inf>pk</inf>). To mine effective information features, 149 are derived enrich feature set original data with covering more characteristics traffic. these effectively, an ensemble model En<inf>pk</inf>,...

Due to the increasing arriving rate and complex relationship of behavior data streams, how detect sequential anomaly in an efficient accurate manner has become emerging challenge. However, most existing literature simply calculates score for segmented sequence, there is limited work going deep investigate stream segment structural relationship. Moreover, studies cannot meet efficiency requirements because large number projected subsequences. In this article, we propose EADetection, detection...

Distance Measuring between two mixed data objects is the basis of many learning algorithms. The complex relevance heterogeneous – various types/scales attributes has a significant influence on measured results. In this paper, we propose an End-to-End method for mixe d based deep learning, called E2DM. Existing methods confuse space by mapping discrete attribute values to new continuous values, or discretize without considering relevance. contrast, E2DM directly manipulates original with...

Payload anomaly detection can discover malicious behaviors hidden in network packets. It is hard to handle payload due its various possible characters and complex semantic context, thus identifying abnormal also a non-trivial task. Prior art only uses the n-gram language model extract features, which directly leads ultra-high-dimensional feature space fails capture context semantics fully. Accordingly, this paper proposes word embedding-based context-sensitive flow method (termed WECAD)....