Current Time Slice Research Articles

Successfully remembering a belief and the problem of forgotten evidence

The problem of forgotten evidence consists of a pair of scenarios originally proposed by Alvin Goldman. In the “forgotten good evidence” and “forgotten bad evidence” scenarios, subjects hold the same memory belief while irreversibly forgetting its original, though different, pieces of evidence. The two scenarios pose a series of challenges to current time slice (CTS) theories, which posit that memory beliefs are justified solely by contemporaneous states. Goldman’s two scenarios pose an apparent dilemma to CTS theories given a naïve picture of how a memory belief is successfully retained while its evidence is irreversibly forgotten. In my view, however, CTS theories may find a solution to the apparent problem by carefully examining the conditions under which a memory belief is successfully retained while its evidence is completely forgotten. Namely, the two scenarios overlook an important difference between forgetting good evidence and forgetting bad evidence.

Community Detection Based on Graph Representation Learning in Evolutionary Networks

Aiming at analyzing the temporal structures in evolutionary networks, we propose a community detection algorithm based on graph representation learning. The proposed algorithm employs a Laplacian matrix to obtain the node relationship information of the directly connected edges of the network structure at the previous time slice, the deep sparse autoencoder learns to represent the network structure under the current time slice, and the K-means clustering algorithm is used to partition the low-dimensional feature matrix of the network structure under the current time slice into communities. Experiments on three real datasets show that the proposed algorithm outperformed the baselines regarding effectiveness and feasibility.

A time-aware dynamic service quality prediction approach for services

Dynamic Quality of Service (QoS) prediction for services is currently a hot topic and a challenge for research in the fields of service recommendation and composition. Our paper addresses the problem with a Time-aWare service Quality Prediction method (named TWQP), a two-phase approach with one phase based on historical time slices and one on the current time slice. In the first phase, if the user had invoked the service in a previous time slice, the QoS value for the user calling the service on the next time slice is predicted on the basis of the historical QoS data; if the user had not invoked the service in a previous time slice, then the Covering Algorithm (CA) is applied to predict the missing values. In the second phase, we predict the missing values for the current time slice according to the results of the previous phase. A large number of experiments on a real-world dataset, WS-Dream, show that, when compared with the classical QoS prediction algorithms, our proposed method greatly improves the prediction accuracy.

Polynomial-Time Algorithm for Learning Optimal BFS-Consistent Dynamic Bayesian Networks.

Dynamic Bayesian networks (DBN) are powerful probabilistic representations that model stochastic processes. They consist of a prior network, representing the distribution over the initial variables, and a set of transition networks, representing the transition distribution between variables over time. It was shown that learning complex transition networks, considering both intra- and inter-slice connections, is NP-hard. Therefore, the community has searched for the largest subclass of DBNs for which there is an efficient learning algorithm. We introduce a new polynomial-time algorithm for learning optimal DBNs consistent with a breadth-first search (BFS) order, named bcDBN. The proposed algorithm considers the set of networks such that each transition network has a bounded in-degree, allowing for p edges from past time slices (inter-slice connections) and k edges from the current time slice (intra-slice connections) consistent with the BFS order induced by the optimal tree-augmented network (tDBN). This approach increases exponentially, in the number of variables, the search space of the state-of-the-art tDBN algorithm. Concerning worst-case time complexity, given a Markov lag m, a set of n random variables ranging over r values, and a set of observations of N individuals over T time steps, the bcDBN algorithm is linear in N, T and m; polynomial in n and r; and exponential in p and k. We assess the bcDBN algorithm on simulated data against tDBN, revealing that it performs well throughout different experiments.

Iterative Feedback Based Manifold-Ranking for Update Summary

The update summary as defined for the DUC2007 new task aims to capture evolving information of a single topic over time. It delivers focused information to a user who has already read a set of older documents covering the same topic. This paper presents a novel manifold-ranking frame based on iterative feedback mechanism to this summary task. The topic set is extended by using the summarization of previous timeslices and the first sentences of documents in current timeslice. Iterative feedback mechanism is applied to model the dynamically evolving characteristic and represent the relay propagation of information in temporally evolving data. Modified manifold-ranking process also can naturally make use of both the relationships among all the sentences in the documents and relationships between the topic and the sentences. The ranking score for each sentence obtained in the manifold-ranking process denotes the importance of sentence biased towards topic, and then the greedy algorithm is employed to rerank the sentences for removing the redundant information. The summary is produced by choosing the sentences with high ranking score. Experiments on dataset of DUC2007 update task demonstrate the encouraging performance of the proposed approach.