Optimized Query Algorithms for Top- K Group Skyline

Skyline query is a typical multiobjective query and optimization problem, which aims to find out the information that all users may be interested in a multidimensional data set. Multiobjective optimization has been applied in many scientific fields, including engineering, economy, and logistics. It is necessary to make the optimal decision when two or more conflicting objectives are weighed. For example, maximize the service area without changing the number of express points, and in the existing business district distribution, find out the area or target point set whose target attribute is most in line with the user’s interest. Group Skyline is a further extension of the traditional definition of Skyline. It considers not only a single point but a group of points composed of multiple points. These point groups should not be dominated by other point groups. For example, in the previous example of business district selection, a single target point in line with the user’s interest is not the focus of the research, but the overall optimality of all points in the whole target area is the final result that the user wants. This paper focuses on how to efficiently solve top- k group Skyline query problem. Firstly, based on the characteristics that the low levels of Skyline dominate the high level points, a group Skyline ranking strategy and the corresponding SLGS algorithm on Skyline layer are proposed according to the number of Skyline layer and vertices in the layer. Secondly, a group Skyline ranking strategy based on vertex coverage is proposed, and corresponding VCGS algorithm and optimized algorithm VCGS+ are proposed. Finally, experiments verify the effectiveness of this method from two aspects: query response time and the quality of returned results.

CRank: Reusable Word Importance Ranking for Text Adversarial Attack

Deep learning models have been widely used in natural language processing tasks, yet researchers have recently proposed several methods to fool the state-of-the-art neural network models. Among these methods, word importance ranking is an essential part that generates text adversarial examples, but suffers from low efficiency for practical attacks. To address this issue, we aim to improve the efficiency of word importance ranking, making steps towards realistic text adversarial attacks. In this paper, we propose CRank, a black box method utilized by our innovated masking and ranking strategy. CRank improves efficiency by 75% at the ’cost’ of only a 1% drop of the success rate when compared to the classic method. Moreover, we explore a new greedy search strategy and Unicode perturbation methods.

Domain Model Discovery from Textbooks for Computer Programming Intelligent Tutors

We present a novel approach to intro-to-programming domain model discovery from textbooks using an over-generation and ranking strategy. We first extract candidate key phrases from each chapter in a Computer Science textbook focusing on intro-to-programming and then rank those concepts according to a number of metrics such as the standard tf-idf weight used in information retrieval and metrics produced by other text ranking algorithms. Specifically, we conduct our work in the context of developing an intelligent tutoring system for source code comprehension for which a specification of the key programming concepts is needed - the system monitors students' performance on those concepts and scaffolds their learning process until they show mastery of the concepts. Our experiments with programming concept instruction from Java textbooks indicate that the statistical methods such as KP Miner method are quite competitive compared to other more sophisticated methods. Automated discovery of domain models will lead to more scalable Intelligent Tutoring Systems (ITSs) across topics and domains, which is a major challenge that needs to be addressed if ITSs are to be widely used by millions of learners across many domains.

Author Correction: The development of a scoring and ranking strategy for a patient-tailored adverse drug reaction prediction in polypharmacy

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Multi-Objective Task Scheduling Optimization in Spatial Crowdsourcing

Recently, with the development of mobile devices and the crowdsourcing platform, spatial crowdsourcing (SC) has become more widespread. In SC, workers need to physically travel to complete spatial–temporal tasks during a certain period of time. The main problem in SC platforms is scheduling a set of proper workers to achieve a set of spatial tasks based on different objectives. In actuality, real-world applications of SC need to optimize multiple objectives together, and these objectives may sometimes conflict with one another. Furthermore, there is a lack of research dealing with the multi-objective optimization (MOO) problem within an SC environment. Thus, in this work we focused on task scheduling based on multi-objective optimization (TS-MOO) in SC, which is based on maximizing the number of completed tasks, minimizing the total travel costs, and ensuring the balance of the workload between workers. To solve the previous problem, we developed a new method, i.e., the multi-objective task scheduling optimization (MOTSO) model that consists of two algorithms, namely, the multi-objective particle swarm optimization (MOPSO) algorithm with our fitness function Alabbadi, et al. and the ranking strategy algorithm based on the task entropy concept and task execution duration. The main purpose of our ranking strategy is to improve and enhance the performance of our MOPSO. The primary goal of the proposed MOTSO model is to find an optimal solution based on the multiple objectives that conflict with one another. We conducted our experiment with both synthetic and real datasets; the experimental results and statistical analysis showed that our proposed model is effective in terms of maximizing the number of completed tasks, minimizing the total travel costs, and balancing the workload between workers.