Integrated Multiple Features for Tumor Image Retrieval Using Classifier and Feedback Methods

AbstractThe content based image retrieval method greatly assists in retrieving medical images close to the query image from a large database basing on their visual features. This paper presents an effective approach in which the region of the object is extracted with the help of multiple features ignoring the background of the object by employing edge following segmentation method followed by extracting texture and shape characteristics of the images. The former is extracted with the help of Steerable filter at different orientations and radial Chebyshev moments are used for extracting the later. Initially the images similar to the query image are extracted from a large group of medical images. Then the search is by accelerating the retrieval process with the help of Support Vector Machine (SVM) classifier. The performance of the retrieval system is enhanced by adapting the subjective feedback method. The experimental results show that the proposed region based multiple features and integrated with classifier and subjective feedback method yields better results than classical retrieval systems

Supporting Young People With Psychosis In The Community: An ICT Enabled Relapse Prevention Tool

Psychotic disorders are the most disabling of all mental illnesses and place a heavy demand on limited mental health services. Consequently, this research aims to develop novel approaches to care which are less demanding of public resources. This research-in-progress is based in an innovative youth mental health service in Australia. It presents a model for a web-based platform which provides support for relapse prevention in young people with first-episode psychosis (FEP). This study combines Information Systems and Psychology theory and research to develop an advanced web-based interactive psychosocial intervention for relapse prevention and recovery in FEP. While web-based applications to support schizophrenia, depression and anxiety have been researched such approaches have not been applied to the problem of relapse prevention in young people with FEP. The research uses focus groups to study case managers, clients of the support organisation and usability experts to inform an initial prototype which has then been presented to a group of clients for evaluation. It concludes that an intervention based on intelligent technologies which combine social networking and web-based treatment to promote independent home based care would best suit the characteristics of the target group and should be tested in large-scale in the community

Technology as Folklore: A Study of Change Through New Technology

We are in the second year of a three year, longitudinal, field-based study of work group life and technology change. Our view is that present organizational life has two dominant characteristics. The first characteristic is an increasing interdependence between members of the organization to do work. The second characteristic is the increasing dependence on information technology to support work. The interaction of these two forces becomes a key issue confronting the modern organization. In that context, this research seeks to describe: • How is client/server computing effecting technology-supported, group-based, work? • How are these effects shaped by organizational, temporal and social structures? This study focuses on chronicling the change in I/T infrastructure at one large academic organization. This change is viewed from a multi-theoretic perspective. We have the opportunity to observe and document the move of a large academic organization as it embraces the client-server computing infrastructure. Present, interim, findings include: (1) technical changes are difficult, social and organizational changes are more difficult; (2) change requires they maintain two systems; (3) there are two types of users and they are both important; (4) the technologists are now in the middle of the value chain

Integral Field Spectroscopy based H\alpha\ sizes of local Luminous and Ultraluminous Infrared Galaxies. A Direct Comparison with high-z Massive Star Forming Galaxies

Aims. We study the analogy between local U/LIRGs and high-z massive SFGs by comparing basic H{\alpha} structural characteristics, such as size, and luminosity (and SFR) surface density, in an homogeneous way (i.e. same tracer and size definition, similar physical scales). Methods. We use Integral Field Spectroscopy based H{\alpha} emission maps for a representative sample of 54 local U/LIRGs (66 galaxies). From this initial sample we select 26 objects with H{\alpha} luminosities (L(H{\alpha})) similar to those of massive (i.e. M\ast \sim 10^10 M\odot or larger) SFGs at z \sim 2, and observed on similar physical scales. Results. The sizes of the H{\alpha} emitting region in the sample of local U/LIRGs span a large range, with r1/2(H{\alpha}) from 0.2 to 7 kpc. However, about 2/3 of local U/LIRGs with Lir > 10^11.4 L\odot have compact H{\alpha} emission (i.e. r1/2 < 2 kpc). The comparison sample of local U/LIRGs also shows a higher fraction (59%) of objects with compact H{\alpha} emission than the high-z sample (25%). This gives further support to the idea that for this luminosity range the size of the star forming region is a distinctive factor between local and distant galaxies of similar SF rates. However, when using H{\alpha} as a tracer for both local and high-z samples, the differences are smaller than the ones recently reported using a variety of other tracers. Despite of the higher fraction of galaxies with compact H{\alpha} emission, a sizable group (\sim 1/3) of local U/LIRGs are large (i.e. r1/2 > 2 kpc). These are systems showing pre-coalescence merger activity and they are indistinguishable from the massive high-z SFGs galaxies in terms of their H{\alpha} sizes, and luminosity and SFR surface densities.Comment: Accepted for publication in A&A. (!5 pages, 7 figures, 2 tables

Computer supported mathematics with Ωmega

AbstractClassical automated theorem proving of today is based on ingenious search techniques to find a proof for a given theorem in very large search spaces—often in the range of several billion clauses. But in spite of many successful attempts to prove even open mathematical problems automatically, their use in everyday mathematical practice is still limited.The shift from search based methods to more abstract planning techniques however opened up a paradigm for mathematical reasoning on a computer and several systems of that kind now employ a mix of interactive, search based as well as proof planning techniques.The Ωmega system is at the core of several related and well-integrated research projects of the Ωmega research group, whose aim is to develop system support for a working mathematician as well as a software engineer when employing formal methods for quality assurance. In particular, Ωmega supports proof development at a human-oriented abstract level of proof granularity. It is a modular system with a central proof data structure and several supplementary subsystems including automated deduction and computer algebra systems. Ωmega has many characteristics in common with systems like NuPrL, CoQ, Hol, Pvs, and Isabelle. However, it differs from these systems with respect to its focus on proof planning and in that respect it is more similar to the proof planning systems Clam and λClam at Edinburgh

Efficient Communication and Coordination for Large-Scale Multi-Agent Systems

The growth of the computational power of computers and the speed of networks has made large-scale multi-agent systems a promising technology. As the number of agents in a single application approaches thousands or millions, distributed computing has become a general paradigm in large-scale multi-agent systems to take the benefits of parallel computing. However, since these numerous agents are located on distributed computers and interact intensively with each other to achieve common goals, the agent communication cost significantly affects the performance of applications. Therefore, optimizing the agent communication cost on distributed systems could considerably reduce the runtime of multi-agent applications. Furthermore, because static multi-agent frameworks may not be suitable for all kinds of applications, and the communication patterns of agents may change during execution, multi-agent frameworks should adapt their services to support applications differently according to their dynamic characteristics. This thesis proposes three adaptive services at the agent framework level to reduce the agent communication and coordination cost of large-scale multi-agent applications. First, communication locality-aware agent distribution aims at minimizing inter-node communication by collocating heavily communicating agents on the same platform and maintaining agent group-based load sharing. Second, application agent-oriented middle agent services attempt to optimize agent interaction through middle agents by executing application agent-supported search algorithms on the middle agent address space. Third, message passing for mobile agents aims at reducing the time of message delivery to mobile agents using location caches or by extending the agent address scheme with location information. With these services, we have achieved very impressive experimental results in large- scale UAV simulations including up to 10,000 agents. Also, we have provided a formal definition of our framework and services with operational semantics

Effects of litter floor access and inclusion of experienced hens in aviary housing on floor eggs, litter condition, air quality, and hen welfare

To better understand how relevant intensive systems’ characteristics simultaneously affect the performance and welfare of broiler chickens, a meta-analysis of recent literature was carried out. The study determined the effects of gender, genetics, experimental initial age (EIA, d), stocking density (SD; kg/m2), group size (GS; n), bedding material (yes/no), duration of photoperiod (DP; h), divided scotoperiod (yes/no), feeding phases (1/2/3/\u3e3), environmental control (EC; yes/no), environmental enrichment (yes/no), use of vaccines and other medications (yes/no), experimental duration (d), and relevant 2-way interactions on average daily gain (g/d), average daily feed intake (g/d), FCR (g: g), mortality (%), behavior (%), and gait score (mean value). Predictive equations for response variables were calculated using multiple regression models including a random experiment effect. Among other results, EIA × SD interaction indicated that relatively high SD may improve FCR at older ages, but parallel increased mortality would pose concerns about the actual productive benefits and welfare. Combining large GS and relatively low SD seem to improve performance and decrease flock disturbance. They would also increase leg problems, and so their actual benefits on welfare remain unclear. A gradual increase in FCR seems to occur with longer DP at older EIA (EIA × DP interaction), highlighting the importance of adapting light programs to flock age to optimize performance. The SD × DP and GS × DP interactions predicted increased FCR for longer DP at low SD or large GS, that is, with more effective space available. Longer DP combined with low SD or large GS would overall promote enhanced leg conditions, and therefore welfare. Predictions would not support scotoperiod division from both performance and welfare perspectives. The SD × EC interaction indicated that EC would benefit chicken performance at low SD, although EC would seem to increase leg problems. Our study highlights the complex, interactive nature of production systems’ characteristics on broiler chicken performance and welfare

From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers’ personal devices act as sources of data flows that are eventually fed into software running on automatic devices, actuators or control systems producing, in turn, complex information flows among users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded in this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.This work was supported in part by the Basque Government for its funding support through the EMAITEK program (3KIA, ref. KK-2020/00049). It has also received funding support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of the Basque Government