title: Oxytocin receptor (OXTR) and serotonin transporter (5-HTT) genes associated with observed parenting

abstract: Both oxytocin and serotonin modulate affiliative responses to partners and offspring. Animal studies suggest a crucial role of oxytocin in mammalian parturition and lactation but also in parenting and social interactions with offspring. The serotonergic system may also be important through its influence on mood and the release of oxytocin. We examined the role of serotonin transporter (5-HTT) and oxytocin receptor (OXTR) genes in explaining differences in sensitive parenting in a community sample of 159 Caucasian, middle-class mothers with their 2-year-old toddlers at risk for externalizing behavior problems, taking into account maternal educational level, maternal depression and the quality of the marital relationship. Independent genetic effects of 5-HTTLPR SCL6A4 and OXTR rs53576 on observed maternal sensitivity were found. Controlling for differences in maternal education, depression and marital discord, parents with the possibly less efficient variants of the serotonergic (5-HTT ss) and oxytonergic (AA/AG) system genes showed lower levels of sensitive responsiveness to their toddlers. Two-way and three-way interactions with marital discord or depression were not significant. This first study on the role of both OXTR and 5-HTT genes in human parenting points to molecular genetic differences that may be implicated in the production of oxytocin explaining differences in sensitive parenting.


title: Aquaporins in Renal Diseases

abstract: Aquaporins (AQPs) are a family of highly selective transmembrane channels that mainly transport water across the cell and some facilitate low-molecular-weight solutes. Eight AQPs, including AQP1, AQP2, AQP3, AQP4, AQP5, AQP6, AQP7, and AQP11, are expressed in different segments and various cells in the kidney to maintain normal urine concentration function. AQP2 is critical in regulating urine concentrating ability. The expression and function of AQP2 are regulated by a series of transcriptional factors and post-transcriptional phosphorylation, ubiquitination, and glycosylation. Mutation or functional deficiency of AQP2 leads to severe nephrogenic diabetes insipidus. Studies with animal models show AQPs are related to acute kidney injury and various chronic kidney diseases, such as diabetic nephropathy, polycystic kidney disease, and renal cell carcinoma. Experimental data suggest ideal prospects for AQPs as biomarkers and therapeutic targets in clinic. This review article mainly focuses on recent advances in studying AQPs in renal diseases.


title: Yacc is dead

abstract: We present two novel approaches to parsing context-free languages. The first approach is based on an extension of Brzozowski's derivative from regular expressions to context-free grammars. The second approach is based on a generalization of the derivative to parser combinators. The payoff of these techniques is a small (less than 250 lines of code), easy-to-implement parsing library capable of parsing arbitrary context-free grammars into lazy parse forests. Implementations for both Scala and Haskell are provided. Preliminary experiments with S-Expressions parsed millions of tokens per second, which suggests this technique is efficient enough for use in practice.


title: Bidirectional current-mode capacitor multiplier in DC-DC converter compensation

abstract: Bidirectional current-mode capacitor multipliers for on-chip DC-DC converter compensation are presented in this paper. The increasing demand for portable devices is a driving force toward higher integration. Reducing physical area with the same or better performance is carried out. Based on TSMC 0.35/spl mu/m technology, we demonstrate that a small capacitor is multiplied by a factor about 200. It allows the control system compensating circuit of DC-DC converter be easily integrated on a chip and occupy less silicon area. The experimental results show that the DC-DC converter with proposed architecture is stable for wide loading conditions from 10mA to 400mA while input voltage is 3.3V and output voltage is 2.0V. The quiescent current consumed is 9/spl mu/A by single-ended capacitor multiplier and 19/spl mu/A by two-ended capacitor multiplier.


title: Anthropometry of elderly residents in the city of São Paulo, Brazil.

abstract: The article presents gender and age-specific selected anthropometric data for a representative sample of elderly Brazilians in the city of São Paulo. This was a cross-sectional, population-based household survey. A total of 1,894 older adults (men and women, > 60 years) were examined from January to March 2001. Data were presented as means and percentiles for body mass (BM); height or stature (ST); body mass index (BMI); waist (WC), hip (HC), arm (AC), and calf (CC) circumferences; triceps skinfold thickness (TST); and arm muscle circumference (AMC), and differences were described according to age (all variables) and gender (BMI). Except for HC (men), all anthropometric variables were lower in the oldest than in the youngest individuals (p < 0.01) in both genders. BMI was significantly higher (p < 0.01) in women than men (all age groups). The observations suggest that there is loss of muscle mass and redistribution and reduction of fat mass with age (both genders). The data can be used in clinical practice and epidemiological studies based on interpretation of anthropometric measurements in the elderly in São Paulo.


title: Real Time Mapping and Dynamic Navigation for Mobile Robots

abstract: This paper discusses the importance, the complexity and the challenges of mapping mobile robot's unknown and dynamic environment, besides the role of sensors and the problems inherited in map building. These issues remain largely an open research problems in developing dynamic navigation systems for mobile robots. The paper presenst the state of the art in map building and localization for mobile robots navigating within unknown environment, and then introduces a solution for the complex problem of autonomous map building and maintenance method with focus on developing an incremental grid based mapping technique that is suitable for real-time obstacle detection and avoidance. In this case, the navigation of mobile robots can be treated as a problem of tracking geometric features that occur naturally in the environment of the robot. The robot maps its environment incrementally using the concept of occupancy grids and the fusion of multiple ultrasonic sensory information while wandering in it and stay away from all obstacles. To ensure real-time operation with limited resources, as well as to promote extensibility, the mapping and obstacle avoidance modules are deployed in parallel and distributed framework. Simulation based experiments has been conducted and illustrated to show the validity of the developed mapping and obstacle avoidance approach.


title: Measurement and Modeling Mutual Capacitance of Electrical Wiring and Humans

abstract: In a recent series of electric field sensing experiments, a theremin was used to measure the mutual capacitance between a human being and a length of electrical wiring. The instrument, based on the LM555 circuit, measures the deflections in capacitance due to the proximity of a human. The measurements are repeatable, and the difference in capacitance for a person at 0.5 m with a person at 1 m is consistent with the difference computed, assuming the human acts as a ground plane for the wiring. Much of the current literature in electric field sensing focuses on measures and models of mutual capacitance for humans interacting with plate conductors [J. R. Smith, Electric field imaging, Ph.D. dissertation, Mass. Inst. Technol., Cambridge, MA, 1999; N. Karlsson and J. O. Jarrhed, A capacitive sensor for the detection of humans in a robot cell, in Proc. IEEE IMTC Rec., May 18-20, 1993 pp. 164-166.], especially fingers near touch screens [D. Wiebe, A. Machynia, K. Mazur, and J. Epp, Human-computer interface device based on electric field sensing, Ph.D. dissertation, Univ. Manitoba, Winnipeg, MB, Canada, 2004]. The present investigation considers conducting wires to allow the development of portable rapidly deployable human proximity sensing systems that exploit existing electrical infrastructure in buildings. The experiment described here demonstrates that sensing with wires is possible at ranges on the order of a meter and provides evidence that modeling the person as a ground plane of finite extent provides a rough estimate of the change in mutual capacitance


Least squares support vector machine employing model-based methods coefficients for analysis of EEG signals


title: Classification of movement disorders

abstract: The classification of movement disorders has evolved. Even the terminology has shifted, from an anatomical one of extrapyramidal disorders to a phenomenological one of movement disorders. The history of how this shift came about is described. The history of both the definitions and the classifications of the various neurologic conditions is then reviewed. First is a review of movement disorders as a group; then, the evolving classifications for 3 of them—parkinsonism, dystonia, and tremor—are covered in detail. © 2011 Movement Disorder Society


title: A small omni-directional patch antenna with ultra wide impedance bandwidth

abstract: This paper presents a novel omni-directional small Ultra Wide Band (UWB) patch antenna. The proposed geometry is composed of a circular patch which is inlayed with a bucket shaped patch top loaded on a monopole and shorted by 4 shorting strips to the ground is presented. The antenna is excited by a coaxial probe feed. It has a stable radiation pattern and a constant gain in the band of operation. Proposed geometry offers a very wide impedance bandwidth of 127% (for VSWR <2) from the frequency range of 2.3 GHz to 10.6 GHz. The antenna is low profile and small in size which has the resonance length of 0.25 lambdao. A novel method to excite the axial probe feed via a conical tube patch broadened the bandwidth of the structure.


title: Learning to Translate in Real-time with Neural Machine Translation

abstract: Translating in real-time, a.k.a. simultaneous translation, outputs translation words before the input sentence ends, which is a challenging problem for conventional machine translation methods. We propose a neural machine translation (NMT) framework for simultaneous translation in which an agent learns to make decisions on when to translate from the interaction with a pre-trained NMT environment. To trade off quality and delay, we extensively explore various targets for delay and design a method for beam-search applicable in the simultaneous MT setting. Experiments against state-of-the-art baselines on two language pairs demonstrate the efficacy of the proposed framework both quantitatively and qualitatively.


title: The impacts of using different methods to sense emotion in computer-mediated communication

abstract: As the ubiquity of computers, computer-mediated communications (CMC) has evolved to satisfy one of humanity's basic needs — communication today. Text-based message exchanging has established itself as the most popular CMC medium in our lives than other modes of communication. Compared to face-to-face communication, CMC is not restricted in terms of time and place However, text-based communications lack much of the richness of contextual information. It has been argued that nonverbal cues such as emotions are effective in helping users understand the meaning and nature of the message. The aim of this paper is to use empirical evidence to gain insight in how different CMC interaction methods influence the sensing of emotions and how this embed breaks down emotion barriers between CMC users. We also report how emotion can affect different user groups in CMC.


title: Vibrotactile haptic feedback for human-robot interaction in leader-follower tasks

abstract: In this paper we explore a vibrotactile feedback paradigm which allows the human to intuitively interact in human-robot applications. In particular we focus on a haptic bracelet which helps the human to move along trajectories that are feasible for the leader-follower formation tasks. The bracelet consists of three vibrating motors circling the forearm and represents a non invasive way to provide essential information to the human. Experiments performed on a public of 15 subjects revealed the effectiveness of the proposed device.


title: Navigation System for Omni-directional Automatic Guided Vehicle with Mecanum Wheel

abstract: This paper presents navigation system for an omni-directional AGV (automatic guided vehicle) with Mecanum wheels. The Mecanum wheel, one design for the wheel which can move in any direction, is a conventional wheel with a series of rollers attached to its circumference. The localization techniques for the general mobile robot use basically encoder. Otherwise, they use gyro and electronic compass with encoder. However, it is difficult to use the encoder because in the Mecanum wheel the slip occurs frequently by the rollers attached to conventional wheel's circumference. Hence, we propose the localization of the omnidirectional AGV with the Mecanum wheel. The proposed localization uses encoder, gyro, and accelerometer. In this paper, we ourselves designed and made the AGV with the Mecanum wheels for experiment. And we analyzed the accuracy of the localization when the AGV moves sideways a 20m distance at about 20cm/s and 38cm/s, respectively. In experimental result, we verified that the accuracies of the proposed localization are 27.4944mm and 29.2521mm respectively.


title: Can mobile augmented reality systems assist in portion estimation? A user study

abstract: Accurate assessment of nutrition information is an important part in the prevention and treatment of a multitude of diseases, but remains a challenging task. We present a novel mobile augmented reality application, which assists users in the nutrition assessment of their meals. Using the realtime camera image as a guide, the user overlays a 3D form of the food. Additionally the user selects the food type. The corresponding nutrition information is automatically computed. Thus accurate volume estimation is required for accurate nutrition information assessment. This work presents an evaluation of our mobile augmented reality approaches for portion estimation and offers a comparison to conventional portion estimation approaches. The comparison is performed on the basis of a user study (n=28). The quality of nutrition assessment is measured based on the error in energy units. In the results of the evaluation one of our mobile augmented reality approaches significantly outperforms all other methods. Additionally we present results on the efficiency and effectiveness of the approaches.


title: Smart Agriculture Based on Cloud Computing and IOT

abstract: Issues concerning agriculture, countryside and farmers have been always hindering China’s development. The only solution to these three problems is agricultural modernization. However, China's agriculture is far from modernized. The introduction of cloud computing and internet of things into agricultural modernization will probably solve the problem. Based on major features of cloud computing and key techniques of internet of things, cloud computing, visualization and SOA technologies can build massive data involved in agricultural production. Internet of things and RFID technologies can help build plant factory and realize automatic control production of agriculture. Cloud computing is closely related to internet of things. A perfect combination of them can promote fast development of agricultural modernization, realize smart agriculture and effectively solve the issues concerning agriculture, countryside and farmers.


title: Convolutional Neural Networks combined with Runge-Kutta Methods

abstract: A convolutional neural network for image classification can be constructed following some mathematical ways since it models the ventral stream in visual cortex which is regarded as a multi-period dynamical system. In this paper, a new point of view is proposed for constructing network models as well as providing a direction to get inspiration or explanation for neural network. If each period in ventral stream was deemed to be a dynamical system with time as the independent variable, there should be a set of ordinary differential equations (ODEs) for this system. Runge-Kutta methods are common means to solve ODE. Thus, network model ought to be built using these methods. Moreover, convolutional networks could be employed to emulate the increments within every time-step. The model constructed in the above way is named Runge-Kutta Convolutional Neural Network (RKNet). According to this idea, Dense Convolutional Networks (DenseNets) and Residual Networks (ResNets) were varied to RKNets. To prove the feasibility of RKNets, these variants were verified on benchmark datasets, CIFAR and ImageNet. The experimental results show that the RKNets transformed from DenseNets gained similar or even higher parameter efficiency. The success of the experiments denotes that Runge-Kutta methods can be utilized to construct convolutional neural networks for image classification efficiently. Furthermore, the network models might be structured more rationally in the future basing on RKNet and priori knowledge.


From storyline to box office: A new approach for green-lighting movie scripts. Management Science Forthcoming


title: Mental Imagery: In search of a theory

abstract: It is generally accepted that there is something special about reasoning by using mental images. The question of how it is spe- cial, however, has never been satisfactorily spelled out, despite more than thirty years of research in the post-behaviorist tradition. This article considers some of the general motivation for the assumption that entertaining mental images involves inspecting a picture-like object. It sets out a distinction between phenomena attributable to the nature of mind to what is called the cognitive architecture, and ones that are attributable to tacit knowledge used to simulate what would happen in a visual situation. With this distinction in mind, the paper then considers in detail the widely held assumption that in some important sense images are spatially displayed or are depictive, and that examining images uses the same mechanisms that are deployed in visual perception. I argue that the assumption of the spatial or depictive nature of images is only explanatory if taken literally, as a claim about how images are physically instantiated in the brain, and that the literal view fails for a number of empirical reasons - for example, because of the cognitive penetrability of the phenomena cited in its favor. Similarly, while it is arguably the case that imagery and vision involve some of the same mechanisms, this tells us very little about the nature of mental imagery and does not support claims about the pictorial nature of mental images. Finally, I consider whether recent neuroscience evidence clarifies the debate over the nature of mental images. I claim that when such questions as whether images are depictive or spatial are formulated more clearly, the evidence does not provide support for the picture-theory over a symbol- structure theory of mental imagery. Even if all the empirical claims were true, they do not warrant the conclusion that many people have drawn from them: that mental images are depictive or are displayed in some (possibly cortical) space. Such a conclusion is incompatible with what is known about how images function in thought. We are then left with the provisional counterintuitive conclusion that the available evidence does not support rejection of what I call the "null hypothesis"; namely, that reasoning with mental images involves the same form of representation and the same processes as that of reasoning in general, except that the content or subject matter of thoughts experienced as images includes information about how things would look.


title: Optimal Grid Pattern for Automated Matching Using Cross Ratio

abstract: We design an optimal grid pattern such that an observed image of a small portion of it can be matched to its corresponding position in the pattern easily. The grid shape is so determined that the cross ratio of adjacent intervals is different everywhere. The cross ratios are generated by an optimal Markov process that maximizes the accuracy of matching. Finally, we show a virtual studio application of our pattern.


title: Current Challenges and Application of Speech Recognition Process using Natural Language Processing: A Survey

abstract: Speech recognition is a vast research field for researchers in modern era. Earlier, the human language was processed by the computer system for speech recognition. Thus, the main objective is to develop recognition system which improves human to human communication by enabling human-machine communication by processing of text or speech. Various applications of speech recognition systems are present and these all includes various research challenges. A critical machine learning based review is defined which addresses the various challenging tasks of speech recognition system in NLP. In the existing systems, the recognition rate is very less and the noise ration during the recognition process creates a problem. Thus in this literature review we try to address such kind of challenges and provides a solution to work further in future.


title: Sketched Floor plans versus SLAM maps: A Comparison

abstract: Maps --- specifically floor plans --- are useful for a variety of tasks from arranging furniture to designating conceptual or functional spaces (e.g., kitchen, walkway). We present a simple algorithm for quickly laying a floor plan (or other conceptual map) onto a SLAM map, creating a one-to-one mapping between them. Our goal was to enable using a floor plan (or other hand-drawn or annotated map) in robotic applications instead of the typical SLAM map created by the robot. We look at two use cases, specifying "no-go" regions within a room and locating objects within a scanned room. Although a user study showed no statistical difference between the two types of maps in terms of performance on this spatial memory task, we argue that floor plans are closer to the mental maps people would naturally draw to characterize spaces.


title: Navigation System for the Blind: Auditory Display Modes and Guidance

abstract: The research we are reporting here is part of our effort to develop a navigation system for the blind. Our long-term goal is to create a portable, self-contained system that will allow visually impaired individuals to travel through familiar and unfamiliar environments without the assistance of guides. The system, as it exists now, consists of the following functional components: (1) a module for determining the traveler's position and orientation in space, (2) a Geographic Information System comprising a detailed database of our test site and software for route planning and for obtaining information from the database, and (3) the user interface. The experiment reported here is concerned with one function of the navigation system: guiding the traveler along a predefined route. We evaluate guidance performance as a function of four different display modes: one involving spatialized sound from a virtual acoustic display, and three involving verbal commands issued by a synthetic speech display. The virtual display mode fared best in terms of both guidance performance and user preferences.


title: Photorealistic rendering for augmented reality using environment illumination

abstract: Mixing 3D computer-generated images with real-scene images seamlessly in augmented reality has many desirable and wide areas of applications such as entertainment, cinematography, design visualization and medical trainings. The challenging task is to make virtual objects blend harmoniously into the real scene and appear as if they are like real. Apart from constructing detailed geometric 3D model representation and obtaining accurate surface properties for virtual objects, adopting real scene lighting information to render virtual objects is another important factor to achieve photorealistic rendering. Such a factor not only improves visual complexity of virtual objects, but also determines the consistency of illumination between the virtual objects and the surrounding real objects in the scene. Conventional rendering techniques such as ray tracing, and radiosity require intensive computation and data preparation to solve the lighting transport equation. Hence, they are less practical for rendering virtual objects in augmented reality, which demands a real-time performance. This work explores an image-based and hardware-based approach to improve photorealism for rendering synthetic objects in augmented reality. It uses a recent technique of image-based lighting, environment illumination maps, and a simple yet practical multi-pass rendering framework for augmented reality.


A Blame for All ( revised )


title: Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula

abstract: Factorizing low-rank matrices has many applications in machine learning and statistics. For probabilistic models in the Bayes optimal setting, a general expression for the mutual information has been proposed using heuristic statistical physics computations, and proven in few specific cases. Here, we show how to rigorously prove the conjectured formula for the symmetric rank-one case. This allows to express the minimal mean-square-error and to characterize the detectability phase transitions in a large set of estimation problems ranging from community detection to sparse PCA. We also show that for a large set of parameters, an iterative algorithm called approximate message-passing is Bayes optimal. There exists, however, a gap between what currently known polynomial algorithms can do and what is expected information theoretically. Additionally, the proof technique has an interest of its own and exploits three essential ingredients: the interpolation method introduced in statistical physics by Guerra, the analysis of the approximate message-passing algorithm and the theory of spatial coupling and threshold saturation in coding. Our approach is generic and applicable to other open problems in statistical estimation where heuristic statistical physics predictions are available.


title: Current status and key issues in image steganography

abstract: Steganography and steganalysis are the prominent research fields in information hiding paradigm. Steganography is the science of invisible communication while steganalysis is the detection of steganography. Steganography means "covered writing" that hides the existence of the message itself. Digital steganography provides potential for private and secure communication that has become the necessity of most of the applications in today's world. Various multimedia carriers such as audio, text, video, image can act as cover media to carry secret information. In this paper, we have focused only on image steganography. This article provides a review of fundamental concepts, evaluation measures and security aspects of steganography system, various spatial and transform domain embedding schemes. In addition, image quality metrics that can be used for evaluation of stego images and cover selection measures that provide additional security to embedding scheme are also highlighted. Current research trends and directions to improve on existing methods are suggested.


title: Control strategies in object manipulation tasks

abstract: The remarkable manipulative skill of the human hand is not the result of rapid sensorimotor processes, nor of fast or powerful effector mechanisms. Rather, the secret lies in the way manual tasks are organized and controlled by the nervous system. At the heart of this organization is prediction. Successful manipulation requires the ability both to predict the motor commands required to grasp, lift, and move objects and to predict the sensory events that arise as a consequence of these commands.


Type studies in the Clavariaceae. VI. Four pivotal types from the Pacific tropics [Fungi].


The Retinex Theory of Color Vision SCIENTIFIC AMERICAN


I want you to bring me a slab: Remarks on the opening sections of the Philosophical Investigations


title: Rationalizing meat consumption. The 4Ns

abstract: Recent theorizing suggests the 4Ns—that is, the belief that eating meat is natural, normal, necessary, and nice—are common rationalizations people use to defend their choice of eating meat. However, such theorizing has yet to be subjected to empirical testing. Six studies were conducted on the 4Ns. Studies 1a-1b demonstrated that the 4N classification captures the vast majority (83%-91%) of justifications people naturally offer in defense of eating meat. In Study 2, individuals who endorsed the 4Ns tended also to objectify (dementalize) animals and included fewer animals in their circle of moral concern, and this was true independent of social dominance orientation. Subsequent studies (Studies 3-5) showed that individuals who endorsed the 4Ns tend not to be motivated by ethical concerns when making food choices, are less involved in animal-welfare advocacy, less driven to restrict animal products from their diet, less proud of their animal-product decisions, tend to endorse Speciesist attitudes, tend to consume meat and animal products more frequently, and are highly committed to eating meat. Furthermore, omnivores who strongly endorsed the 4Ns tended to experience less guilt about their animal-product decisions, highlighting the guilt-alleviating function of the 4Ns.


title: All-frequency shadows using non-linear wavelet lighting approximation

abstract: We present a method, based on pre-computed light transport, for real-time rendering of objects under all-frequency, time-varying illumination represented as a high-resolution environment map. Current techniques are limited to small area lights, with sharp shadows, or large low-frequency lights, with very soft shadows. Our main contribution is to approximate the environment map in a wavelet basis, keeping only the largest terms (this is known as a non-linear approximation). We obtain further compression by encoding the light transport matrix sparsely but accurately in the same basis. Rendering is performed by multiplying a sparse light vector by a sparse transport matrix, which is very fast. For accurate rendering, using non-linear wavelets is an order of magnitude faster than using linear spherical harmonics, the current best technique.


title: The Relationship Between User Perception and User Behaviour in Interactive Information Retrieval Evaluation

abstract: Measures of user behaviour and user perception have been used to evaluate interactive information retrieval systems. However, there have been few efforts taken to understand the relationship between these two. In this paper, we investigated both using user actions from log files, and the results of the User Engagement Scale, both of which came from a study of people interacting with a novel interface to an image collection, but with a non-purposeful task. Our results suggest that selected behavioural actions are associated with selected user perceptions (i.e., focused attention, felt involvement, and novelty), while typical search and browse actions have no association with aesthetics and perceived usability. This is a novel finding that can lead toward a more systematic user-centered evaluation.


ACIPE - Adaptive Cognitive Interaction in Production Environments


title: Utilizing X3D for immersive environments

abstract: Conceptually, the semantics of X3D describe an abstract functional behaviour of time-based, interactive 3D, multimedia information and do not at all specify a specific software or hardware setup. However, X3D clients and applications today are mainly built for desktop systems running a web-browser.In this paper we explore how suitable X3D and W3C technologies can be utilized as an application and programming model for im-mersive virtual environments. We present a system implementation, necessary extensions to the X3D specification and discuss how standard nodes can be interpreted differently in an immersive setting.


title: Software-Defined Firewall: Enabling Malware Traffic Detection and Programmable Security Control

abstract: Network-based malware has posed serious threats to the security of host machines. When malware adopts a private TCP/IP stack for communications, personal and network firewalls may fail to identify the malicious traffic. Current firewall policies do not have a convenient update mechanism, which makes the malicious traffic detection difficult. In this paper, we propose Software-Defined Firewall (SDF), a new security design to protect host machines and enable programmable security policy control by abstracting the firewall architecture into control and data planes. The control plane strengthens the easy security control policy update, as in the SDN (Software-Defined Networking) architecture. The difference is that it further collects host information to provide application-level traffic control and improve the malicious traffic detection accuracy. The data plane accommodates all incoming/outgoing network traffic in a network hardware to avoid malware bypassing it. The design of SDF is easy to be implemented and deployed in today's network. We implement a prototype of SDF and evaluate its performance in real-world experiments. Experimental results show that SDF can successfully monitor all network traffic (i.e., no traffic bypassing) and improves the accuracy of malicious traffic identification. Two examples of use cases indicate that SDF provides easier and more flexible solutions to today's host security problems than current firewalls.


title: Situating evaluation in scenarios of use

abstract: We report on the use of scenario-based methods for evaluating collaborative systems. We describe the method, the case study where it was applied, and provide results of its efficacy in the field. The results suggest that scenario-based evaluation is effective in helping to focus evaluation efforts and in identifying the range of technical, human, organizational and other contextual factors that impact system success. The method also helps identify specific actions, for example, prescriptions for design to enhance system effectiveness. However, we found the method somewhat less useful for identifying the measurable benefits gained from a CSCW implementation, which was one of our primary goals. We discuss challenges faced applying the technique, suggest recommendations for future research, and point to implications for practice.


title: Towards an Automated Approach to Extract and Compare Fictional Networks: An Initial Evaluation

abstract: Within the field of Digital Humanities, fictional networks are defined by the social interactions among characters, capturing unique features associated with each different textual source. The ability to automatically extract, analyse, and assess such properties would open new research directions, and lead to relevant and important results. However, these are challenging tasks which require a multi-disciplinary approach due to their inherent complexity. In this paper, we will describe the initial implementation of a system to address the above. This is part of an ongoing research investigation aiming to define and implement a similarity measure to compare specific properties associated with fictional networks extracted from across different genres and sources, as well as from different historical periods. The preliminary evaluation clearly shows the potential of our approach, as well as the applications to a variety of multi-disciplinary research fields.


title: WG-8: A Lightweight Stream Cipher for Resource-Constrained Smart Devices

abstract: Lightweight cryptographic primitives are essential for securing pervasive embedded devices like RFID tags, smart cards, and wireless sensor nodes. In this paper, we present a lightweight stream cipher WG-8, which is tailored from the well-known Welch-Gong (WG) stream cipher family, for resource-constrained devices. WG-8 inherits the good randomness and cryptographic properties of the WG stream cipher family and is resistant to the most common attacks against stream ciphers. The software implementations of the WG-8 stream cipher on two popular low-power microcontrollers as well as the extensive comparison with other lightweight cryptography implementations highlight that in the context of securing lightweight embedded applications WG-8 has favorable performance and low energy consumption.


title: Deep Network Guided Proof Search

abstract: Deep learning techniques lie at the heart of several significant AI advances in recent years including object recognition and detection, image captioning, machine translation, speech recognition and synthesis, and playing the game of Go. Automated first-order theorem provers can aid in the formalization and verification of mathematical theorems and play a crucial role in program analysis, theory reasoning, security, interpolation, and system verification. Here we suggest deep learning based guidance in the proof search of the theorem prover E. We train and compare several deep neural network models on the traces of existing ATP proofs of Mizar statements and use them to select processed clauses during proof search. We give experimental evidence that with a hybrid, two-phase approach, deep learning based guidance can significantly reduce the average number of proof search steps while increasing the number of theorems proved. Using a few proof guidance strategies that leverage deep neural networks, we have found first-order proofs of 7.36% of the first-order logic translations of the Mizar Mathematical Library theorems that did not previously have ATP generated proofs. This increases the ratio of statements in the corpus with ATP generated proofs from 56% to 59%.


title: Rheological evaluation of the physical properties of hyaluronic acid dermal fillers.

abstract: BACKGROUND ::: Hyaluronic acid (HA) gels are commonly injected into the skin to lift rhytides and to improve facial appearance. The different processes used in their manufacture and formulation yield products with unique physical characteristics that play an important role in predicting their clinical performance. ::: ::: ::: OBJECTIVE ::: The following rheologic evaluation was performed to objectively measure the physical characteristics of HA dermal filler products derived from similar bacterial sources and containing the same butanediol diglycidyl ether cross-linker, but formulated using different manufacturing techniques. The objective of this study was to evaluate the physical characteristics of two distinct families of HA products, thereby providing clinicians with a greater understanding of these products' attributes and the ability to optimize their use in the treatment of patients seeking facial rejuvenation. ::: ::: ::: MATERIALS AND METHODS ::: The physical properties of commercially-available dermal fillers containing HA were evaluated using rheologic testing methods under clinically-relevant conditions. Additionally, light microscopy was used to assess the particulate nature of each product. ::: ::: ::: RESULTS ::: The gels tested demonstrated a broad range of elasticity, firmness and viscosity. Light microscopy confirmed the particulate nature of each product and revealed HA particles of varying size and distribution. ::: ::: ::: CONCLUSION ::: This rheologic evaluation demonstrates that differences exist among the HA products tested including gel elasticity, viscosity, and the range and distribution of gel particle sizes. Understanding the distinct physical characteristics of different HA dermal fillers and how these characteristics may predict their clinical behavior can assist clinicians in achieving the desired results in patients seeking facial rejuvenation.


title: The Determining Factors of Customer Loyalty for Luxury Hotels in US

abstract: This research aims to examine which determinants (i .e. perceived quality, trust, satisfaction, perceived value, and membership programs) have significant effect(s) on customer loyalty for luxury hotel. In order to get the answer, this study surveyed 308 online users to investigate the determining factors that affect customer loyalty fo r luxury hotel in US. The multiple regression statistical method, ANOVA, frequency tests and factor analysis were used for d ata analysis. The study results show that among the determining facto rs and their relationship against attitudinal and b ehavioral loyalty, “satisfaction” was consistently valued the most, fo llowed by “trust.” The attitudinal loyalty measurem ent regarded “membership program” as its third most valued while the behavioral loyalty positioned “perceived quali ty” as its third. Results for the fourth most valued factor, indicate d “perceived quality” from the attitudinal loyalty measurement, while “perceived value” was behavioral loyalty’s fourth m ost valued. The least valued factors for attitudina l and behavioral loyalty were “perceived value” and “membership prog ram,” respectively. On a greater scale, in measurin g overall loyalty, the determining factors were ranked as fol lows, in descending order from most to least import ant: “satisfaction,” “trust,” perceived quality,” “membe rship program,” and “perceived value.” This study can serve as a model to be adopted for yto come in the determining factors of loyalty in luxury hotels in US. The constructs, specifically “perceiv ed value,” “perceived quality,” “satisfaction,” and “trust” have been of use for years, through various industries. These particular determinants will continue to be of use across industries as efforts are made to measure customer loyalty.


title: Coprime array adaptive beamforming based on compressive sensing virtual array signal

abstract: In this paper, we propose a novel adaptive beamforming algorithm for coprime array by compressive sensing the virtual uniform linear array signal. Based on the idea of coprime sampling, a much longer virtual uniform linear array can be generated from a coprime array. With a compressive sensing matrix, a connection can be built between the coprime array with fewer physical sensors and the virtual uniform linear array with much more virtual sensors. Hence, the proposed adaptive beamforming algorithm takes full advantage of the longer virtual array. The performance increment provided by the virtual array is much larger than the performance loss due to the introduced compressive sensing. Hence, the beam-former using the virtual array is expected to obtain much better performance than those using the coprime array directly. Simulation results demonstrate the effectiveness of the proposed adaptive beamforming algorithm.


title: Measuring and Monitoring ICP in Neurocritical Care: Results from a National Practice Survey

abstract: INTRODUCTION ::: The use of intracranial pressure (ICP) monitors is nearly synonymous with Neurocritical Care. Recent studies in nursing literature have report high levels of practice variance associated with ICP monitoring and treatment. There are no recent practice surveys to describe how critical care physicians and nurses who are familiar with ICP management provide care to their patients. ::: ::: ::: METHODS ::: A short survey was developed and disseminated electronically to the members of the Neurocritical Care Society. ::: ::: ::: RESULTS ::: The summary from 241 professionals provides evidence that there is significant practice variation associated with ICP monitoring and management. ::: ::: ::: CONCLUSION ::: The results highlight the need to develop standardized approaches to measuring, monitoring, recording, and treating ICP.


title: Model-driven development: The

abstract: In large software development organizations, increased complexity of products, shortened development cycles, and heightened expectations of quality have created major challenges at all the stages of the software life cycle. As this issue of the IBM Systems Journal illustrates, there are exciting improvements in the technologies of model-driven development (MDD) to meet many of these challenges. Even though the prevalent software-development practices in the industry are still immature, tools that embody MDD technologies are finally being incorporated in large-scale commercial software development. Assuming MDD pervades the industry over the next several years, there is reason to hope for significant improvements in software quality and time to value, but it is far from a foregone conclusion that MDD will succeed where previous software-engineering approaches have failed.


title: Hidden anomaly detection in telecommunication networks

abstract: Nowadays one of the challenges of telecommunication systems management is to detect in real-time unexpected or hidden malfunctions in extremely complex environments. In this article, we present an on-line algorithm that performs a flow of messages analysis. More precisely, it is able to highlight hidden abnormal behaviors that existing network management methods would not detect. Our algorithm uses the notion of constraint curves, introduced in the Network Calculus theory, defining successive time windows that bound the flow.


title: A resiliency model for high performance infrastructure based on logical encapsulation

abstract: An emerging trend in distributed systems is the creation of dynamically provisioned heterogeneous high performance platforms that include the co-allocation of both virtualized computing and network attached storage volumes offering NAS and SAN level data services. These high performance computing environments support parallel applications performing traditional file system operations. As with any parallel platform the ability to continue computation in the face of component failures is an important characteristic. Achieving resiliency in heterogeneous environments presents unique challenges and opportunities not found in homogeneous aggregations of computing resources. We present a logical encapsulation model for heterogeneous high performance infrastructure, which enables a reactive resiliency approach for federations of virtual machines and externally hosted physical storage volumes. Asynchronous state capture and restoration models are presented for individual resources, which are composed into non-blocking resiliency models for logical encapsulations. We perform an evaluation that demonstrates our methodology has greater overall flexibility and significant performance improvements when compared to current resiliency approaches in virtualized distributed execution environments.


title: Image retrieval from scientific publications: Text and image content processing to separate multipanel figures

abstract: Images contained in scientific publications are widely considered useful for educational and research purposes, and their accurate indexing is critical for efficient and effective retrieval. Such image retrieval is complicated by the fact that figures in the scientific literature often combine multiple individual subfigures (panels). Multipanel figures are in fact the predominant pattern in certain types of scientific publications. The goal of this work is to automatically segment multipanel figures—a necessary step for automatic semantic indexing and in the development of image retrieval systems targeting the scientific literature. We have developed a method that uses the image content as well as the associated figure caption to: (1) automatically detect panel boundaries; (2) detect panel labels in the images and convert them to text; and (3) detect the labels and textual descriptions of each panel within the captions. Our approach combines the output of image‐content and text‐based processing steps to split the multipanel figures into individual subfigures and assign to each subfigure its corresponding section of the caption. The developed system achieved precision of 81% and recall of 73% on the task of automatic segmentation of multipanel figures.


title: Rating Computer-Generated Questions with Mechanical Turk

abstract: We use Amazon Mechanical Turk to rate computer-generated reading comprehension questions about Wikipedia articles. Such application-specific ratings can be used to train statistical rankers to improve systems' final output, or to evaluate technologies that generate natural language. We discuss the question rating scheme we developed, assess the quality of the ratings that we gathered through Amazon Mechanical Turk, and show evidence that these ratings can be used to improve question generation.


title: An orthotropic viscoelastic model for the passive myocardium: continuum basis and numerical treatment

abstract: This study deals with the viscoelastic constitutive modeling and the respective computational analysis of the human passive myocardium. We start by recapitulating the locally orthotropic inner structure of the human myocardial tissue and model the mechanical response through invariants and structure tensors associated with three orthonormal basis vectors. In accordance with recent experimental findings the ventricular myocardial tissue is assumed to be incompressible, thick-walled, orthotropic and viscoelastic. In particular, one spring element coupled with Maxwell elements in parallel endows the model with viscoelastic features such that four dashpots describe the viscous response due to matrix, fiber, sheet and fiber-sheet fragments. In order to alleviate the numerical obstacles, the strictly incompressible model is altered by decomposing the free-energy function into volumetric-isochoric elastic and isochoric-viscoelastic parts along with the multiplicative split of the deformation gradient which enables the three-field mixed finite element method. The crucial aspect of the viscoelastic formulation is linked to the rate equations of the viscous overstresses resulting from a 3-D analogy of a generalized 1-D Maxwell model. We provide algorithmic updates for second Piola-Kirchhoff stress and elasticity tensors. In the sequel, we address some numerical aspects of the constitutive model by applying it to elastic, cyclic and relaxation test data obtained from biaxial extension and triaxial shear tests whereby we assess the fitting capacity of the model. With the tissue parameters identified, we conduct (elastic and viscoelastic) finite element simulations for an ellipsoidal geometry retrieved from a human specimen.


title: A Novel ILP Framework for Summarizing Content with High Lexical Variety

abstract: Summarizing content contributed by individuals can be challenging, because people make different lexical choices even when describing the same events. However, there remains a significant need to summarize such content. Examples include the student responses to post-class reflective questions, product reviews, and news articles published by different news agencies related to the same events. High lexical diversity of these documents hinders the system's ability to effectively identify salient content and reduce summary redundancy. In this paper, we overcome this issue by introducing an integer linear programming-based summarization framework. It incorporates a low-rank approximation to the sentence-word co-occurrence matrix to intrinsically group semantically-similar lexical items. We conduct extensive experiments on datasets of student responses, product reviews, and news documents. Our approach compares favorably to a number of extractive baselines as well as a neural abstractive summarization system. The paper finally sheds light on when and why the proposed framework is effective at summarizing content with high lexical variety.


title: Real-time global localization with a pre-built visual landmark database

abstract: In this paper, we study how to build a vision-based system for global localization with accuracies within 10 cm. for robots and humans operating both indoors and outdoors over wide areas covering many square kilometers. In particular, we study the parameters of building a landmark database rapidly and utilizing that database online for real-time accurate global localization. Although the accuracy of traditional short-term motion based visual odometry systems has improved significantly in recent years, these systems alone cannot solve the drift problem over large areas. Landmark based localization combined with visual odometry is a viable solution to the large scale localization problem. However, a systematic study of the specification and use of such a landmark database has not been undertaken. We propose techniques to build and optimize a landmark database systematically and efficiently using visual odometry. First, topology inference is utilized to find overlapping images in the database. Second, bundle adjustment is used to refine the accuracy of each 3D landmark. Finally, the database is optimized to balance the size of the database with achievable accuracy. Once the landmark database is obtained, a new real-time global localization methodology that works both indoors and outdoors is proposed. We present results of our study on both synthetic and real datasets that help us determine critical design parameters for the landmark database and the achievable accuracies of our proposed system.


title: A Multifractal Cantor Antenna for Multiband Wireless Applications

abstract: A novel printed Cantor fractal monopole antenna using multifractal technique is proposed. The antenna has multi-band characteristics covering GSM, DCS, PCS, UMTS, and WLAN applications. Prototypes of the multiband antenna have been implemented and the return loss has been compared with that of measured value. The use of multifractal concept in the Cantor antenna design makes it flexible in terms of controlling resonance and bandwidth.


title: Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis

abstract: Financial time-series forecasting has long been a challenging problem because of the inherently noisy and stochastic nature of the market. In the high-frequency trading, forecasting for trading purposes is even a more challenging task, since an automated inference system is required to be both accurate and fast. In this paper, we propose a neural network layer architecture that incorporates the idea of bilinear projection as well as an attention mechanism that enables the layer to detect and focus on crucial temporal information. The resulting network is highly interpretable, given its ability to highlight the importance and contribution of each temporal instance, thus allowing further analysis on the time instances of interest. Our experiments in a large-scale limit order book data set show that a two-hidden-layer network utilizing our proposed layer outperforms by a large margin all existing state-of-the-art results coming from much deeper architectures while requiring far fewer computations.


title: Augmented reality based indoor positioning navigation tool

abstract: Indoor positioning has gained popularity recently due to its potential to be used in the increasing complexity of indoor environment. Unfortunately GPS signals are restricted to outdoor purposes. The main objective of this work is to design a new method to develop indoor positioning navigation system without using wireless technology through image processing. Apart from that, the work also aims to develop an interactive indoor navigation system. Augmented reality is being used to superimpose the directional signage on the real view of the indoor environment in 3D form. Along with the 3D guides, voice guidance will be output from the system to assist users in identifying their locations easily. Overall, the scope of study involves research on augmented reality, audio API, and other additional techniques that can improve the program in computing the route. The idea of this work can be broadly applied to mobile devices such as mobile phones and PDA as an added indoor navigation functionality without the use of GPS and wireless communication. The proposed system has been tested at the ground floor of the Information Resource Centre (IRC) in Universiti Teknologi PETRONAS and the results show the flexibility of the system in navigating 12 locations and ability to handle up to 30 possible routes.


title: Teacher Efficacy: A Study of Construct Dimensions

abstract: This study examined the structure of a construct generally labeled teacher efficacy. A sample of 342 prospective and experienced teachers was administered an efficacy questionnaire adapted from the research of Gibson and Dembo (1984). Factor analytic procedures with varimax rotation were used to generate a two-factor solution that accounted for 32 % of the variance in scale scores. Contrary to previous research, these factors corresponded not to a personal versus teaching efficacy distinction, but instead to a simpler internal versus external distinction, similar to locus-of-control measures of causal attribution. Implications of these findings for past and future research involving this construct are discussed.


title: A Dynamic Zoom ADC With 109-dB DR for Audio Applications

abstract: This paper presents the first dynamic zoom ADC. Intended for audio applications, it achieves 109-dB DR, 106-dB signal-to-noise ratio, and 103-dB SNDR in a 20-kHz bandwidth, while dissipating only 1.12 mW. This translates into the state-of-the-art energy efficiency as expressed by a Schreier FoM of 181.5 dB. It also achieves the state-of-the-art area efficiency, occupying only 0.16 mm2 in the 0.16- $\mu \text{m}$ CMOS. These advances are enabled by the use of concurrent fine and coarse conversions, dynamic error-correction techniques, and a dynamically biased inverter-based operational transconductance amplifier.


title: Modeling occluded areas in dynamic grid maps

abstract: The dynamic grid map illustrates the environment of robots with moving and static obstacles. Nuss et al. describe in [1] an implementation of this grid map, in which the state of the grid cells is to be modeled as a random finite set (RFS) based on a stochastic measurement system. For a real-time implementation this approach was approximated with Dempster-Shafer (DS). For this Nuss et al. design the areas without information (unknown areas) so, that no probabilistic calculations are executed. Only in the field of view, hypotheses represent the dynamic behavior of objects. This hypotheses are generated with particles. Therefore, in [1] it was proposed to extend this modeling. In this paper a pure Bayes approach is presented, which calculates all areas of the dynamic grid map probabilistic. Now, the resulting modeling generates hypotheses, which represent the dynamic behavior of unobservable objects. Thus, objects moving out of unknown areas can be detected more quickly. This leads to a more intuitive understanding as well as representation of the environment.


title: Active Recognition through Next View Planning: A Survey

abstract: Abstract 3-D object recognition involves using image-computable features to identify 3-D object. A single view of a 3-D object may not contain sufficient features to recognize it unambiguously. One needs to plan different views around the given object in order to recognize it. Such a task involves an active sensor—one whose parameters (external and/or internal) can be changed in a purposive manner. In this paper, we review two important applications of an active sensor. We first survey important approaches to active 3-D object recognition. Next, we review existing approaches towards another important application of an active sensor namely, that of scene analysis and interpretation.


title: Non-interactive OS fingerprinting through memory de-duplication technique in virtual machines

abstract: OS fingerprinting tries to identify the type and version of a system based on gathered information of a target host. It is an essential step for many subsequent penetration attempts and attacks. Traditional OS fingerprinting depends on banner grabbing schemes or network traffic analysis results to identify the system. These interactive procedures can be detected by intrusion detection systems (IDS) or fooled by fake network packets. In this paper, we propose a new OS fingerprinting mechanism in virtual machine hypervisors that adopt the memory de-duplication technique. Specifically, when multiple memory pages with the same contents occupy only one physical page, their reading and writing access delay will demonstrate some special properties. We use the accumulated access delay to the memory pages that are unique to some specific OS images to derive out whether or not our VM instance and the target VM are using the same OS. The experiment results on VMware ESXi hypervisor with both Windows and Ubuntu Linux OS images show the practicability of the attack. We also discuss the mechanisms to defend against such attacks by the hypervisors and VMs.


title: Stable Matching-Based Selection in Evolutionary Multiobjective Optimization

abstract: Multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem into a set of scalar optimization subproblems and optimizes them in a collaborative manner. Subproblems and solutions are two sets of agents that naturally exist in MOEA/D. The selection of promising solutions for subproblems can be regarded as a matching between subproblems and solutions. Stable matching, proposed in economics, can effectively resolve conflicts of interests among selfish agents in the market. In this paper, we advocate the use of a simple and effective stable matching (STM) model to coordinate the selection process in MOEA/D. In this model, subproblem agents can express their preferences over the solution agents, and vice versa. The stable outcome produced by the STM model matches each subproblem with one single solution, and it tradeoffs convergence and diversity of the evolutionary search. Comprehensive experiments have shown the effectiveness and competitiveness of our MOEA/D algorithm with the STM model. We have also demonstrated that user-preference information can be readily used in our proposed algorithm to find a region that decision makers are interested in.


title: Study of frontal hairline patterns in Spanish Caucasian women.

abstract: BACKGROUND ::: Anterior hairline measurements and their possible relationship with androgen levels, sebum production, and skin hydration have not been reported in white Spanish women. ::: ::: ::: MATERIAL AND METHODS ::: This was a prospective descriptive and analytical observational study conducted on 103 healthy premenopausal white Spanish women recruited from the health staff of Hospital Universitario Virgen Macarena in Seville, Spain. Measurements were made of anterior hairline implantation, sebum levels, and the degree of hydration of the stratum corneum. Androgen levels were also determined in 50 volunteers from this group 3 to 5 days after the end of the menstrual cycle. ::: ::: ::: RESULTS ::: The mean age of the women was 29.7 years. A widow's peak was observed in 94.17% of the group. The mean dimensions of the widow's peak were a height of 1.01 cm and width of 2.13 cm. The mean hormone levels were within normal limits for our hospital's laboratory with the exception of 17-hydroxyprogesterone, with a mean level of 1.39 ng/mL (range, 0.6-5.9ng/mL; normal limits, 0.15-1.10 ng/mL). The mean prostate specific antigen level was 0.04ng/mL (range 0.02-0.08ng/mL; normal limits, 0.00-0.02ng/mL). ::: ::: ::: CONCLUSIONS ::: The hairline measurements of the white Spanish women in this study differ from those reported in American women. Knowledge of this normal pattern of anterior hairline implantation can be important in the evaluation of women with female androgenetic alopecia with male pattern, frontal fibrosing alopecia, or other established scarring alopecia seeking a surgical solution by hair transplantation.


title: Obstacle surpassing and posture control of a stair-climbing robotic mechanism

abstract: Abstract In this study, we propose a new kinematic control for a robotic stair-climbing mechanism which allows successful obstacle surpassing when faced with typical architectural barriers such as curbs, ramps or staircases while maintaining the passenger's comfort and the seat's inclination within security margins. The scheme also takes into account perturbations in the system due to the fact that the environment is not perfectly known. The actuated degrees of freedom in charge of the locomotion device and the posture device are controlled in a different way. The locomotion control is achieved with a feedforward term and a standard proportional derivative (PD) control position. The posture control is obtained with a novel variant of multilevel feedback controller design via a suitable combination of a Lyapunov feedback controller design along with a multilevel generalization of the sliding mode based Sigma–Delta ( Σ – Δ ) modulator coupled with an adequate selection of the trajectory of the center of mass. The main features of this novel kinematic control algorithm are: maintenance of the passenger comfort, high degree of perturbation rejection regarding modeling error, independence of the designed control law on the environment parameters, computational efficiency and minimal sensor requirements. Numerical simulations illustrate the performance of the proposed method when the prototype is to surpass an obstacle with different heights with the value of the height obstacle being unknown. Finally, experimental results validate the behavior of the prototype for, both, obstacle surpassing and posture control when the wheelchair prototype ascends a curb of 180 mm.


title: Copy-Move Forgery Detection using DCT and SIFT

abstract: Digital images are the most prevalent way to spread a message. So the authenticity of images is very essential. But due to advancement of the technology the editing of images has become very effortless. Copy-move forgery is most basic technique to alter an image. In this one part of image is copied, called as snippet, and pasted within same image and most likely post-processing it. Considerable number of algorithms is proposed to detect different post-processing on snippet of image. In this paper novel approach is proposed to detect combination of different post-processing operations by single method. It is analyzed that block-based features method DCT is robust to Gaussian noise and JPEG compression, secondly the keypoint-based feature method SIFT is robust to rotation and scaling. Thus by combining SIFT and DCT we are able to detect forgery under post-processing operations of rotation, scaling, Gaussian noise, and JPEG compression and thus the efficiency to detect forgery improves.


A 3 D data model and topological analyses for emergency response in urban areas


title: Antibacterial Activity of the Ethanolic and Methanolic Leaf Extracts of Some Tropical Plants on Some Human Pathogenic Microbes

abstract: 2 Abstract: Ethanol and Methanol Extracts of Chromolaena odorata, Azadirachta indica, Cymbopogon citratus, Piper guineense, P sidium guajava, Morinda lucida and Gynandropsis gynandra were screened for antibacterial activity against s ix human pathogenic microbes. The highes t i nhibition was obtained with the ethanol extracts(300-500 mg/ml) The s tudy also revealed that all the s ix plants used had similar inhibitory activities with Tetracycline, Chloramphenicol, Erythromycin, Gentamycin and Cotrimoxazole against E.coli, S. aureus, P.mirabilis and S.albus


title: Paying for performance: Performance incentives increase desire for the reward object.

abstract: The current research examines how exposure to performance incentives affects one’s desire for the reward object. We hypothesized that the flexible nature of performance incentives creates an attentional fixation on the reward object (e.g., money), which leads people to become more desirous of the rewards. Results from 5 laboratory experiments and 1 large-scale field study provide support for this prediction. When performance was incentivized with monetary rewards, participants reported being more desirous of money (Study 1), put in more effort to earn additional money in an ensuing task (Study 2), and were less willing to donate money to charity (Study 4). We replicated the result with nonmonetary rewards (Study 5). We also found that performance incentives increased attention to the reward object during the task, which in part explains the observed effects (Study 6). A large-scale field study replicated these findings in a real-world setting (Study 7). One laboratory experiment failed to replicate (Study 3). (PsycINFO Database Record (c) 2016 APA, all rights reserved)


title: Estimation of Arrival Flight Delay and Delay Propagation in a Busy Hub-Airport

abstract: In recent years, flight delay problem blocks the development of the civil aviation industry all over the world. And delay propagation always is a main factor that impacts the flight's delay. All kinds of delays often happen in nearly-saturated or overloaded airports. This paper we take one busy hub-airport as the main research object to estimate the arrival delay in this airport, and to discuss the influence of propagation within and from this airport. First, a delay propagation model is described qualitatively in mathematics after sorting and analyzing the relationships between all flights, especially focused on the frequently type, named aircraft correlation. Second, an arrival delay model is established based on Bayesian network. By training the model, the arrival delay in this airport can be estimated. Third, after clarifying the arrival status of one airport, the impact from propagation of arrival delays within and from this busy airport is discussed, especially between the flights belonging to one same air company. All the data used in our experiments is come from real records, for the industry secret, the name of the airport and the air company is hidden.


title: The requirements, challenges, and technologies for 5G of terrestrial mobile telecommunication

abstract: In this article, we summarize the 5G mobile communication requirements and challenges. First, essential requirements for 5G are pointed out, including higher traffic volume, indoor or hotspot traffic, and spectrum, energy, and cost efficiency. Along with these changes of requirements, we present a potential step change for the evolution toward 5G, which shows that macro-local coexisting and coordinating paths will replace one macro-dominated path as in 4G and before. We hereafter discuss emerging technologies for 5G within international mobile telecommunications. Challenges and directions in hardware, including integrated circuits and passive components, are also discussed. Finally, a whole picture for the evolution to 5G is predicted and presented.


title: Phinding Phish: An Evaluation of Anti-Phishing Toolbars

abstract: There are currently dozens of freely available tools to help combat phishing and other web-based scams. Many of these tools come in the form of web browser extensions that warn users when they are browsing a suspected phishing site. We used verified phishing URLs and legitimate URLs to test the effectiveness of 10 popular antiphishing toolbars. Overall, we found that the anti-phishing toolbars that were examined in this study left a lot to be desired. SpoofGuard did a very good job at identifying fraudulent sites, but it also incorrectly identified a large fraction of legitimate sites as fraudulent. EarthLink, Google, Netcraft, Cloudmark, and Internet Explorer 7 identified most fraudulent sites correctly and had few, if any, false positives, but they still missed more than 15% of fraudulent sites. The TrustWatch, eBay, and Netscape 8 toolbars could correctly identify less than half the fraudulent sites, and McAfee SiteAdvisor did not correctly identify any fraudulent sites. Many of the toolbars we tested were vulnerable to some simple exploits as well. In this paper we describe the anti-phishing toolbar test bed we developed, summarize our findings, and offer observations about the usability and overall effectiveness of these toolbars. Finally, we suggest ways to improve anti-phishing toolbars.


title: Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation

abstract: Segmentation of anatomical and pathological structures in ophthalmic images is crucial for the diagnosis and study of ocular diseases. However, manual segmentation is often a time-consuming and subjective process. This paper presents an automatic approach for segmenting retinal layers in Spectral Domain Optical Coherence Tomography images using graph theory and dynamic programming. Results show that this method accurately segments eight retinal layer boundaries in normal adult eyes more closely to an expert grader as compared to a second expert grader.


title: Scaling up Link Prediction with Ensembles

abstract: A network with $n$ nodes contains O(n2) possible links. Even for networks of modest size, it is often difficult to evaluate all pairwise possibilities for links in a meaningful way. Furthermore, even though link prediction is closely related to missing value estimation problems, such as collaborative filtering, it is often difficult to use sophisticated models such as latent factor methods because of their computational complexity over very large networks. Due to this computational complexity, most known link prediction methods are designed for evaluating the link propensity over a specified subset of links, rather than for performing a global search over the entire networks. In practice, however, it is essential to perform an exhaustive search over the entire networks. In this paper, we propose an ensemble enabled approach to scaling up link prediction, which is able to decompose traditional link prediction problems into subproblems of smaller size. These subproblems are each solved with the use of latent factor models, which can be effectively implemented over networks of modest size. Furthermore, the ensemble enabled approach has several advantages in terms of performance. We show the advantage of using ensemble-based latent factor models with experiments on very large networks. Experimental results demonstrate the effectiveness and scalability of our approach.


title: Self-presentation and emotional contagion on Facebook: new experimental measures of profiles' emotional coherence

abstract: Social Networks allow users to self-present by sharing personal contents with others which may add comments. Recent studies highlighted how the emotions expressed in a post affect others' posts, eliciting a congruent emotion. So far, no studies have yet investigated the emotional coherence between wall posts and its comments. This research evaluated posts and comments mood of Facebook profiles, analyzing their linguistic features, and a measure to assess an excessive self-presentation was introduced. Two new experimental measures were built, describing the emotional loading (positive and negative) of posts and comments, and the mood correspondence between them was evaluated. The profiles "empathy", the mood coherence between post and comments, was used to investigate the relation between an excessive self-presentation and the emotional coherence of a profile. Participants publish a higher average number of posts with positive mood. To publish an emotional post corresponds to get more likes, comments and receive a coherent mood of comments, confirming the emotional contagion effect reported in literature. Finally, the more empathetic profiles are characterized by an excessive self-presentation, having more posts, and receiving more comments and likes. To publish emotional contents appears to be functional to receive more comments and likes, fulfilling needs of attention-seeking.


title: What is the Philosophy of Information?

abstract: Preface 1. What is the Philosophy of Information? 2. Open Problems in the Philosophy of Information 3. The Method of Levels of Abstraction 4. Semantic Information and the Veridicality Thesis 5. Outline of a Theory of Strongly Semantic Information 6. The Symbol Grounding Problem 7. Action-Based Semantics 8. Semantic Information and the Correctness Theory of Truth 9. The Logical Unsolvability of the Gettier Problem 10. The Logic of Being Informed 11. Understanding Epistemic Relevance 12. Semantic Information and the Network Theory of Account 13. Consciousness, Agents and the Knowledge Game 14. Against Digital Ontology 15. A Defence of Informational Structural Realism References


title: Group Decision Making with Dual Hesitant Fuzzy Preference Relations

abstract: Background/Introduction ::: Due to the complexity and uncertainty of socioeconomic environments and cognitive diversity of group members, the cognitive information over alternatives provided by a decision organization consisting of several experts is usually uncertain and hesitant. Hesitant fuzzy preference relations provide a useful means to represent the hesitant cognitions of the decision organization over alternatives, which describe the possible degrees that one alternative is preferred to another by using a set of discrete values. However, in order to depict the cognitions over alternatives more comprehensively, besides the degrees that one alternative is preferred to another, the decision organization would give the degrees that the alternative is non-preferred to another, which may be a set of possible values. To effectively handle such common cases, in this paper, the dual hesitant fuzzy preference relation (DHFPR) is introduced and the methods for group decision making (GDM) with DHFPRs are investigated.


title: Applications of nanoparticles in biology and medicine

abstract: Nanomaterials are at the leading edge of the rapidly developing field of nanotechnology. Their unique size-dependent properties make these materials superior and indispensable in many areas of human activity. This brief review tries to summarise the most recent developments in the field of applied nanomaterials, in particular their application in biology and medicine, and discusses their commercialisation prospects.


title: Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification

abstract: Relation classification is an important semantic processing task in the field of natural language processing (NLP). State-ofthe-art systems still rely on lexical resources such as WordNet or NLP systems like dependency parser and named entity recognizers (NER) to get high-level features. Another challenge is that important information can appear at any position in the sentence. To tackle these problems, we propose Attention-Based Bidirectional Long Short-Term Memory Networks(AttBLSTM) to capture the most important semantic information in a sentence. The experimental results on the SemEval-2010 relation classification task show that our method outperforms most of the existing methods, with only word vectors.


Indoor Localization Method Based on Wi-Fi Trilateration Technique


title: Online Multi-object Tracking-by-Clustering for Intelligent Transportation System with Neuromorphic Vision Sensor

abstract: Instead of wastefully sending entire images at fixed frame rates, neuromorphic vision sensors only transmits the local pixel-level changes caused by movement in a scene at the time they occur. This results in a stream of events, with a latency in the order of micro-seconds. While these sensors offer tremendous advantages in terms of latency and bandwidth, they require new, adapted approaches to computer vision, due to their unique event-based pixel-level output. In this contribution, we propose an online multi-target tracking system utilizing for neuromorphic vision sensors, which is the first neuromorphic vision system in intelligent transportation systems. In order to track moving targets, a fast and simple object detection algorithm using clustering techniques is developed. To make full use of the low latency, we integrate an online tracking-by-clustering system running at a high frame rate, which far exceeds the real-time capabilities of traditional frame based industry cameras. The performance of the system is evaluated using real world dynamic vision sensor data of a highway bridge scenario. We hope that our attempt will motivate further research on neuromorphic vision sensors for intelligent transportation systems.


title: Contextual Classification by Entropy-Based Polygonization

abstract: To improve the performance of pixel-wise classification results for remotely sensed imagery, several contextual classification schemes have been proposed that aim at avoiding classification noise by local averaging. These algorithms, however, bear the serious disadvantage of smoothing the segment boundaries and producing rounded segments that hardly match the true shapes. The authors present a novel contextual classification algorithm that overcomes these shortcomings. Using a hierarchical approach for generating a triangular mesh, it decomposes the image into a set of polygons that, in our application, represent individual land-cover types. Compared to classical contextual classification approaches, this method has the advantage of generating output that matches the intuitively expected type of segmentation. Besides, it achieves excellent classification results.


title: Molecular dynamics studies on the NMR structures of rabbit prion protein wild-type and mutants: surface electrostatic charge distributions

abstract: Prion diseases are invariably fatal and highly infectious neurodegenerative diseases that affect a wide variety of mammalian species such as sheep and goats, cattle, deer and elk, and humans. But for rabbits, studies have shown that they have a low susceptibility to be infected by prion diseases. This paper does molecular dynamics (MD) studies of rabbit NMR structures (of the wild type and its two mutants of two surface residues), in order to understand the specific mechanism of rabbit prion proteins (RaPrP(C)). Protein surface electrostatic charge distributions are specially focused to analyze the MD trajectories. This paper can conclude that surface electrostatic charge distributions indeed contribute to the structural stability of wild-type RaPrP(C); this may be useful for the medicinal treatment of prion diseases.


title: On the Potential for Open-Endedness in Neural Networks

abstract: Natural evolution gives the impression of leading to an open-ended process of increasing diversity and complexity. If our goal is to produce such open-endedness artificially, this suggests an appro...


title: No Unbiased Estimator of the Variance of K-Fold Cross-Validation

abstract: Most machine learning researchers perform quantitative experiments to estimate generalization error and compare algorithm performances. In order to draw statistically convincing conclusions, it is important to estimate the uncertainty of such estimates. This paper studies the estimation of uncertainty around the K-fold cross-validation estimator. The main theorem shows that there exists no universal unbiased estimator of the variance of K-fold cross-validation. An analysis based on the eigende-composition of the covariance matrix of errors helps to better understand the nature of the problem and shows that naive estimators may grossly underestimate variance, as confirmed by numerical experiments.


title: Fraud detection in water meters using pattern recognition techniques

abstract: Water supply utilities have been increasingly looking for solutions to reduce water wastage. Many efforts have been made aiming to promote a better management of this resource. Fraud detection is one of these actions, as the irregular violations are usually held precariously, thus, causing leaks. In this context, the use of technology in order to automate the identification of potential frauds can be an important support tool to avoid water waste. Thus, this research aims to apply pattern recognition techniques in the implementation of an automated detection of suspected irregularities cases in water meters, through image analysis. The proposed computer vision system is composed of three steps: the detection of the water meter location, obtained by OPF classifier and HOG descriptor, detecting the seals through morphological image processing and segmentation methods; and the classification of frauds, in which the condition of the water meter seals is assessed. We validated the proposed framework using a dataset containing images of water meter inspections. At the last step, the proposed framework reached an average accuracy up to 81.29%. We concluded that a computer vision system is a promising strategy and has potential to benefit the analysis of fraud detection.


title: A scaling mitigation approach during direct contact membrane distillation

abstract: Membrane scaling during the treatment of aqueous solutions containing sparingly soluble salts by direct contact membrane distillation (DCMD) was investigated. The results reveal that membrane scaling caused by CaSO4 was more severe than that by CaCO3 or silicate. However, under the experimental con- dition used in this study and at feed and distillate temperature of 20 C and 40 C, respectively, CaSO4 scaling occurred only after a sufficiently long induction time of up to 25 h (corresponding to a saturation index of up to 1.5). The induction period decreased and the size of the CaSO4 crystals increased as the feed temperature increased. SEM analysis reveals that prior to the onset of CaSO4 scaling, the membrane sur- face was relatively clean and was completely free of any large crystals. Subsequently, a simple opera- tional regime involving regular membrane flushing to reset the induction period was developed and was proven to be effective in controlling CaSO4 scaling. At a low system recovery, the permeate flux was constant despite the fact that the feed solution was always at a super saturation condition. Results reported here also confirm the interplay between induction time and the saturation index.


title: FINGERPRINT IMAGE ENHANCEMENT USING STFT ANALYSIS

abstract: Contrary to popular belief, despite decades of research in fingerprints, reliable fingerprint recognition is still an open problem. Extracting features out of poor quality prints is the most challenging problem faced in this area. This paper introduces a new approach for fingerprint enhancement based on Short Time Fourier Transform(STFT) Analysis. STFT is a well known technique in signal processing to analyze non-stationary signals. Here we extend its application to 2D fingerprint images.The algorithm simultaneously estimates all the intrinsic properties of the fingerprints such as the foreground region mask, local ridge orientation and local frequency orientation. We have evaluated the algorithm over a set of 800 images from FVC2002 DB3 database and obtained a 17% relative improvement in the recognition rate.


Genetic Algorithm Optimization of a High-Directivity Microstrip Patch Antenna Having a Rectangular Profile


title: A global-ranking local feature selection method for text categorization

abstract: In this paper, we propose a filtering method for feature selection called ALOFT (At Least One FeaTure). The proposed method focuses on specific characteristics of text categorization domain. Also, it ensures that every document in the training set is represented by at least one feature and the number of selected features is determined in a data-driven way. We compare the effectiveness of the proposed method with the Variable Ranking method using three text categorization benchmarks (Reuters-21578, 20 Newsgroup and WebKB), two different classifiers (k-Nearest Neighbor and Naive Bayes) and five feature evaluation functions. The experiments show that ALOFT obtains equivalent or better results than the classical Variable Ranking.


title: Introduction to Distributed Algorithms: Solutions and Suggestions

abstract: This booklet contains (partial) solutions to most of the exercises in the book Introduction to Distributed Algorithms [Tel00]. Needless to say, the book as well as this document contain numerous errors; please find them and mail them to me! I have not included answers or hints for the following exercises and projects: 2.3, 2.9, 2.10, 3.2, 3.7, 6.16, 10.2, 12.8, 12.10, 12.11, 14.6, 17.5, 17.9, 17.11, B.5.


title: DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation

abstract: Accurate medical image segmentation is essential for diagnosis, surgical planning and many other applications. Convolutional Neural Networks (CNNs) have become the state-of-the-art automatic segmentation methods. However, fully automatic results may still need to be refined to become accurate and robust enough for clinical use. We propose a deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy. We use one CNN to obtain an initial automatic segmentation, on which user interactions are added to indicate mis-segmentations. Another CNN takes as input the user interactions with the initial segmentation and gives a refined result. We propose to combine user interactions with CNNs through geodesic distance transforms, and propose a resolution-preserving network that gives a better dense prediction. In addition, we integrate user interactions as hard constraints into a back-propagatable Conditional Random Field. We validated the proposed framework in the context of 2D placenta segmentation from fetal MRI and 3D brain tumor segmentation from FLAIR images. Experimental results show our method achieves a large improvement from automatic CNNs, and obtains comparable and even higher accuracy with fewer user interventions and less time compared with traditional interactive methods.


title: Optical Flow in Mostly Rigid Scenes

abstract: The optical flow of natural scenes is a combination of the motion of the observer and the independent motion of objects. Existing algorithms typically focus on either recovering motion and structure under the assumption of a purely static world or optical flow for general unconstrained scenes. We combine these approaches in an optical flow algorithm that estimates an explicit segmentation of moving objects from appearance and physical constraints. In static regions we take advantage of strong constraints to jointly estimate the camera motion and the 3D structure of the scene over multiple frames. This allows us to also regularize the structure instead of the motion. Our formulation uses a Plane+Parallax framework, which works even under small baselines, and reduces the motion estimation to a one-dimensional search problem, resulting in more accurate estimation. In moving regions the flow is treated as unconstrained, and computed with an existing optical flow method. The resulting Mostly-Rigid Flow (MR-Flow) method achieves state-of-the-art results on both the MPI-Sintel and KITTI-2015 benchmarks.


title: Revisiting Summarization Evaluation for Scientific Articles

abstract: Evaluation of text summarization approaches have been mostly based on metrics that measure similarities of system generated summaries with a set of human written gold-standard summaries. The most widely used metric in summarization evaluation has been the ROUGE family. ROUGE solely relies on lexical overlaps between the terms and phrases in the sentences; therefore, in cases of terminology variations and paraphrasing, ROUGE is not as effective. Scientific article summarization is one such case that is different from general domain summarization (e.g. newswire data). We provide an extensive analysis of ROUGE's effectiveness as an evaluation metric for scientific summarization; we show that, contrary to the common belief, ROUGE is not much reliable in evaluating scientific summaries. We furthermore show how different variants of ROUGE result in very different correlations with the manual Pyramid scores. Finally, we propose an alternative metric for summarization evaluation which is based on the content relevance between a system generated summary and the corresponding human written summaries. We call our metric SERA (Summarization Evaluation by Relevance Analysis). Unlike ROUGE, SERA consistently achieves high correlations with manual scores which shows its effectiveness in evaluation of scientific article summarization.


title: Spectrum Management of Cognitive Radio Using Multi-agent Reinforcement Learning

abstract: Wireless cognitive radio (CR) is a newly emerging paradigm that attempts to opportunistically transmit in licensed frequencies, without affecting the pre-assigned users of these bands. To enable this functionality, such a radio must predict its operational parameters, such as transmit power and spectrum. These tasks, collectively called spectrum management, is difficult to achieve in a dynamic distributed environment, in which CR users may only take local decisions, and react to the environmental changes. In this paper, we introduce a multi-agent reinforcement learning approach based spectrum management. Our approach uses value functions to evaluate the desirability of choosing different transmission parameters, and enables efficient assignment of spectrums and transmit powers by maximizing long-term reward. We then investigate various real-world scenarios, and compare the communication performance using different sets of learning parameters. We also apply Kanerva-based function approximation to improve our approach's ability to handle large cognitive radio networks and evaluate its effect on communication performance. We conclude that our reinforcement learning based spectrum management can significantly reduce the interference to the licensed users, while maintaining a high probability of successful transmissions in a cognitive radio ad hoc network.


title: Rough Set Theory in analyzing the attributes of combination values for the insurance market

abstract: Based on Rough Set Theory, this research addresses the effect of attributes/features on the combination values of decisions that insurance companies make to satisfy customers’ needs. Attributes impact on combination values by yielding sets with fewer objects (such as one or two objects), which increases both the lower and upper approximations. It also increases the decision rules, and degrades the precision of decisions. Our approach redefines the value set of attributes through expert knowledge by reducing the independent data set and reclassifying it. This approach is based on an empirical study. The results demonstrate that the redefined combination values of attributes can contribute to the precision of decisions in insurance marketing. Following an empirical analysis, we use a hit test that incorporates 50 validated sample data into the decision rule so that the hit rate reaches 100%. The results of the empirical study indicate that the generated decision rules can cover all new data. Consequently, we believe that the effects of attributes on combination values can be fully applied in research into insurance marketing.


title: A CLASSIFICATION MODEL FOR PREDICTING THE SUITABLE STUDY TRACK FOR SCHOOL STUDENTS

abstract: One of the most important issues to succeed in the academic life is to assign students to the right track when they arrive to the end of the basic education stage. The main problem in the selection of an academic track in basic Jordanian schools is the lack of useful knowledge for students to support their planning. This paper utilized data mining techniques to provide a classification approach to support basic school students in selecting the suitable track. For this purpose, a decision tree classification model was developed to determine which track is suitable for each student. There are a set classification rules that were extracted from the decision tree to predict and classify the class label for each student. A confusion matrix is built to evaluate the model where the 10-fold Cross Validation method was used for accuracy estimation of the model. The overall accuracy of the model was 87.9% where 218 students were correctly classified out of the 248 students.


title: Position estimation approach by Complementary Filter-aided IMU for indoor environment

abstract: This paper proposes a foot-mounted Zero Velocity Update (ZVU) aided Inertial Measurement Unit (IMU) filtering algorithm for pedestrian tracking in indoor environment. The algorithm outputs are the foot kinematic parameters, which include foot orientation, position, velocity, acceleration, and gait phase. The foot motion filtering algorithm incorporates methods for orientation estimation, gait detection, and position estimation. A novel Complementary Filter (CF) is introduced to better pre-process the sensor data from a foot-mounted IMU containing tri-axial angular rate sensors, accelerometers, and magnetometers and to estimate the foot orientation without resorting to GPS data. A gait detection is accomplished using a simple states detector that transitions between states based on acceleration measurements. Once foot orientation is computed, position estimates are obtained by using integrating acceleration and velocity data, which has been corrected at step stance phase for drift using an implemented ZVU algorithm, leading to a position accuracy improvement. We illustrate our findings experimentally by using of a commercial IMU during regular human walking trial in a typical public building. Experiment results show that the positioning approach achieves approximately a position accuracy less than 1 m and improves the performance regarding a previous work of literature.


title: Design and control of a second-generation hyper-redundant mechanism

abstract: We present a refined, second-generation design, construction and integration, of a compact hyper-redundant snakelike robot, called "Woodstock." This robot has substantial advantages over our previous design iteration, "Snoopy," in terms of cost and performance. The robot is composed of six actuated universal joints which are serially chained to construct a twelve degrees of freedom snake-like robot optimized for strength and compactness. Any joint in the robot is strong enough to produce a torque that is capable of cantilevering the entire robot. This paper also presents the low-level system- control architecture, which is based on a high-speed RS-485 data bus; this allows the entire system to be operated with only two power and two data wires. The system is controlled from a remote computer on a wireless network and can also run over the Internet.