Query: Power to the People: The Role of Humans in Interactive Machine Learning

Relevant passages:
Human model evaluation in interactive supervised learning <=SEP=> Model evaluation plays a special role in interactive machine learning (IML) systems in which users rely on their assessment of a model's performance in order to determine how to improve it. A better understanding of what model criteria are important to users can therefore inform the design of user interfaces for model evaluation as well as the choice and design of learning algorithms. We present work studying the evaluation practices of end users interactively building supervised learning systems for real-world gesture analysis problems. We examine users' model evaluation criteria, which span conventionally relevant criteria such as accuracy and cost, as well as novel criteria such as unexpectedness. We observed that users employed evaluation techniques---including cross-validation and direct, real-time evaluation---not only to make relevant judgments of algorithms' performance and interactively improve the trained models, but also to learn to provide more effective training data. Furthermore, we observed that evaluation taught users about what types of models were easy or possible to build, and users sometimes used this information to modify the learning problem definition or their plans for using the trained models in practice. We discuss the implications of these findings with regard to the role of generalization accuracy in IML, the design of new algorithms and interfaces, and the scope of potential benefits of incorporating human interaction in the design of supervised learning systems.

Teachable robots: Understanding human teaching behavior to build more effective robot learners <=SEP=> While Reinforcement Learning (RL) is not traditionally designed for interactive supervisory input from a human teacher, several works in both robot and software agents have adapted it for human input by letting a human trainer control the reward signal. In this work, we experimentally examine the assumption underlying these works, namely that the human-given reward is compatible with the traditional RL reward signal. We describe an experimental platform with a simulated RL robot and present an analysis of real-time human teaching behavior found in a study in which untrained subjects taught the robot to perform a new task. We report three main observations on how people administer feedback when teaching a robot a task through Reinforcement Learning: (a) they use the reward channel not only for feedback, but also for future-directed guidance; (b) they have a positive bias to their feedback — possibly using the signal as a motivational channel; and (c) they change their behavior as they develop a mental model of the robotic learner. Given this, we made specific modifications to the simulated RL robot, and analyzed and evaluated its learning behavior in four additional experiments with human trainers. We report significant improvements on several learning measures. This work demonstrates the importance of understanding the human-teacher/robot-learner partnership in order to design algorithms that support how people want to teach while simultaneously improving the robot’s learning behavior.

Natural methods for robot task learning: instructive demonstrations, generalization and practice <=SEP=> Among humans, teaching various tasks is a complex process which relies on multiple means for interaction and learning, both on the part of the teacher and of the learner. Used together, these modalities lead to effective teaching and learning approaches, respectively. In the robotics domain, task teaching has been mostly addressed by using only one or very few of these interactions. In this paper we present an approach for teaching robots that relies on the key features and the general approach people use when teaching each other: first give a demonstration, then allow the learner to refine the acquired capabilities by practicing under the teacher's supervision, involving a small number of trials. Depending on the quality of the learned task, the teacher may either demonstrate it again or provide specific feedback during the learner's practice trial for further refinement. Also, as people do during demonstrations, the teacher can provide simple instructions and informative cues, increasing the performance of learning. Thus, instructive demonstrations, generalization over multiple demonstrations and practice trials are essential features for a successful human-robot teaching approach. We implemented a system that enables all these capabilities and validated these concepts with a Pioneer 2DX mobile robot learning tasks from multiple demonstrations and teacher feedback.

XWand: UI for intelligent spaces <=SEP=> The XWand is a novel wireless sensor package that enables styles of natural interaction with intelligent environments. For example, a user may point the wand at a device and control it using simple gestures. The XWand system leverages the intelligence of the environment to best determine the user's intention. We detail the hardware device, signal processing algorithms to recover position and orientation, gesture recognition techniques, a multimodal (wand and speech) computational architecture and a preliminary user study examining pointing performance under conditions of tracking availability and audio feedback.

GloveTalkII: An Adaptive Gesture-to-Formant Interface <=SEP=> Glove-TaikII is a system which translates hand gestures-· to speech through an adaptive interface. Hand gestures are mapped continuously to 10 control parameters of a parallel formant speech synthesizer. The mapping allows the hand to act as an artificial vocal tract that produces speech in real time. This gives an unlimited vocabulary, multiple languages in addition to direct control of fundamental frequency and volume. Currently, the best version of Glove-TaikII uses several input devices (including a Cyberglove, a ContactGlove, a polhemus sensor, and a foot-pedal), a parallel formant speech synthesizer and 3 neural networks. The gestureto-speech task is divided into vowel and consonant production by using a gating network to weight the outputs of a vowel and a consonant neural network. The gating network and the consonant network are trained with examples from the user. The vowel network implements a fixed, user-defined relationship between hand-position and vowel sound and does not require any training examples from the user. Volume, fundamental frequency and stop consonants are produced with a fixed mapping from the input devices. One subject has trained for about 100 hours to speak intelligibly with Glove-TalkII. He passed through eight distinct stages while learning to speak. He speaks slowly with speech quality similar to a text-to-speech synthesizer but with far more natural-sounding pitch variations.

Collecting and analyzing qualitative data for system dynamics: methods and models <=SEP=> System dynamics depends heavily upon quantitative data to generate feedback models. Qualitative data and their analysis also have a central role to play at all levels of the modeling process. Although the classic literature on system dynamics strongly supports this argument, the protocols to incorporate this information during the modeling process are not detailed by the most influential authors. Data gathering techniques such as interviews and focus groups, and qualitative data analysis techniques such as grounded theory methodology and ethnographic decision models could have a strong, critical role in rigorous system dynamics efforts. This paper describes some of the main qualitative, social science techniques and explores their suitability in the different stages of the modeling process. Additionally, the authors argue that the techniques described in the paper could contribute to the understanding of the modeling process, facilitate

Skip graphs <=SEP=> Skip graphs are a novel distributed data structure, based on skip lists, that provide the full functionality of a balanced tree in a distributed system where elements are stored in separate nodes that may fail at any time. They are designed for use in searching peer-to-peer networks, and by providing the ability to perform queries based on key ordering, they improve on existing search tools that provide only hash table functionality. Unlike skip lists or other tree data structures, skip graphs are highly resilient, tolerating a large fraction of failed nodes without losing connectivity. In addition, constructing, inserting new elements into, searching a skip graph and detecting and repairing errors in the data structure introduced by node failures can be done using simple and straight-forward algorithms.

Sensors for 3D Imaging: Metric Evaluation and Calibration of a CCD/CMOS Time-of-Flight Camera <=SEP=> 3D imaging with Time-of-Flight (ToF) cameras is a promising recent technique which allows 3D point clouds to be acquired at video frame rates. However, the distance measurements of these devices are often affected by some systematic errors which decrease the quality of the acquired data. In order to evaluate these errors, some experimental tests on a CCD/CMOS ToF camera sensor, the SwissRanger (SR)-4000 camera, were performed and reported in this paper. In particular, two main aspects are treated: the calibration of the distance measurements of the SR-4000 camera, which deals with evaluation of the camera warm up time period, the distance measurement error evaluation and a study of the influence on distance measurements of the camera orientation with respect to the observed object; the second aspect concerns the photogrammetric calibration of the amplitude images delivered by the camera using a purpose-built multi-resolution field made of high contrast targets.

Traceable Air Baggage Handling System Based on RFID Tags in the Airport <=SEP=> The RFID is not only a feasible, novel, and cost-effective candidate for daily object identification but it is also considered as a significant tool to provide traceable visibility along different stages of the aviation supply chain. In the air baggage handing application, the RFID tags are used to enhance the ability for baggage tracking, dispatching and conveyance so as to improve the management efficiency and the users’ satisfaction. We surveyed current related work and introduce the IATA RP1740c protocol used for the standard to recognize the baggage tags. One distributed aviation baggage traceable application is designed based on the RFID networks. We describe the RFID-based baggage tracking experiment in the BCIA (Beijing Capital International Airport). In this experiment the tags are sealed in the printed baggage label and the RFID readers are fixed in the certain interested positions of the BHS in the Terminal 2. We measure the accurate recognition rate and monitor the baggage’s real-time situation on the monitor’s screen. Through the analysis of the measured results within two months we emphasize the advantage of the adoption of RFID tags in this high noisy BHS environment. The economical benefits achieved by the extensive deployment of RFID in the baggage handing system are also outlined.

Learning continuous grasp stability for a humanoid robot hand based on tactile sensing <=SEP=> Grasp stability estimation with complex robots in environments with uncertainty is a major research challenge. Analytical measures such as force closure based grasp quality metrics are often impractical because tactile sensors are unable to measure contacts accurately enough especially in soft contact cases. Recently, an alternative approach of learning the stability based on examples has been proposed. Current approaches of stability learning analyze the tactile sensor readings only at the end of the grasp attempt, which makes them somewhat time consuming, because the grasp can be stable already earlier. In this paper, we propose an approach for grasp stability learning, which estimates the stability continuously during the grasp attempt. The approach is based on temporal filtering of a support vector machine classifier output. Experimental evaluation is performed on an anthropomorphic ARMAR-IIIb. The results demonstrate that the continuous estimation provides equal performance to the earlier approaches while reducing the time to reach a stable grasp significantly. Moreover, the results demonstrate for the first time that the learning based stability estimation can be used with a flexible, pneumatically actuated hand, in contrast to the rigid hands used in earlier works.

Authorship attribution based on Life-Like Network Automata <=SEP=> The authorship attribution is a problem of considerable practical and technical interest. Several methods have been designed to infer the authorship of disputed documents in multiple contexts. While traditional statistical methods based solely on word counts and related measurements have provided a simple, yet effective solution in particular cases; they are prone to manipulation. Recently, texts have been successfully modeled as networks, where words are represented by nodes linked according to textual similarity measurements. Such models are useful to identify informative topological patterns for the authorship recognition task. However, there is no consensus on which measurements should be used. Thus, we proposed a novel method to characterize text networks, by considering both topological and dynamical aspects of networks. Using concepts and methods from cellular automata theory, we devised a strategy to grasp informative spatio-temporal patterns from this model. Our experiments revealed an outperformance over structural analysis relying only on topological measurements, such as clustering coefficient, betweenness and shortest paths. The optimized results obtained here pave the way for a better characterization of textual networks.

High-Sensitivity Software-Configurable 5.8-GHz Radar Sensor Receiver Chip in 0.13-$\mu$ m CMOS for Noncontact Vital Sign Detection <=SEP=> In this paper, analyses on sensitivity and link budget have been presented to guide the design of high-sensitivity noncontact vital sign detector. Important design issues such as flicker noise, baseband bandwidth, and gain budget have been discussed with practical considerations of analog-to-digital interface and signal processing methods in noncontact vital sign detection. Based on the analyses, a direct-conversion 5.8-GHz radar sensor chip with 1-GHz bandwidth was designed and fabricated. This radar sensor chip is software configurable to set the operation point and detection range for optimal performance. It integrates all the analog functions on-chip so that the output can be directly sampled for digital signal processing. Measurement results show that the fabricated chip has a sensitivity of better than -101 dBm for ideal detection in the absence of random body movement. Experiments have been performed successfully in laboratory environment to detect the vital signs of human subjects.

The social impact of a systematic floor cleaner <=SEP=> Mint is an automatic cleaning robot that sweeps and mops hard-surface floors using dusting and mopping cloths. Thanks to the Northstar navigation technology it systematically cleans and navigates in people's homes. Since it first became commercially available in mid 2010, hundreds of thousands of Mint cleaners are nowadays in use at home. In this paper we investigate the product's social impact with respect to the attitude of customers towards a systematic floor cleaner and how such a robot influences their lifestyle. We first report feedback from users owning the product, and demonstrate how Mint changed their everyday life at home. We then evaluate the results of a survey launched in 2012 that addresses the technical understanding of the product and what impact it has on the social life of their users. Our findings suggest that Mint, on average, saves more than one hour of people's time per week, floors are cleaner leading to healthier homes and lives, systematic cleaning is seen as an important feature, and modifications to the environment to support the navigation of the robot are largely accepted.

Audit and Analysis of Impostors: An experimental approach to detect fake profile in online social network <=SEP=> In the present generation, the social life of every person has become associated with online social networks (OSN). These sites have made drastic changes in the way we socialize. Making friends and keeping in contact with them as well as being updated of their activities, has become easier. But with their rapid growth, problems like fake profiles, online impersonation have also increased. The risk lies in the fact that anybody can create a profile to impersonate a real person on the OSN. The fake profile could be exploited to build online relationship with a targeted person purely through online interactions with the friends of victim.
 In present work, we have proposed experimental framework with which detection of fake profile is feasible within the friend list, however this framework is restricted to a specific online social networking site namely Facebook. This framework extracts data from the friend list and uses it to classify them as real or fake by using unsupervised and supervised machine learning.

Blind PSF estimation and methods of deconvolution optimization <=SEP=> We have shown that the left side null space of the autoregression (AR) matrix operator is the lexicographical presentation of the point spread function (PSF) on condition the AR parameters are common for original and blurred images. The method of inverse PSF evaluation with regularization functional as the function of surface area is offered. The inverse PSF was used for primary image estimation. Two methods of original image estimate optimization were designed basing on maximum entropy generalization of sought and blurred images conditional probability density and regularization. The first method uses balanced variations of convolution and deconvolution transforms to obtaining iterative schema of image optimization. The variations balance was defined by dynamic regularization basing on condition of iteration process convergence. The regularization has dynamic character because depends on current and previous image estimate variations. The second method implements the regularization of deconvolution optimization in curved space with metric defined on image estimate surface. It is basing on target functional invariance to fluctuations of optimal argument value. The given iterative schemas have faster convergence in comparison with known ones, so they can be used for reconstruction of high resolution images series in real time.

The FEELING of WHAT HAPPENS : BODY AND EMOTION IN THE MAKING OF CONSCIOUSNESS <=SEP=> Antonio Damasio is a Portuguese-born American behavioral neurologist and neuroscientist. For a person without any knowledge of neuroscience, biology, or psychology, The Feeling of What Happens would presumably be a fascinating read. How often does one typically contemplate their own contemplation? The concepts presented by Damasio are abstract and uncommon, and undoubtedly are a new phenomenon for the average reader.

MELOGRAPH: Multi-Engine WorkfLOw Graph Processing <=SEP=> This paper introduces MELOGRAPH, a new system that exposes in the front-end a domain specific language(DSL) for graph processing tasks and in the back-end identifies, ranks and generates source code for the top-N ranked engines. This approach lets the specialized MELOGRAPH be part of a more general multi-engine workflow optimizer. The candidate execution engines are chosen from the contemporaneous Big Data ecosystem: graph databases (e.g. Neo4j, TitanDB, OrientDB, Sparksee/DEX) and robust graph processing frameworks with Java API or packaged libraries of algorithms (e.g. Giraph, Okapi, Flink Gelly, Hama, Gremlin). As MELOGRAPH is work in progress, our current paper stresses upon the state of the art in this field, provides a general architecture and some early implementation insights.

Trusted Click: Overcoming Security issues of NFV in the Cloud <=SEP=> Network Function Virtualization has received a large amount of research and recent efforts have been made to further leverage the cloud to enhance NFV. However, since there are privacy and security issues with using cloud computing, work has been done to allow for operating on encrypted data, which introduces a large amount of overhead in both computation and data, while only providing a limited set of operations, since these encryption schemes are not fully homomorphic. We propose using trusted computing to circumvent these limitations by having hardware enforce data privacy and provide guaranteed computation. Prior work has shown that Intel's Software Guard Extensions can be used to protect the state of network functions, but there are still questions about the usability of SGX in arbitrary NFV applications and the performance of SGX in these applications. We extend prior work to show how SGX can be used in network deployments by extending the Click modular router to perform secure packet processing with SGX. We also present a performance evaluation of SGX on real hardware to show that processing inside of SGX has a negligible performance impact, compared to performing the same processing outside of SGX.

Parathyroid Hormone PTH(1-34) Formulation that Enables Uniform Coating on a Novel Transdermal Microprojection Delivery System <=SEP=> Assess formulation parameters to enable >24-h continuous accurate and uniform coating of PTH(1-34) on a novel transdermal microprojection array delivery system. Surface activity and rheology of the liquid formulation was determined by contact angle measurement and cone-plate viscometry. The formulation’s delivery performance was assessed in vivo using the hairless guinea pig model. Peptide gelation was investigated by rheological and viscoelastic behavior changes. Accurate and uniform coating was achieved by formulating the liquid formulation to a preferred contact angle range of 30–60° with a surfactant and by establishing a Newtonian fluid (defined as a fluid maintaining a constant viscosity with shear rate and time) with a viscosity of ≥20 cps via adjusting the peptide concentration and using an appropriate acidic counterion. A non-volatile acidic counterion was found critical to compensate for the loss of the volatile acetate counterion to maintain the peptide formulation’s solubility upon rehydration in the skin. Finally, the 15.5% w/w PTH(1-34) concentration was found to be the most physically stable formulation (delayed gelation) in the roll-coating reservoir. With a properly designed coating reservoir for shear force reduction, the liquid formulation could last for more than 24 h without gelation. The study successfully offered scientific rationales for developing an optimal liquid formulation for a novel titanium microprojection array coating process. The resultant formulation has an enduring physical stability (>24 h) in the coating reservoir and maintained good in vivo dissolution performance.

An Ethics Evaluation Tool for Automating Ethical Decision-Making in Robots and Self-Driving Cars <=SEP=> As we march down the road of automation in robotics and artificial intelligence, we will need to automate an increasing amount of ethical decision-making in order for our devices to operate independently from us. But automating ethical decision-making raises novel questions for engineers and designers, who will have to make decisions about how to accomplish that task. For example, some ethical decisionmaking involves hard moral cases, which in turn requires user input if we are to respect established norms surrounding autonomy and informed consent. The author considers this and other ethical considerations that accompany the automation of ethical decision-making. He proposes some general ethical requirements that should be taken into account in the design room, and sketches a design tool that can be integrated into the design process to help engineers, designers, ethicists, and policymakers decide how best to automate certain forms of ethical decision-making.

Correctness By Construction: Better Can Also Be Cheaper <=SEP=> I n December 1999 CrossTalk [3], David Cook provided a well-reasoned historical analysis of programming language development and considered the role languages play in the software development process. The article was valuable because it showed that programming language developments are not sufficient to ensure success; however, it would be dangerous to conclude from this that they are not necessary for success. Cook rightly identifies other issues such as requirements capture, specifications, and verification and validation (V&V) that need to be addressed. Perhaps we need to look at programming languages not just in terms of their ability to code some particular design but in the influence the language has on some of these other vital aspects of the development process. The key notion is that of the benefit of a precise language or language subset. If the term subset has set anyone thinking " oh no, not another coding standard, " then read on, the topic is much more interesting and useful than that! Language Issues Programming languages have evolved in three main ways. First came improvements in structure; then attempts at improving compile-time error detection through such things as strong typing; and, most significantly , facilities to improve our ability to express abstractions. All of these have shaped the way we think about problem solving. However, programming languages have not evolved in their precision of expression. In fact, they may have actually gotten worse since the meaning of a sample of machine code is exact and unequivocal , whereas the meaning of the constructs of typical modern high-order languages are substantially less certain. The evolution of C into C++ certainly improved its ability to express design abstractions but, if anything, the predictability of the compiled code decreased. These ambiguities arise either from deficiencies in the original language definition or from implementation freedoms given to the compiler writer for ease of implementation or efficiency reasons. None of this may look like a very serious problem. We can still do code walk-throughs and reviews and, after all, we still have to do dynamic testing that should flush out any remaining ambiguities. In fact the evidence is quite strong that it does matter because it creates an environment where we are encouraged to make little attempt to reason about the software we are producing at each stage of its development. Since we typically do not have formal mathematical specifications and we use imprecise …

Peppermint oil for treatment of irritable bowel syndrome. <=SEP=> 22 AM J HEALTH-SYST PHARM | VOLUME 73 | NUMBER 2 | JANUARY 15, 2016 Peppermint oil for treatment of irritable bowel syndrome Irritable bowel syndrome (IBS) is a chronic gastrointestinal disorder that affects 5–15% of people worldwide. Typically, patients with IBS experience abdominal pain or discomfort and constipation or diarrhea with other gastrointestinal symptoms, such as abdominal distention and bloating; these symptoms reduce quality of life and can be difficult to control with currently available prescription agents. For these reasons, patients are often compelled to try nonprescription therapies, including natural products. In studies conducted in Australia and the United Kingdom, about 20–50% of patients with IBS reported using complementary and alternative medicines. Thus, it is important that healthcare professionals are knowledgeable of these therapies, including dietary supplements such as peppermint oil. Historically, the ingestion of peppermint oil has been associated with effects on the gastrointestinal tract. It is a classic essential oil known to have carminative properties (i.e., it is a naturally occurring remedy thought to help decrease bloating and gas by allowing passage of flatus) and may even be beneficial as an antiemetic. Peppermint oil comes from a perennial herb (Mentha × piperita), a plant found across North America and Europe. Mentha × piperita is a sterile hybrid of two herbs, spearmint (Menthaspicata) and water mint (Menthaaquatica). The main constituents of peppermint oil include menthol (35–55%), menthone (20–31%), menthyl acetate (3–10%), isomenthone, 1,8-cineole, limonene, b-myrcene, and carvone. Peppermint oil may be obtained through steam distillation of flowering parts that grow aboveground. It is a volatile oil, with menthol accounting for a majority of its potency. Due to the various constituents of peppermint oil, it has a variety of uses, including topical application as an antiseptic and for aches and pains, inhalation as aromatherapy, and oral formulations for flavoring or for use as digestive aids. Relaxation of intestinal muscle, both in vivo and in vitro, and relaxation of the lower esophageal sphincter have been reported with the use of peppermint oil, which is thought of as an antispasmodic that may confer benefits in conditions such as IBS. Several clinical studies of the use of peppermint oil for the treatment of

Human motion and emotion parameterization <=SEP=> This paper describes the methods and data structures used for generating new human motions out of existing motion data. These existing animation data are derived from live motion capture or produced through the traditional animation tools. We present a survey of techniques for generating new animations by physical and emotional parameterizations. Discussed new animations are generated by Fourier transformations, transformations of velocity and amplitude and scattered data interpolation in this report.

An Algorithm for Drawing General Undirected Graphs Tomihisa Kamada and Satoru Kawai <=SEP=> Graphs (networks) are very common data structures which are handled in computers. Diagrams are widely used to represent the graph structures visually in many information systems. In order to automatically draw the diagrams which are, for example, state graphs, data-flow graphs, Petri nets, and entity-relationship diagrams, basic graph drawing algorithms are required. The state of the art in automatic drawing is surveyed comprehensively in [7,19]. There have been only a few algorithms for general undirected graphs. This paper presents a simple but successful algorithm for drawing undirected graphs and weighted graphs. The basic idea of our algorithm is as follows. We regard the desirable "geometric" (Euclidean) distance between two vertices in the drawing as the "graph theoretic" distance between them in the corresponding graph. We introduce a virtual dynamic system in which every two vertices are connected by a "spring" of such desirable length. Then, we regard the optimal layout of vertices as the state in which the total spring energy of the system is minimal. The "spring" idea for drawing general graphs was introduced in [6], and similar methods were used for drawing planar graphs with fixed boundary [2,20]. This paper brings a new significant result in graph drawing based on the spring model.

Topic Model for Identifying Suicidal Ideation in Chinese Microblog <=SEP=> Suicide is one of major public health problems worldwide. Traditionally, suicidal ideation is assessed by surveys or interviews, which lacks of a real-time assessment of personal mental state. Online social networks, with large amount of user-generated data, offer opportunities to gain insights of suicide assessment and prevention. In this paper, we explore potentiality to identify and monitor suicide expressed in microblog on social networks. First, we identify users who have committed suicide and collect millions of microblogs from social networks. Second, we build suicide psychological lexicon by psychological standards and word embedding technique. Third, by leveraging both language styles and online behaviors, we employ Topic Model and other machine learning algorithms to identify suicidal ideation. Our approach achieves the best results on topic-500, yielding F1 − measure of 80.0%, Precision of 87.1%, Recall of 73.9%, and Accuracy of 93.2%. Furthermore, a prototype system for monitoring suicidal ideation on several social networks is deployed.

Unusual coexistence of caudal duplication and caudal regression syndromes. <=SEP=> Caudal duplication syndrome includes anomalies of the genitourinary system, gastrointestinal tract, and the distal neural tube. Caudal regression syndrome presents with lumbosacral hypogenesis, anomalies of the lower gastrointestinal tract, genitourinary system, and limb anomalies. Both happen as a result of insult to the caudal cell mass. We present a child having features consistent with both entities.

Location Assisted Coding (LAC): Embracing Interference in Free Space Optical Communications <=SEP=> As the number of wireless devices grows, the increasing demand for the shared radio frequency (RF) spectrum becomes a critical problem. Unlike wired communications in which, theoretically, more fibers can be used to accommodate the increasing bandwidth demand, wireless spectrum cannot be arbitrarily increased due to the fundamental limitations imposed by the physical laws. On the other hand, recent advances in free space optical (FSO) technologies promise a complementary approach to increase wireless capacity. However, high-speed FSO technologies are currently confined to short distance transmissions, resulting in limited mobility. In this paper, we briefly describe WiFO, a hybrid WiFi-FSO network for Gbps wireless local area network (WLAN) femtocells that can provide up to one Gbps per user while maintaining seamless mobility. While typical RF femtocells are non-overlapped to minimize inter-cell interference, there are advantages of using overlapped femtocells to increase mobility and throughput when the number of users is small. That said, the primary contribution of this paper will be a novel location assisted coding (LAC) technique used in the WiFO network that aims to increase throughput and reduce interference for multiple users in a dense array of femtocells. Both theoretical analysis and numerical experiments show orders of magnitude increase in throughput using LAC over basic codes.

Biodynamic Excisional Skin Tension Lines for Cutaneous Surgery <=SEP=> 

Qualitative research methods in mental health. <=SEP=> As the evidence base for the study of mental health problems develops, there is a need for increasingly rigorous and systematic research methodologies. Complex questions require complex methodological approaches. Recognising this, the MRC guidelines for developing and testing complex interventions place qualitative methods as integral to each stage of intervention development and implementation. However, mental health research has lagged behind many other healthcare specialities in using qualitative methods within its evidence base. Rigour in qualitative research raises many similar issues to quantitative research and also some additional challenges. This article examines the role of qualitative methods within mental heath research, describes key methodological and analytical approaches and offers guidance on how to differentiate between poor and good quality qualitative research.

Reliability in the utility computing era: Towards reliable Fog computing <=SEP=> This paper considers current paradigms in computing and outlines the most important aspects concerning their reliability. The Fog computing paradigm as a non-trivial extension of the Cloud is considered and the reliability of the networks of smart devices are discussed. Combining the reliability requirements of grid and cloud paradigms with the reliability requirements of networks of sensor and actuators it follows that designing a reliable Fog computing platform is feasible.