The k-nearest neighbour (KNN) technique is a simple yet effective method for classification. In this paper, we propose an efficient weighted nearest neighbour classification algorithm, called PINE, using vertical data representation. A metric called HOBBit is used as the distance metric. The PINE algorithm applies a Gaussian podium function to set weights to different neighbours. We compare PIN...

We introduce the language LP that extends logic programs under the stable model semantics to allow weighted rules similar to the way Markov Logic considers weighted formulas. LP is a proper extension of the stable model semantics to enable probabilistic reasoning, providing a way to handle inconsistency in answer set programming. We also show that the recently established logical relationship b...

We describe an infinitary logic for metric structures which is analogous to Lω1,ω . We show that this logic is capable of expressing several concepts from analysis that cannot be expressed in finitary continuous logic. Using topological methods, we prove an omitting types theorem for countable fragments of our infinitary logic. We use omitting types to prove a two-cardinal theorem, which yields...

In the last years renewed investigation of operator precedence languages (OPL) led to discover important properties thereof: OPL are closed with respect to all major operations, are characterized, besides the original grammar family, in terms of an automata family and an MSO logic; furthermore they significantly generalize the well-known visibly pushdown languages (VPL). In another area of rese...

This study aims to develop content-based image retrieval (CBIR) system for the retrieval of T1-weighted contrast-enhanced MR (CE-MR) images of brain tumors. When a tumor region is fed to the CBIR system as a query, the system attempts to retrieve tumors of the same pathological category. The bag-of-visual-words (BoVW) model with partition learning is incorporated into the system to extract info...

We introduce the concept of weighted rules under the stable model semantics following the log-linear models of Markov Logic. This provides versatile methods to overcome the deterministic nature of the stable model semantics, such as resolving inconsistencies in answer set programs, ranking stable models, associating probability to stable models, and applying statistical inference to computing w...

This paper presents a new complexity metric for Object-Oriented (OO) design measurement to find at the design stage whether the classes become more complex, Moderate complex or less complex. The proposed metric is theoretically evaluated against the Weyuker’s properties as well as empirically evaluated against three open source software system. Furthermore, for validating the validity of new co...

Dynamic programming algorithms in statistical natural language processing can be easily described as weighted logic programs. We give a notation and semantics for such programs. We then describe several source-to-source transformations that affect a program’s efficiency, primarily by rearranging computations for better reuse or by changing the search strategy. We present practical examples of u...

different industrial decision makers and managers in our country, try to select and organize locations for aggregating industrial units, estates and areas with respect to land use planning visions and industrial development strategies. in this regard, considering large quantity of the input data and diverse criteria affecting this application, it is complicated and difficult to optimally make d...

This dissertation deals with the problem of characterizing the controllers which are potentially optimal in the robustness optimization for (weighted) normalized coprime factor/gap metric uncertainty, which is the basis for the H1 Loop Shaping Design Method. This dissertation considers single-input-single-output systems. Given a plant P and a stabilizing controller C, we ask if C can be obtaine...