An approach to efficient business intelligent system for financial prediction

A concept of business intelligent system for financial prediction is considered in this paper. It provides data needed for fast, precise and good business decision support to all levels of management. The aim of the project is the development of a new online analytical processing oriented on case-based reasoning (CBR) where a previous experience for every new problem is taken into account. Methodological aspects have been tested in practice as a part of the management information system development project of “Novi Sad Fair”. A case study of an improved application of CBR in prediction of future payments is discussed in the paper. This paper is originally presented at The International Conference on Hybrid Information Technology 2006, at the special session on “Intelligent Information Systems for Financial Engineering”, November 2006 in Cheju Island, Korea.     

Inductive proximity sensor with a flat coil and a new differential relaxation oscillator

A high-sensitivity flat-coil inductive proximity sensor microsystem has been realized and successfully tested. The flat coil, made with CMOS-compatible post processes, is connected to a versatile differential relaxation oscillator electronic interface. In this new and simple interface, the excitation, the signal extraction and amplification are assured by only one operational amplifier. Experimental results on the microsystem confirm that large output frequency variations occur when a metallic target approaches the sensor.     

Machine learning in control of functional electrical stimulationsystems for locomotion

Two machine learning techniques were evaluated for automatic design of a rule-based control of functional electrical stimulation (FES) for locomotion of spinal cord injured humans. The task was to learn the invariant characteristics of the relationship between sensory information and the FES-control signal by using off-line supervised training. Sensory signals were recorded using pressure sensors installed in the insoles of a subject's shoes and goniometers attached across the joints of the affected leg. The FES-control consisted of pulses corresponding to time intervals when the subject pressed on the manual push-button to deliver the stimulation during FES-assisted ambulation. The machine learning techniques used were the adaptive logic network (ALN) and the inductive learning algorithm (IL). Results to date suggest that, given the same training data, the IL learned faster than the ALN while both performed the test rapidly. The generalization was estimated by measuring the test errors and it was better with an ALN, especially if past points were used to reflect the time dimension. Both techniques were able to predict future stimulation events. An advantage of the ALN over the IL was that ALN's can be retrained with new data without losing previously collected knowledge. The advantages of the IL over the ALN were that the IL produces small, explicit, comprehensible trees and that the relative importance of each sensory contribution can be quantified     

Transistor oscillator and amplifier grids

Although quasi-optical techniques are applicable to a large variety of solid-state devices, special attention is given to transistors, which are attractive because they can be used as either amplifiers or oscillators. Experimental results for MESFET bar-grid and planar grid oscillators are presented. A MESFET grid amplifier that receives only vertically polarized waves at the input and radiates horizontally polarized waves at the output is discussed. These planar grids can be scaled for operation at millimeter- and submillimeter-wave frequencies. By using modern IC fabrication technology, planar grid oscillators and amplifiers containing thousands of devices can be built, thereby realizing an efficient means for large-scale power combining     

Air trenches for sharp silica waveguide bends

Air trench structures for reduced-size bends in low-index contrast waveguides are proposed. To minimize junction loss, the structures are designed to provide adiabatic mode shaping between low- and high-index contrast regions, which is achieved by the introduction of "cladding tapers." Drastic reduction in effective bend radius is predicted. We present two-dimensional (2-D) finite-difference time-domain/effective index method simulations of bends in representative silica index contrasts. We also argue that substrate loss, while present, can be controlled with such air trenches and reduced to arbitrarily low levels limited only by fabrication capabilities. The required trench depth, given an acceptable substrate loss, is calculated in three dimensions using an approximate equivalent current sheet method and also by a numerical solver for full-vector leaky modes. A simple, compact waveguide T-splitter using air trench bends is presented.     

Challenges and opportunities of multimodal data in human learning: The computer science students' perspective

Multimodal data have the potential to explore emerging learning practices that extend human cognitive capacities. A critical issue stretching in many multimodal learning analytics (MLA) systems and studies is the current focus aimed at supporting researchers to model learner behaviours, rather than directly supporting learners. Moreover, many MLA systems are designed and deployed without learners' involvement. We argue that in order to create MLA interfaces that directly support learning, we need to gain an expanded understanding of how multimodal data can support learners' authentic needs. We present a qualitative study in which 40 computer science students were tracked in an authentic learning activity using wearable and static sensors. Our findings outline learners' curated representations about multimodal data and the non-technical challenges in using these data in their learning practice. The paper discusses 10 dimensions that can serve as guidelines for researchers and designers to create effective and ethically aware student-facing MLA innovations.     

A propositional probabilistic logic with discrete linear time for reasoning about evidence

The aim of the paper is to present a sound, strongly complete and decidable probabilistic temporal logic that can model reasoning about evidence. The formal system developed here is actually a solution of a problem proposed by Halpern and Pucella (J Artif Intell Res 26:1–34, 2006).