An Approach to Capture Role-Based Access Control Models from Spring Web Applications

To mitigate potential misinterpretation and security violations, software developers should use tools that reflect the state of web applications and visualise them as graphical models. Modelling helps to ensure that functionality and access control mechanisms are consistently interconnected. In this paper, we propose an approach to support Web application development using the Spring platform. Our proposal is supported by the Eclipse IDE plugin tool, which recognises Spring Security configuration captures, its notations, and visualises them in the role-based access control (RBAC) models. The RBAC models are represented using SecureUML modelling language. The plugin is validated through survey taken by software developers.


CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images.

With the powerfulness of convolution neural networks (CNN), CNN based face reconstruction has recently shown promising performance in reconstructing detailed face shape from 2D face images. The success of CNN-based methods relies on a large number of labeled data. The state-of-the-art synthesizes such data using a coarse morphable face model, which however has difficulty to generate detailed photo-realistic images of faces (with wrinkles). This paper presents a novel face data generation method. Specifically, we render a large number of photo-realistic face images with different attributes based on inverse rendering. Furthermore, we construct a fine-detailed face image dataset by transferring different scales of details from one image to another. We also construct a large number of video-type adjacent frame pairs by simulating the distribution of real video data. With these nicely constructed datasets, we propose a coarse-to-fine learning framework consisting of three convolutional networks. The networks are trained for real-time detailed 3D face reconstruction from monocular video as well as from a single image. Extensive experimental results demonstrate that our framework can produce high-quality reconstruction but with much less computation time compared to the state-of-the-art. Moreover, our method is robust to pose, expression and lighting due to the diversity of data.


Comparison of Shunt and Series/Shunt nMOS Single-Pole Double-Throw Switches for X-Band Phased Array T/R Modules

This paper compares the performance of shunt and series/shunt single-pole double-throw nMOS switches designed in a 0.13 mum SiGe BiCMOS process for X-band phased array transmit/receive modules. From 8.5 to 10.5 GHz, the worst case return loss, insertion loss, and isolation are 14.5, 1.89, and 20.5 dB, respectively, for the reflective shunt switch, and 22.2, 2.33, and 22.5 dB, respectively, for the absorptive series/shunt switch. Both switches exhibit an IIP3 of about 28 dBm and dissipate no dc power. The performance of these switches are comparable to other CMOS switches found in triple well technologies, on non-standard substrates, using special device structures, or using extra dc biases


Is it possible to cure Internet addiction with the Internet?

Significant technological advancements over the last two decades have led to enhanced accessibility to computing devices and the Internet. Our society is experiencing an ever-growing integration of the Internet into everyday lives, and this has transformed the way we obtain and exchange information, communicate and interact with one another as well as conduct business. However, the term ‘Internet addiction’ (IA) has emerged from problematic and excessive Internet usage which leads to the development of addictive cyber-behaviours, causing health and social problems. The most commonly used intervention treatments such as motivational interviewing, cognitive-behavioural therapy, and retreat or inpatient care mix a variety of psychotherapy theories to treat such addictive behaviour and try to address underlying psychosocial issues that are often coexistent with IA, but the efficacy of these approaches is not yet proved. The aim of this paper is to address the question of whether it is possible to cure IA with the Internet. After detailing the current state-of-the-art including various IA definitions, risk factors, assessment methods and IA treatments, we outline the main research challenges that need to be solved. Moreover, we propose an Internet-based IA Recovery Framework (IARF) which uses AI to closely observe, visualize and analyse patient’s Internet usage behaviour for possible staged intervention. The proposal to use smart Internet-based systems to control IA can be expected to be controversial. This paper is intended to stimulate further discussion and research in IA recovery through Internet-based frameworks.


LDAExplore: Visualizing Topic Models Generated Using Latent Dirichlet Allocation

We present LDAExplore, a tool to visualize topic distributions in a given document corpus that are generated using Topic Modeling methods. Latent Dirichlet Allocation (LDA) is one of the basic methods that is predominantly used to generate topics. One of the problems with methods like LDA is that users who apply them may not understand the topics that are generated. Also, users may find it difficult to search correlated topics and correlated documents. LDAExplore, tries to alleviate these problems by visualizing topic and word distributions generated from the document corpus and allowing the user to interact with them. The system is designed for users, who have minimal knowledge of LDA or Topic Modelling methods. To evaluate our design, we run a pilot study which uses the abstract’s of 322 Information Visualization papers, where every abstract is considered a document. The topics generated are then explored by users. The results show that users are able to find correlated documents and group them based on topics that are similar.


Barriers to the acceptance of electronic medical records by physicians from systematic review to taxonomy and interventions

BACKGROUND
The main objective of this research is to identify, categorize, and analyze barriers perceived by physicians to the adoption of Electronic Medical Records (EMRs) in order to provide implementers with beneficial intervention options.

METHODS
A systematic literature review, based on research papers from 1998 to 2009, concerning barriers to the acceptance of EMRs by physicians was conducted. Four databases, "Science", "EBSCO", "PubMed" and "The Cochrane Library", were used in the literature search. Studies were included in the analysis if they reported on physicians' perceived barriers to implementing and using electronic medical records. Electronic medical records are defined as computerized medical information systems that collect, store and display patient information.

RESULTS
The study includes twenty-two articles that have considered barriers to EMR as perceived by physicians. Eight main categories of barriers, including a total of 31 sub-categories, were identified. These eight categories are: A) Financial, B) Technical, C) Time, D) Psychological, E) Social, F) Legal, G) Organizational, and H) Change Process. All these categories are interrelated with each other. In particular, Categories G (Organizational) and H (Change Process) seem to be mediating factors on other barriers. By adopting a change management perspective, we develop some barrier-related interventions that could overcome the identified barriers.

CONCLUSIONS
Despite the positive effects of EMR usage in medical practices, the adoption rate of such systems is still low and meets resistance from physicians. This systematic review reveals that physicians may face a range of barriers when they approach EMR implementation. We conclude that the process of EMR implementation should be treated as a change project, and led by implementers or change managers, in medical practices. The quality of change management plays an important role in the success of EMR implementation. The barriers and suggested interventions highlighted in this study are intended to act as a reference for implementers of Electronic Medical Records. A careful diagnosis of the specific situation is required before relevant interventions can be determined.


Automated camera dysfunctions detection

Surveillance systems depend greatly on the robustness and availability of the video streams. The cameras must deliver reliable streams from an angle corresponding to the correct viewpoint. In other words, the field of view and video quality must remain unchanged after the initial installation of a surveillance camera. The paper proposes an approach to detect changes (such as displacement, out of focus, obstruction) automatically in a difficult environment with illumination variations and dynamic background and foreground objects.


An analogue approach to the travelling salesman problem using an elastic net method

The travelling salesman problem1 is a classical problem in the field of combinatorial optimization, concerned with efficient methods for maximizing or minimizing a function of many independent variables. Given the positions of N cities, which in the simplest case lie in the plane, what is the shortest closed tour in which each city can be visited once? We describe how a parallel analogue algorithm, derived from a formal model2–3 for the establishment of topographically ordered projections in the brain4–10, can be applied to the travelling salesman problem1,11,12. Using an iterative procedure, a circular closed path is gradually elongated non-uniformly until it eventually passes sufficiently near to all the cities to define a tour. This produces shorter tour lengths than another recent parallel analogue algorithm13, scales well with the size of the problem, and is naturally extendable to a large class of optimization problems involving topographic mappings between geometrical structures14.


Improving Text Proposals for Scene Images with Fully Convolutional Networks

Text Proposals have emerged as a class-dependent version of object proposals – efficient approaches to reduce the search space of possible text object locations in an image. Combined with strong word classifiers, text proposals currently yield top state of the art results in end-to-end scene text recognition. In this paper we propose an improvement over the original Text Proposals algorithm of [1], combining it with Fully Convolutional Networks to improve the ranking of proposals. Results on the ICDAR RRC and the COCO-text datasets show superior performance over current state-of-the-art.


Sum-Rate Analysis for High Altitude Platform (HAP) Drones With Tethered Balloon Relay

High altitude platform (HAP) drones can provide broadband wireless connectivity to ground users in rural areas by establishing line-of-sight links and exploiting effective beamforming techniques. However, at high altitudes, acquiring the channel state information (CSI) for HAPs, which is a key requirement to perform beamforming, is challenging. In this paper, by exploiting an interference alignment (IA) technique, a novel method for achieving the maximum sum-rate in HAP-based communications without CSI is proposed. In particular, to realize IA, a multiple-antenna tethered balloon is used as a relay between multiple HAP drones and ground stations (GSs). Here, a multiple-input multiple-output X network system is considered. The capacity of the considered  $M \times N$  X network with a tethered balloon relay is derived in closed-form. Simulation results corroborate the theoretical findings and show that the proposed approach yields the maximum sum-rate in multiple HAPs-GSs communications in absence of CSI. The results also show the existence of an optimal balloon’s altitude for which the sum-rate is maximized.


Feature Generating Networks for Zero-Shot Learning

Suffering from the extreme training data imbalance between seen and unseen classes, most of existing state-of-the-art approaches fail to achieve satisfactory results for the challenging generalized zero-shot learning task. To circumvent the need for labeled examples of unseen classes, we propose a novel generative adversarial network (GAN) that synthesizes CNN features conditioned on class-level semantic information, offering a shortcut directly from a semantic descriptor of a class to a class-conditional feature distribution. Our proposed approach, pairing a Wasserstein GAN with a classification loss, is able to generate sufficiently discriminative CNN features to train softmax classifiers or any multimodal embedding method. Our experimental results demonstrate a significant boost in accuracy over the state of the art on five challenging datasets - CUB, FLO, SUN, AWA and ImageNet - in both the zero-shot learning and generalized zero-shot learning settings.


Compact and Efficient Bipolar Coupler for Wireless Power Chargers: Design and Analysis

Compactness and efficiency are the two basic considerations of the wireless battery chargers for electric vehicles (EVs) and plug-in hybrid EVs. The double-sided LCC compensation topology for wireless power transfer (WPT) has been proved to be one of the efficient solutions lately. However, with the increase of the numbers of compensation components, the volume of the system may become larger, which makes it less attractive. To improve the compactness, a bipolar coupler structure with a compensation-integrated feature is proposed. The inductors of the LCC compensation networks are designed as planar-type and attached to the power-transferring main coils. Extra space and magnetic cores for the compensated inductors outside of the coupler are saved. The cost is that extra couplings between the compensated coils (inductors) and the main coils are induced. To validate the feasibility, the proposed coupler is modeled and investigated by 3-D finite-element analysis tool first. The positioning of the compensated coils, the range of the extra couplings, and the tolerance to misalignment are studied. This is followed by the circuit modeling and characteristic analysis of the proposed WPT topology based on the fundamental harmonic approximation. At last, a 600 mm × 600 mm with a nominal 150-mm-gap wireless charger prototype, operated at a resonant frequency of 95 kHz and a rated power of 5.6 kW has been built and tested. A peak efficiency of 95.36% from a dc power source to the battery load is achieved at rated operation condition.


Addressing Female Genital Mutilation/Cutting (FGM/C) in the Era of Clitoral Reconstruction: Plastic Surgery

Purpose of the Review
The aim of this review is to give an overview of the recent evidence on clitoral reconstruction and other relevant reconstructive plastic surgery measures after female genital mutilation/cutting (FGM/C).

Recent Findings
Recent publications present refinements and modifications of the surgical technique of clitoral reconstruction along with reconstruction of the labia majora and clitoral hood. Novel approaches with reposition of the clitoral nerve, anchoring of the labia majora, fat grafting, and full thickness mucosa grafts have been introduced. The current evidence on outcomes of clitoral reconstruction shows potential benefits. However, there is a risk of postoperative complications and a negative outcome. Experts in the field advocate for a multidisciplinary approach including psychosexual counseling and health education with or without subsequent clitoral reconstructive surgery.

Summary
The evolution of reconstructive treatment for women with FGM/C is expanding, however at a slow rate. The scarcity of evidence on clitoral reconstruction halters availability of clinical guidelines and consensus regarding best practice. Clitoral reconstruction should be provided by multidisciplinary referral centers in a research setting with long-term follow-up on outcomes of postoperative morbidity and possible benefits.


Design and development of a heart rate measuring device using fingertip

In this paper, we presented the design and development of a new integrated device for measuring heart rate using fingertip to improve estimating the heart rate. As heart related diseases are increasing day by day, the need for an accurate and affordable heart rate measuring device or heart monitor is essential to ensure quality of health. However, most heart rate measuring tools and environments are expensive and do not follow ergonomics. Our proposed Heart Rate Measuring (HRM) device is economical and user friendly and uses optical technology to detect the flow of blood through index finger. Three phases are used to detect pulses on the fingertip that include pulse detection, signal extraction, and pulse amplification. Qualitative and quantitative performance evaluation of the device on real signals shows accuracy in heart rate estimation, even under intense of physical activity. We compared the performance of HRM device with Electrocardiogram reports and manual pulse measurement of heartbeat of 90 human subjects of different ages. The results showed that the error rate of the device is negligible.


HEART RATE MONITORING SYSTEM USING FINGER TIP THROUGH ARDUINO AND PROCESSING SOFTWARE

Technological innovations in the field of disease prevention and maintenance of patient health have enabled the evolution of fields such as monitoring systems. Heart rate is a very vital health parameter that is directly related to the soundness of the human cardiovascular system. Heart rate is the number of times the heart beats per minute, reflects different physiological conditions such as biological workload, stress at work and concentration on tasks, drowsiness and the active state of the autonomic nervous system. It can be measured either by the ECG waveform or by sensing the pulse the rhythmic expansion and contraction of an artery as blood is forced through it by the regular contractions of the heart. The pulse can be felt from those areas where the artery is close to the skin. This paper describes a technique of measuring the heart rate through a fingertip and Arduino. It is based on the principal of photophelthysmography (PPG) which is non-invasive method of measuring the variation in blood volume in tissue using a light source and detector. While the heart is beating, it is actually pumping blood throughout the body, and that makes the blood volume inside the finger artery to change too. This fluctuation of blood can be detected through an optical sensing mechanism placed around the fingertip. The signal can be amplified and is sent to arduino with the help of serial port communication. With the help of processing software heart rate monitoring and counting is performed. The sensor unit consists of an infrared light-emitting-diode (IR LED) and a photo diode. The IR LED transmits an infrared light into the fingertip, a part of which is reflected back from the blood inside the finger arteries. The photo diode senses the portion of the light that is reflected back. The intensity of reflected light depends upon the blood volume inside the fingertip. So, every time the heart beats the amount of reflected infrared light changes, which can be detected by the photo diode. With a high gain amplifier, this little alteration in the amplitude of the reflected light can be converted into a pulse.


Microcontroller Based Wireless Temperature And Heart Beat Read-Out

In this paper, we propose a Simple Wireless transmission System using common approach Sensor Platform called The wireless based Patient Sensor platform (WSP, Sensor Node) which has remote access capability. The goals of the WSP are to establish: Standard sensor node (System on module), a common software .The proposed platform architecture (Sensor Node) offers flexibility, easy customization for different vital parameter collecting and sending. An prototype has been established based on wireless communication channel. Wireless lan (IEEE .802.15.4)) has been used as communication channel on our prototype (Sensor node). Desire sensor information (vital parameter) can be viewed remotely, and also vital parameter can be adjusted to meet demand.


Augmented Reality for the Study of Human Heart Anatomy

Augmented reality is increasingly applied in medical education mainly because educators can share knowledge through virtual objects. This research describes the development of a web application, which enhances users' medical knowledge with regards to the anatomy of the human heart by means of augmented reality. Evaluation is conducted in two different facets. In the first one, the authors of this paper evaluate the feasibility of a three-dimensional human heart module using one investigator under the supervision of an expert. In the second, evaluation aims at identifying usability issues by means of the cognitive walkthrough method. Three medical students (naive users) are called upon three target tasks in the web application. Task completion is appreciated in the light of the standard set of cognitive walkthrough questions. Augmented reality content miss hits are revealed by means of the first evaluation in an effort to enhance the educational utility of the three-dimensional human heart. Cognitive walkthrough provides further improvement points, which may further enhance usability in the next software release. The current piece of work constitutes the pre-pilot evaluation. Standardized methodologies are utilized in an effort to improve the application before its wider piloting to proper student populations. Such evaluations are considered important in experiential learning methods aiding online education of anatomy courses.


Semantic Sensor Data Search in a Large-Scale Federated Sensor Network

Sensor network deployments are a primary source of massive amounts of data about the real world that surrounds us, measuring a wide range of physical properties in real time. However, in large-scale deployments it becomes hard to effectively exploit the data captured by the sensors, since there is no precise information about what devices are available and what properties they measure. Even when metadata is available, users need to know low-level details such as database schemas or names of properties that are specific to a device or platform. Therefore the task of coherently searching, correlating and combining sensor data becomes very challenging. We propose an ontology-based approach, that consists in exposing sensor observations in terms of ontologies enriched with semantic metadata, providing information such as: which sensor recorded what, where, when, and in which conditions. For this, we allow defining virtual semantic streams, whose ontological terms are related to the underlying sensor data schemas through declarative mappings, and can be queried in terms of a high level sensor network ontology.


Design of fractal based microstrip rectangular patch antenna for multiband applications

In this paper a multiband fractal based rectangular microstrip patch antenna is designed. FR4 substrate having thickness of 1.58 mm is used as substrate material for the design of proposed antenna and microstrip feed line provides the excitation to the antenna. The antenna operating frequency range is from 1 to 10 GHz. The proposed antenna resonate at twelve different frequencies as 1.86, 2.33, 3.67, 4.57, 5.08, 6.06, 7.03, 7.75, 8.08, 8.84, 9.56 and 10 GHz and the return losses are -15.39, -16.48, -10.02, -17.29, -13.15, -23.41, -10.22, -11.28, -17.02, -10.94, -15.15 and -15.48 dB respectively. The proposed antenna is designed and simulated by using the Ansoft HFSS V13 (high frequency structure simulator) software.


Multiview Stereo via Volumetric Graph-Cuts and Occlusion Robust Photo-Consistency

This paper presents a volumetric formulation for the multiview stereo problem which is amenable to a computationally tractable global optimization using Graph-cuts. Our approach is to seek the optimal partitioning of 3D space into two regions labeled as "object" and "empty" under a cost functional consisting of the following two terms: 1) A term that forces the boundary between the two regions to pass through photo-consistent locations; and 2) a ballooning term that inflates the "object" region. To take account of the effect of occlusion on the first term, we use an occlusion robust photo-consistency metric based on normalized cross correlation, which does not assume any geometric knowledge about the reconstructed object. The globally optimal 3D partitioning can be obtained as the minimum cut solution of a weighted graph.


A Novel Design of $4 \times 4$ Butler Matrix With Relatively Flexible Phase Differences

This letter presents a novel topology of a 4 ×4 Butler matrix, which can realize relatively flexible phase differences at the output ports. The proposed Butler matrix employs couplers with arbitrary phase-differences to replace quadrature couplers in the conventional Butler matrix. By controlling the phase differences of the applied couplers, the progressive phase differences among output ports of the proposed Butler matrix can be relatively flexible. To facilitate the design, closed-form design equations are derived and presented. For verifying the design concept, a planar 4×4 Butler matrix with four unique progressive phase differences ( - 30°, + 150°, - 120°, and + 60°) is designed and fabricated. At the operating frequency, the amplitude imbalance is less than 0.75 dB, and the phase mismatch is within ±6°. The measured return loss is better than 16 dB, and the isolation is better than 18 dB. The bandwidth with 10 dB return loss is about 15%.


Linked REST APIs: A Middleware for Semantic REST API Integration

Over the last decade, an exponentially increasing number of REST services have been providing a simple and straightforward syntax for accessing rich data resources. To use these services, however, developers have to understand "information-use contracts" specified in natural language, and, to build applications that benefit from multiple existing services they have to map the underlying resource schemas in their code. This process is difficult and error-prone, especially as the number and overlap of the underlying services increases, and the mappings become opaque, difficult to maintain, and practically impossible to reuse. The more recent advent of the Linked Data formalisms can offer a solution to the challenge. In this paper, we propose a conceptual framework for REST-service integration based on Linked Data models. In this framework, the data exposed by REST services is mapped to Linked Data schemas, based on these descriptions, we have developed a middleware that can automatically compose API calls to respond to data queries (in SPARQL). Furthermore, we have developed a RDF model for characterizing the access-control protocols of these APIs and the quality of the data they expose, so that our middleware can develop "legal" compositions with desired qualities. We report our experience with the implementation of a prototype that demonstrates the usefulness of our framework in the context of a research-data management application.


Cooperation between distributed agents through self-organisation

The paper discussu some antral irrw in the cooperation between disuibuted agcnu using lhe following case study: The objective is to explore a distant planet. more concretely to collect samples of a parricular typ of precious ruck. The location of the ruck .iamplu is unknown in advance but they arc rypiwlly clustered in cenain spm There i S a vehicle that can drive around on the planet and later reenter the s p x d (0 g0 back to ewlh There is no deuiled map of the t e m h although it h known hat the terrain is fuU of obstacles. hills. valleys. etc.


Privacy Preserving Data

In this paper we introduce the concept of privacy preserving data mining. In our model, two parties owning conndential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information. This problem has many practical and important applications, such as in medical research with conndential patient records. Data mining algorithms are usually complex, especially as the size of the input is measured in megabytes, if not gigabytes. A generic secure multi-party computation solution, based on evaluation of a circuit computing the algorithm on the entire input, is therefore of no practical use. We focus on the problem of decision tree learning and use ID3, a popular and widely used algorithm for this problem. We present a solution that is considerably more eecient than generic solutions. It demands very few rounds of communication and reasonable bandwidth. In our solution, each party performs by itself a computation of the same order as computing the ID3 algorithm for its own database. The results are then combined using eecient cryptographic protocols, whose overhead is only logarithmic in the number of transactions in the databases. We feel that our result is a substantial contribution, demonstrating that secure multi-party computation can be made practical, even for complex problems and large inputs.


The Role of Requirements Engineering Practices in Agile Development: An Empirical Study


Cross-modal Image-Graphics Retrieval by Neural Transfer Learning


Gaze-Adaptive Above and On-Surface Interaction

We explore the combination of above-surface sensing with eye tracking to facilitate concurrent interaction with multiple regions on touch screens. Conventional touch input relies on positional accuracy, thereby requiring tight visual monitoring of one's own motor action. In contrast, above-surface sensing and eye tracking provides information about how user's hands and gaze are distributed across the interface. In these situations we facilitate interaction by 1) showing the visual feedback of the hand hover near user's gaze point and 2) decrease the requisite of positional accuracy by employing gestural information. We contribute input and visual feedback techniques that combine these modalities and demonstrate their use in example applications. A controlled study showed the effectiveness of our techniques for manipulation tasks against conventional touch, while the effectiveness in acquisition tasks depended on the amount of mid-air motion, leading to our conclusion that the techniques can benefit interacting with multiple interface regions.


Enterprise Content Management (ECM): Needs, challenges and recommendations

Availability of information, agility of business process, interoperability of diverse business functions and conformity of the legal requirements are few of the critical success factors for enterprises to survive in the modern and competing global market space. Enterprises while striving to achieve these critical success factors are further exposed to the complexity of massive volumes of variant data and information, which exists in a broad array of formats. Complex and extended business processes spanning across the business functions and partners around the globe, need for integration and interoperability, fulfillment of compliance to legal and regulatory requirements. Enterprise content management (ECM) answers the above questions and offers the capabilities to efficiently and effectively manage the challenges imposed by the demanding business requirements of the modern world. The study in-hand reviews and analyzes the existing literature on enterprise content management domain. In order to conduct a structured literature review, three broader areas of considerations for ECM are identified i.e. business needs & drivers, challenges and recommendations. The review inhand aims at consolidating valuable domain knowledge and research work accomplished in the recent years for the benefits of the industry and academia.


Web Application Testing: A Review on Techniques, Tools and State of Art

Web applications are meant to be viewed by human user. While this implies that quality of web application has importance in our daily life. Web application quality is our prime concern. To ensure the quality of web application, web testing is having a dandy role in Software Testing as well as Web Community. Web Applications are erring because of features provided for rising of web application. In the last years, various web testing problems have been addressed by research work. Several tools, techniques and methods have been determined to test w eb application eff icaciously. This paper w ill present the contribution of researchers in the f ield of web application in previous years and state of art of web testing and challenges primarily because of distributed and heterogeneous nature of web


A study of interactive 3D point location in a computer simulated virtual environment

The present study investigated the ability to inter actively locate points in a three dimensional computer environment using a six degree of freedom input device. Four different visu al feedback modes were tested: fixed viewpoint monoscopic persp ective, fixed viewpoint stereoscopic perspective, head-tracked mo noscopic perspective and head-tracked stereoscopic perspecti ve. Targets were located at plus and minus 10cm along the X, Y or Z axes, from a fixed starting location. Data about the time to complete the task and positioning accuracy (error) are gathered for each trial. In addition, subjective feedback regarding the apparat us, visual mode and task difficulty was solicited from subject s. The results indicate that stereoscopic performance is superior to monoscopic performance and that asymmetries exist both across and within axes. Head tracking had no appreciable effect upon performance. Subjective feedback regarding performance is usuall y consistent with objective measures, but some inconsistencies a re present.


Pavlov: Programming by Stimulus-Response Demonstration

Pavlov is a Programming By Demonstration (PBD) system that allows animated interfaces to be created without programming. Using a drawing editor and a clock, designers specify the behavior of a target interface by demonstrating stimuli (end-user actions or time) and the (time-stamped) graphical transformations that should be executed in response. This stimulus-response model allows interaction and animation to be defined in a uniform manner, and it allows for the demonstration of interactive animation, i.e., game-like behaviors in which the end-user (player) controls the speed and direction of object movement.


Contextual Equivalence for Probabilistic Programs with Continuous Random Variables and Scoring

We present a logical relation for proving contextual equivalence in a probabilistic programming language (PPL) with continuous random variables and with a scoring operation for expressing observations and soft constraints. Our PPL model is based on a big-step operational semantics that represents an idealized sampler with likelihood weighting. The semantics treats probabilistic non-determinism as a deterministic process guided by a source of entropy. We derive a measure on result values by aggregating (that is, integrating) the behavior of the operational semantics over the entropy space. Contextual equivalence is defined in terms of these measures, taking real events as observable behavior. We define a logical relation and prove it sound with respect to contextual equivalence. We demonstrate the utility of the logical relation by using it to prove several useful examples of equivalences, including the equivalence of a βv-redex and its contractum and a general form of expression re-ordering. The latter equivalence is sound for the sampling and scoring effects of probabilistic programming but not for effects like mutation or control.


Data-Driven Vehicle Trajectory Prediction

Vehicle trajectory or route prediction is useful in online, data-driven transportation simulation to predict future traffic patterns and congestion, among other uses. The various approaches to route prediction have varying degrees of data required to predict future vehicle trajectories. Three approaches to vehicle trajectory prediction, along with extensions, are examined to assess their accuracy on an urban road network. These include an approach based on the intuition that drivers attempt to reduce their travel time, an approach based on neural networks, and an approach based on Markov models. The T-Drive trajectory data set consisting of GPS trajectories of over ten thousand taxicabs and including 15 million data points in Beijing, China is used for this evaluation. These comparisons illustrate that using trajectory data from other vehicles can substantially improve the accuracy of forward trajectory prediction in the T-Drive data set. These results highlight the benefit of exploiting dynamic data to improve the accuracy of transportation simulation predictions.


Survey of failures and fault tolerance in cloud

Cloud computing provides support for hosting client's application. Cloud is a distributed platform that provides hardware, software and network resources to both execute consumer's application and also to store and mange user's data. Cloud is also used to execute scientific workflow applications that are in general complex in nature when compared to other applications. Since cloud is a distributed platform, it is more prone to errors and failures. In such an environment, avoiding a failure is difficult and identifying the source of failure is also complex. Because of this, fault tolerance mechanisms are implemented on the cloud platform. This ensures that even if there are failures in the environment, critical data of the client is not lost and user's application running on cloud is not affected in any manner. Fault tolerance mechanisms also help in improving the cloud's performance by proving the services to the users as required on demand. In this paper a survey of existing fault tolerance mechanisms for the cloud platform are discussed. This paper also discusses the failures, fault tolerant clustering methods and fault tolerant models that are specific for scientific workflow applications.


Tracking the student's performance in Web-based education using Scrum methodology

The Internet, communication and mobile technologies are emerging as predominant paradigm in the learning of students. Various technologies have created numerous opportunities for the learners to explore in the learning landscape. Even though traditional teaching and learning are still popular in India, the scope for Web based education is positive. It is commonly believed that the Web based education is poised to penetrate deep into the educational system in India in near future. While global web-based education system faces different subjects, ideas and different kind of talented student's, the biggest challenge is communication and tracking the performance of student's. To deal with this challenge, we can use scrum. Scrum is the essence and one of the powerful agile methodologies. This paper presents about scrum and scrum backlog.


Design of Incoherent Frames via Convex Optimization

This paper describes a new procedure for the design of incoherent frames used in the field of sparse representations. We present an efficient algorithm for the design of incoherent frames that works well even when applied to the construction of relatively large frames. The main advantage of the proposed method is that it uses a convex optimization formulation that operates directly on the frame, and not on its Gram matrix. Solving a sequence of convex optimization problems allows for the introduction of constraints on the frame that were previously considered impossible or very hard to include, such as non-negativity. Numerous experimental results validate the approach.


Hyper-parameter selection in non-quadratic regularization-based radar image formation

We consider the problem of automatic parameter selection in regularization-based radar image formation techniques. It has previously been shown that non-quadratic regularization produces feature-enhanced radar images; can yield superresolution; is robust to uncertain or limited data; and can generate enhanced images in non-conventional data collection scenarios such as sparse aperture imaging. However, this regularized imaging framework involves some hyper-parameters, whose choice is crucial because that directly affects the characteristics of the reconstruction. Hence there is interest in developing methods for automatic parameter choice. We investigate Stein’s unbiased risk estimator (SURE) and generalized cross-validation (GCV) for automatic selection of hyper-parameters in regularized radar imaging. We present experimental results based on the Air Force Research Laboratory (AFRL) “Backhoe Data Dome,” to demonstrate and discuss the effectiveness of these methods.


Automatic fusion and classification using random forests and features extracted with deep learning

Fusion of different sensor modalities has proven very effective in numerous remote sensing applications. However, in order to benefit from fusion, advanced feature extraction mechanisms that rely on domain expertise are typically required. In this paper we present an automated feature extraction scheme based on deep learning. The feature extraction is unsupervised and hierarchical. Furthermore, computational efficiency (often a challenge for deep learning methods) is a primary goal in order to make certain that the method can be applied in large remote sensing datasets. Promising classification results show the applicability of the approach for both reducing the gap between naive feature extraction and methods relying on domain expertise, as well as further improving the performance of the latter in two challenging datasets.


Who, where, when and what: discover spatio-temporal topics for twitter users

Micro-blogging services, such as Twitter, and location-based social network applications have generated short text messages associated with geographic information, posting time, and user ids. The availability of such data received from users offers a good opportunity to study the user's spatial-temporal behavior and preference. In this paper, we propose a probabilistic model W4 (short for Who+Where+When+What) to exploit such data to discover individual users' mobility behaviors from spatial, temporal and activity aspects. To the best of our knowledge, our work offers the first solution to jointly model individual user's mobility behavior from the three aspects. Our model has a variety of applications, such as user profiling and location prediction; it can be employed to answer questions such as ``Can we infer the location of a user given a tweet posted by the user and the posting time?" Experimental results on two real-world datasets show that the proposed model is effective in discovering users' spatial-temporal topics, and outperforms state-of-the-art baselines significantly for the task of location prediction for tweets.


Modelling the scaling properties of human mobility

Individual human trajectories are characterized by fat-tailed distributions of jump sizes and waiting times, suggesting the relevance of continuous-time random-walk (CTRW) models for human mobility. However, human traces are barely random. Given the importance of human mobility, from epidemic modelling to traffic prediction and urban planning, we need quantitative models that can account for the statistical characteristics of individual human trajectories. Here we use empirical data on human mobility, captured by mobile-phone traces, to show that the predictions of the CTRW models are in systematic conflict with the empirical results. We introduce two principles that govern human trajectories, allowing us to build a statistically self-consistent microscopic model for individual human mobility. The model accounts for the empirically observed scaling laws, but also allows us to analytically predict most of the pertinent scaling exponents.


Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks

Recently, location-based social networks (LBSNs), such as Gowalla, Foursquare, Facebook, and Brightkite, etc., have attracted millions of users to share their social friendship and their locations via check-ins. The available check-in information makes it possible to mine users’ preference on locations and to provide favorite recommendations. Personalized Point-of-interest (POI) recommendation is a significant task in LBSNs since it can help targeted users explore their surroundings as well as help third-party developers to provide personalized services. To solve this task, matrix factorization is a promising tool due to its success in recommender systems. However, previously proposed matrix factorization (MF) methods do not explore geographical influence, e.g., multi-center check-in property, which yields suboptimal solutions for the recommendation. In this paper, to the best of our knowledge, we are the first to fuse MF with geographical and social influence for POI recommendation in LBSNs. We first capture the geographical influence via modeling the probability of a user’s check-in on a location as a Multi-center Gaussian Model (MGM). Next, we include social information and fuse the geographical influence into a generalized matrix factorization framework. Our solution to POI recommendation is efficient and scales linearly with the number of observations. Finally, we conduct thorough experiments on a large-scale real-world LBSNs dataset and demonstrate that the fused matrix factorization framework with MGM utilizes the distance information sufficiently and outperforms other state-of-the-art methods significantly.


The scaling laws of human travel

The dynamic spatial redistribution of individuals is a key driving force of various spatiotemporal phenomena on geographical scales. It can synchronize populations of interacting species, stabilize them, and diversify gene pools. Human travel, for example, is responsible for the geographical spread of human infectious disease. In the light of increasing international trade, intensified human mobility and the imminent threat of an influenza A epidemic, the knowledge of dynamical and statistical properties of human travel is of fundamental importance. Despite its crucial role, a quantitative assessment of these properties on geographical scales remains elusive, and the assumption that humans disperse diffusively still prevails in models. Here we report on a solid and quantitative assessment of human travelling statistics by analysing the circulation of bank notes in the United States. Using a comprehensive data set of over a million individual displacements, we find that dispersal is anomalous in two ways. First, the distribution of travelling distances decays as a power law, indicating that trajectories of bank notes are reminiscent of scale-free random walks known as Lévy flights. Second, the probability of remaining in a small, spatially confined region for a time T is dominated by algebraically long tails that attenuate the superdiffusive spread. We show that human travelling behaviour can be described mathematically on many spatiotemporal scales by a two-parameter continuous-time random walk model to a surprising accuracy, and conclude that human travel on geographical scales is an ambivalent and effectively superdiffusive process.


Estimating surface normals in noisy point cloud data

In this paper we describe and analyze a method based on local least square fitting for estimating the normals at all sample points of a point cloud data (PCD) set, in the presence of noise. We study the effects of neighborhood size, curvature, sampling density, and noise on the normal estimation when the PCD is sampled from a smooth curve in R2 or a smooth surface in R3 and noise is added. The analysis allows us to find the optimal neighborhood size using other local information from the PCD. Experimental results are also provided.


Big data: the next frontier for innovation in therapeutics and healthcare.

Advancements in genomics and personalized medicine not only effect healthcare delivery from patient and provider standpoints, but also reshape biomedical discovery. We are in the era of the '-omics', wherein an individual's genome, transcriptome, proteome and metabolome can be scrutinized to the finest resolution to paint a personalized biochemical fingerprint that enables tailored treatments, prognoses, risk factors, etc. Digitization of this information parlays into 'big data' informatics-driven evidence-based medical practice. While individualized patient management is a key beneficiary of next-generation medical informatics, this data also harbors a wealth of novel therapeutic discoveries waiting to be uncovered. 'Big data' informatics allows for networks-driven systems pharmacodynamics whereby drug information can be coupled to cellular- and organ-level physiology for determining whole-body outcomes. Patient '-omics' data can be integrated for ontology-based data-mining for the discovery of new biological associations and drug targets. Here we highlight the potential of 'big data' informatics for clinical pharmacology.


Forecasting Real Estate Prices ∗

This chapter reviews the evidence of predictability in US residential and commercial real estate markets. First, we highlight the main methodologies used in the construction of real estate indices, their underlying assumptions and their impact on the stochastic properties of the resultant series. We then survey the key empirical findings in the academic literature, including short-run persistence and long-run reversals in the log changes of real estate prices. Next, we summarize the ability of local as well as aggregate variables to forecast real estate returns. We illustrate a number of these results by relying on six aggregate indexes of the prices of unsecuritized (residential and commercial) real estate and REITs. The effect of leverage and monetary policy is also discussed.


The Beta Generalized Exponential Distribution

We introduce the beta generalized exponential distribution that includes the beta exponential and generalized exponential distributions as special cases. We provide a comprehensive mathematical treatment of this distribution. We derive the moment generating function and the rth moment thus generalizing some results in the literature. Expressions for the density, moment generating function and rth moment of the order statistics also are obtained. We discuss estimation of the parameters by maximum likelihood and provide the information matrix. We observe in one application to real data set that this model is quite flexible and can be used quite effectively in analyzing positive data in place of the beta exponential and generalized exponential distributions. keywords: Beta exponential distribution, Information matrix, Generalized exponential distribution, Maximum likelihood estimation.


Motivation and job satisfaction

The movement of workers to act in a desired manner has always consumed the thoughts of managers. In many ways, this goal has been reached through incentive programs, corporate pep talks, and other types of conditional administrative policy. However, as the workers adjust their behaviour in response to one of the aforementioned stimuli, is job satisfaction actualized? The instilling of satisfaction within workers is a crucial task of management. Satisfaction creates confidence, loyalty and ultimately improved quality in the output of the employed. Satisfaction, though, is not the simple result of an incentive program. Employees will most likely not take any more pride in their work even if they win the weekend getaway for having the highest sales. This paper reviews the literature of motivational theorists and draws from their approaches to job satisfaction and the role of motivation within job satisfaction. The theories of Frederick Herzberg and Edwin Locke are presented chronologically to show how Locke’s theory was a response to Herzberg’s theory. By understanding these theories, managers can focus on strategies of creating job satisfaction. This is followed by a brief examination of Kenneth Blanchard and Paul Hersey’s theory on leadership within management and how this art is changing through time. Herzberg and job satisfaction


Recognizing Objects in Range Data Using Regional Point Descriptors

Recognition of three dimensional (3D) objects in noisy and cluttered scenes is a challenging problem in 3D computer vision. One approach that has been successful in past research is the regional shape descriptor. In this paper, we introduce two new regional shape descriptors: 3D shape contexts and harmonic shape contexts. We evaluate the performance of these descriptors on the task of recognizing vehicles in range scans of scenes using a database of 56 cars. We compare the two novel descriptors to an existing descriptor, the spin image, showing that the shape context based descriptors have a higher recognition rate on noisy scenes and that 3D shape contexts outperform the others on cluttered scenes.


How Smart is Smart Money ? An Empirical Two-Sided Matching Model of Venture Capital

In capital markets, top-tier investors may have better abilities to monitor and manage their investments. In addition, there may be sorting in these markets, with top-tier investors investing in the best deals, second-tier investors investing in the second-best deals, and so forth. To separate and quantify these two effects, a structural model of the market for venture capital is developed and estimated. The model is a two-sided matching model that allows for sorting in equilibrium. It is found that more experienced venture capitalists make more successful investments. This is explained both by their value-adding influence on their investments, and by their access to late stage and biotechnology companies, companies that are more successful on average. Sorting is found to be prevalent and has general implications for the interpretation of empirical evidence of the impact of investors on their investments.


Supervised Topic Models

We introduce supervised latent Dirichlet allocation (sLDA), a statistical model of labelled documents. The model accommodates a variety of response types. We derive a maximum-likelihood procedure for parameter estimation, which relies on variational approximations to handle intractable posterior expectations. Prediction problems motivate this research: we use the fitted model to predict response values for new documents. We test sLDA on two real-world problems: movie ratings predicted from reviews, and web page popularity predicted from text descriptions. We illustrate the benefits of sLDA versus modern regularized regression, as well as versus an unsupervised LDA analysis followed by a separate regression.


A Circuit-Compatible SPICE model for Enhancement Mode Carbon Nanotube Field Effect Transistors

This paper presents a circuit-compatible compact model for short channel length (5 nm~100 nm), quasi-ballistic single wall carbon nanotube field-effect transistors (CNFETs). For the first time, a universal circuit-compatible CNFET model was implemented with HSPICE. This model includes practical device non-idealities, e.g. the quantum confinement effects in both circumferential and channel length direction, the acoustical/optical phonon scattering in channel region and the resistive source/drain, as well as the real time dynamic response with a transcapacitance array. This model is valid for CNFET for a wide diameter range and various chiralities as long as the carbon nanotube (CNT) is semiconducting


Fear bradycardia and activation of the human periaqueductal grey

Animal models of predator defense distinguish qualitatively different behavioral modes that are activated at increasing levels of predation threat. A defense mode observed at intermediate threat levels is freezing: a cessation of locomotion that is characterized by a parasympathetically dominated autonomic nervous system response that causes heart rate deceleration, or fear bradycardia. Studies in rodents have shown that freezing depends on amygdalar projections to the periaqueductal grey (PAG). In humans, freezing-like behaviors are implicated in development and maintenance of psychopathology, but neural mechanisms underlying freezing or its characteristic autonomic response profile have not been identified. Here, we combined event-related blood oxygenation level-dependent functional MRI (BOLD-fMRI) with autonomic response measures in a picture viewing paradigm to probe activity and interconnectivity within the amygdala-PAG pathway and test for an association with parasympathetic as opposed to sympathetic activation. In response to negatively arousing pictures, we observed parasympathetic (bradycardia) and sympathetic (pupil dilation) autonomic responses, BOLD responses in the amygdala and PAG, and effective connectivity between these regions. Critically, BOLD responses in the PAG to negative pictures correlated on a trial-by-trial basis with bradycardia but not pupil dilation. This correlation with bradycardia remained significant when partialling out pupil dilation. Additionally, activity in regions associated with motor planning and inhibition mirrored the PAG response. Thus, our findings implicate the human PAG in a parasympathetically dominated defense mode that subserves a state of attentive immobility. Mechanistic insight into this qualitatively distinct defense mode may importantly advance translational models of anxiety disorders.


RSSI informed phase method for distance calculations

In an attempt to find an accurate, environmentally robust, and fast process for RFID distance estimations, a method was developed called RSSI Informed Phase. This method uses both phase and RSSI measurements to estimate the distance, and potentially location, of a moving RFID tag. RSSI is initially used to find an approximate reader to tag separation distance. This distance value is applied to find an approximate slope of the phase angle vs. frequency curve. The estimated slope can inform the phase distance calculation, meaning fewer reads are required to find the actual phase angle vs. frequency slope. The reduction in the number of necessary reads accelerates the localization process and makes this method more robust for dynamic environments.


Teleoperated robot writing using EMG signals

In this paper, we have developed a method to tele-control a robot arm to imitate human writing skills using electromyography (EMG) signals. The control method is implemented on the Baxter® robot with a brush attached on the endpoint of its arm to imitate human writing skills, and a MYO sensor is used to measure the surface electromyographic (sEMG) signals generated by contractions of human muscles involved in writing tasks. A haptic device Sensable® Omni is used to control the motion of the Baxter® robot arm, and V-Rep® is employed to simulate the movement of the arm and to calculate the inverse kinematic of Baxter® robot arm. All the communications for Baxter® robot, V-Rep simulator, Omni device and MYO are integrated in MATLAB®/Simulink®. The main test is to make the Baxter® arm following the movement of a human subject when writing with Omni stylus, and the EMG signals are processed using low pass filter and moving average technique to extract the smoothed envelope which is utilised to control the variation of position instead of variation of force along the vertical Z axis, so when the operator writes with force variations, the Baxter® can draw lines with variable thickness. This imitation system is successfully tested to make the Baxter® arm to follow the writing movement of the operator's hand and would have potential applications in more complicated human robot teleoperation tasks such as tele-rehabilitation and tele-surgery.


Parallel computations for controlling an arm.

In order to control a reaching movement of the arm and body, several different computational problems must be solved. Some parallel methods that could be implemented in networks of neuron-like processors are described. Each method solves a different part of the overall task. First, a method is described for finding the torques necessary to follow a desired trajectory. The methods is more economical and more versatile than table look-up and requires very few sequential steps. Then a way of generating an internal representation of a desired trajectory is described. This method shows the trajectory one piece at a time by applying a large set of heuristic rules to a "motion blackboard" that represents the static and dynamic parameters of the state of the body at the current point in the trajectory. The computations are simplified by expressing the positions, orientations, and motions of parts of the body in terms of a single, non-accelerating, world-based frame of reference, rather than in terms of the joint-angles or an egocentric frame based on the body itself.


TimeMachine: Timeline Generation for Knowledge-Base Entities

We present a method called TIMEMACHINE to generate a timeline of events and relations for entities in a knowledge base. For example for an actor, such a timeline should show the most important professional and personal milestones and relationships such as works, awards, collaborations, and family relationships. We develop three orthogonal timeline quality criteria that an ideal timeline should satisfy: (1) it shows events that are relevant to the entity; (2) it shows events that are temporally diverse, so they distribute along the time axis, avoiding visual crowding and allowing for easy user interaction, such as zooming in and out; and (3) it shows events that are content diverse, so they contain many different types of events (e.g., for an actor, it should show movies and marriages and awards, not just movies). We present an algorithm to generate such timelines for a given time period and screen size, based on submodular optimization and web-co-occurrence statistics with provable performance guarantees. A series of user studies using Mechanical Turk shows that all three quality criteria are crucial to produce quality timelines and that our algorithm significantly outperforms various baseline and state-of-the-art methods.


Sustainable construction: construction and demolition waste reconsidered.

Construction activity in Europe has increased substantially in the past decade. Likewise, there has also been a commensurate rise in the generation of construction and demolition waste (C&DW). This, together with the fact that in many European countries the rate of recycling and reuse of C&DW is still quite low has engendered a serious environmental problem and a motivation to develop strategies and management plans to solve it. Due to its composition, there is a significant potential to reuse and/or recycle C&DW, and thereby, contribute to improving the sustainability of construction and development, but practical procedures are not yet widely known or practiced in the construction industry. This article (a) summarizes the different applications that are presently practiced to optimize the recovery and/or application of C&DW for reuse, and (b) proposes various measures and strategies to improve the processing of this waste. The authors suggest that to enhance environmental effectiveness, a conscious and comprehensive C&DW management plan should be implemented in each jurisdiction. More precisely, this study presents a holistic approach towards C&DW management, through which environmental benefits can be achieved through the application of new construction methods that can contribute to sustainable growth.


Wireless image communication system for fire-fighting robots

Recently, it has sometimes been impossible for fire-fighting personnel to access the site of a fire, even as the fire causes tremendous property damage and loss of human life, due to high temperatures or the presence of explosive materials. In such environments, fire-fighting robots can be useful for extinguishing a fire, and they should be controlled by remote operators. In order to help a remote operator who is located far away from the fire-fighting robot, wireless image communication systems have been investigated in this study. The developed system uses a High-Speed Downlink Packet Access (HSDPA) network to transmit images, showing the view from a fire-fighting robot. The fire-fighting robots and remote controllers are equipped with industrial computers, and image-transmitting and image-receiving programs function according to various protocols to allow communication between the robots and controllers


Low voltage low power analog circuit design OTA using signal attenuation technique in universal filter application

This paper presents a new configuration for a linear operational transconductance amplifier (OTA) using a signal attenuation technique. The OTA is designed to operate with a ±0.8V supply voltage and consumes 0.45mW power. All simulations are performed by ELDO model BSIM3v3 technology CMOS TSMC 0.18μm. The simulation results of this circuit showed a high DC gain of 73.6dB with a unity frequency of 50.19MHz and a total harmonic distortion of -60.81dB at 100 kHz for an input voltage of 1Vpp. Based on this circuit, a voltage mode universal filter has been implemented. The simulation results are in a good agreement with the theoretical calculations.


Competitive intelligence: construct exploration, validation and equivalence

Purpose – Little empirical research has been conducted on competitive intelligence (CI). This paper aims to contribute to the quantitative strand of the CI literature by exploring and validating the theoretical constructs of the CI process. Design/methodology/approach – Data from 601 questionnaires filled out by South African and Flemish exporters were subjected to exploratory factor analysis and construct equivalence analysis between the sub-samples. Findings – The results showed that the CI process consists of three constructs, while the context in which CI takes place consists of four constructs. This agrees to some extent with the literature. When verifying the constructs for both cultures it was found that all but one CI context construct can be viewed as equivalent in both groups. Bias analysis identified one item in the questionnaire that was biased. Via regression analysis it was also indicated that the context in which CI takes place influences the CI process to a large extent. The research identified size as an important influencing factor in a business’ CI process. Practical implications – Businesses involved in CI should take note that an improvement in their formal infrastructure, employee involvement and internal information processes could enhance their CI capability. Originality/value – This paper contributes towards the formalising of the constructs of competitive intelligence.


Empirical studies of software engineering: a roadmap

In this article we summarize the strengths and weaknesses of empirical research in software engineering. We argue that in order to improve the current situation we must create better studies and draw more credible interpretations from them. We finally present a roadmap for this improvement, which includes a general structure for software empirical studies and concrete steps for achieving these goals: designing better studies, collecting data more effectively, and involving others in our empirical enterprises.


Hidost: a static machine-learning-based detector of malicious files

Malicious software, i.e., malware, has been a persistent threat in the information security landscape since the early days of personal computing. The recent targeted attacks extensively use non-executable malware as a stealthy attack vector. There exists a substantial body of previous work on the detection of non-executable malware, including static, dynamic, and combined methods. While static methods perform orders of magnitude faster, their applicability has been hitherto limited to specific file formats. This paper introduces Hidost, the first static machine-learning-based malware detection system designed to operate onmultiple file formats. Extending a previously published, highly effective method, it combines the logical structure of files with their content for even better detection accuracy. Our system has been implemented and evaluated on two formats, PDF and SWF (Flash). Thanks to its modular design and general feature set, it is extensible to other formats whose logical structure is organized as a hierarchy. Evaluated in realistic experiments on timestamped datasets comprising 440,000 PDF and 40,000 SWF files collected during several months, Hidost outperformed all antivirus engines deployed by the website VirusTotal to detect the highest number of malicious PDF files and ranked among the best on SWF malware.


Thinking in circuits: toward neurobiological explanation in cognitive neuroscience

Cognitive theory has decomposed human mental abilities into cognitive (sub) systems, and cognitive neuroscience succeeded in disclosing a host of relationships between cognitive systems and specific structures of the human brain. However, an explanation of why specific functions are located in specific brain loci had still been missing, along with a neurobiological model that makes concrete the neuronal circuits that carry thoughts and meaning. Brain theory, in particular the Hebb-inspired neurocybernetic proposals by Braitenberg, now offers an avenue toward explaining brain–mind relationships and to spell out cognition in terms of neuron circuits in a neuromechanistic sense. Central to this endeavor is the theoretical construct of an elementary functional neuronal unit above the level of individual neurons and below that of whole brain areas and systems: the distributed neuronal assembly (DNA) or thought circuit (TC). It is shown that DNA/TC theory of cognition offers an integrated explanatory perspective on brain mechanisms of perception, action, language, attention, memory, decision and conceptual thought. We argue that DNAs carry all of these functions and that their inner structure (e.g., core and halo subcomponents), and their functional activation dynamics (e.g., ignition and reverberation processes) answer crucial localist questions, such as why memory and decisions draw on prefrontal areas although memory formation is normally driven by information in the senses and in the motor system. We suggest that the ability of building DNAs/TCs spread out over different cortical areas is the key mechanism for a range of specifically human sensorimotor, linguistic and conceptual capacities and that the cell assembly mechanism of overlap reduction is crucial for differentiating a vocabulary of actions, symbols and concepts.


Face, Age and Gender Recognition using Local Descriptors

This thesis focuses on the area of face processing and aims at designing a reliable framework to facilitate face, age, and gender recognition. A Bag-of-Words framework has been optimized for the task of face recognition by evaluating different feature descriptors and different bag-of-words configurations. More specifically, we choose a compact set of features (e.g., descriptors, window locations, window sizes, dictionary sizes, etc.) in order to produce the highest possible rate of accuracy. Experiments on a challenging dataset shows that our framework achieves a better level of accuracy when compared to other popular approaches such as dimension reduction techniques, edge detection operators, and texture and shape feature extractors. The second contribution of this thesis is the proposition of a general framework for age and gender classification. Although the vast majority of the existing solutions focus on a single visual descriptor that often only encodes a certain characteristic of the image regions, this thesis aims at integrating multiple feature types. For this purpose, feature selection is employed to obtain more accurate and robust facial descriptors. Once descriptors have been computed, a compact set of features is chosen, which facilitates facial image processing for age and gender analysis. In addition to this, a new color descriptor (CLR-LBP) is proposed and the results obtained is shown to be comparable to those of other pre-existing color descriptors. The experimental results indicates that our age and gender framework outperforms other proposed methods when examined on two challenging databases, where face objects are present with different expressions and levels of illumination. This achievement demonstrates the effectiveness of our proposed solution and allows us to achieve a higher accuracy over the existing state-of-the-art methods.


Mate choice decisions: the role of facial beauty

For most people, facial beauty appears to play a prominent role in choosing a mate. Evidence from research on facial attractiveness indicates that physical beauty is a sexually selected trait mediated, in part, by pubertal facial hormone markers that signal important biological information about the displayer. Such signals would be ineffective if they did not elicit appropriate cognitive and/or emotional responses in members of the opposite sex. In this article, I argue that the effectiveness of these hormonal displays varies with perceivers' brains, which have been organized by the degree of steroid hormone exposure in the uterus, and activated by varying levels of circulating steroids following puberty. I further propose that the methodology used for examining mate choice decisions has general applicability for determining how cognitive and emotional evaluations enter into decision processes.


Learning from the past: answering new questions with past answers

Community-based Question Answering sites, such as Yahoo! Answers or Baidu Zhidao, allow users to get answers to complex, detailed and personal questions from other users. However, since answering a question depends on the ability and willingness of users to address the asker's needs, a significant fraction of the questions remain unanswered. We measured that in Yahoo! Answers, this fraction represents 15% of all incoming English questions. At the same time, we discovered that around 25% of questions in certain categories are recurrent, at least at the question-title level, over a period of one year.
 We attempt to reduce the rate of unanswered questions in Yahoo! Answers by reusing the large repository of past resolved questions, openly available on the site. More specifically, we estimate the probability whether certain new questions can be satisfactorily answered by a best answer from the past, using a statistical model specifically trained for this task. We leverage concepts and methods from query-performance prediction and natural language processing in order to extract a wide range of features for our model. The key challenge here is to achieve a level of quality similar to the one provided by the best human answerers.
 We evaluated our algorithm on offline data extracted from Yahoo! Answers, but more interestingly, also on online data by using three "live" answering robots that automatically provide past answers to new questions when a certain degree of confidence is reached. We report the success rate of these robots in three active Yahoo! Answers categories in terms of both accuracy, coverage and askers' satisfaction. This work presents a first attempt, to the best of our knowledge, of automatic question answering to questions of social nature, by reusing past answers of high quality.


Netprobe: a fast and scalable system for fraud detection in online auction networks

Given a large online network of online auction users and their histories of transactions, how can we spot anomalies and auction fraud? This paper describes the design and implementation of NetProbe, a system that we propose for solving this problem. NetProbe models auction users and transactions as a Markov Random Field tuned to detect the suspicious patterns that fraudsters create, and employs a Belief Propagation mechanism to detect likely fraudsters. Our experiments show that NetProbe is both efficient and effective for fraud detection. We report experiments on synthetic graphs with as many as 7,000 nodes and 30,000 edges, where NetProbe was able to spot fraudulent nodes with over 90% precision and recall, within a matter of seconds. We also report experiments on a real dataset crawled from eBay, with nearly 700,000 transactions between more than 66,000users, where NetProbe was highly effective at unearthing hidden networks of fraudsters, within a realistic response time of about 6 minutes. For scenarios where the underlying data is dynamic in nature, we propose IncrementalNetProbe, which is an approximate, but fast, variant of NetProbe. Our experiments prove that Incremental NetProbe executes nearly doubly fast as compared to NetProbe, while retaining over 99% of its accuracy.


Multicommodity max-flow min-cut theorems and their use in designing approximation algorithms

In this paper, we establish max-flow min-cut theorems for several important classes of multicommodity flow problems. In particular, we show that for any n-node multicommodity flow problem with uniform demands, the max-flow for the problem is within an O(log n) factor of the upper bound implied by the min-cut. The result (which is existentially optimal) establishes an important analogue of the famous 1-commodity max-flow min-cut theorem for problems with multiple commodities. The result also has substantial applications to the field of approximation algorithms. For example, we use the flow result to design the first polynomial-time (polylog n-times-optimal) approximation algorithms for well-known NP-hard optimization problems such as graph partitioning, min-cut linear arrangement, crossing number, VLSI layout, and minimum feedback arc set. Applications of the flow results to path routing problems, network reconfiguration, communication in distributed networks, scientific computing and rapidly mixing Markov chains are also described in the paper.


Requirements Traceability: a Systematic Review and Industry Case Study

Requirements traceability enables software engineers to trace a requirement from its emergence to its ful ̄llment. In this paper we examine requirements traceability de ̄nitions, challenges, tools and techniques, by the use of a systematic review performing an exhaustive search through the years 1997 2007. We present a number of common de ̄nitions, challenges, available tools and techniques (presenting empirical evidence when found), while complementing the results and analysis with a static validation in industry through a series of interviews.


StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones

Much of the stress and strain of student life remains hidden. The StudentLife continuous sensing app assesses the day-to-day and week-by-week impact of workload on stress, sleep, activity, mood, sociability, mental well-being and academic performance of a single class of 48 students across a 10 week term at Dartmouth College using Android phones. Results from the StudentLife study show a number of significant correlations between the automatic objective sensor data from smartphones and mental health and educational outcomes of the student body. We also identify a Dartmouth term lifecycle in the data that shows students start the term with high positive affect and conversation levels, low stress, and healthy sleep and daily activity patterns. As the term progresses and the workload increases, stress appreciably rises while positive affect, sleep, conversation and activity drops off. The StudentLife dataset is publicly available on the web.


Finite Element Analysis Based Design of Mobile Robot for Removing Plug Oil Well

In order to develop the mobile robot for removing the plug oil well, the robot was designed based on the wheeltype and leg-type robot mechanism. A well functioning prototype has been manufactured. To demonstrate the validity and the benefit of the mobile robot, supporting mechanism and guiding rod were chosen to design based on the FEM. The mathematical model of the supporting mechanism is established and the mechanical property is analyzed using the FEM. The deformation and stress of some components of the supporting mechanism and the guiding rod is investigated. The results show that the supporting mechanism and the guiding rod have excellent performance with little displacement and small stress under working condition. The strength and rigidity of supporting mechanism and the guiding rod are good enough to ensure the reliability of the whole robot mechanism.


Cognitive load and detection thresholds in car following situations: safety implications for using mobile (cellular) telephones while driving.

This study was aimed at investigating drivers' ability to detect a car ahead decelerating, while doing mobile phone related tasks. Nineteen participants aged between 20 and 29 years, (2000-125000 km driving experience) drove at 80 km/h, 50 m behind a lead car, on a 30 km section of motorway in normal traffic. During each trial the lead car started to decelerate at an average of 0.47 m/s2 while the participant either looked at the car in front (control), continuously dialed series of three random integers on a numeric keypad (divided visual attention), or performed a memory and addition task (non-visual attention). The results indicated that drivers' detection ability was impaired by about 0.5 s in terms of brake reaction time and almost 1 s in terms of time-to-collision, when they were doing the non-visual task whilst driving. This impairment was similar to when the drivers were dividing their visual attention between the road ahead and dialing numbers on the keypad. It was concluded that neither a hands-free option nor a voice controlled interface removes the safety problems associated with the use of mobile phones in a car.


Telerobotics, automation, and human supervisory control [Review]

In Telerobotics, Automation, and Human Supervisory Control, Thomas B. Sheridan shows that progress in robotics depends not only on change in technology, but also on advances in humans’ relationships to machines. Sheridan, Professor of Engineering and Applied Psychology at M.I.T., has spent decades studying human-machine relations. His idea of “human supervisory control” (just “supervisory control” for short) has the potential to bring robotics out of the laboratory and into the difficult and messy world. Traditionally, the public and researchers alike have focused on automation which seeks to build into a machine all necessary capabilities for achieving a particular task, including sensors, actuators, servos, and algorithms. In contrast to automatic control, supervisory control puts a human back “in the loop,” usually at the highest levels of abstraction, while maintaining lower-level functions within the machine. In a supervisory control system human and machine work together. The operator may intervene and operate manually in a particular situation, or she (Sheridan refers to the generic operator as female) might turn over control to an automatic controller for a time, acting as monitor of the task at hand. Typically the “system” (including the human) will operate somewhere on the spectrum between manual and automatic, dividing the task in different degrees depending on the situation. When the operator and the machine are not collocated but are connected by some communication channel, the system is said to be telerobotic. Determining the proper balance of man and machine, manual and automatic control, or providing tools to do so, is the goal of Sheridan’s book. An important modern example of a supervisory control system (although not a telerobotic one) is a modem aircraft. The pilot can command the flight surfaces directly and fly manually, or she can program an autopilot to fly a complete route, including nearly complete takeoff and landing. Typically, the pilot performs some


Internet Traffic Classification by Aggregating Correlated Naive Bayes Predictions

This paper presents a novel traffic classification scheme to improve classification performance when few training data are available. In the proposed scheme, traffic flows are described using the discretized statistical features and flow correlation information is modeled by bag-of-flow (BoF). We solve the BoF-based traffic classification in a classifier combination framework and theoretically analyze the performance benefit. Furthermore, a new BoF-based traffic classification method is proposed to aggregate the naive Bayes (NB) predictions of the correlated flows. We also present an analysis on prediction error sensitivity of the aggregation strategies. Finally, a large number of experiments are carried out on two large-scale real-world traffic datasets to evaluate the proposed scheme. The experimental results show that the proposed scheme can achieve much better classification performance than existing state-of-the-art traffic classification methods.


New relationships between breast microcalcifications and cancer

Background:Breast microcalcifications are key diagnostically significant radiological features for localisation of malignancy. This study explores the hypothesis that breast calcification composition is directly related to the local tissue pathological state.Methods:A total of 236 human breast calcifications from 110 patients were analysed by mid-Fouries transform infrared (FTIR) spectroscopy from three different pathology types (112 invasive carcinoma (IC), 64 in-situ carcinomas and 60 benign). The biochemical composition and the incorporation of carbonate into the hydroxyapatite lattice of the microcalcifications were studied by infrared microspectroscopy. This allowed the spectrally identified composition to be directly correlated with the histopathology grading of the surrounding tissue.Results:The carbonate content of breast microcalcifications was shown to significantly decrease when progressing from benign to malignant disease. In this study, we report significant correlations (P<0.001) between microcalcification chemical composition (carbonate content and protein matrix : mineral ratios) and distinct pathology grades (benign, in-situ carcinoma and ICs). Furthermore, a significant correlation (P<0.001) was observed between carbonate concentrations and carcinoma in-situ sub-grades. Using the two measures of pathology-specific calcification composition (carbonate content and protein matrix : mineral ratios) as the inputs to a two-metric discriminant model sensitivities of 79, 84 and 90% and specificities of 98, 82 and 96% were achieved for benign, ductal carcinoma in situ and invasive malignancies, respectively.Conclusions:We present the first demonstration of a direct link between the chemical nature of microcalcifications and the grade of the pathological breast disease. This suggests that microcalcifications have a significant association with cancer progression, and could be used for future objective analytical classification of breast pathology. A simple two-metric model has been demonstrated, more complex spectral analysis may yeild greater discrimination performance. Furthermore there appears to be a sequential progression of calcification composition.


Link prediction in complex networks: a local naïve Bayes model

Common-neighbor-based method is simple yet effective to predict missing links, which assume that two nodes are more likely to be connected if they have more common neighbors. In such method, each common neighbor of two nodes contributes equally to the connection likelihood. In this Letter, we argue that different common neighbors may play different roles and thus lead to different contributions, and propose a local näıve Bayes model accordingly. Extensive experiments were carried out on eight real networks. Compared with the common-neighbor-based methods, the present method can provide more accurate predictions. Finally, we gave a detailed case study on the US air transportation network. Introduction. – The problem of link prediction aims at estimating the likelihood of the existence of a link between two nodes in a given network, based on the observed links [1]. Recently, the study of link prediction has attracted much attention from disparate scientific communities. In the theoretical aspect, accurate prediction indeed gives evidence to some underlying mechanisms that drives the network evolution [2]. Moveover, it is very possible to build a fair evaluation platform for network modeling under the framework of link prediction, which might be interested by network scientists [1, 3]. In the practical aspect, for biological networks such as protein-protein interaction networks and metabolic networks [4–6], the experiments of uncovering the new links or interactions are costly, and thus to predict in advance and focus on the links most likely to exist can sharply reduce the experimental costs [7]. In addition, some of the representative methods of link prediction have been successfully applied to address the classification problem in partially labeled networks [8, 9], as well as identified the spurious links resulted from the inaccurate information in the data [10]. Motivated by the theoretical interests and practical significance, many methods for link predication have been proposed. Therein some algorithms are based on Markov chains [11–13] and machine learning [14, 15]. Another group of algorithms are based on node similarity [1, 16]. (a)Corresponding author:linyuan.lue@unifr.ch Common Neighbors (CN) is one of the simplest similarity indices. Empirically, Kossinets and Watts [17] analyzed a large-scale social network, suggesting that two students having many mutual friends are very probable to be friend in the future. Also in online society like Facebook, the users tend to be friend if they have common friends and therefore form a community. Extensive analysis on disparate networks suggests that CN index is of good performance on predicting the missing links [16, 18]. Lü et al. [19] suggested that in a network with large clustering coefficient, CN can provide competitively accurate predictions compared with the indices making use of global information. Very recently, Cui et al. [20] revealed that the nodes with more common neighbors are more likely to form new links in a growing network. The basic assumption of CN is that two nodes are more likely to be connected if they have more common neighbors. Simply counting the number of common neighbors indicates that each common neighbor gives equal contribution to the connection likelihood. However, sometimes different common neighbors may play different roles. For instance, the common close friends of two people who don’t know each other may contribute more to their possibly future friendship than their common nodding acquaintances. In this Letter, we propose a probabilistic model based on the Bayesian theory, called Local Näıve Bayes (LNB) model, to predict the missing links in complex networks. Based on the LNB model,


A Rank-Switching, Open-Row DRAM Controller for Time-Predictable Systems

We introduce ROC, a Rank-switching, Open-row Controller for Double Data Rate Dynamic RAM (DDR DRAM). ROC is optimized for mixed-criticality multicore systems using modern DDR devices: compared to existing real-time memory controllers, it provides significantly lower worst case latency bounds for hard real-time tasks and supports throughput-oriented optimizations for soft real-time applications. The key to improved performance is an innovative rank-switching mechanism which hides the latency of write-read transitions in DRAM devices without requiring unpredictable request reordering. We further employ open row policy to take advantage of the data caching mechanism (row buffering) in each device. ROC provides complete timing isolation between hard and soft tasks and allows for compositional timing analysis over the number of cores and memory ranks in the system. We implemented and synthesized the ROC back end in Verilog RTL, and evaluated its performance on both synthetic tasks and a set of representative benchmarks.


Interprofessional education: effects on professional practice and health care outcomes.

BACKGROUND
As patient care becomes more complex, effective collaboration between health and social care professionals is required. However, evidence suggests that these professionals do not collaborate well together. Interprofessional education (IPE) offers a possible way forward in this area.

OBJECTIVES
To assess the usefulness of IPE interventions compared to education in which the same professions were learning separately from one another.

SEARCH STRATEGY
We searched the Cochrane Effective Practice and Organisation of Care Group specialised register, MEDLINE (1968 to 1998) and Cinahl (1982 to 1998). We also hand searched the Journal of Interprofessional Care (1992 to 1998), the Centre for the Advancement of Interprofessional Education Bulletin (1987 to 1998), conference proceedings, the 'grey literature' held by relevant organisations, and reference lists of articles.

SELECTION CRITERIA
Randomised trials, controlled before and after studies and interrupted time series studies of IPE interventions designed to improve collaborative practice between health/social care practitioners and/or the health/well being of patients/clients. The participants included chiropodists/podiatrists, complementary therapists, dentists, dietitians, doctors/physicians, hygienists, psychologists, psychotherapists, midwives, nurses, pharmacists, physiotherapists, occupational therapists, radiographers, speech therapists and/or social workers. The outcomes included objectively measured or self reported (validated instrument) patient/client outcomes and reliable (objective or validated subjective) health care process measures.

DATA COLLECTION AND ANALYSIS
Two reviewers independently assessed the eligibility of potentially relevant studies.

MAIN RESULTS
The total yield from the search strategy was 1042, of which 89 were retained for further consideration. However none of these studies met the inclusion criteria.

REVIEWER'S CONCLUSIONS
Despite finding a large body of literature on the evaluation of IPE, these studies lacked the methodological rigour needed to begin to convincingly understand the impact of IPE on professional practice and/or health care outcomes.


Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking

Online multi-object tracking aims at estimating the tracks of multiple objects instantly with each incoming frame and the information provided up to the moment. It still remains a difficult problem in complex scenes, because of the large ambiguity in associating multiple objects in consecutive frames and the low discriminability between objects appearances. In this paper, we propose a robust online multi-object tracking method that can handle these difficulties effectively. We first define the tracklet confidence using the detectability and continuity of a tracklet, and decompose a multi-object tracking problem into small subproblems based on the tracklet confidence. We then solve the online multi-object tracking problem by associating tracklets and detections in different ways according to their confidence values. Based on this strategy, tracklets sequentially grow with online-provided detections, and fragmented tracklets are linked up with others without any iterative and expensive association steps. For more reliable association between tracklets and detections, we also propose a deep appearance learning method to learn a discriminative appearance model from large training datasets, since the conventional appearance learning methods do not provide rich representation that can distinguish multiple objects with large appearance variations. In addition, we combine online transfer learning for improving appearance discriminability by adapting the pre-trained deep model during online tracking. Experiments with challenging public datasets show distinct performance improvement over other state-of-the-arts batch and online tracking methods, and prove the effect and usefulness of the proposed methods for online multi-object tracking.


The Mini Nutritional Assessment (MNA) review of the literature--What does it tell us?

UNLABELLED
To review the literature on the MNA to Spring 2006, we searched MEDLINE, Web of Science and Scopus, and did a manual search in J Nutr Health Aging, Clin Nutr, Eur J Clin Nutr and free online available publications.

VALIDATION AND VALIDITY
The MNA was validated against two principal criteria, clinical status and comprehensive nutrition assessment using principal component and discriminant analysis. The MNA shortform (MNA-SF) was developed and validated to allow a 2-step screening process. The MNA and MNA-SF are sensitive, specific, and accurate in identifying nutrition risk.

NUTRITIONAL SCREENING
The prevalence of malnutrition in community-dwelling elderly (21 studies, n = 14149 elderly) is 2 +/- 0.1% (mean +/- SE, range 0- 8%) and risk of malnutrition is 24 +/- 0.4% (range 8-76%). A similar pattern is seen in out-patient and home care elderly (25 studies, n = 3119 elderly) with prevalence of undernutrition 9 +/- 0.5% (mean +/- SE, range 0-30%) and risk of malnutrition 45 +/- 0.9% (range 8-65%). A high prevalence of undernutrition has been reported in hospitalized and institutionalized elderly patients: prevalence of malnutrition is 23 +/- 0.5% (mean +/- SE, range 1- 74%) in hospitals (35 studies, n = 8596) and 21 +/- 0.5% (mean +/- SE, range 5-71%) in institutions (32 studies, n = 6821 elderly). An even higher prevalence of risk of malnutrition was observed in the same populations, with 46 +/- 0.5% (range 8-63%) and 51 +/- 0.6% (range 27-70%), respectively. In cognitively impaired elderly subjects (10 studies, n = 2051 elderly subjects), detection using the MNA, prevalence of malnutrition was 15 +/- 0.8% (mean +/- SE, range 0-62%), and 44 +/- 1.1% (range 19-87%) of risk of malnutrition.

CHARACTERISTICS
The large variability is due to differences in level of dependence and health status among the elderly. In hospital settings, a low MNA score is associated with an increase in mortality, prolonged length of stay and greater likelihood of discharge to nursing homes. Malnutrition is associated with functional and cognitive impairment and difficulties eating. The MNA(R) detects risk of malnutrition before severe change in weight or serum proteins occurs.

NUTRITIONAL INTERVENTION
Intervention studies demonstrate that timely intervention can stop weight loss in elderly at risk of malnutrition or undernourished and is associated with improvements in MNA scores. The MNA can also be used as a follow up assessment tool.

CONCLUSION
The MNA is a screening and assessment tool with a reliable scale and clearly defined thresholds, usable by health care professionals. It should be included in the geriatric assessment and is proposed in the minimum data set for nutritional interventions.