Tuning user profiles based on analyzing dynamic preference in document retrieval systems

Modeling users’ preferences and needs is one of the most important personalization tasks in information retrieval domain. In this paper a model for user profile tuning in document retrieval systems is considered. Methods for tuning the user profile based on analysis of user preferences dynamics are experimentally evaluated to check whether with growing history of user activity the created user profile can converge to his preferences. As the statistical analysis of series of simulations has shown, proposed methods of user profile actualization are effective in the sense that the distance between user preferences and his profile becomes smaller and smaller along with time.     

Enhanced security in internet voting protocol using blind signature and dynamic ballots

In this paper, we introduce an internet voting protocol which satisfies desired security requirements of electronic voting. In the newly proposed protocol, we allow the adversaries to get more power than in any previous works. They can be coercers or vote buyers outside, and corrupted parties inside our system. These adversaries also have ability to collude with each other to ruin the whole system. Our main contribution is to design an internet voting protocol which is unsusceptible to most of sophisticated attacks. We employ the blind signature technique and the dynamic ballots instead of complex cryptographic techniques to preserve privacy in electronic voting. Moreover, we also aim at the practical system by improving the blind signature scheme and removing physical assumptions which have often been used in the previous works.     

The fatigue life of contoured cobalt chrome posterior spinal fusion rods

Intraoperative contouring of posterior rods in lumbar arthrodesis constructs introduces stress concentrations that can substantially reduce fatigue life. The sensitivity of titanium (Ti) and stainless steel (SS) to intraoperative contouring has been established in the literature; however, notch sensitivity has yet to be quantified for cobalt chrome (CoCr), which is now being advocated for use in posterior arthrodesis constructs. The goal of this study is to evaluate the sensitivity of CoCr rods to intraoperative contouring for posterior lumbar screwrod arthrodesis constructs. In this paper lumbar bilateral vertebrectomy models are constructed based on ASTM F1717-01 with curved rods (26-30 degrees total curvature) and poly-axial pedicle screws. Three types of constructs are assembled: first, 5.5 mm SS rods with SS screws (6.5 x 35 mm), second, 6.0 mm Ti rods with Ti screws (7.5 x 35 mm), and third, 6.0 mm CoCr rods with Ti screws (7.5 x 35 mm). All specimens are tested at 4 Hz in dynamic axial compression-bending with a load ratio of ten and maximum load levels of 250, 400, and 700 N until run-out at 2 000 000 cycles. Results are presented that show that the fatigue life of CoCr constructs tend to be greater than Ti constructs at all levels. At the 400 N maximum loading, CoCr lasts an average of 350 000 cycles longer than the Ti constructs. The CoCr constructs are able to sustain the 250 N load until run-out at 2 000 000 cycles but they fail at high load levels (maximum 700 N). The CoCr constructs fail at the neck of the Ti screw at high loads whereas Ti screws fail at the notch induced by contouring. Since CoCr is compatible with magnetic resonance imaging and has high static strength characteristics, the results of this study suggest that it may be an appropriate substitute for Ti.     

Flow measurements in sewers based on image analysis: automatic flow velocity algorithm

Discharges of combined sewer overflows (CSOs) and stormwater are recognized as an important source of environmental contamination. However, the harsh sewer environment and particular hydraulic conditions during rain events reduce the reliability of traditional flow measurement probes. An in situ system for sewer water flow monitoring based on video images was evaluated. Algorithms to determine water velocities were developed based on image-processing techniques. The image-based water velocity algorithm identifies surface features and measures their positions with respect to real world coordinates. A web-based user interface and a three-tier system architecture enable remote configuration of the cameras and the image-processing algorithms in order to calculate automatically flow velocity on-line. Results of investigations conducted in a CSO are presented. The system was found to measure reliably water velocities, thereby providing the means to understand particular hydraulic behaviors.     

Sources and leaching of manganese and iron in the Saigon River Basin, Vietnam

High concentrations of manganese and iron in the Saigon River are major problems for the water supply in Ho Chi Minh City (HCMC), Viet Nam. To identify their sources and leaching processes, we surveyed water quality along the Saigon River and ran batch leaching tests using soil and sediment samples. Two important leaching processes were identified: acidic leaching from acid sulfate soil (ASS) in the middle reaches of the river, and Mn dissolution and Fe reduction from sediments in the downstream reaches. Low pH caused the concurrent release of Fe and Mn from the ASS. In contrast, anoxia caused the release of Fe but not Mn from the sediments, whereas low pH facilitated Mn dissolution. Sediments are a more important source of Mn because of their higher Mn contents (10 times) and release rates (14 times) than those from ASS.     

From big smartphone data to worldwide research: The Mobile Data Challenge

This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of mobile data analysis methodologies. First, we review the Lausanne Data Collection Campaign (LDCC), an initiative to collect unique longitudinal smartphone dataset for the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC, describe the specific datasets used in each of them, discuss the key design and implementation aspects introduced in order to generate privacy-preserving and scientifically relevant mobile data resources for wider use by the research community, and summarize the main research trends found among the 100+ challenge submissions. We finalize by discussing the main lessons learned from the participation of several hundred researchers worldwide in the MDC Tracks.     

Human interaction discovery in smartphone proximity networks

Since humans are fundamentally social beings and interact frequently with others in their daily life, understanding social context is of primary importance in building context-aware applications. In this paper, using smartphone Bluetooth as a proximity sensor to create social networks, we present a probabilistic approach to mine human interaction types in real life. Our analysis is conducted on Bluetooth data continuously sensed with smartphones for over one year from 40 individuals who are professionally or personally related. The results show that the model can automatically discover a variety of social contexts. We objectively validated our model by studying its predictive and retrieval performance.     

VICUR: A human-vision-based algorithm for curve reconstruction

In this paper, a new algorithm, named VICUR, is presented for curve reconstruction problem. From a set of unorganized points, the proposed algorithm can construct curves that look natural to human vision. The VICUR algorithm is based on two connectivity criteria: proximity and good continuation from the prominent Gestalt principles of perception. Experimental results are presented to show the effectiveness of VICUR.     

Design of neural network-based estimator for tool wear modeling in hard turning

Hard turning with cubic boron nitride (CBN) tools has been proven to be more effective and efficient than traditional grinding operations in machining hardened steels. However, rapid tool wear is still one of the major hurdles affecting the wide implementation of hard turning in industry. Better prediction of the CBN tool wear progression helps to optimize cutting conditions and/or tool geometry to reduce tool wear, which further helps to make hard turning a viable technology. The objective of this study is to design a novel but simple neural network-based generalized optimal estimator for CBN tool wear prediction in hard turning. The proposed estimator is based on a fully forward connected neural network with cutting conditions and machining time as the inputs and tool flank wear as the output. Extended Kalman filter algorithm is utilized as the network training algorithm to speed up the learning convergence. Network neuron connection is optimized using a destructive optimization algorithm. Besides performance comparisons with the CBN tool wear measurements in hard turning, the proposed tool wear estimator is also evaluated against a multilayer perceptron neural network modeling approach and/or an analytical modeling approach, and it has been proven to be faster, more accurate, and more robust. Although this neural network-based estimator is designed for CBN tool wear modeling in this study, it is expected to be applicable to other tool wear modeling applications.