Temporal Ranking Research Articles

Learning rewards from exploratory demonstrations using probabilistic temporal ranking

AbstractInformative path-planning is a well established approach to visual-servoing and active viewpoint selection in robotics, but typically assumes that a suitable cost function or goal state is known. This work considers the inverse problem, where the goal of the task is unknown, and a reward function needs to be inferred from exploratory example demonstrations provided by a demonstrator, for use in a downstream informative path-planning policy. Unfortunately, many existing reward inference strategies are unsuited to this class of problems, due to the exploratory nature of the demonstrations. In this paper, we propose an alternative approach to cope with the class of problems where these sub-optimal, exploratory demonstrations occur. We hypothesise that, in tasks which require discovery, successive states of any demonstration are progressively more likely to be associated with a higher reward, and use this hypothesis to generate time-based binary comparison outcomes and infer reward functions that support these ranks, under a probabilistic generative model. We formalise this probabilistic temporal ranking approach and show that it improves upon existing approaches to perform reward inference for autonomous ultrasound scanning, a novel application of learning from demonstration in medical imaging while also being of value across a broad range of goal-oriented learning from demonstration tasks.

A Timely Application-Temporal methods, past, present, and future.

Eating is a dynamic experience, and temporal sensory methods have been proposed to document how products change over the course of consumption or use (nonfood). A search of online databases yielded approximately 170 sources related to temporal evaluation of food products that were compiled and reviewed. This review summarizes the evolution of temporal methodologies (past), offers guidance in selecting appropriate methods (present), and provides insights into the future of temporal methodologies in the sensory space. Temporal methods have evolved to document a variety of characteristics in food products including how the intensity of a specific attribute changes over time (Time-Intensity), which specific attribute is dominant at each time during evaluation (Temporal Dominance of Sensations), which attributes are present at each time point during evaluation (Temporal Check-All-That-Apply), and many others (Temporal Order of Sensations, Attack-Evolution-Finish, and Temporal Ranking). In addition to documenting the evolution of temporal methods, this review considers the selection of an appropriate temporal method based on the objective and scope of research. When choosing a temporal method, researchers should also consider the selection of panelists to perform the temporal evaluation. Future temporal research should focus on validation of new temporal methods and explore how methods can be implemented and improved to add to the usefulness of temporal techniques for researchers.

Ranking Models for the Temporal Dimension of Text

Temporal features of text have been shown to improve clustering and organization of documents, text classification, visualization, and ranking. Temporal ranking models consider the temporal expressions found in text (e.g., “in 2021” or “last year”) as time units, rather than as keywords, to define a temporal relevance and improve ranking. This article introduces a new class of ranking models called Temporal Metric Space Models (TMSM), based on a new domain for representing temporal information found in documents and queries, where each temporal expression is represented as a time interval . Furthermore, we introduce a new frequency-based baseline called Temporal BM25 (TBM25). We evaluate the effectiveness of each proposed metric against a purely textual baseline, as well as several variations of the metrics themselves, where we change the aggregate function, the time granularity and the combination weight. Our extensive experiments on five test collections show statistically significant improvements of TMSM and TBM25 over state-of-the-art temporal ranking models. Combining the temporal similarity scores with the text similarity scores always improves the results, when the combination weight is between 2% and 6% for the temporal scores. This is true also for test collections where only 5% of queries contain explicit temporal expressions.

Temporal ranking for characterization and improved discrimination of protein beverages

AbstractWe propose a new temporal sensory method called temporal ranking (TR) in which assessors indicate and rank the three most noticeable sensations at every time point. The TR method was compared to temporal‐check‐all‐that‐apply (TCATA) in two trained‐panel studies, one study involving six ready‐to‐mix (RTM) protein beverages and one study involving seven ready‐to‐drink (RTD) protein beverages. In each study, the same attributes were used in both methods; six attributes were evaluated for RTMs and 10 attributes for RTDs. A trained sensory panel (n = 10) completed TCATA and temporal ranking (TR) training exercises, then evaluated each beverage in triplicate using each method in a replicated balanced randomized design. To evaluate each temporal method (TR and TCATA), each test beverage was compared with the sucrose‐ or sucralose‐sweetened control beverage within each study (RTM and RTD). Although results from TR and TCATA often coincided, TR better differentiated the protein beverage formulations on more sensory attributes and detected differences between the test and control beverages (p < .05) when TCATA did not. Overall, TR was found to be more sensitive in detecting sensory differences than TCATA, and thus could improve the guidance for the development and formulation of foods.Practical applicationsThis study proposes a new temporal method, temporal ranking, which has assessors continuously rank the three most noticeable attributes when evaluating a beverage. Temporal ranking data can give improved guidance, especially for products that might have side flavors, such as natural nonnutritive sweeteners or alternative protein sources. Further application of findings and methodologies from this study may help guide development and formulation of foods.

Time-aware evidence ranking for fact-checking

Truth can vary over time. Fact-checking decisions on claim veracity should therefore take into account temporal information of both the claim and supporting or refuting evidence. In this work, we investigate the hypothesis that the timestamp of a Web page is crucial to how it should be ranked for a given claim. We delineate four temporal ranking methods that constrain evidence ranking differently and simulate hypothesis-specific evidence rankings given the evidence timestamps as gold standard. Evidence ranking in three fact-checking models is ultimately optimized using a learning-to-rank loss function. Our study reveals that time-aware evidence ranking not only surpasses relevance assumptions based purely on semantic similarity or position in a search results list, but also improves veracity predictions of time-sensitive claims in particular.

Time-Aware Evidence Ranking for Fact-Checking

Truth can vary over time. Fact-checking decisions on claim veracity should therefore take into account temporal information of both the claim and supporting or refuting evidence. In this work, we investigate the hypothesis that the timestamp of a Web page is crucial to how it should be ranked for a given claim. We delineate four temporal ranking methods that constrain evidence ranking differently and simulate hypothesis-specific evidence rankings given the evidence timestamps as gold standard. Evidence ranking in three fact-checking models is ultimately optimized using a learning-to-rank loss function. Our study reveals that time-aware evidence ranking not only surpasses relevance assumptions based purely on semantic similarity or position in a search results list, but also improves veracity predictions of time-sensitive claims in particular.

RankBrushers: interactive analysis of temporal ranking ensembles

Temporal ranking ensembles indicate time-evolving multivariate rankings. Such data can be commonly found in our daily life, for example, different rankings of universities (QS, ARWU, THE, and USNews) over year and those of NBA players over season. Effective analysis and tracking of rankings allow users to gain insights into the overall ranking change over time and seek the explanation for the change. This paper introduces a novel visual analytics approach for characterizing and visualizing the uncertainty, dynamics, and differences of ranking ensemble data. A novel visual design is proposed to characterize the evolution pattern, distribution, and uncertainty of a large number of temporal ranking ensembles. The evolutionary ranking ensembles are progressively explored, tracked, and compared by means of an intuitive visualization system. Two case studies and a task-driven user study conducted on real datasets demonstrate the effectiveness and feasibility of the implemented system.

LEARNING WORD RELATEDNESS OVER TIME FOR TEMPORAL RANKING

Queries and ranking with temporal aspects gain significant attention in field of Information Retrieval. While searching for articles published over time, the relevant documents usually occur in certain temporal patterns. Given a query that is implicitly time sensitive, we develop a temporal ranking using the important times of query by drawing from the distribution of query trend relatedness over time. We also combine the model with Dual Embedding Space Model (DESM) in the temporal model according to document timestamp. We apply our model using three temporal word embeddings algorithms to learn relatedness of words from news archive in Bahasa Indonesia: (1) QT-W2V-Rank using Word2Vec (2) QT-OW2V-Rank using OrthoTrans-Word2Vec (3) QT-DBE-Rank using Dynamic Bernoulli Embeddings. The highest score was achieved with static word embeddings learned separately over time, called QT-W2V-Rank, which is 66% in average precision and 68% in early precision. Furthermore, studies of different characteristics of temporal topics showed that QT-W2V-Rank is also more effective in capturing temporal patterns such as spikes, periodicity, and seasonality than the baselines.

Venus: Scalable Real-Time Spatial Queries on Microblogs with Adaptive Load Shedding

Microblogging services have become among the most popular services on the web in the last few years. This led to significant increase in data size, speed, and applications. This paper presents Venus ; a system that supports real-time spatial queries on microblogs. Venus supports its queries on a spatial boundary $R$ and a temporal boundary $T$ , from which only the top- $k$ microblogs are returned in the query answer based on a spatio-temporal ranking function. Supporting such queries requires Venus to digest hundreds of millions of real-time microblogs in main-memory with high rates, yet, it provides low query responses and efficient memory utilization. To this end, Venus employs: (1) an efficient in-memory spatio-temporal index that digests high rates of incoming microblogs in real time, (2) a scalable query processor that prune the search space, $R$ and $T$ , effectively to provide low query latency on millions of items in real time, and (3) a group of memory optimization techniques that provide system administrators with different options to save significant memory resources while keeping the query accuracy almost perfect. Venus memory optimization techniques make use of the local arrival rates of microblogs to smartly shed microblogs that are old enough not to contribute to any query answer. In addition, Venus can adaptively, in real time, adjust its load shedding based on both the spatial distribution and the parameters of incoming query loads. All Venus components can accommodate different spatial and temporal ranking functions that are able to capture the importance of each dimension differently depending on the applications requirements. Extensive experimental results based on real Twitter data and actual locations of Bing search queries show that Venus supports high arrival rates of up to 64 K microblogs/second and average query latency of 4 msec.

A query expansion approach for social media data extraction

Social media data analysis has been a significant topic of interest in both academia and industry over the past few years. The availability of the increasingly huge amount of social media data is changing our attitude towards social media data analytics which has moved from extracting more meaningful information to predicting social trends. Recently, an increasing number of approaches have been proposed on the applications of social media data. However, they mostly focus on processing the query result data which is often inadequate as the query terms are not easily determined by the users. Taking into consideration this shortcoming, this paper aims to examine the query side in order to provide a better solution for social media data analytics. We propose a Wikipedia-based query expansion and temporal ranking technique for social media data to extract more meaningful answers.

A Survey on Temporal Information Retrieval Systems

Temporal Information Retrieval is an emerging research area in the field of Information Retrieval. Due to the immense amount of data in the WWW, and because the contents of documents are strongly time-dependent, it is very tough for the user to retrieve the relevant documents. Traditional Information Retrieval approaches based on topic similarity alone is not sufficient for the search in temporal document collections. The time dimension available in the documents should be incorporated with document ranking for efficient retrieval. This survey gives an introduction to Temporal Information Retrieval and explores the different time-aware retrieval methods and temporal ranking methods for specific types of time-sensitive queries.

Utilizing Related Samples to Enhance Interactive Concept-Based Video Search

One of the main challenges in interactive concept-based video search is the problem of insufficient relevant samples, especially for queries with complex semantics. In this paper, “related samples” are exploited to enhance interactive video search. The related samples refer to those video segments that are relevant to part of the query rather than the entire query. Compared to the relevant samples which may be rare, the related samples are usually plentiful and easy to find in search results. Generally, the related samples are visually similar and temporally neighboring to the relevant samples. Based on these two characters, we develop a visual ranking model that simultaneously exploits the relevant, related, and irrelevant samples, as well as a temporal ranking model to leverage the temporal relationship between related and relevant samples. An adaptive fusion method is then proposed to optimally explore these two ranking models to generate search results. We conduct extensive experiments on two real-world video datasets: TRECVID 2008 and YouTube datasets. As the experimental results show, our approach achieves at least 96% and 167% performance improvements against the state-of-the-art approaches on the TRECVID 2008 and YouTube datasets, respectively.

Top-k queries on temporal data

The database community has devoted extensive amount of efforts to indexing and querying temporal data in the past decades. However, insufficient amount of attention has been paid to temporal ranking queries. More precisely, given any time instance t, the query asks for the top-k objects at time t with respect to some score attribute. Some generic indexing structures based on R-trees do support ranking queries on temporal data, but as they are not tailored for such queries, the performance is far from satisfactory. We present the Seb-tree, a simple indexing scheme that supports temporal ranking queries much more efficiently. The Seb-tree answers a top-k query for any time instance t in the optimal number of I/Os in expectation, namely, $${O\left({\rm log}_B\,\frac{N}{B}+\frac{k}{B}\right)}$$ I/Os, where N is the size of the data set and B is the disk block size. The index has near-linear size (for constant and reasonable k max values, where k max is the maximum value for the possible values of the query parameter k), can be constructed in near-linear time, and also supports insertions and deletions without affecting its query performance guarantee. Most of all, the Seb-tree is especially appealing in practice due to its simplicity as it uses the B-tree as the only building block. Extensive experiments on a number of large data sets, show that the Seb-tree is more than an order of magnitude faster than the R-tree based indexes for temporal ranking queries.

Basic Insights in Pricing Basket Credit Derivatives

Basket credit derivatives are those financial contracts whose pay–out depends on the credit events (‘failure to pay’, ‘default’, etc.) characterizing a portfolio of bonds or loans over a determined time horizon.We have two main categories of basket credit derivatives. The first is characterized by a pay–out depending on the temporal ranking of the credit events: first–to–default, second–to–default, etc. The second is characterized by a pay–out depending on the percentiles of the portfolio's loss distribution induced by the credit events. The latter is often embedded in securitizations of portfolios of bonds or loans, i.e. CDO.This paper proposes some basic insights in the pricing of these particularly complex credit derivatives. Whenever possible, we will try to find an analytical approximation to the exact pricing formula, if a closed form solution is not available.(J.E.L.: G13).

Using Q-mode factor analysis in biostratigraphic ordination

Although traditionally restricted to the analysis of closed compositional data, the Imbrie Q-mode method appears also to be of utility when applied to binary data sets common in biostratigraphy. Its advantages include its utilization of the tally concept, and its ability to analyze very large numbers of species simultaneously. When the structure of the data set is simple, the method appears to be capable of ranking samples along a temporal gradient, or coenocline, defined by the second unroated factor. Varimax and oblique rotations do not appear to increase the efficacy of this ranking. Even when facies effects are important, a temporal ranking of samples can be achieved, providing that the variation due to time is greater than that due to facies.