Ranking Approach for the User Story Prioritization Methods

The AHP （analytic hierarchy process） has been applied in many fields and especially to complex engineering problems and applications. AHP is capable of structuring decision problems and finding mathematically determined judgments built on knowledge and experience. This suggests that AHP should prove useful in agile software development, where complex decisions occur routinely. This paper describes a ranking approach to help stakeholders select the best prioritization method for prioritizing the user stories.     

Telehealth and ubiquitous computing for bandwidth-constrained rural and remote areas

The information and communication technology infrastructure available in rural and remote areas may often not have the bandwidth to support all types of telehealth applications; therefore, for example, some traditionally envisaged videoconferencing-based telehealth applications may not be able to be used or not used in their anticipated form at this time. While the level of broadband services available may impose limitations on these types of telehealth applications, in this review article, we identify applications that allow the maximizing of telehealth benefits in the presence of low-bandwidth connectivity and have potential benefits well-matched to rural and remote area healthcare challenges. In particular, we include consideration of how ubiquitous computing might potentially bring non-traditional approaches to telehealth that can also come into usage more immediately in bandwidth-constrained rural and regional areas. In this article, we review the benefits of ubiquitous computing for rural and remote telehealth including social media-based preventative, peer support and public health communication, mobile phone platforms for the detection and notification of emergencies, wearable and ambient biosensors, the utilization of personal health records including in conjunction with mobile and sensor platforms, chronic condition care and management information systems, and mobile device–enabled video consultation.     

Cryptanalysis of the RCES/RSES image encryption scheme

Recently, a chaos-based image encryption scheme called RCES (also called RSES) was proposed. This paper analyses the security of RCES, and points out that it is insecure against the known/chosen-plaintext attacks: the number of required known/chosen plain-images is only one or two to succeed an attack. In addition, the security of RCES against the brute-force attack was overestimated. Both theoretical and experimental analyses are given to show the performance of the suggested known/chosen-plaintext attacks. The insecurity of RCES is due to its special design, which makes it a typical example of insecure image encryption schemes. A number of lessons are drawn from the reported cryptanalysis of RCES, consequently suggesting some common principles for ensuring a high level of security of an image encryption scheme.     

The development of robot art

Going through a few examples of robot artists who are recognized worldwide, we try to analyze the deepest meaning of what is called “robot art” and the related art field definition. We also try to highlight its well-marked borders, such as kinetic sculptures, kinetic art, cyber art, and cyberpunk. A brief excursion into the importance of the context, the message, and its semiotics is also provided, case by case, together with a few hints on the history of this discipline in the light of an artistic perspective. Therefore, the aim of this article is to try to summarize the main characteristics that might classify robot art as a unique and innovative discipline, and to track down some of the principles by which a robotic artifact can or cannot be considered an art piece in terms of social, cultural, and strictly artistic interest. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Outlier detection using default reasoning

Default logics are usually used to describe the regular behavior and normal properties of domain elements. In this paper we suggest, conversely, that the framework of default logics can be exploited for detecting outliers. Outliers are observations expressed by sets of literals that feature unexpected semantical characteristics. These sets of literals are selected among those explicitly embodied in the given knowledge base. Hence, essentially we perceive outlier detection as a knowledge discovery technique. This paper defines the notion of outlier in two related formalisms for specifying defaults: Reiter's default logic and extended disjunctive logic programs. For each of the two formalisms, we show that finding outliers is quite complex. Indeed, we prove that several versions of the outlier detection problem lie over the second level of the polynomial hierarchy. We believe that a thorough complexity analysis, as done here, is a useful preliminary step towards developing effective heuristics and exploring tractable subsets of outlier detection problems.     

Scheduling algorithms for fault-tolerance in hard-real-time systems

Many time-critical applications require predictable performance in the presence of failures. This paper considers a distributed system with independent periodic tasks which can checkpoint their state on some reliable medium in order to handle failures. The problem of preemptively scheduling a set of such tasks is discussed where every occurrence of a task has to be completely executed before the next occurrence of the same task can start. Efficient scheduling algorithms are proposed which yield sub-optimal schedules when there is provision for fault-tolerance. The performance of the solutions proposed is evaluated in terms of the number of processors and the cost of the checkpoints needed. Moreover, analytical studies are used to reveal interesting trade-offs associated with the scheduling algorithms.This work has been supported by grants from the Italian Ministero dell'Università e della Ricerca Scientifica e Tecnologica and the Consiglio Nazionale delle Ricerche-Progetto Finalizzato Sistemi Informatici e Calcolo Parallelo.     

Artificial convolution neural network techniques and applications for lung nodule detection

We have developed a double-matching method and an artificial visual neural network technique for lung nodule detection. This neural network technique is generally applicable to the recognition of medical image pattern in gray scale imaging. The structure of the artificial neural net is a simplified network structure of human vision. The fundamental operation of the artificial neural network is local two-dimensional convolution rather than full connection with weighted multiplication. Weighting coefficients of the convolution kernels are formed by the neural network through backpropagated training. In addition, we modeled radiologists' reading procedures in order to instruct the artificial neural network to recognize the image patterns predefined and those of interest to experts in radiology. We have tested this method for lung nodule detection. The performance studies have shown the potential use of this technique in a clinical setting. This program first performed an initial nodule search with high sensitivity in detecting round objects using a sphere template double-matching technique. The artificial convolution neural network acted as a final classifier to determine whether the suspected image block contains a lung nodule. The total processing time for the automatic detection of lung nodules using both prescan and convolution neural network evaluation was about 15 seconds in a DEC Alpha workstation.     

On measuring network robustness for weighted networks

Knowledge and Information Systems - Network robustness measures how well network structure is strong and healthy when it is under attack, such as vertices joining and leaving. It has been widely...     

Hyperprolactinemia in end‐stage renal disease and effects of frequent hemodialysis

Introduction: End‐stage renal disease is associated with elevations in circulating prolactin concentrations, but the association of prolactin concentrations with intermediate health outcomes and the effects of hemodialysis frequency on changes in serum prolactin have not been examined. Methods: The FHN Daily and Nocturnal Dialysis Trials compared the effects of conventional thrice weekly hemodialysis with in‐center daily hemodialysis (6 days/week) and nocturnal home hemodialysis (6 nights/week) over 12 months and obtained measures of health‐related quality of life, self‐reported physical function, mental health and cognition. Serum prolactin concentrations were measured at baseline and 12‐month follow‐up in 70% of the FHN Trial cohort to examine the associations among serum prolactin concentrations and physical, mental and cognitive function and the effects of hemodialysis frequency on serum prolactin. Findings: Among 177 Daily Trial and 60 Nocturnal Trial participants with baseline serum prolactin measurements, the median serum prolactin concentration was 65 ng/mL (25th–75th percentile 48–195 ng/mL) and 81% had serum prolactin concentrations >30 ng/mL. While serum prolactin was associated with sex (higher in women), we observed no association between baseline serum prolactin and age, dialysis vintage, and baseline measures of physical, mental and cognitive function. Furthermore, there was no significant effect of hemodialysis frequency on serum prolactin in either of the two trials. Discussion: Serum prolactin concentrations were elevated in the large majority of patients with ESRD, but were not associated with several measures of health status. Circulating prolactin levels also do not appear to decrease in response to more frequent hemodialysis over a one‐year period.