Query: SDN docker: Enabling application auto-docking/undocking in edge switch

Relevant passages:
OpenFlow: enabling innovation in campus networks <=SEP=> This whitepaper proposes OpenFlow: a way for researchers to run experimental protocols in the networks they use every day. OpenFlow is based on an Ethernet switch, with an internal flow-table, and a standardized interface to add and remove flow entries. Our goal is to encourage networking vendors to add OpenFlow to their switch products for deployment in college campus backbones and wiring closets. We believe that OpenFlow is a pragmatic compromise: on one hand, it allows researchers to run experiments on heterogeneous switches in a uniform way at line-rate and with high port-density; while on the other hand, vendors do not need to expose the internal workings of their switches. In addition to allowing researchers to evaluate their ideas in real-world traffic settings, OpenFlow could serve as a useful campus component in proposed large-scale testbeds like GENI. Two buildings at Stanford University will soon run OpenFlow networks, using commercial Ethernet switches and routers. We will work to encourage deployment at other schools; and We encourage you to consider deploying OpenFlow in your university network too

Software-Defined Wireless Networking Opportunities and Challenges for Internet-of-Things: A Review <=SEP=> With the emergence of Internet-of-Things (IoT), there is now growing interest to simplify wireless network controls. This is a very challenging task, comprising information acquisition, information analysis, decision-making, and action implementation on large scale IoT networks. Resulting in research to explore the integration of software-defined networking (SDN) and IoT for a simpler, easier, and strain less network control. SDN is a promising novel paradigm shift which has the capability to enable a simplified and robust programmable wireless network serving an array of physical objects and applications. This paper starts with the emergence of SDN and then highlights recent significant developments in the wireless and optical domains with the aim of integrating SDN and IoT. Challenges in SDN and IoT integration are also discussed from both security and scalability perspectives.

Publish/subscribe-enabled software defined networking for efficient and scalable IoT communications <=SEP=> The Internet of Things (IoT) is the result of many different enabling technologies such as embedded systems, wireless sensor networks, cloud computing, big-data, etc., which are used to gather, process, infer, and transmit data. Combining all these technologies requires a research effort to address all the challenges of these technologies, especially for sensing and delivering information from the physical world to cloud-hosted services. In this article we outline the most important issues related to standardization efforts, mobility of objects, networking and gateway access, and QoS support. In particular, we describe a novel IoT network architecture that integrates software defined networking (SDN) and data distribution service (DDS) middleware. The proposed architecture will improve service delivery of IoT systems and will bring flexibility to the network.

Maestro: A System for Scalable OpenFlow Control <=SEP=> The fundamental feature of an OpenFlow network is that the controller is responsible for the initial establishment of every flow by contacting related switches. Thus the performance of the controller could be a bottleneck. This paper shows how this fundamental problem is addressed by parallelism. The state of the art OpenFlow controller, called NOX, achieves a simple programming model for control function development by having a single-threaded event-loop. Yet NOX has not considered exploiting parallelism. We propose Maestro which keeps the simple programming model for programmers, and exploits parallelism in every corner together with additional throughput optimization techniques. We experimentally show that the throughput of Maestro can achieve near linear scalability on an eight core server machine. Keywords-OpenFlow, network management, multithreading, performance optimization

A Survey of Fog Computing: Concepts, Applications and Issues <=SEP=> Despite the increasing usage of cloud computing, there are still issues unsolved due to inherent problems of cloud computing such as unreliable latency, lack of mobility support and location-awareness. Fog computing can address those problems by providing elastic resources and services to end users at the edge of network, while cloud computing are more about providing resources distributed in the core network. This survey discusses the definition of fog computing and similar concepts, introduces representative application scenarios, and identifies various aspects of issues we may encounter when designing and implementing fog computing systems. It also highlights some opportunities and challenges, as direction of potential future work, in related techniques that need to be considered in the context of fog computing.

CBIR Evaluation using Different Distances and DWT <=SEP=> In this paper, evaluation is made on the result of CBIR system based on haar wavelet transform with different distances for similarity matching to calculate the deviation from query image. So, different distances used are Chessboard distance, Cityblock distance and Euclidean distance. In this paper discrete wavelet transform is used to decompose the image. The image is decomposed till sixth level and last level approximate component is saved as feature vector. Comparison is made between different distances to see the best suited distance for CBIR. The wavelet used is &quot;Haar&quot;. Haar has compact support and it is the simplest orthogonal wavelet. It is very fast also.

Grounding New Words on the Physical World in Multi-Domain Human-Robot Dialogues <=SEP=> This paper summarizes our ongoing project on developing an architecture for a robot that can acquire new words and their meanings while engaging in multidomain dialogues. These two functions are crucial in making conversational service robots work in real tasks in the real world. Household robots and office robots need to be able to work in multiple task domains and they also need to engage in dialogues in multiple domains corresponding to those task domains. Lexical acquisition is necessary because speech understanding cannot be done without enough knowledge on words that are possibly spoken in the task domain. Our architecture is based on a multi-expert model in which multiple domain experts are employed and one of them is selected based on the user utterance and the situation to engage in the control of the dialogue and physical behaviors. We incorporate experts that have an ability to acquire new lexical entries and their meanings grounded on the physical world through spoken interactions. By appropriately selecting those experts, lexical acquisition in multi-domain dialogues becomes possible. An example robotic system based on this architecture that can acquire object names and location names demonstrates the viability of the architecture.

A Brief Tour Through Provenance in Scientific Workflows and Databases <=SEP=> Within computer science, the term provenance has multiple meanings, due to different motivations, perspectives, and assumptions prevalent in the respective communities. This chapter provides a high-level “sightseeing tour” of some of those different notions and uses of provenance in scientific workflows and databases.

The raven roosting optimisation algorithm <=SEP=> A significant stream of literature which draws inspiration from the foraging activities of various organisms to design optimisation algorithms has emerged over the past decade. The success of these algorithms across a wide variety of application domains has spurred interest in the examination of the foraging behaviours of other organisms to develop novel and powerful, optimisation algorithms. A variety of animals, including some species of birds and bats, engage in social roosting whereby large numbers of conspecifics gather together to roost, either overnight or for longer periods. It has been claimed that these roosts can serve as information centres to spread knowledge concerning the location of food resources in the environment. In this paper we look at the social roosting and foraging behaviour of one species of bird, the common raven, and take inspiration from this to design a novel optimisation algorithm which we call the raven roosting optimisation algorithm. The utility of the algorithm is assessed on a series of benchmark problems and the results are found to be competitive. We also provide a novel taxonomy which classifies foraging-inspired optimisation algorithms based on the underlying social communication mechanism embedded in the algorithms.

Quantifying driver stress: developing a system for collecting and processing bio-metric signals in natural situations. <=SEP=> A system for quantifying the physiological features of emotional stress is being developed for use during a driving task. Two prototypes, using sensors that measure the driver's skin conductance, respiration, muscle activity, and heart activity are presented. The first system allows sampling rates of 200 Hz on two fast channels and 20 Hz on six additional channels. It uses a wearable computer to do real-time processing on the signals and has an attached digital camera which was used to capture images of the driver's facial expression once every minute. The second system uses a car-based computer that allows a sampling rate of 1984 samples per second on eight channels. This system uses multiple video cameras to continuously capture the driver's facial expression and road conditions. The data is then synchronized with the physiological signals using a video quad-splitter. The methods for extracting physiological features in the driving environment are discussed, including measurement of the skin conductance orienting response, muscle activity, pulse, and respiration patterns. Preliminary studies show how using multiple modalities of sensors can help discriminate reactions to driving events and how individual's response to similar driving conditions can vary from day to day.

SOCIAL SPACE AND SYMBOLIC POWER * <=SEP=> Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://www.jstor.org/journals/asa.html. Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission.

A customizable pipeline for social media text normalization <=SEP=> Social networks are persistently generating text-based data that encapsulate vast amounts of knowledge. However, the presence of non-standard terms and misspellings in texts originating from social networks poses a crucial challenge for natural language processing and machine learning systems that attempt to mine this knowledge. To address this problem, we propose a sequential, modular, and hybrid pipeline for social media text normalization. In the first phase, text preprocessing techniques and social media-specific vocabularies gathered from publicly available sources are used to transform, with high precision, out-of-vocabulary terms into in-vocabulary terms. A sequential language model, generated using the partially normalized texts from the first phase, is then utilized to normalize short, high-frequency, ambiguous terms. A supervised learning module is employed to normalize terms based on a manually annotated training corpus. Finally, a tunable, distributed language model-based backoff module at the end of the pipeline enables further customization of the system to specific domains of text. We performed intrinsic evaluations of the system on a publicly available domain-independent dataset from Twitter, and our system obtained an F-score of 0.836, outperforming other benchmark systems for the task. We further performed brief, task-oriented evaluations of the system to illustrate the customizability of the system to domain-specific tasks and the effects of normalization on downstream applications. The modular design enables the easy customization of the system to distinct types domain-specific social media text, in addition to its off-the-shelf application to generic social media text.

Friend or Foe: Cyberbullying in Social Network Sites <=SEP=> As the use of social media technologies proliferates in organizations, it is important to understand the nefarious behaviors, such as cyberbullying, that may accompany such technology use and how to discourage these behaviors. We draw from neutralization theory and the criminological theory of general deterrence to develop and empirically test a research model to explain why cyberbullying may occur and how the behavior may be discouraged. We created a research model of three second-order formative constructs to examine their predictive influence on intentions to cyberbully. We used PLS- SEM to analyze the responses of 174 Facebook users in two different cyberbullying scenarios. Our model suggests that neutralization techniques enable cyberbullying behavior and while sanction certainty is an important deterrent, sanction severity appears ineffective. We discuss the theoretical and practical implications of our model and results.

Face Recognition: Features Versus Templates <=SEP=> Over the last 20 years, several different techniques have been proposed for computer recognition of human faces. The purpose of this paper is to compare two simple but general strategies on a common database (frontal images of faces of 47 people: 26 males and 21 females, four images per person). We have developed and implemented two new algorithms; the first one is based on the computation of a set of geometrical features, such as nose width and length, mouth position, and chin shape, and the second one is based on almost-grey-level template matching. The results obtained on the testing sets (about 90% correct recognition using geometrical features and perfect recognition using template matching) favor our implementation of the template-matching approach.

Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond <=SEP=> ive Text Summarization using Sequence-to-sequence RNNs and Beyond Ramesh Nallapati IBM Watson nallapati@us.ibm.com Bowen Zhou IBM Watson zhou@us.ibm.com Cicero dos Santos IBM Watson

QR Code Security: A Survey of Attacks and Challenges for Usable Security <=SEP=> QR (Quick Response) codes are two-dimensional barcodes with the ability to encode different types of information. Because of their high information density and robustness, QR codes have gained popularity in various fields of application. Even though they offer a broad range of advantages, QR codes pose significant security risks. Attackers can encode malicious links that lead e.g. to phishing sites. Such malicious QR codes can be printed on small stickers and replace benign ones on billboard advertisements. Although many real world examples of QR code based attacks have been reported in the media, only little research has been conducted in this field and almost no attention has been paid on the interplay of security and human-computer interaction. In this work, we describe the manifold use cases of QR codes. Furthermore, we analyze the most significant attack scenarios with respect to the specific use cases. Additionally, we systemize the research that has already been conducted and identified usable security and security awareness as the main research challenges. Finally we propose design requirements with respect to the QR code itself, the reader application and usability aspects in order to support further research into to making QR code processing both secure and usable.

Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit <=SEP=> This paper demonstrates theoretically and empirically that a greedy algorithm called orthogonal matching pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal. This is a massive improvement over previous results, which require O(m2) measurements. The new results for OMP are comparable with recent results for another approach called basis pursuit (BP). In some settings, the OMP algorithm is faster and easier to implement, so it is an attractive alternative to BP for signal recovery problems.

Assessment of exposure to sexually explicit materials and factors associated with exposure among preparatory school youths in Hawassa City, Southern Ethiopia: a cross-sectional institution based survey <=SEP=> BACKGROUND
According to the 2007 Ethiopian census, youths aged 15-24 years were more than 15.2 million which contributes to 20.6% of the whole population. These very large and productive groups of the population are exposed to various sexual and reproductive health risks. The aim of this study was to assess exposure to Sexually Explicit Materials (SEM) and factors associated with exposure among preparatory school students in Hawassa city, Southern Ethiopia.


METHODOLOGY
A cross-sectional institution based study involving 770 randomly selected youth students of preparatory schools at Hawassa city. Multi stage sampling technique was used to select study subjects. Data was collected using pre-tested and self-administered questionnaire. Data was entered by EPI INFO version 3.5.1 and analyzed using SPSS version 20.0 statistical software packages. The result was displayed using descriptive, bivariate and multivariate analysis. Statistical association was done for independent predictors (at p < 0.05).


RESULT AND DISCUSSION
About 750 students were participated in this study with a response rate of 97.4%. Among this, about 77.3% of students knew about the presence of SEM and most of the respondents 566(75.5%) were watched SEM films/movies and 554(73.9%) were exposed to SE texts. The overall exposure to SEM in school youths was 579(77.2%). Among the total respondents, about 522(70.4%) claimed as having no open discussion on sexual issues with in their family. Furthermore, About 450 (60.0%) respondents complained for having no sexual and reproductive health education at their school. Male students had faced almost two times higher exposure to SEM than female students (95 % CI: AOR 1.84(C.I = 1.22, 2.78). Students who attended private school were more than two times more likely exposed to SEM than public schools (95 % CI: AOR 2.07(C.I = 1.29, 3.30). Students who drink alcohol and labelled as 'sometimes' were two times more likely exposed to SEM than those who never drink alcohol (95 % CI = AOR 2.33(C.I = 1.26, 4.30). Khat chewers who labelled "rarely", "sometimes" and "often" had shown higher exposure (95 % CI: AOR 3.02(CI = 1.65, 5.52), (95 % CI: AOR 3.40(CI = 1.93, 6.00) and (95 % CI: AOR 2.67(CI = 1.46, 4.86) than those who never chew khat, respectively. Regarding SEM access, school youths with label 'easy access were exposed in odds of six folds than youths of no access (95 % CI: AOR 5.64(C.I = 3.56, 8.9).


CONCLUSION
High number of students was exposed to sexually explicit materials. Sex, school type, substance use and access to SEM were observed independent predictors of exposure to SEM.


MOTIVATION
The current generation of young people is the healthiest, most educated, and most urbanized in history. However, there still remain some serious concerns. Most people become sexually active during adolescence. Premarital sexual activity is common and is on the rise worldwide. Rates are highest in sub Saharan Africa, where more than half of girls aged 15-19 are sexually experienced. Millions of adolescents are bearing children, in sub-Saharan Africa. More than half of women give birth before age 20. The need for improved health and social services aimed at adolescents, including reproductive health services, is being increasingly recognized throughout the world. Approximately 85 % of world adolescents live in developing countries. Each year, up to 100 million becomes infected with a curable sexually transmitted disease (STI). About 40 % of all new global human immunodeficiency virus (HIV) infections occur among 15-24 year olds; with recent estimates of 7000 infected each day. These health risks are influenced by many interrelated factors, such as expectations concerning early marriage and sexual relationships, access to education and employment, gender inequities, sexual violence, and the influence of mass media and popular culture. Furthermore, many adolescents lack strong stable relationships with parents or other adults whom they can talk to about their reproductive health concerns. Despite these challenges, programs that meet the information and service needs of adolescents can make a real difference. Successful programs help young people develop life-planning skill, respect the needs and concerns of young people, involve communities in their efforts, and provide respectful and confidential clinical services. Accordingly, the government of Ethiopia now works on improving adolescent's health as one part of MDG (Goal VI-halting transmission of HIV/AIDS, STI, and other communicable diseases) with a focus on adolescents, since they are most affected population. This finding, therefore, will benefit the government to partly evaluate the goal achieving through adolescents exposure status to sexually explicit materials and improvement of sexual issues free talk with in school with class mates and their family at home. For that matter, we authors decided to publish this finding in BMC Reproductive Health Journal so that on line access will be easy to all governing bodies that they use to re-plan their strategies for better product of plan. Moreover, Researchers, Practitioners, policy makers, Students, school leaders and professionals will also benefit from this finding for their future researches references, knowledge gain and practice.

A framework for thermal building parameter identification and simulation <=SEP=> Studies related to Smart Energy technologies have been highlighted in the last years, specially by significantly contributing to the saving of energy consumption, to the reduction of CO2 emissions and to the diminishing of the paid values in electricity bills. The electrical-thermal analogy can be used in order to simulate heating behavior in real situations and it also may be considered a contributor to Smart Energy technologies. This is the reason why several simulation tools are used. Taking this into account, this paper presents a framework using NGSpice simulator to identify both, the thermal capacitance and the thermal resistance parameters from a hypothetical monitored room which in a Smart Building context. The results from the proposed framework are compared to OpenModelica software and to the contributions from literature.

Additive Manufacturing Defect Detection using Neural Networks <=SEP=> Currently defect detection in additive manufacturing is predominately done by traditional image processing, approximation, and statistical methods. Two important aspects of defect detection are edge detection and porosity detection. Both problems appear to be good candidates to apply machine learning. This project describes the implementation of neural networks as an alternative method for defect detection. Results from current methods and the neural networks are compared for both speed and accuracy.

Secure and light IoT protocol (SLIP) for anti-hacking <=SEP=> In the elemental technologies, it is necessary to realize the Internet service of things (IoT), sensors and devices, network, platform (hardware platforms, open software platform, such as specific OS platforms). Web services, data analysis and prediction, big data processing, such as security and privacy protection technology, there are a variety of techniques. These elements technology provide a specific function. The element technology is integrated with each other. However, by several techniques are integrated, it can be problems with integration of security technologies that existed for each element technology. Even if individual technologies basic security features are constituting Internet Services (CIA: Confidentiality, integrity, authentication or authorization). It also offers security technology not connected to each other. Therefore, I will look at the security technology and proposes a lightweight routing protocol indispensable for realizing a secure Internet services things.

A search engine for 3D models <=SEP=> As the number of 3D models available on the Web grows, there is an increasing need for a search engine to help people find them. Unfortunately, traditional text-based search techniques are not always effective for 3D data. In this article, we investigate new shape-based search methods. The key challenges are to develop query methods simple enough for novice users and matching algorithms robust enough to work for arbitrary polygonal models. We present a Web-based search engine system that supports queries based on 3D sketches, 2D sketches, 3D models, and/or text keywords. For the shape-based queries, we have developed a new matching algorithm that uses spherical harmonics to compute discriminating similarity measures without requiring repair of model degeneracies or alignment of orientations. It provides 46 to 245% better performance than related shape-matching methods during precision--recall experiments, and it is fast enough to return query results from a repository of 20,000 models in under a second. The net result is a growing interactive index of 3D models available on the Web (i.e., a Google for 3D models).

Robotic excavation in construction automation <=SEP=> C onstruction is of prime economic significance to many industry sectors. Intense competition, shortages of skilled labor, and technological advances are forcing rapid change in the construction industry, thus motivating construction automation [1]. Earthmoving machines, such as bulldozers, wheel loaders, excavators, scrapers, and graders, are common in construction. Excavationbased operations are used in general earthmoving, digging, and sheet-piling for displacing large amounts of material. On a smaller scale, operations such as trenching and footing formation require precisely controlled excavation. Although the fully automated construction site is still a dream of some civil engineers, research developments have shown the promise of robotics and automation in construction [2]. Despite the apparent economic importance of excavation in construction, there have been few implementations of autonomous or teleoperated excavators. A number of researchers have investigated the feasibility of automating excavation. Many of these studies have addressed the possible use of autonomous excavators during unmanned phases of establishing manned Lunar or Martian research stations [3], [4]. Much of the work on terrestrial excavation has focused on teleoperation, rather than on the system requirements for autonomous operation. Although there have been a number of valuable theoretical and experimental contributions to the field of autonomous, robotic or teleoperated excavation [5]-[8], autonomous operation of a full-scale excavator has not been commercially demonstrated. Many of the experimental studies reported in the literature involve using conventional industrial robots fitted with buckets to excavate in a bed of loose sand. While there are parallels between “classical” robotics and robotic excavation, there are also some pronounced differences. In particular, an excavator is not fixed relative to the work piece; it plastically deforms the work piece by applying large forces and is caused to move relative to the soil by the same large forces. Furthermore, strategic and bucket trajectory planning must necessarily occur in a dynamic environment; if the excavator is not changing the profile of the soil being worked, it is not doing useful work. This article presents some results of the autonomous excavation project conducted at the Australian Centre for Field Robotics (ACFR) with a focus on construction automation. The application of robotic technology and computer control is one key to construction industry automation. Excavation automation is a multidisciplinary task, encompassing a broad area of research and development planning monitoring environment sensing and modeling navigation machine modeling and control. The ultimate goal of the ACFR excavation project is to demonstrate fully autonomous execution of excavation tasks in

Convolutional CRFs for Semantic Segmentation <=SEP=> For the challenging semantic image segmentation task the most efficient models have traditionally combined the structured modelling capabilities of Conditional Random Fields (CRFs) with the feature extraction power of CNNs. In more recent works however, CRF post-processing has fallen out of favour. We argue that this is mainly due to the slow training and inference speeds of CRFs, as well as the difficulty of learning the internal CRF parameters. To overcome both issues we propose to add the assumption of conditional independence to the framework of fully-connected CRFs. This allows us to reformulate the inference in terms of convolutions, which can be implemented highly efficiently on GPUs. Doing so speeds up inference and training by two orders of magnitude. All parameters of the convolutional CRFs can easily be optimized using backpropagation. To facilitating further CRF research we make our implementation publicly available. Please visit: https://github.com/MarvinTeichmann/ConvCRF

CBR: Strengths and Weaknesses <=SEP=> There is considerable enthusiasm about Case-Based Reasoning as a means of developing knowledge-based systems. There are two broad reasons for this enthusiasm. First, it is evident that much of human expert competence is experience based and it makes sense to adopt a reuse-based methodology for developing knowledge based systems. The other reason is the expectation that using Case-Based Reasoning to develop knowledge based systems will i nvolve less knowledge engineering than alternative ‘ first-principles’ based approaches. In this paper I explore the veracity of this assertion and outline the types of situation in which it will be true. CBR is perceived to have this knowledge engineering advantage because it allows the development of knowledge based systems in weak theory domains. If CBR can work without formalising a domain theory then there is a question about the quality of solutions produced by case-based systems. This is the other issue discussed in this paper and situations where CBR will and will not produce good quality solutions are outlined.

Modeling of braided pneumatic actuators for robotic control <=SEP=> Braided Pneumatic Actuators exhibit non-linear forcelength properties grossly similar to muscle, and have a high strength-to-weight ratio. These properties make them desirable for legged robots. This work emphasizes understanding the actuator properties for later use in simulation and control of legged robots. Static and dynamic mathematical models for the actuators are reported and verified through testing and simulation. In addition, a dynamic air flow model modulated by a solenoid valve was developed to provide modular actuation subroutines for simulation.

Review of performance metrics for green data centers: a taxonomy study <=SEP=> Data centers now play an important role in modern IT infrastructures. Although much research effort has been made in the field of green data center computing, performance metrics for green data centers have been left ignored. This paper is devoted to categorization of green computing performance metrics in data centers, such as basic metrics like power metrics, thermal metrics and extended performance metrics i.e. multiple data center indicators. Based on a taxonomy of performance metrics, this paper summarizes features of currently available metrics and presents insights for the study on green data center computing.

A Graph Traversal Based Approach to Answer Non-Aggregation Questions Over DBpedia <=SEP=> We present a question answering system over DBpedia, filling the gap between user information needs expressed in natural language and a structured query interface expressed in SPARQL over the underlying knowledge base (KB). Given the KB, our goal is to comprehend a natural language query and provide corresponding accurate answers. Focusing on solving the non-aggregation questions, in this paper, we construct a subgraph of the knowledge base from the detected entities and propose a graph traversal method to solve both the semantic item mapping problem and the disambiguation problem in a joint way. Compared with existing work, we simplify the process of query intention understanding and pay more attention to the answer path ranking. We evaluate our method on a non-aggregation question dataset and further on a complete dataset. Experimental results show that our method achieves best performance compared with several state-of-the-art systems.

Detection of money laundering groups using supervised learning in networks <=SEP=> Money laundering is a major global problem, enabling criminal organisations to hide their ill-gotten gains and to finance further operations. Prevention of money laundering is seen as a high priority by many governments, however detection of money laundering without prior knowledge of predicate crimes remains a significant challenge. Previous detection systems have tended to focus on individuals, considering transaction histories and applying anomaly detection to identify suspicious behaviour. However, money laundering involves groups of collaborating individuals, and evidence of money laundering may only be apparent when the collective behaviour of these groups is considered. In this paper we describe a detection system that is capable of analysing group behaviour, using a combination of network analysis and supervised learning. This system is designed for real-world application and operates on networks consisting of millions of interacting parties. Evaluation of the system using real-world data indicates that suspicious activity is successfully detected. Importantly, the system exhibits a low rate of false positives, and is therefore suitable for use in a live intelligence environment.

Camera Augmented Mobile C-Arm (CAMC): Calibration, Accuracy Study, and Clinical Applications <=SEP=> Mobile C-arm is an essential tool in everyday trauma and orthopedics surgery. Minimally invasive solutions, based on X-ray imaging and coregistered external navigation created a lot of interest within the surgical community and started to replace the traditional open surgery for many procedures. These solutions usually increase the accuracy and reduce the trauma. In general, they introduce new hardware into the OR and add the line of sight constraints imposed by optical tracking systems. They thus impose radical changes to the surgical setup and overall procedure. We augment a commonly used mobile C-arm with a standard video camera and a double mirror system allowing real-time fusion of optical and X-ray images. The video camera is mounted such that its optical center virtually coincides with the C-arm's X-ray source. After a one-time calibration routine, the acquired X-ray and optical images are coregistered. This paper describes the design of such a system, quantifies its technical accuracy, and provides a qualitative proof of its efficiency through cadaver studies conducted by trauma surgeons. In particular, it studies the relevance of this system for surgical navigation within pedicle screw placement, vertebroplasty, and intramedullary nail locking procedures. The image overlay provides an intuitive interface for surgical guidance with an accuracy of <;1 mm, ideally with the use of only one single X-ray image. The new system is smoothly integrated into the clinical application with no additional hardware especially for down-the-beam instrument guidance based on the anteroposterior oblique view, where the instrument axis is aligned with the X-ray source. Throughout all experiments, the camera augmented mobile C-arm system proved to be an intuitive and robust guidance solution for selected clinical routines.