Inductive Logic Programming (ILP) [9, 11] is the area of AI which deals with the induction of hypothesised predicate definitions from examples and background knowledge. Logic programs are used as a single representation for examples, background knowledge and hypotheses. ILP is differentiated from most other forms of Machine Learning (ML) both by its use of an expressive representation language ...

this research compared ratings of task performance and contextual performance from three different sources: self, peer, and supervisor. participants were service industry employees in the service industries in yogyakarta, indonesia. a sample of 146 employees and 40 supervisors   from the service industries provided ratings of task performance and contextual performance. the results indicated th...

Cross efficiency evaluation was developed as an extension of DEA. But the traditional DEA models usually have alternative optimal solutions and, as a result, cross efficiency scores may not be unique. It is recommended that without changing the DEA efficiency scores, the secondary goal should be introduced for optimization of the inputs/outputs weights.  Several reports evaluated the perfo...

In our study, we improve our Parallel Inductive Logic Programming (ILP) System to enable super linear speedup. This improvement redesigns several features of our ILP learning system and our parallel mechanism. The redesigned ILP learning system searches and gathers all rules that have the same evaluation in the learning. The redesigned parallel mechanism adds a communication protocol for sharin...

The aim of the study was to determine differences in hand-grip strength and strength-related anthropometric parameters between highest-performance international paddlers (HILP) other (ILP) canoe slalom. In male competitors (n=48; 23 canoeists 25 kayakers) who took part 2018 European championship event slalom Prague, Czech Republic representing 15 countries were measured two days prior event. Di...

Current microprocessors improve performance by exploiting instruction-level parallelism (ILP). ILP hardware techniques such as multiple instruction issue, out-of-order (dynamic) issue, and non-blocking reads can accelerate both computation and data memory references. Since computation speeds have been improving faster than data memory access times, memory system performance is quickly becoming ...

This paper presents an application of stochastic logic programs (SLPs) to learning probabilistic logic rules in protein fold prediction. We apply SLP parameter estimation algorithm to a previous study in which rules have been learned by inductive logic programming (ILP). On the basis of experiments, we demonstrate that probabilistic ILP approaches (eg. SLPs) have advantages for solving multi-cl...

Current microprocessors exploit high levels of instruction level parallelism ILP through techniques such as multiple issue dynamic scheduling and non blocking reads This paper presents the rst detailed analysis of the impact of such processors on shared memory multiprocessors using a detailed execution driven simulator Using this analysis we also exam ine the validity of common direct execution...

Integrated Layer Processing (ILP) has several limitations on current system environments. Performance evaluations show that the main benefits from ILP are rather a reduced number of memory accesses than an improved cache hit rate. Data manipulation characteristics have significant impact on the cache behavior and on the achievable performance gain of ILP. Future protocol characteristics and eff...

In this thesis we describe a novel approach to application of Evolutionary Algorithms (EAs) into the field of Inductive Logic Programming (ILP). One of the main issues of ILP is the time complexity that comes out of the large size of ILP search space. Improving the search efficiency is therefore the main objective of this thesis. To reach this, EAs were used in this work as they have proven to ...