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Work in information retrieval has largely been centered around ranking and relevance: given a query, return some number of results ordered by relevance to the user. The problem of result list truncation, or where to truncate the ranked list of results, ...

Key points detection is crucial for signal analysis by marking the identification points of specific events. Deep learning methods have been introduced into key points detection tasks due to their significant representation learning ability. ...

The utility of a match in a two-sided matching market often depends on a variety of characteristics of the two agents (e.g., a buyer and a seller) to be matched. In contrast to the matching market literature, this utility may best be modeled by a general ...

We consider a matching market where buyers and sellers arrive according to independent Poisson processes at the same rate and independently abandon the market if not matched after an exponential amount of time with the same mean. In this centralized ...

Modeling extreme latencies in communication networks can contribute information to network planning and flow admission under service level agreements. Extreme Value Theory is such an approach that utilizes real-world measurement data. It is often ...

Zero-inflated, heavy-tailed spatiotemporal data is common across science and engineering, from climate science to meteorology and seismology. A central modeling objective in such settings is to forecast the intensity, frequency, and timing of extreme and ...

In this paper, we study an N server fork-join queue with nearly deterministic arrival and service times. Specifically, we present a fluid limit for the maximum queue length as N→∞. This fluid limit depends on the initial number of tasks. In order to prove ...

Data‐driven anomaly detection methods typically build a model for the normal behavior of the target system, and score each data instance with respect to this model. A threshold is invariably needed to identify data instances with high (or low) ...

Fairness is a hot topic in recommender system research in recent years. Researchers have resorted to regularization and other techniques to reduce fairness problems. However, a lot of research literature adopts classic evaluation metrics for recommender ...

Regarding extreme value theory, the unseen novel classes in the open-set recognition can be seen as the extreme values of training classes. Following this idea, we introduce the margin and coverage distribution to model the training classes. A ...

With the rapid development of big data, smart city and artificial intelligence, anomaly detection technology for data streams has been widely used in big data detection, video surveillance, network intrusion detection, intelligent security analysis and ...

As computer architectures evolve, guaranteeing that Real-Time Systems’ (RTSs’) timing requirements are met through Worst Case Execution Time (WCET) upper bounds becomes increasingly difficult. Techniques such as Measurement-Based Probabilistic Timing ...

Anomaly detection in time series has attracted considerable attention due to its importance in many real-world applications including intrusion detection, energy management and finance. Most approaches for detecting outliers rely on either manually set ...

Extreme Value Theory (EVT) has been historically used in domains such as finance and hydrology to model worst-case events (e.g., major stock market incidences). EVT takes as input a sample of the distribution of the variable to model and fits the tail ...

Novelty detection or one-class classification starts from a model describing some type of 'normal behaviour' and aims to classify deviations from this model as being either novelties or anomalies.

In this paper the problem of novelty detection for point ...

The main goal of a retrieval model is to measure the degree of relevance of a document with respect to the given query. Probabilistic models are widely used to measure the likelihood of relevance of a document by combining within document term frequency ...

It is important to understand to what extent, and in what perspectives, cyber attacks can be predicted. Despite its evident importance, this problem was not investigated until very recently, when we proposed using the innovative methodology of gray-box ...

This work presents a computationally efficient, semiparametric, and nonstationary framework for statistical regression analysis of threshold excesses with systematically missing covariates based on the Generalized Pareto Distribution (GPD). The involved ...

We propose and evaluate a framework for creating and running approximation-enabled MapReduce programs. Specifically, we propose approximation mechanisms that fit naturally into the MapReduce paradigm, including input data sampling, task dropping, and ...

We explore the dependence structure in the sampled sequence of complex networks. We consider randomized algorithms to sample the nodes and study external properties in any associated stationary sequence of characteristics of interest like node degrees, ...

Racing algorithms are often used for offline model selection, where models are compared in terms of their average performance over a collection of problems. In this paper, we present a new racing algorithm variant, Max-Race, which makes decisions based ...