In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Example Input: The subpath planning problem is a branch of the path planning problem, which has widespread applications in automated manufacturing process as well as vehicle and robot navigation. This problem is to find the shortest path or tour subject for travelling a set of given subpaths. The current approaches for dealing with the subpath planning problem are all based on meta-heuristic approaches. It is well-known that meta-heuristic based approaches have several deficiencies. To address them, we propose a novel approximation algorithm in the O(n3) time complexity class, which guarantees to solve any subpath planning problem instance with the fixed ratio bound of 2. Beside the formal proofs of the claims, our empirical evaluation shows that our approximation method acts much better than a state-of-the-art method, both in result and execution time. Note to Practitioners—In some real world applications such as robot and vehicle navigation in structured and industrial environments as well as some of the manufacturing processes such as electronic printing and polishing, it is required for the agent to travel a set of predefined paths. Automating this process includes three steps: 1) capturing the environment of the actual problem and formulating it as a subpath planning problem; 2) solving subpath planning problem to find the near optimal path or tour; 3) command the robot to follow the output. The most challenging phase is the second one that this paper tries to tackle it. To design an effective automation for the aforementioned applications, it is essential to make use of methods with low computational cost but near optimal outputs in the second phase. According to the fact that the length of the final output has a direct effect on the cost of performing the task, it is desirable to incorporate methods with low complexity that can guarantee a bound for the difference between length of the optimal path and 1 ar X iv :1 60 3. 06 21 7v 1 [ cs .R O ] 2 0 M ar 2 01 6 the output. Current approaches for solving subpath planning problem are all meta-heuristic based. These methods do not provide such a bound. And plus, they are usually very time consuming. They may find promising results for some instances of problems, but there is no guarantee that they always exhibit such a good behaviour. In this paper, in order to avoid the issues of metaheuristics methods, we present an approximation algorithm, which provides an appropriate bound for the optimality of its solution. To gauge the performance of proposed methods, we conducted a set of experiments the results of which show that our proposed method finds shorter paths in less time in comparison with a state-of-the-art method.
Example Output: An Approximation Approach for Solving the Subpath Planning Problem

Example Input: The ability to monitor the progress of students’ academic performance is a critical issue to the academic community of higher learning. A system for analyzing students’ results based on cluster analysis and uses standard statistical algorithms to arrange their scores data according to the level of their performance is described. In this paper, we also implemented k-mean clustering algorithm for analyzing students’ result data. The model was combined with the deterministic model to analyze the students’ results of a private Institution in %igeria which is a good benchmark to monitor the progression of academic performance of students in higher Institution for the purpose of making an effective decision by the academic planners. Keywordsk – mean, clustering, academic performance, algorithm.
Example Output: Application of k-Means Clustering algorithm for prediction of Students’ Academic Performance

Example Input: We propose a learning setting in which unlabeled data is free, and the cost of a label depends on its value, which is not known in advance. We study binary classification in an extreme case, where the algorithm only pays for negative labels. Our motivation are applications such as fraud detection, in which investigating an honest transaction should be avoided if possible. We term the setting auditing, and consider the auditing complexity of an algorithm: the number of negative labels the algorithm requires in order to learn a hypothesis with low relative error. We design auditing algorithms for simple hypothesis classes (thresholds and rectangles), and show that with these algorithms, the auditing complexity can be significantly lower than the active label complexity. We also discuss a general competitive approach for auditing and possible modifications to the framework.
Example Output:
Auditing: Active Learning with Outcome-Dependent Query Costs