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With the exponential growth of information on the Internet, it has become increasingly difficult to find and organize relevant material. Topic detection and tracking (TDT) is a research area addressing this problem. As one of the basic tasks of TDT, topic detection is the problem of grouping all stories, based on the topics they discuss. This paper proposes a new topic detection method (TPIC) based on an incremental clustering algorithm. The proposed topic detection strives to achieve a high accuracy and the capability of estimating the true number of topics in the document corpus. Term reweighing algorithm is used to accurately and efficiently cluster the given document corpus, and a self-refinement process of discriminative feature identification is proposed to improve the performance of clustering. Furthermore, topics' "aging" nature is used to precluster stories, and Bayesian information criterion (BIC) is used to estimate the true number of topics. Experimental results on linguistic data consortium (LDC) datasets TDT-4 show that the proposed model can improve both efficiency and accuracy

The problem of ranking has recently gained attention in data learning. The goal ranking is to learn a real-valued ranking function that induces a ranking or ordering over an instance space. In this paper, we apply popular Bayesian techniques on ranking support vector machine. We propose a novel differentiable loss function called trigonometric loss function with the desirable characteristic of natural normalization in the likelihood function, and then follow standard Gaussian processes techniques to set up a Bayesian framework. In this framework, Bayesian inference is used to implement model adaptation, while keeping the merits of ranking SVM. Experimental results on data sets indicate the usefulness of this approach.

Dynamic symbolic execution or concolic execution is a powerful method for program analysis and software testing by attaching symbolic execution to the concrete running of a program. This paper proposes an approach to handle aggregate types e.g., pointers, arrays, structures and their complex combinations for the dynamic symbolic execution of C programs. The main idea of the approach is splitting a complex type program variable into a series of primitive type variables. During the concrete execution of a program, a concolic execution engine is provided to observe the operations on every program variable at the level of primitive types, and then the symbolic state of the program is updated. The path constraints which must be satisfied to drive the program running along the current execution path are collected to generate new test data for other paths. Our approach guarantees that only primitive type variables can appear in the symbolic states and path constraints. Based on LLVM byte code instrumentation, we present a new tool, called PathWalker, which implements this approach. Experimental results reveal that PathWalker is effective to deal with complex types in C codes.

Courses scheduling is indeed momentously obligatory event placement job in the presence of mutually interlinked conditions. The problem has been broadly noticed by the research community due to its complexity. In the research paper, a novel triphasic approach is applied. First phase is consisted of creating and initialize the population, however the phase is wholly concentrated over eliminating all the hard constraints from genome. Second phase is intended to move down the number of violations and spread up the events over layout. First two phases are employed by two distinguished Local Search algorithms. On the other hand, third phase which is comprised over Genetic Algorithm, eventually tries to converge the solution to best available fitness point of the search space. The research method is testified on genuine real world data set. Promising results validate the adopted methodology. The main advantages are observed, elimination of hard constraints on very first stage. Second, noticeably drop of computational time for GA by using preprocessed genome of partial solutions. Additionally, efficient deployments and convenience to end-users are prime objectives.

University timetabling is very hectic resources allocation job against tough constraints. The problem is broadly recognized on account of its crucial significance for curriculum activities. Its intensive complexity has challenged the researchers from diverse disciplines for several decades. In the research paper, a novel interrelated approach is employed that primarily depends on Genetic Algorithm supported by Local Search algorithm. Local Search systematizes the events in each timetabling chromosome up to certain degree. Later on GA is likely to obtain more feasible solution available on the search space. The approach has been applied on real dataset and the research direction is validated by promising outcome. The bottom line is minimizing computational time for GA by initializing the set of partial solutions. In addition, exploitation of the resources usage and effective events deployment are key objectives.

University Timetabling is a challenging problem has caught the eye of researchers for numerous decades due to its unavoidable significance along with NP-hard complexity. The research paper presents the approach consisted of two distinguishable phases. First one completely neutralizes the search space from hard constraints and consequently ends with a preliminary solution which feeds the successive processing step. Subsequently the second phase deals with the soft constraints initially scan throughout the layout to detect and mark the time slots with their associated penalties afterward the mechanism iteratively pushes the penalized events to the locations raised with minimum penalty cost. Key advantages of using this technique are to build an efficient deployment and maximum utilization of the resources.

Rough set theory has been widely and successfully used in data mining, especially in classification field. But most existing rough set based classification approaches require computing optimal attribute reduction, which is usually intractable and many problems related to it have been shown to be NP-hard. Although approximate algorithms exist, they also tend to be computationally expensive. This paper presents a novel rough set method for classification, which does not require computing attribute reduction. It stepwise investigates condition attributes and outputs the classification rules induced by them, which is just like the strategy of "on the fly". The theoretical analysis and the empirical study show that the proposed method is effective and efficient. Index Terms—rough set, attribute reduction, data mining, classification

During the process of mining maximal frequent item sets, when minimum support is little, superset checking is a kind of time-consuming and frequent operation in the mining algorithm. In this paper, a new algorithm FPMFI (frequent pattern tree for maximal frequent item sets) for mining maximal frequent item sets is proposed. It adopts a new superset checking method based on projection of the maximal frequent item sets, which efficiently reduces the cost of superset checking. In addition, FPMFI also compresses the conditional FP-Tree (frequent pattern tree) greatly by deleting the redundant information, which can reduce the cost of accessing the tree. It is proved by theoretical analysis that FPMFI has superiority and it is revealed by experimental comparison that the performance of FPMFI is superior to that of the similar algorithm based on FP-Tree more than one time.